research on health care behaviors

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• Published: 14 June 2021

Accomplishing breakthroughs in behavioural medicine research

• Karina W. Davidson   ORCID: orcid.org/0000-0002-9162-477X 1 ,
• Simon L. Bacon   ORCID: orcid.org/0000-0001-7075-0358 2 ,
• Gary G. Bennett 3 ,
• Elizabeth Brondolo 4 ,
• Susan M. Czajkowski 5 ,
• Michael A. Diefenbach 1 ,
• Elissa S. Epel 6 ,
• Karen Matthews 7 ,
• Tracey A. Revenson 8 ,
• John Manuel Ruiz 9 ,
• Suzanne C. Segerstrom 10 &

Behavioral Medicine Research Council

Nature Human Behaviour volume  5 ,  pages 813–815 ( 2021 ) Cite this article

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To the Editor — Research progress for behavioural medicine has been observably fragmented and incremental, and it often occurs by accident rather than design. The Behavioral Medicine Research Council aims to lead coordinated efforts to identify target priorities for the science proposed, conducted, and implemented by the field of behavioural medicine.

Globally, behavioural risks are associated with the largest decrement in disability-adjusted life-years 1 . In the United States, an analysis of the 2016 Global Burden of Disease study results revealed that 19.5% of total disability-adjusted life-years could be attributed to behavioural risk factors 2 . Consequently, many governmental agencies, health policy entities, and funding agencies now focus on behaviour as a way to improve the health of their nations’ people.

Behavioural medicine is the interdisciplinary field of science focused on developing and integrating behavioural, psychosocial, and biomedical science knowledge and methods to better understand health and illness. The field aspires to improve primarily through tertiary prevention, diagnosis, and treatment of all diseases and health states affected by behaviours 3 . However, research progress for behavioural medicine has been fragmented and frequently happens by accident rather than design. The field’s four primary professional societies (the Academy of Behavioral Medicine, the American Psychosomatic Society, the Society of Behavioral Medicine Research, and the Society for Health Psychology) recognized the need to accomplish breakthroughs in the area of behavioural medicine and build on our accomplishments to achieve a more cumulative, rather than fragmented, science. In December 2018, the Behavioral Medicine Research Council (BMRC) was created to disrupt the scientific culture of the behavioural medicine research community 4 , address the field’s fragmentation, and lead coordinated efforts to identify target priorities for transformative science needed to advance behavioural medicine 5 .

The problem

Solutions to contemporary health challenges require an orchestrated plan of discovery, intervention development, optimization, and scaling/implementation. Tackling the complex behavioural issues involved in managing the COVID-19 pandemic is just one of many such challenges. The BMRC is developing key strategies about the processes that should be considered with behaviour-based research in response to an acute crisis. However, designing immense strategic, complex, or discovery-based behavioural medicine research is, for many researchers, not perceived as a current possibility. Reinforcements for this type of behavioural medicine research do not exist, nor do the resources to enact it.

A minimum of two levels are integral in considering how incentives shape scientific communities to focus on simple rather than complex approaches 6 . First is the process by which individual scientists make choices about the focus and level of complexity of their work. Second are selective processes that govern new scientists’ training and the formation of manageable research groups, areas, and research capacities 6 . The BMRC was created to tackle both of these issues and focus on select target priorities.

Individual efforts and happenstance currently drive most of the accomplished progress in the field of behavioural medicine. As academics, researchers, and scientists, we propose projects that have the best chance of receiving funding; in evaluating proposals worth funding, scholars tend to select (often without making a prior decision to do so) conformist science rated as having the highest funding merit 6 . We focus exclusively on testing scientific ideas that can be accomplished in just a few years, as most federally funded grants are slated for 5 years (or fewer) of funding. Furthermore, we complete science in areas in which only a few expertise domains are needed so as to avoid excessive complexity. Given that the field was established 40 years ago 3 , perhaps the time has come for a revolution in the manner in which we identify, prioritize, and advance behavioural medicine research 7 .

The proposed solution: learn from the best

We propose reviewing best practices from other fields and determining which may offer new ways for behavioural medicine to advance more rapidly with an increased focus on the larger complex problems the field needs to address.

Set up the right structure

Children’s Oncology Group is the world’s largest organization devoted exclusively to childhood and adolescent cancer research. Its structure began with four competing pediatric groups that merged in 2000 (ref. 8 ). The Children’s Oncology Group has nearly 100 active clinical trials open at any given time 9 (they received more than US$146,000,000 in 2019). It offers care and clinical trial enrollment for more than 90% of the 14,000 children and adolescents diagnosed with cancer each year in the United States. Imagine a world in which any person with health behaviour problems was enrolled in a trial and provided care based on the most recent scientific advances. Though establishing a single entity of clinical trial infrastructure for all behavioural medicine trials seems unimaginable, others have accomplished it for the sake of moving the science and its clinical applications forward.

Currently, the field of behavioural medicine has isolated groups of investigators who have sporadically conducted multi-site trials. However, several actions are needed to accomplish such a network for behavioural medicine trials. The International Behavioural Trials Network ( https://www.ibtnetwork.org/ ) was established in 2013 to facilitate global improvement in methodological quality of behavioural trials, a critical first action. Next, the trials needed should be identified, prioritized, and conducted—actions the BMRC is charged to fulfill. However, the barrier of securing long-term, stable funding, as in the pediatric oncology field, must also be overcome.

Use the right process

The United States Department of Defense’s Defense Advanced Research Projects Agency (DARPA) has produced an unprecedented number of breakthroughs. Arguably, it has the longest-standing, most consistent program of radical inventions in history 10 . With its unconventional approach, speed, and effectiveness, DARPA has created a “special forces” process of innovation. Reflections from past leaders suggest that this is an instantiation of the ‘use-inspired basic research’ section of Pasteur’s Quadrant 11 . As described by others, it entails pushing the frontiers of basic science to solve a well-defined, use-inspired need. This innovative approach is the type of process that the BMRC could adopt to focus behavioural medicine on the selected priorities.

The DARPA process consists of three parts and one overarching culture. First, it sets an ambitious goal that tackles the most critical problems and thus advances science. Second, for a time-limited period, it assembles the best minds from industry and academia in creating diverse, agile, and scalable teams in one specific science area that can select and execute science in a way that deviates substantially from mainstream processes. Third, it ‘flips the script’: instead of starving scientists for resources so that large amounts of time are required for seeking funding, it instead starves scientists of time and provides unlimited resources. Its culture empowers scientists and the DARPA project managers to take risks on innovative ideas, expect failures, experience autonomy, and contribute to an environment of excitement and trust. Other innovators, such as the Gates Foundation, use similar processes and create a similar culture. They issue challenges such as the Healthy Longevity Global Competition 12 that ask for the most crucial goal to be attained and then provide extraordinary resources to the teams who offer unique and exciting proposals to accomplish it. The overall function of these programs of science would not be recognizable to most scientists currently conducting research in behavioural medicine; we are rarely, if ever, asked, “if you had unlimited resources and a tight timeframe, what single behavioural medicine problem would you solve now?” and then directed to “propose your solution and execute it.” Though DARPA’s and the Gates Foundation’s processes are unique to their respective endeavours, their general approaches to pursuing and accomplishing progress are noteworthy. Imagine if the entire behavioural medicine field was tasked with solving structural racism with regard to healthcare, climate change, or COVID-19 vaccine hesitancy and then provided the necessary funds to test their proposed scientific hypotheses immediately without penalizing initial failure. What could the field achieve?

Incentivize the right behaviour

Creating incentives for long-term capacity-building and science system change is particularly difficult. We need a critical mass of national and international pioneering institutions that embrace the principles of transformative science as part of their strategic mission, and these have traditionally been nonprofit foundations. MacArthur Genius awards—created to serve long-term research capacity-building to address neglected or absent science areas—are one way that creative, risky, and innovative advances are awarded and reinforced for individual scientists. The National Cancer Institute of the National Institutes of Health and other entities such as Cancer Research U.K. have experimented with ‘sandpits’ as a way to incentivize the proper behaviour at the individual-scientist level. Sandpits are creativity competitions among randomly formed teams of scientists wherein the best idea is funded for an investment of only about 5 days. Partnering with foundations and funding agencies to run these sandpits regularly and on a focused topic would allow the BMRC to enact its mission and is therefore one of the committee’s initial actions.

Moving forward

These are daunting challenges and ambitious undertakings to alter the course of an area such as behavioural medicine. Building on the vision and initial plans for the BMRC 4 , the organization will focus on:

Drawing from the successful efforts of the best organizations and agencies to create processes that advance science rapidly

Establishing transparent and public processes for rapid topic identification and prioritization

Conducting comprehensive evidence synthesis and identifying critical research gaps needed in discovery-based, clinical, and implementation behavioural science

Locating funding partners

Providing the intellectual infrastructure to permit researchers to advance towards solutions as quickly as possible

Ensuring inclusivity in every part of the initiative

Building a pipeline of researchers with the interpersonal, strategic, and creative skills necessary for collaboration and innovation

We are in an exciting age for the science of behavioural medicine research. Our scientific community is in a prime position to creatively disrupt the scientific methods and approaches employed in the field. More than ever, researchers who recognize the limitations of individual research groups and single methods are connecting across disciplines and countries and sharing resources through Open Science practices 13 , 14 . Designing structures, processes, and incentives to transform our field and its science can be done explicitly, and the BMRC will do so. Uniting these and other initiatives will cultivate a more cumulative science of behavioural medicine that can inform policy, practice, and scientific progress.

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Acknowledgements

This work was supported by the National Cancer Institute (R13CA228496) and the National Library of Medicine (R01LM012836) of the National Institutes of Health in addition to the professional organizations of the Academy of Behavioral Medicine Research, the American Psychosomatic Society, the Society for Health Psychology, and the Society of Behavioral Medicine. No sources of funding had any role in this opinion piece. We thank A. Dominello and S. D’Angelo for their contributions to this manuscript.

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A list of authors appears at the end of the paper.

Authors and Affiliations

Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA

Karina W. Davidson, Michael A. Diefenbach, Karina W. Davidson & Michael A. Diefenbach

Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, Quebec, Canada

Simon L. Bacon & Simon L. Bacon

Trinity College of Arts & Sciences, Duke University, Durham, NC, USA

Gary G. Bennett & Gary G. Bennett

College of Liberal Arts and Sciences, St. John’s University, New York, NY, USA

Elizabeth Brondolo & Elizabeth Brondolo

Health Behaviors Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA

Susan M. Czajkowski & Susan M. Czajkowski

Weill Institute for Neurosciences, School of Medicine, University of California, San Francisco, CA, USA

Elissa S. Epel & Elissa S. Epel

Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA

Karen Matthews & Karen Matthews

Department of Psychology, Hunter College, New York, NY, USA

Tracey A. Revenson & Tracey A. Revenson

College of Science, University of Arizona, Tucson, AZ, USA

John Manuel Ruiz & John Manuel Ruiz

College of Arts & Sciences, University of Kentucky, Lexington, KY, USA

Suzanne C. Segerstrom & Suzanne C. Segerstrom

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The views expressed in this paper are those of the authors and do not represent the views of the National Institutes of Health, the United States Department of Health and Human Services, or any other government entity. K.W.D. is a member of the United States Preventive Services Task Force (USPSTF). This article does not represent the views and policies of the USPSTF. The authors report no financial or personal relationships with other people or organizations that could inappropriately influence their work in this manuscript.

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Davidson, K.W., Bacon, S.L., Bennett, G.G. et al. Accomplishing breakthroughs in behavioural medicine research. Nat Hum Behav 5 , 813–815 (2021). https://doi.org/10.1038/s41562-021-01134-4

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Using these brief interventions, you can help your patients make healthy behavior changes.

STEPHANIE A. HOOKER, PHD, MPH, ANJOLI PUNJABI, PHARMD, MPH, KACEY JUSTESEN, MD, LUCAS BOYLE, MD, AND MICHELLE D. SHERMAN, PHD, ABPP

Fam Pract Manag. 2018;25(2):31-36

Author disclosures: no relevant financial affiliations disclosed.

Effectively encouraging patients to change their health behavior is a critical skill for primary care physicians. Modifiable health behaviors contribute to an estimated 40 percent of deaths in the United States. 1 Tobacco use, poor diet, physical inactivity, poor sleep, poor adherence to medication, and similar behaviors are prevalent and can diminish the quality and length of patients' lives. Research has found an inverse relationship between the risk of all-cause mortality and the number of healthy lifestyle behaviors a patient follows. 2

Family physicians regularly encounter patients who engage in unhealthy behaviors; evidence-based interventions may help patients succeed in making lasting changes. This article will describe brief, evidence-based techniques that family physicians can use to help patients make selected health behavior changes. (See “ Brief evidence-based interventions for health behavior change .”)

Modifiable health behaviors, such as poor diet or smoking, are significant contributors to poor outcomes.

Family physicians can use brief, evidence-based techniques to encourage patients to change their unhealthy behaviors.

Working with patients to develop health goals, eliminate barriers, and track their own behavior can be beneficial.

Interventions that target specific behaviors, such as prescribing physical activity for patients who don't get enough exercise or providing patient education for better medication adherence, can help patients to improve their health.

CROSS-BEHAVIOR TECHNIQUES

Although many interventions target specific behaviors, three techniques can be useful across a variety of behavioral change endeavors.

“SMART” goal setting . Goal setting is a key intervention for patients looking to make behavioral changes. 3 Helping patients visualize what they need to do to reach their goals may make it more likely that they will succeed. The acronym SMART can be used to guide patients through the goal-setting process:

Specific. Encourage patients to get as specific as possible about their goals. If patients want to be more active or lose weight, how active do they want to be and how much weight do they want to lose?

Measurable. Ensure that the goal is measurable. For how many minutes will they exercise and how many times a week?

Attainable. Make sure patients can reasonably reach their goals. If patients commit to going to the gym daily, how realistic is this goal given their schedule? What would be a more attainable goal?

Relevant. Ensure that the goal is relevant to the patient. Why does the person want to make this change? How will this change improve his or her life?

Timely. Help patients define a specific timeline for the goal. When do they want to reach their goal? When will you follow-up with them? Proximal, rather than distal, goals are preferred. Helping patients set a goal to lose five pounds in the next month may feel less overwhelming than a goal of losing 50 pounds in the next year.

Problem-solving barriers . Physicians may eagerly talk with patients about making changes — only to become disillusioned when patients do not follow through. Both physicians and patients may grow frustrated and less motivated to work on the problem. One way to prevent this common phenomenon and set patients up for success is to brainstorm possible obstacles to behavior change during visits.

After offering a suggestion or co-creating a plan, physicians can ask simple, respectful questions such as, “What might get in the way of your [insert behavior change]?” or “What might make it hard to [insert specific step]?” Physicians may anticipate some common barriers raised by patients but be surprised by others. Once the barriers are defined, the physician and patient can develop potential solutions, or if a particular barrier cannot be overcome, reevaluate or change the goal. This approach can improve clinical outcomes for numerous medical conditions and for patients of various income levels. 4

For example, a patient wanting to lose weight may commit to regular short walks around the block. Upon further discussion, the patient shares that the cold Minnesota winters and the violence in her neighborhood make walking in her area difficult. The physician and patient may consider other options such as walking around a local mall or walking with a family member instead. Anticipating every barrier may be impossible, and the problem-solving process may unfold over several sessions; however, exploring potential challenges during the initial goal setting can be helpful.

Self-monitoring . Another effective strategy for facilitating a variety of behavioral changes involves self-monitoring, defined as regularly tracking some specific element of behavior (e.g., minutes of exercise, number of cigarettes smoked) or a more distal outcome (e.g., weight). Having patients keep diaries of their behavior over a short period rather than asking them to remember it at a visit can provide more accurate and valuable data, as well as provide a baseline from which to track change.

When patients agree to self-monitor their behavior, physicians can increase the chance of success by discussing the specifics of the plan. For example, at what time of day will the patient log his or her behavior? How will the patient remember to observe and record the behavior? What will the patient write on the log? Logging the behavior soon after it occurs will provide the most accurate data. Although patients may be tempted to omit unhealthy behaviors or exaggerate healthy ones, physicians should encourage patients to be completely honest to maximize their records' usefulness. For self-monitoring to be most effective, physicians should ask patients to bring their tracking forms to follow-up visits, review them together, celebrate successes, discuss challenges, and co-create plans for next steps. (Several diary forms are available in the Patient Handouts section of the FPM Toolbox .)

A variety of digital tracking tools exist, including online programs, smart-phone apps, and smart-watch functions. Physicians can help patients select which method is most convenient for daily use. Most online programs can present data in charts or graphs, allowing patients and physicians to easily track change over time. SuperTracker , a free online program created by the U.S. Department of Agriculture, helps patients track nutrition and physical activity plans, set goals, and work with a group leader or coach. Apps like Lose It! or MyFitnessPal can also help.

The process of consistently tracking one's behavior is sometimes an intervention itself, with patients often sharing that it created self-reflection and resulted in some changes. Research shows self-monitoring is effective across several health behaviors, especially using food intake monitoring to produce weight loss. 5

BEHAVIOR-SPECIFIC TECHNIQUES

The following evidence-based approaches can be useful in encouraging patients to adopt specific health behaviors.

Physical activity prescriptions . Many Americans do not engage in the recommended amounts of physical activity, which can affect their physical and psychological health. Physicians, however, rarely discuss physical activity with their patients. 6 Clinicians ought to act as guides and work with patients to develop personalized physical activity prescriptions, which have the potential to increase patients' activity levels. 7 These prescriptions should list creative options for exercise based on the patient's experiences, strengths, values, and goals and be adapted to a patient's condition and treatment goals over time. For example, a physician working with a patient who has asthma could prescribe tai chi to help the patient with breathing control as well as balance and anxiety.

In creating these prescriptions, physicians should help the patient recognize the personal benefits of physical activity; identify barriers to physical activity and how to overcome them; set small, achievable goals; and give patients the confidence to attempt their chosen activity. Physicians should also put the prescriptions in writing, give patients logs to track their activity, and ask them to bring those logs to follow-up appointments for further discussion and coaching. 8 More information about exercise prescriptions and sample forms are available online.

Healthy eating goals . Persuading patients to change their diets is daunting enough without unrealistic expectations and the constant bombardment of fad diets, cleanses, fasts, and other food trends that often leave both patients and physicians uncertain about which food options are actually healthy. Moreover, physicians in training receive little instruction on what constitutes sound eating advice and ideal nutrition. 9 This confusion can prevent physicians from broaching the topic with patients. Even if they identify healthy options, common setbacks can leave both patients and physicians less motivated to readdress the issue. However, physicians can help patients set realistic healthy eating goals using two simple methods:

Small steps. Studies have shown that one way to combat the inertia of unhealthy eating is to help patients commit to small, actionable, and measurable steps. 10 First, ask the patient what small change he or she would like to make — for example, decrease the number of desserts per week by one, eat one more fruit or vegetable serving per day, or swap one fast food meal per week with a homemade sandwich or salad. 11 Agree on these small changes to empower patients to take control of their diets.

The Plate Method. This model of meal design encourages patients to visualize their plates split into the following components: 50 percent fruits and non-starchy vegetables, 25 percent protein, and 25 percent grains or starchy foods. 12 Discuss healthy options that would fit in each of the categories, or combine this method with the small steps described above. By providing a standard approach that patients can adapt to many forms of cuisine, the model helps physicians empower their patients to assess their food options and adopt healthy eating behaviors.

Brief behavioral therapy for insomnia . Many adults struggle with insufficient or unrestful sleep, and approximately 18.8 percent of adults in the United States meet the criteria for an insomnia disorder. 13 The first-line treatment for insomnia is Cognitive Behavioral Therapy for Insomnia (CBT-I), which involves changing patients' behaviors and thoughts related to their sleep and is delivered by a trained mental health professional. A physician in a clinic visit can easily administer shorter versions of CBT-I, such as Brief Behavioral Therapy for Insomnia (BBT-I). 14 BBT-I is a structured therapy that includes restricting the amount of time spent in bed but not asleep and maintaining a regular sleep schedule from night to night. Here's how it works:

Sleep diary. Have patients maintain a sleep diary for two weeks before starting the treatment. Patients should track when they got in bed, how long it took to fall asleep, how frequently they woke up and for how long, what time they woke up for the day, and what time they got out of bed. Many different sleep diaries exist, but the American Academy of Sleep Medicine's version is especially user-friendly.

Education. In the next clinic appointment, briefly explain how the body regulates sleep. This includes the sleep drive (how the pressure to sleep is based on how long the person has been awake) and circadian rhythms (the 24-hour biological clock that regulates the sleep-wake cycle).

Set a wake-up time. Have patients pick a wake-up time that will work for them every day. Encourage them to set an alarm for that time and get up at that time every day, no matter how the previous night went.

Limit “total time in bed.” Review the patient's sleep diary and calculate the average number of hours per night the patient slept in the past two weeks. Add 30 minutes to that average and explain that the patient should be in bed only for that amount of time per night until your next appointment.

Set a target bedtime. Subtract the total time in bed from the chosen wake-up time, and encourage patients to go to bed at that “target” time only if they are sleepy and definitely not any earlier.

For example, if a patient brings in a sleep diary with an average of six hours of sleep per night for the past two weeks, her recommended total time in bed will be 6.5 hours. If she picks a wake-up time of 7 a.m., her target bedtime would be 12:30 a.m. It usually takes up to three weeks of regular sleep scheduling and sleep restriction for patients to start seeing improvements in their sleep. As patients' sleep routines become more solid (i.e., they are falling asleep quickly and sleeping more than 90 percent of the time they are in bed), slowly increase the total time in bed to possibly increase time asleep. Physicians should encourage patients to increase time in bed in increments of 15 to 30 minutes per week until the ideal amount of sleep is reached. This amount is different for each patient, but patients generally have reached their ideal amount of sleep when they are sleeping more than 85 percent of the time in bed and feel rested during the day.

Patient education to prevent medication nonadherence . Medication adherence can be challenging for many patients. In fact, approximately 20 percent to 30 percent of prescriptions are never picked up from the pharmacy, and 50 percent of medications for chronic diseases are not taken as prescribed. 15 Nonadherence is associated with poor therapeutic outcomes, further progression of disease, and decreased quality of life. To help patients improve medication adherence, physicians must determine the reason for nonadherence. The most common reasons are forgetfulness, fear of side effects, high drug costs, and a perceived lack of efficacy. To help patients change these beliefs, physicians can take several steps:

Educate patients on four key aspects of drug therapy — the reason for taking it (indication), what they should expect (efficacy), side effects and interactions (safety), and how it structurally and financially fits into their lifestyle (convenience). 16

Help patients make taking their medication a routine of their daily life. For example, if a patient needs to use a controller inhaler twice daily, recommend using the inhaler before brushing his or her teeth each morning and night. Ask patients to describe their day, including morning routines, work hours, and other responsibilities to find optimal opportunities to integrate this new behavior.

Ask patients, “Who can help you manage your medications?” Social networks, including family members or close friends, can help patients set up pillboxes or provide medication reminders.

The five Rs to quitting smoking . Despite the well-known consequences of smoking and nationwide efforts to reduce smoking rates, approximately 15 percent of U.S. adults still smoke cigarettes. 17 As with all kinds of behavioral change, patients present in different stages of readiness to quit smoking. Motivational interviewing techniques can be useful to explore a patient's ambivalence in a way that respects his or her autonomy and bolsters self-efficacy. Discussing the five Rs is a helpful approach for exploring ambivalence with patients: 18

Relevance. Explore why quitting smoking is personally relevant to the patient.

Risks. Advise the patient on negative consequences of continuing to smoke.

Rewards. Ask the patient to identify the benefits of quitting smoking.

Roadblocks. Help the patient determine obstacles he or she may face when quitting. Common barriers include weight gain, stress, fear of withdrawal, fear of failure, and having other smokers such as coworkers or family in close proximity.

Repeat. Incorporate these aspects into each clinical contact with the patient.

Many patients opt to cut back on the amount of tobacco they use before their quit date. However, research shows that cutting back on the number of cigarettes is no more effective than quitting abruptly, and setting a quit date is associated with greater long-term success. 19

Once the patient sets a quit date, repeated physician contact to reinforce smoking cessation messages is key. Physicians, care coordinators, or clinical staff should consider calling or seeing the patient within one to three days of the quit date to encourage continued efforts to quit, as this time period has the highest risk for relapse. Evidence shows that contacting the patient four or more times increases the success rate in staying abstinent. 18 Quitting for good may take multiple a empts, but continued encouragement and efforts such as setting new quit dates or offering other pharmacologic and behavioral therapies can be helpful.

GETTING STARTED

Family physicians are uniquely positioned to provide encouragement and evidence-based advice to patients to change unhealthy behaviors. The proven techniques described in this article are brief enough to attempt during clinic visits. They can be used to encourage physical activity, healthy eating, better sleep, medication adherence, and smoking cessation, and they can help patients adjust their lifestyle, improve their quality of life, and, ultimately, lower their risk of early mortality.

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• Research article
• Open access
• Published: 27 March 2020

A population-based study on healthcare-seeking behaviour of persons with symptoms of respiratory and gastrointestinal-related infections in Hong Kong

• Qiqi Zhang 1 ,
• Shuo Feng 1 ,
• Irene O. L. Wong 1 ,
• Dennis K. M. Ip 1 ,
• Benjamin J. Cowling 1 &
• Eric H. Y. Lau 1  

BMC Public Health volume  20 , Article number:  402 ( 2020 ) Cite this article

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Metrics details

Studies on healthcare-seeking behaviour usually adopted a patient care perspective, or restricted to specific disease conditions. However, pre-diagnosis symptoms may be more relevant to healthcare-seeking behaviour from a patient perspective. We described healthcare-seeking behaviours by specific symptoms related to respiratory and gastrointestinal-related infections.

We conducted a longitudinal population-based telephone survey in Hong Kong. We collected data on healthcare-seeking behaviour specific to symptoms of respiratory and gastrointestinal-related infections and also associated demographic factors. We performed descriptive analyses and estimated the proportion of participants who sought medical consultation, types of services utilized and duration from symptom onset to healthcare seeking, by different age groups. Post-stratification was used to compensate non-response and multiple imputation to handle missing and right-censored data.

We recruited 2564 participants who reported a total of 4370 illness episodes and 7914 symptoms. Fatigue was the most frequently reported symptom, followed by headache and runny nose, with 30-day incidence rate of 9.1, 7.7, and 7.7% respectively. 78% of the participants who had fever sought medical consultation, followed by those with rash (60%) and shortness of breath (58%). Older adults (aged ≥55y) who had symptoms including fever, sore throat, and headache had a significantly higher consultation rate comparing to the other age groups. The 30-day incidence rates of influenza-like illness (ILI) and acute respiratory illness (ARI) were 0.8 and 7.2% respectively, and the consultation rates among these participants were 91 and 64%. Private general practitioner clinics was the main service utilized by participants for most of the symptoms considered, especially those related to acute illness such as fever, diarrhoea and vomiting. Chinese medicine clinics were mostly likely to be visited by participants with low back pain, myalgia and fatigue. Among participants who have sought medical services, most were within 3 days of symptom onset.

Conclusions

Healthcare-seeking behaviour were different by symptoms and age. Characterization of these patterns provides crucial parameters for estimating the full burden of common infectious diseases from facility-based surveillance system, for planning and allocation of healthcare resources.

Peer Review reports

Healthcare-seeking behaviour is defined as “any activity undertaken by individuals who perceived themselves to have a health problem or to be ill for purpose of finding an appropriate remedy” [ 1 ]. Healthcare-seeking behaviour includes the timing and types of healthcare service utilization and may affect population health outcomes [ 2 ]. Delayed medical attention has been shown to associate with an increased risk of unfavourable outcomes [ 3 ]. For patients with infectious diseases, delay in seeking care may also result in increased transmission risk in the community. Understanding the pattern of healthcare-seeking behaviour could help public health practitioners and policy makers to improve the healthcare system and health promotion strategies.

From a patients’ perspective, healthcare-seeking behaviour tends to be responsive to discomfort or symptoms, rather than to specific diagnosed diseases which were unknown to them before medical consultation. However, many studies examined healthcare-seeking behaviour either focused on a patient care perspective, or restricted to a specific disease related to a few limited symptoms [ 4 , 5 , 6 ]. In this study, we focused on healthcare-seeking behaviour specific to symptoms and syndromes, which may more realistically reflect personal responses to sickness in the general population. Such data is still limited in the literature.

A previous study in Denmark showed that for patients with any symptoms, on average < 40% of the patients actually sought healthcare service, though the proportion varied substantially by symptoms [ 7 ]. Here we reported the findings in Hong Kong which also has a well-developed healthcare system composed of both public and private sectors but with very different share in the outpatients and inpatients services: 70% of outpatient services were delivered by private sectors, whereas 90–95% of inpatient services were provided by public sectors [ 8 ]. Also, Hong Kong has its unique mixed culture, which provides and promotes both western and Chinese medicine in the healthcare system. Western medicine has been widely accepted and is the dominant medical system for a long time, but the Hong Kong Government has also actively promoted the development of Chinese medicine.

The objective of this study is to describe the characteristics of healthcare-seeking behaviour due to different symptoms and syndromes related to respiratory and gastrointestinal-related diseases, such as the proportions of patients seeking medical consultation, types of healthcare service utilized, and time from symptom onset to consultation. Data describing healthcare-seeking behaviour could characterize the utilization of the healthcare services, and facilitate risk communication during outbreaks, planning of health care resources, and interpretation of practitioner-based surveillance system.

Study design and study population

A longitudinal survey consisting of 4 rounds of telephone interviews was carried out from February 2014 to May 2015. We selected different times of the year to capture the variation in different infectious disease activity and also to avoid over-representation of a specific timing (e.g. winter) (Fig.  1 ). We avoided long holidays (e.g. Chinese New Year, Easter) which may alter typical healthcare-seeking behaviour.

Timing of the surveys (shaded bars) and influenza-like illness consultation rates (lines) in the community from private general practitioners (GP) and public general outpatient clinics (GOPC) influenza surveillance

The study population was the general population including children and adults in Hong Kong, a subtropical city of 7.5 million people with an ageing population of which more than 95% speak Cantonese [ 9 ]. We adopted a two-stage sampling where participants were recruited by trained interviewers through telephone calls to landlines generated by random-digit dialling. The sample size was calculated based on a previous household telephone survey, with an average of 3.5 symptoms per illness episode and a follow-up rate of about 60%,, assuming a conservative prevalence of 50% [ 10 , 11 ]. Allowing for an error margin of 3% and assuming a 95% confidence level, 3000 participants would provide enough sample size to obtain accurate estimates for the top 10 symptoms. From each household, one household member aged 16 years or above was invited to participate in the study. The person who answered the phone was first recruited. To increase the sample size of the young population, we also recruited caregivers of children aged below 16 years as a booster samplevia telephone and online survey in parallel of the main survey. Up to two follow-up calls were made at different times of the day for unanswered calls. We only recruited Cantonese-speaking participants to our study. Verbal or online informed consent was obtained from participants or from parents prior to the survey. In our longitudinal study, we followed up all participants recruited in the first round and did not recruit new participants.

Data collection

We asked the respondents about any symptoms in 30 days preceding the interview, and the corresponding healthcare-seeking behaviour. To minimize recall and reporting bias, we provided a list of 30 symptoms related to respiratory and gastrointestinal-related infections in Hong Kong [ 12 ], each of which was read out during the interview. The questionnaire was developed for this study, adopted or modified from previous questionnaires in similar studies [ 8 , 10 ] (see Additional file  1 ). The questionnaire consisted of six main sections, including questions on [ 1 ] self-reported symptoms of the most recent illness episode [ 2 ]; healthcare-seeking behaviour (including specific symptoms leading to healthcare-seeking, types of healthcare service utilized and time from symptom onset to medical consultation) [ 3 ]; risk perception of the symptoms [ 4 ]; behavioural change and change of contact pattern due to the symptoms [ 5 ]; social-economic and host determinant of healthcare-seeking behaviour (e.g. social economic status, medical insurance, and perceived benefit of consultation) [ 6 ]; demographic information (including age, sex, education, place of living) of the participants and caregiver (if the participants are aged below 16 years). Types of healthcare service considered in our questionnaire included private general practitioner clinics (GP), general out-patient clinics (GOPC) from the public sector, Chinese medicine practitioner clinics (CMP), and Accident and Emergency Department (A&E). For the main outcome healthcare-seeking behaviour, we specifically asked the participants which symptoms directly triggered their healthcare-seeking behaviour. Besides studying healthcare-seeking behaviour by specific symptoms, we also grouped symptoms into influenza-like illness (ILI) and acute respiratory illness (ARI). ILI was defined as fever (≥37.8 °C) plus cough or sore throat; ARI was defined as any two of the symptoms including fever (≥37.8 °C), chills, headache, myalgia, cough, runny nose, and sore throat. We collected information on time from symptom onset to healthcare-seeking for each symptom that the participants have reported. We interviewed all participants irrespective of whether they had illness in the 30 days preceding the first interview, hence avoided selection of participants who were sick in the first round of interview.

Statistical analysis

We described the healthcare-seeking behaviour triggered by specific symptoms and by ILI and ARI in all participants and by three age groups: children (0–15 years), adults (16–54 years), and the elderly (≥55 years). We defined a symptom as a trigger if the subject specifically stated that s/he sought medical consultation due to this symptom. We calculated proportion of participants seeking medical consultation by each symptom, by using the number of responses reporting medical consultation due to the symptom as numerator, and the number of episodes of each symptom as denominator. We calculated proportions of healthcare service type utilized and the distribution of the timing by each symptom, using the number of responses reporting medical consultation by each symptom as denominator. To avoid over-representation of healthcare-seeking behaviour triggered by the same symptoms for the same participants, we only included the first episode of a certain symptom for analysis. Also, healthcare-seeking behaviour was right-censored when symptom onset was close to the interview. We assumed that healthcare-seeking behaviour were fully observed for participants who had symptom onset more than 6 days before the interview, or those who have recovered at the time of interview. Participants who reported time from symptom(s) onset to medical consultation more than 30 days were regarded as missing data. Censored healthcare-seeking behaviours were imputed based on the fully observed data, with consideration of different days elapsed since symptom onset. Subjects who attended A&E were excluded when characterizing the duration from symptom onset to medical consultation.

Missing data were handled using multiple imputation with 100 sets of imputed datasets. We applied Rubin’s rules to obtain the overall estimates and 95% confidence intervals [ 13 ]. To achieve population representativeness, we applied post-stratification adjustment by age and sex according to local census data in 2014. Healthcare-seeking behaviours are described by medical consultation rate triggered by the symptom, healthcare service utilized by participants, and time from symptom onset to medical consultation. We used likelihood ratio test to assess potential age differences on healthcare-seeking behaviour using median p -values resulting from multiple imputation [ 14 ]. For better presentation, we combined symptoms which are related (e.g. eye problems) or having fewer than 20 reported illness episodes. All analyses were conducted in R version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria). A p -value of less than 0.05 was considered to be statistically significant.

Incidence of illness and proportion of healthcare seeking

We recruited 3253 participants in the first round of survey, regardless of whether illness was reported 30 days preceding the interview, and received a total of 8727 responses throughout 4 rounds of telephone survey from February 2014 to May 2015. The response rate of the main sample was 29.0% in the first round, with follow-up rates of 73.6, 57.3 and 41.4% in rounds 2 to 4 respectively. The booster samples were recruited by referrals, with follow-up rates of 56.4, 42.0 and 22.0% from rounds 2 to 4 respectively. Among the 8727 responses, a total of 4370 illness episodes were reported from 2564 participants (Table  1 ), resulting in 7914 reported symptoms. The onset of 763 reported illness episodes were within 7 days of the telephone interview and for those participants who have not reported seeking medical consultations, their healthcare-seeking behaviours were considered right-censored and were handled using multiple imputation. After excluding the recurring symptoms, a total of 7120 reported symptoms from 4015 illness episodes were included for analysis (Tables  2 & 3 ). Symptoms related to chronic conditions usually had a larger number of repeated episodes, such as fatigue (201 recurring symptoms) and headache (118 recurring symptoms).

To achieve population representativeness, we applied post-stratification adjustment for age and sex. Young male adults were over-represented, with post-stratification weights ranging from 0.3 to 1.1, while the older population was under represented in our study, with post-stratification weights of 18.0 and 4.6 in female and male participants respectively (Table 1 ).

Fatigue was the most frequently reported symptom (30-day incidence = 9.1%), followed by headache (7.7%) and runny nose (7.7%) (Table  2 ). Fever was the strongest driver to seeking medical consultation: 77.8% of the participants having fever had sought for medical consultation, followed by rash (59.8%) and shortness of breath (58.2%) (Table 2 ). Symptoms related to acute illness were associated with higher medical consultation rates than those related chronic illness, such as nausea, low back pain, myalgia, and fatigue.

Over a 30-day period, almost half of the adults (aged between 16 and 54 years) reported having any symptoms (46.5%), though they are least likely to seek healthcare service when comparing to other age groups (Table  3 ). For children, runny nose had the highest 30-day incidence rate of 11.9%, followed by cough with incidence rate of 9.5%. For adults and the elderly, fatigue (10.0 and 7.9%, respectively) and headache (8.6 and 7.9%, respectively) were most common.

When compared across age groups, incidence rates of fever, rash, vomiting, cough, runny nose, ILI and ARI were highest in children and lowest in the elderly. Incidence rate of loss of appetite was highest in children but lowest in the 16–54 years age group (Table 3 ). For symptoms including headache, dizziness, chills, abdominal pain, low back pain, myalgia, and fatigue, subjects aged 16–54 years had the highest incidence rates and children had the lowest incidence rates.

Older adults who had symptoms including fever, sore throat, and headache had significantly higher consultation rates comparing to other age groups (Table 3 ). Children were most likely to utilize medical services, while younger adults were least likely to seek medical consultation, except when they developed rash.

Types of healthcare service utilized

Regardless of specific symptoms, western medicine, i.e. GP and GOPC, was the most preferred healthcare provider, accounting for 80.9% of consultations. Private GP was the main service utilized by participants with most of the symptoms considered, especially those related to acute illness (Fig.  2 ). CMP was more likely to be utilized for patients with low back pain, myalgia and fatigue, and least utilized by participants with acute symptoms. 50.7% of the participants who utilized medical care due to low back pain visited CMP only. GOPC, as a public service, was only preferred by participants with eye-related symptoms, of which 53.7% visited public doctors. Considering general medical practitioners only, our study found that patients favoured GP (70.5%) over GOPC (9.9%), and relatively few participants utilized both private and public medical services (0.5%). Participants with myalgia (11.6%), shortness of breath (8.8%), and fever (7.0%) have sought both western and CMP services. 16.7% of the participants with any symptoms visited CMP, and 12.9% visiting CMP only. Most of the participants with ILI and ARI visited general medical practitioners, with proportions of 89.4 and 86.3%, respectively (Fig. 2 ).

Type of healthcare services by symptoms in different age groups, among those who sought care. Healthcare services included private general practitioners (GP), public general outpatient clinics (GOPC), Chinese medicine practitioner clinics (CMP), and Accident and Emergency Department (A&E)

Common to each age group, participants mostly consulted western medicine for acute symptoms and CMP for chronic symptoms. Children seemed more likely to consult CMP for several specific symptoms, while the other age groups consulted CMP for broader range of symptoms. Young adults were most likely to seek both western medical service and CMP, compared with other age groups. A&E visits were mostly utilized by the older population, mainly triggered by fever (12.9%), chills (14.9%), ILI (9.6%) and ARI (6.3%).

Duration between symptom onset and medical consultation

Figure  3 shows the duration between symptom onset and medical consultation in the three specific age groups and overall. Most of the participants sought medical consultation within 2 days of symptom onset regardless of symptoms. Among participants with fever, diarrhoea, vomiting, chills, abdominal pain, nausea, and ILI, more than half of the participants sought medical consultation within 12 h due to these symptoms. Among those participants who had sought medical attention due to symptoms related to acute illness and discomfort, these consultations usually took place immediately or within 12 h of symptom onsets, while it usually took longer for patients with symptoms related to chronic illness. Compared to other age groups, older participants tend to delay seeking consultation slightly. In particular, most of the older participants reported with fever either sought medical services immediately, or delay it to 2 days after symptom onset.

Duration from symptom onset to medical consultation for each triggering symptom, by age groups and all participants, among those who sought care. The symptoms were ordered descendingly based on proportion of seeking healthcare service

We studied healthcare-seeking behaviour specific to symptoms, which allows interpretation and application of the results in the patient perspective for Hong Kong Chinese population. Our study found that nearly half of the participants reported infectious diseases-related symptoms over a 30-day period, and 41.4% of whom have sought medical consultation (Table 2 ). Consultation rate varied across symptoms, ranging from 14% due to fatigue, to 78% due to fever, and was usually higher among those with acute/infectious symptoms and lower among those with mild/chronic symptoms (Table 2 ). The consultation rates were highest in the children and lowest in young adults, suggesting that the working population is least likely to seek medical attention when having infectious disease-related symptoms.

An overall consultation rate of about 40% (Table 2 ) for symptomatic patients of respiratory and gastrointestinal-related infections suggested that the majority of patients were not captured by the healthcare system, forming the submerged part of the disease iceberg. Understanding the proportions of medically unattended patients may help policy makers for developing health campaigns targeting these individuals or estimating the full burden of disease.

In Hong Kong, the private sector is the major provider of primary care, delivering about 70% of outpatient consultations [ 8 ], and CMP is used as the main alternative and complementary healthcare service in Hong Kong. In our study, we also found that western medicine is the preferred healthcare provider, contributing more than 80% of the consultations (Fig. 2 ). 16.7% of consultations visited CMP (Fig. 2 ). A local study showed that 85% of people who have sought medical consultation had consulted western medicine, while 10% had consulted CMP [ 8 ]. Another study found that 8.8% of respondents who reported symptoms during the 30 days before survey had visited a CMP for the discomfort [ 15 ]. In comparison, our finding shows that the preference for CMP may have increased slightly in the last decade with the promotion of Chinese Medicine by the Hong Kong Government. Many patients utilized both systems in parallel, taking western medicine to relieve symptoms and Chinese medicine to restore balance and health. In our study, 3.8% of participants had sought both western and Chinese medicine consultation for the same illness episode (Fig. 2 ). This could be interpreted as integrative medicine, or was in fact doctor shopping.

Participants had different preference on the type of health service according to their symptoms. Participants with acute symptoms favoured western medicine, whereas participants with gradually developing symptoms prefer to visit CMP. This preference could be explained by the common perception that western medicine is ‘powerful and quick’ comparing to CMP [ 16 ]. Chan et al. found that older, poorer people who have chronic conditions were more sceptical of western physicians [ 17 ]. In our study, we also found that older people having chronic symptoms such as low back pain, myalgia, and fatigue have 10–20% higher utilization of CMP than those of younger age. Considering western medicine only, our study found that patients favoured GP over GOPC regardless of their symptoms, consistent with a study showing that 76% of patients utilized primary care service provided by GPs [ 4 ].

Meng et al. [ 18 ] investigated the difference in healthcare-seeking behaviour of patients with ILI (defined as “at least two of the signs or symptoms [fever ≥37.8 ̊C, cough, sore throat, headache, or myalgia]”, more similar to the definition of ARI in our study) between summer and winter influenza epidemics. Meng et al. [ 18 ] found that 25.0 and 38.6% of respondents reported ILI in summer and winter peak, respectively. Among those with ILI, 42.3 and 48.5% had sought medical care for each peak, respectively. In our study 64.0% of those with ARI sought medical care (Table 2 ), probably because our surveys were carried out closer to the influenza peak period. In a US study, 40 and 56% of the adults and children respectively who had ILI sought healthcare service during the 2009 H1N1 pandemic [ 19 ], compared to 92 and 84% in our study (Table 2 ). Patients in Hong Kong were much more likely to seek medical attention when presenting with influenza-associated symptoms.

In our study, 91.7 and 75.4% of the children with ILI and ARI respectively sought medical consultation (Table 2 ). In Israel, 81.5% of the children under 13 year-old consulted a physician when they had flu-like symptoms [ 20 ]. Both studies showed that children with flu-related symptoms would have a high consultation rate. Age difference in the consultation rate was statistically significant only for ARI ( p -value < 0.001) but not ILI ( p -value = 0.106), with adults having ARI noticeably less likely to seek medical consultation (Table 3 ). Comparing with ARI, ILI is more specific to influenza infection, and led to high consultation rates irrespective of age (Table 3 ). The high consultation rates due to ILI may result in school or work absence, which probably reduced influenza transmission risk in schools or workplace. In Hong Kong, medical certificate is required for taking sick leave according to the Employment Ordinance. Though this may not be strictly enforced for short sick leave of 1 or 2 days, the need of medical certificate for the working population cannot explain the lower healthcare-seeking behaviour among adults.

Previous studies showed that some influenza patients did not visit doctors. The proportions vary across countries, for example 55% of ILI patients in the US [ 21 ], and 38% of cases of self-defined influenza in France [ 22 ]. From our study, the proportions were lower in Hong Kong (10 and 35% for patients with ILI and ARI respectively, Table 2 ). Most of the influenza surveillance systems are established in the clinical settings, which limits its ability to fully capture the burden of ILI/ARI for patients who have mild symptoms or do not seek any medical consultation. Our findings may help to estimate the proportion not being captured in the surveillance system.

Few studies examined the duration between symptom onset to medical consultation for common infectious diseases, in particular with respective to specific symptoms. In our study, more than 60% of participants had sought medical care within 2 days from symptom onset (Fig.  3 ). A US study showed that among adults with seasonal influenza, 35 and 47% sought medical care within 2 days and within 3–7 days of illness onset respectively [ 21 ], compared to our results for adults with ILI (65 and 35% respectively, combining age groups 16–54 years and ≥ 55 years in Fig. 3 ). The relatively short duration from illness to medical attention in Hong Kong may be attributed to easy access of medical service in a compact city. Delayed access to healthcare might be associated with longer hospital stays and poorer health outcomes [ 23 ]. Shorter duration between symptom onset and medical consultation may allow patient to have more timely diagnosis and better health outcomes.

There are a few limitations in our study. First, our data had a relatively low response rate and might suffer from under-representation of the older population. We addressed this issue by applying post-stratification weighting methods. Second, some other factors that may affect symptom-specific healthcare-seeking behaviour such as self-medication, and vaccination status were not explored in this descriptive study. Third, there may be recall bias for reporting the illness in the past 30 days. We specifically asked the participants to report the latest illness episode, and provided a list of symptoms to minimize under-reporting. However, very mild and unattended symptoms could still be missed from the survey, especially for symptoms reported by parents of younger children. Fourth, there is seasonal variation in disease activities, the associated symptoms and potentially healthcare-seeking behaviour trigged by these symptoms.

Healthcare-seeking behaviour varied substantially by infectious-disease associated symptoms and age for the Hong Kong population. People with acute symptoms were more likely to see western medicine, and people with symptoms related to chronic conditions favoured Chinese medicine. Characterization of these patterns provides crucial parameters for estimating the full burden of common infectious diseases from facility-based surveillance system, for planning and allocation of healthcare resources.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available but are available upon request to the corresponding author.

Abbreviations

Accident and emergency department

Acute respiratory illness

Confidence interval

Chinese medicine practitioner clinics

General out-patient clinics

General practitioner clinics

Healthcare seeking

• Influenza-like illness

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Acknowledgments

We thank the interviewers from Hong Kong Quality Assurance Agency (HKQAA) for conducting telephone and online interviews.

This study was supported by Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region (grant no. 13121262) and Theme-based Research Scheme of the Hong Kong University Grants Committee (grant no. T11–705/14-N). The funding body has no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Qiqi Zhang, Shuo Feng, Irene O. L. Wong, Dennis K. M. Ip, Benjamin J. Cowling & Eric H. Y. Lau

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Contributions

SF, IOLW, DKMI, BJC and EHYL designed study. QZ analysed data and drafted manuscript. QZ, IOLW, BJC, and EHYL have contributed to interpretation of the results. All authors participated in reviewing and revising of the manuscript, and approved the final manuscript as submitted.

Corresponding author

Correspondence to Eric H. Y. Lau .

Ethics declarations

Ethics approval and consent to participate.

Verbal or online informed consent was obtained prior to the survey, from all participants and parents/caregivers of the participants aged below 16 years. Ethics approval has been obtained from the Institutional Review Board (IRB) of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 13–420). The verbal/online consent was approved by the IRB for practical reason due to the nature of the survey and considering only minimal personal information were collected.

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Not applicable.

Competing interests

B. J. Cowling has received research funding from Sanofi for a study of influenza vaccine effectiveness in China, and honoraria from Sanofi and Roche. Other authors declare that they have no conflict of interest

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Supplementary information

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Healthcare-seeking Behavior Survey.

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Zhang, Q., Feng, S., Wong, I.O.L. et al. A population-based study on healthcare-seeking behaviour of persons with symptoms of respiratory and gastrointestinal-related infections in Hong Kong. BMC Public Health 20 , 402 (2020). https://doi.org/10.1186/s12889-020-08555-2

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DOI : https://doi.org/10.1186/s12889-020-08555-2

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• Healthcare seeking behaviour
• Symptom-specific

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Conceptual models of health behavior: research in the emergency care settings

Affiliation.

• 1 Departments of Emergency Medicine and Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA. [email protected]
• PMID: 20053231
• PMCID: PMC5103302
• DOI: 10.1111/j.1553-2712.2009.00543.x

This article provides recommendations for incorporating conceptual models of health behavior change into research conducted in emergency care settings. The authors drafted a set of preliminary recommendations, which were reviewed and discussed by a panel of experienced investigators attending the 2009 Academic Emergency Medicine consensus conference. The original recommendations were expanded and refined based on their input. This article reports the final recommendations. Three recommendations were made: 1) research conducted in emergency care settings that focuses on health behaviors should be grounded in formal conceptual models, 2) investigators should clearly operationalize their outcomes of interest, and 3) expected relations between theoretical constructs and outcomes should be made explicit prior to initiating a study. A priori hypothesis generation grounded in conceptual models of health behavior, followed by empirical validation of these hypotheses, is needed to improve preventive and public health-related interventions in emergency care settings.

(c) 2009 by the Society for Academic Emergency Medicine.

• Consensus Development Conferences as Topic
• Emergency Medical Services
• Health Behavior*
• Health Services Research* / standards
• Models, Theoretical
• Operations Research
• Outcome Assessment, Health Care
• Public Health
• Research Design

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• R01 DA023170/DA/NIDA NIH HHS/United States

Handbook of Health Promotion and Disease Prevention pp 95–121 Cite as

Health-Care-Seeking Behaviors

• M. Janice Gilliland ,
• Martha M. Phillips 4 ,
• James M. Raczynski 5 ,
• Delia E. Smith 6 ,
• Carol E. Cornell 6 &
• Vera Bittner 7  

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2 Citations

Part of the book series: The Springer Series in Behavioral Psychophysiology and Medicine ((SSBP))

Health-care-seeking behavior is that action taken by an individual in response to a stimulus (such as the perception of a symptom) that he or she decides is indicative of a condition needing evaluation by a health professional. This behavior is influenced by personal, physical, and psychological characteristics and by sociocultural and environmental factors. Structural barriers or facilitators can also hinder or abet the decision to seek care. Health-care-seeking behavior is closely related to symptom perception (Chapter 5) in that symptoms are often the stimulus or cue that initiates action by individuals. More urgent, unambiguous symptoms tend to encourage rapid care seeking (Alonzo, 1986; Ell et al. , 1994; Hartford, Herlitz, Karlson, & Risenfors, 1990); but even so, people often delay for days, weeks, or even months with symptoms of acute myocardial infarction (AMI), stroke, or cancer. Symptoms and symptom perceptions may contribute to delay or avoidance of care seeking because of psychological responses such as fear, anxiety, or denial (Bosl {ti et al. }, 1981; Hackett & Cassem, 1969; Millar & Millar, 1996).

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Gilliland, M.J., Phillips, M.M., Raczynski, J.M., Smith, D.E., Cornell, C.E., Bittner, V. (1999). Health-Care-Seeking Behaviors. In: Raczynski, J.M., DiClemente, R.J. (eds) Handbook of Health Promotion and Disease Prevention. The Springer Series in Behavioral Psychophysiology and Medicine. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4789-1_6

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Health Research

Health behaviors.

This article defines health behaviors and then overviews data on key health behaviors (smoking, diet, exercise/physical activity, health screening, sexual behaviors, and alcohol use). Variations in these behaviors by sociodemographic factors are then considered. The next section addresses the psychological determinants of health behaviors and considers key models, such as the theory of planned behavior, health belief model, and social cognitive theory, and how these models might be integrated. The final section discusses how health behaviors might be changed to improve health outcomes.

Introduction

Much of the interest in behaviors that have important impacts on our health and well-being is based upon two assumptions: that a significant proportion of the mortality from the leading causes of death is caused by the behavior of individuals, and that such behavior is modifiable (Conner and Norman, 2005). Behavior is held to exert its influence on health in three basic ways: by producing direct biological changes; by conveying health risks or protecting against them; or by leading to the early detection or treatment of disease (Baum and Posluszny, 1999).

The Definition of Health Behaviors

Health behaviors have been defined in a variety of ways. For example, Conner and Norman (2005) define them as any activity undertaken for the purpose of preventing or detecting disease, or for improving health and well-being. Gochman (1997) in the Handbook of Health Behavior Research defines them as “. behavior patterns, actions and habits that relate to health maintenance, to health restoration and to health improvement” (vol. 1: p. 3). Behaviors within these definitions include medical service usage (e.g., physician visits, vaccination, screening), compliance with medical regimens (e.g., dietary, diabetic, antihypertensive regimens), and self-directed health behaviors (e.g., diet, exercise, smoking, alcohol consumption). Each has received considerable attention from social and behavioral researchers and we now have a growing understanding of the factors determining engagement in such behaviors and ways in which such behavior can be changed.

In describing health behaviors it is common to distinguish health-enhancing from health-impairing behaviors. Health-impairing behaviors have harmful effects on health or otherwise predispose individuals to disease and include smoking, excessive alcohol consumption, and high dietary fat consumption. In contrast, engagement in health-enhancing behaviors convey health benefits or otherwise protect individuals from disease and include physical activity and exercise, fruit and vegetable consumption, and condom use in response to the threat of sexually transmitted diseases. A third set of health behaviors focus on detecting potential health problems and include behaviors such as health screening and testicular/breast self-examination.

Key Health Behaviors

Numerous studies have examined the health-behavior–health-outcome relationship. One of the first such studies identified seven features of lifestyle which were associated with lower morbidity and higher subsequent long-term survival: not smoking, moderate alcohol intake, sleeping 7–8 h per night, exercising regularly, maintaining a desirable body weight, avoiding snacks, and eating breakfast regularly (Belloc and Breslow, 1972). Health behaviors also impact upon individuals’ quality of life by delaying the onset of chronic disease and extending healthy life span. Smoking, alcohol consumption, diet, gaps in primary care services, and low screening uptake are all significant determinants of poor health, and changing such behaviors should lead to improved health. Health recommendations in countries across the Western world currently emphasize the importance of an increase in fruit and vegetable consumption, a reduction in dietary fat consumption, increased physical activity, and reductions in tobacco, alcohol, and drug use as important for health promotion and disease prevention.

Smoking is the health behavior most closely linked with long-term negative health outcomes. Morbidity and mortality from coronary heart disease (CHD) are increased among smokers (Doll et al., 1994). Moreover, there is a strong positive relationship between the number of cigarettes smoked per day and the incidence of CHD (Friedman et al., 1979). Smoking has also been linked to a number of cancers including cancer of the lung, throat, stomach, and bowel as well as a number of more immediate negative health effects such as reduced lung capacity and bronchitis (Royal College of Physicians, 1983). Despite the array of negative health outcomes, smokers often report positive mood effects from smoking and the use of smoking as a strategy for coping with stress.

A variety of studies support the idea that the vast majority of smokers take up this habit as adolescents with 40% of adult smokers having started before they reached 16 years of age (Royal College of Physicians, 1992). The number of people smoking in the USA and UK has shown a steady decline over the past 20 years from over 30% in 1999 to below 20% now (Center for Diseases Control, 2008). Smoking is higher among less educated, lower income, and minority groups (Rigotti, 1989). Those who quit smoking reduce the risk to their health, particularly if they quit before 35 years of age (Doll et al., 1994).

The impacts of diet upon morbidity and mortality are well established (e.g., USDHHS, 1988). In the Third World, the problems related to diet and health are ones of undernutrition; in the First World, the problems are predominantly linked to overconsumption of food. In Western industrialized countries, excessive fat consumption and insufficient fiber, fruit, and vegetable consumption are related to health problems. In addition, excess consumption of calories combined with insufficient exercise has made obesity a major health problem. Diet has been implicated in cardiovascular diseases (CVDs), stroke and high blood pressure, cancer, diabetes, obesity, osteoporosis, and dental disease.

It is generally agreed that elevated blood cholesterol level is a major risk factor for the development of CVD (Consensus Development Conference on Lowering Blood Cholesterol to Prevent Heart Disease, 1985) with significant proportions of the population in countries in the Western world having cholesterol levels higher than the level considered to be healthy. For example, in the USA, it is estimated that 50% of the adult population is at risk of CHD due to elevated blood cholesterol levels (Sampos et al., 1989). The reduction of blood cholesterol via dietary change is now widely accepted as an important way of tackling CHD. Dietary recommendations include reducing fat in the diet and increasing soluble fiber intake. However, their impact upon cholesterol levels may be limited.

Physical Activity/Exercise

The potential health benefits of engaging in physical activity and regular exercise include reduced cardiovascular morbidity and mortality, lowered blood pressure, and the increased metabolism of carbohydrates and fats, as well as a range of psychological benefits such as improved self-esteem, positive mood states, and reduced life stress and anxiety. Nevertheless, many adults in Europe and the US fail to meet physical activity recommendations. For example, the General Household Survey (1989) indicated that only one in three men and one in five women in the UK participate in any sport or recreational physical activity. Moreover, the Allied Dunbar Fitness Survey (1992) of 6000 English adults reported that only one in six adults had engaged in any physical activity that would have been likely to benefit their health (i.e., for 20 min or more at a moderate or vigorous level) in the previous 4 weeks. Participation in regular physical activity and exercise is strongly related to a number of sociodemographic variables. In particular, young people, males, and those from higher socioeconomic status groups are more likely to engage in regular physical activity and exercise.

Health Screening

Individuals may seek to protect their health by participating in various screening programs, which attempt to detect disease at an early, or asymptomatic, stage. In the UK, screening programs have been set up for various diseases including anemia, diabetes, bronchitis, and various cancers (e.g., cervical, bowel, and breast cancers). Taking the example of cervical screening (or PAP testing), it is estimated that if women were screened every 3 years, the cervical cancer mortality rate could be reduced by 70–95% (Greenwald and Sondik, 1986). Research from the UK shows that between 10 and 32% of eligible women remain unscreened (National Audit Office, 1998), while in the USA the number of unscreened women ranges from 13 to 30% (Ruchlin, 1997). Participation tends to be negatively related to age, and positively related to education level and socioeconomic status.

Sexual Behaviors

Sexual behaviors are considered health behaviors because of their impact upon the spread of sexually transmitted infections such as gonorrhea and syphilis. More recently, the role of sexual behaviors in the spread of the human immunodeficiency virus (HIV) has been a focus of attention. While early health education campaigns emphasized the need to reduce the number of sexual partners or avoid particular sexual practices (e.g., anal sex, penetrative sex), more recently the focus has been upon the use of condoms during penetrative sex to reduce the risk of HIV transmission. Condom use is particularly recommended for those with multiple partners or those who do not know their partners’ sexual history. For these reasons, much of the health advice concerning condom use has been focused on young people.

There seems to be considerable variation in the use of condoms in response to the threat of HIV/AIDS, although systematic condom use is not widespread even among single heterosexuals. For example, 78% of respondents in an American study declared they did not always use a condom during sexual intercourse (Choi and Catania, 1996). A Canadian study among a population aged 15 years and older reported similar findings with 28% of respondents reporting not having used a condom during their last sexual intercourse with an occasional partner (Health Canada, 1998). In a European study, among respondents who had more than one partner in the past year, only 52% of the men and 41% of the women declared having used a condom at least once (Guiguet et al., 1994).

Alcohol Use

Moderate alcohol consumption has been linked to positive health outcomes. However, high alcohol consumption has been linked to a range of negative health outcomes including high blood pressure, heart disease, and cirrhosis of the liver. High levels of alcohol consumption have also been associated with accidents, injuries, suicides, crime, domestic violence, rape, murder, and unsafe sex (British Medical Journal, 1982). While many of the adverse effects of high alcohol consumption are due to continued heavy drinking (e.g., cirrhosis of the liver, heart disease), others are more specifically related to excessive alcohol consumption in a single drinking session (e.g., accidents, violence).

The General Household Survey (1992) reported that the average weekly consumption of alcohol in the UK was 15.9 units (1 unit ¼ 1 glass of wine or 1 measure of spirits or 0.5 pints of beer) for men and 5.4 for women. In addition, 27% of men and 11% of women were drinking more than the recommended weekly sensible limits (21 units for men, 14 units for women). Heavy drinking is more likely among younger age groups. In a survey of 12 000 Welsh adults, Moore et al. (1994) reported that 31.1% of drinkers aged 18–24 engaged in binge drinking (i.e., drinking half the recommended weekly consumption of alcohol in a single session) at least once a week.

Relationship of Health Behaviors to Sociodemographic Factors

A clearer understanding of why individuals perform health behaviors might assist in the development of interventions to help individuals gain health benefits. A variety of factors have been found to account for individual differences in the performance of health behaviors. Demographic variables show reliable associations with the performance of health behaviors (e.g., age, gender, ethnic status). For example, there is a nonlinear relationship between many health behaviors and age, with high incidences of many health-risking behaviors such as smoking in young adults and much lower incidences in children and older adults (Blaxter, 1990). Such behaviors also vary by gender, with females being generally less likely to smoke, consume large amounts of alcohol, engage in regular exercise, but more likely to monitor their diet, take vitamins, and engage in dental care (Waldron, 1988). Differences by socioeconomic status are also apparent for behaviors such as diet, exercise, alcohol consumption, and smoking (e.g., Blaxter, 1990) with increases in health-risking behaviors and decreases in health-protective behaviors in lower socioeconomic status groups.

Generally speaking, younger, wealthier, and better-educated individuals are more likely to practice health protective behaviors. Access to medical care has been found to influence the use of such health services (e.g., Black Report, 1988) and may explain some socioeconomic status differences in health behaviors. These factors may interact with other influences such as levels of stress and social support. Higher levels of stress and/ or fewer resources are associated with health-risking behaviors such as smoking and alcohol abuse (Adler and Matthews, 1994). Social factors seem to be important in establishing health behaviors in childhood. Parent, sibling, and peer influences are important, for example, in the initiation of smoking. Cultural values also have a major impact, for instance, in determining the number of women exercising in a particular culture. Steptoe and Wardle (1992) report that between 34 and 95% of women in their European student sample had exercised in the past 14 days.

Understanding the Distribution/Prevalence of Health Behaviors

A considerable body of social and behavioral research has examined individual variables explaining sociodemographic differences in performance of health behaviors. An assumption is that these variables may be more readily modifiable in attempts to change behavior. For example, personality variables such as conscientiousness have been related to mortality. Conscientiousness refers to the ability to control one’s behavior and to complete tasks. Highly conscientious individuals are more organized, careful, dependable, self-disciplined, and achievement-oriented than those low in conscientiousness. There is considerable evidence showing conscientiousness to impact on health behaviors, health outcomes, and even mortality. Friedman et al. (1993) reported that conscientiousness was significantly associated with lower mortality in later life with those high in conscientiousness likely to live longer (by about 2 years), compared to those low in conscientiousness. An important mechanism by which conscientiousness may influence health is through health behaviors. Friedman et al. (1995) showed that the impact of conscientiousness on longevity was partly accounted for by its effect on reducing smoking and alcohol use. A review of work on the relationship between conscientiousness and behavior (Bogg and Roberts, 2004) showed conscientiousness to be positively related to a range of protective health behaviors (e.g., exercise) and negatively related to a range of risky health behaviors (e.g., smoking).

Thoughts and feelings about a health behavior (or health cognitions) also determine whether or not an individual practices health behaviors and may explain how other factors influence behavior. Knowledge about behavior–health links is an important factor in an informed choice concerning health behaviors. Various health cognitions have been studied including perceptions of health risk, efficacy of behaviors in influencing this risk, social pressures to perform the behavior, and control over performance of the behavior. The relative importance of various health cognitions in determining who performs various health behaviors constitutes the basis of several different models. Such models have been labeled social cognition models (SCMs) because of their focus on health cognitions as the primary determinant of individual social behaviors. These SCMs provide a basis for understanding the determinants of behavior and behavior change. Each of these models emphasizes the rationality of human behavior and assumes that behavior is based upon elaborate, but subjective, cost/benefit analysis of the likely outcomes of differing courses of action. It is assumed that individuals generally aim to maximize benefits and minimize costs in selecting a behavior. The effects of sociodemographic variables on health behavior are mediated by health cognitions in these models, although direct and moderated relationships are reported (Conner et al., 2013).

Health Belief Model

The health belief model (HBM) outlines two types of health beliefs that make a behavior in response to illness more or less likely (Abraham and Sheeran, 2005): perceptions of the threat of illness and evaluation of the effectiveness of behaviors to counteract this threat. Threat perceptions depend upon the perceived susceptibility to the illness and the perceived severity of the consequences of the illness. Together these variables determine the likelihood of the individual following a health-related action, although their effect is modified by demographic variables, social pressure, and personality. The particular action undertaken is determined by the evaluation of the possible alternatives. This behavioral evaluation depends upon beliefs concerning the benefits or efficacy of the health behavior and the perceived costs or barriers to performing the behavior. Hence, individuals are likely to follow a particular health behavior if they believe themselves to be susceptible to a particular condition or illness, which they consider to be serious, and believe the benefits of the behavior undertaken to counteract the condition or illness outweigh the costs. It is assumed that this whole process is set in motion by cues to action. Cues to action include a diverse range of triggers to the individual taking action and are commonly divided into factors that are internal (e.g., physical symptoms) or external (e.g., mass media campaigns, advice from others) to the individual. Other influences upon the performance of health behaviors, such as demographic factors or psychological characteristics (e.g., personality, peer pressure, perceived control over behavior), are assumed to exert their effect via changes in the components of the HBM.

Theory of Planned Behavior

The theory of planned behavior (TPB) was developed by social psychologists and has been widely employed to health behaviors (Ajzen, 1991; McEachan et al., 2011). The TPB specifies the influences that determine the individual’s decision to follow a particular behavior. Within the TPB, the determinants of behavior are intentions to engage in that behavior and perceived behavioral control (PBC) over that behavior. Intentions represent a person’s motivation. The construct is conceptualized as an individual’s conscious plan or decision to exert effort in order to engage in a particular behavior. PBC is a person’s expectancy that performance of the behavior is within his or her control or confidence that he or she can perform the behavior. Intentions are determined by three variables. The first is attitudes, which are an individual’s overall evaluation of the behavior. The second is subjective norms, which consist of a person’s beliefs about whether significant others think he or she should engage in the behavior. The third is PBC. Recent revisions to the TPB have suggested that attitudes may divide into instrumental and affective components and subjective norms may divide into injunctive and descriptive norms while PBC may divide into perceived confidence and perceived control (Conner and Sparks, 2005).

The attitude, subjective norm, and PBC components are determined by underlying beliefs. Attitude is a function of a person’s salient behavioral beliefs, which represent perceived likely consequences of the behavior (e.g., taking exercise will reduce my risk of heart disease). Subjective norm is a function of normative beliefs, which represent perceptions of specific salient others’ preferences about whether one should or should not engage in a behavior (e.g., my family think I should take exercise). PBC is based on beliefs concerning access to the necessary resources and opportunities to perform the behavior successfully (e.g., I have easy access to a place where I can exercise). So, according to the TPB, individuals are likely to engage in a health behavior if they believe that the behavior will lead to particular outcomes which they value, if they believe that people whose views they value think they should carry out the behavior, and if they feel that they have the necessary resources and opportunities to perform the behavior.

Social Cognitive Theory

In social cognitive theory (SCT; Bandura, 1982), behavior is held to be determined by four factors: goals, outcome expectancies, self-efficacy, and sociostructural variables. Goals are plans to act and can be conceived of as intentions to perform the behavior (see Luszczynska and Schwarzer, 2005). Outcome expectancies are similar to behavioral beliefs in the TPB but here are split into physical, social, and self-evaluative depending on the nature of the outcomes considered. Self-efficacy is the belief that a behavior is or is not within an individual’s control and is usually assessed as the degree of confidence the individual has that he or she could still perform the behavior in the face of various obstacles (and is similar to PBC in the TPB; e.g., “I am confident that I can refrain from smoking, even if someone offers me a cigarette”). Sociostructural variables are factors assumed to facilitate or inhibit the performance of a behavior and affect behavior via changing goals (e.g., impediments or opportunities associated with particular living conditions, health systems, political, economic, or environmental systems). They are assumed to inform goal setting and be influenced by self-efficacy.

Self-efficacy is one of the most powerful predictors of health behavior (Bandura, 1997). Individuals with a strong sense of self-efficacy are believed to develop stronger intentions to act, to expend more effort to achieve their goals, and to persist longer in the face of barriers and impediments. Perceived self-efficacy is therefore believed to play a crucial role in the determination of health behavior.

Integrated Models of Health Behavior

Models like the HBM, TPB, and SCT show several similarities but also a number of key differences. One approach to combining these models is to consider developing an integrated model. This may be valuable given the overlap in cognitions considered by different models. For example, it is widely accepted that the key cognitions influencing behavior are intention, self-efficacy, and outcome expectancies (or attitudes). One attempt at integration suggested that there were eight key variables determining behavior (Fishbein et al., 2001). The variables are organized into two groups. The first group includes variables viewed as necessary and sufficient determinants of behavior. For behavior to occur an individual must:

• have a strong intention to perform the behavior;
• have the necessary skills to perform the behavior; and
• experience an absence of environmental constraints that could prevent enactment of the behavior.

The second group of variables primarily influences behavior through intention. A strong intention to act is likely to occur when an individual:

• perceives the advantages (or benefits) of performing the behavior to outweigh the perceived disadvantages (or costs);
• perceives the social (normative) pressure to perform the behavior to be greater than that not to perform the behavior;
• believes that the behavior is consistent with his or her self-image;
• anticipates the emotional reaction to performing the behavior to be more positive than negative; and
• has high levels of self-efficacy.

Changing Health Behaviors

The above models detail the key social cognitive determinants of health behavior. To the extent that these models outline the key determinants, interventions, which target these variables should lead to associated changes in behavior. For example, enhancing feelings of self-efficacy could be one means to encourage health behavior change. As Bandura (1997) outlines, there are four main sources of self-efficacy, each of which could be addressed in interventions. First, individuals can develop feelings of self-efficacy from personal mastery experience (e.g., it may be possible to split a behavior into various subgoals, such that the easiest subgoals are achieved before more difficult tasks are attempted). Second, individuals may develop feelings of self-efficacy through observing other people succeed on a task (i.e., vicarious experience). Third, it is possible to use standard persuasive techniques to try to instill feelings of self-efficacy. Finally, one’s physiological state may be used as a source of information, such that high levels of arousal or anxiety may indicate to the individual that he or she is not capable of performing a given action (e.g., relaxation techniques may be employed to help maintain feelings of self-efficacy). Alternatively interventions might target changing attitudes as a means to change behavior. This might be achieved using persuasive messages designed, for example, to target behavioral beliefs (e.g., Brubaker and Fowler, 1990). A further approach would be to target the anticipated emotional reactions to a behavior again using persuasive messages (e.g., Conner et al., 2011).

Another interesting approach has focused directly on the immediate determinant of behavior in several of these models, namely intentions. Where individuals do have an intention to engage in a health behavior (goal intentions), but are having trouble implementing their intention, forming a specific plan about where and when to act has been found helpful (Gollwitzer and Sheeran, 2006). For example, Conner and Higgins (2010) got adolescents who did not want to become smokers to repeatedly form plans about how to refuse the offer of a cigarette. This was shown to significantly reduce rates of smoking initiation over the next 4 years.

Over the past few years, research has also sought to develop a taxonomy of the different behavior change techniques (BCTs) that have been used in attempts to change health behavior and relate them to existing theory. For example, Abraham and Michie (2008) set out a total of 26 distinct BCTs used in relation to a range of behaviors. A good number of these relate directly to SCMs described earlier (e.g., provide information about the consequences of the behavior) although others set out alternative means to changing behavior (e.g., motivational interviewing).

Conclusions

Health behaviors have important consequences for both the quality and length of life partly via influencing disease outcomes. Nevertheless, there is still considerable variation in who performs these behaviors. SCMs provide one approach to understanding the variation in who performs health behaviors. These models are also useful because they suggest ways to change health behaviors in order to improve health. Research on BCTs has provided useful information on how to change health behaviors and further insights into why individuals perform these behaviors. The next big challenge for social and behavioral research on health behaviors is to demonstrate how theory-based interventions can produce effective and long-lasting behavior change that results in real benefits in terms of morbidity and mortality.

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Choosing a health behaviour theory or model for related research projects: a narrative review

Theories are integral to a research project, providing the logic underlying what, how, and/or why a particular phenomenon happens. Alternatively, models are used to guide a research project by representing theories and visualising the structural framework of causal pathways by showing the different levels of analysis. With the rise in chronic and behaviour-related diseases, health behaviour theories and models have a particular importance in designing appropriate and research led behavioural intervention strategies. However, there is a dearth of papers that explain the role of behavioural theories and models in research projects.

The aim of this paper is to synthesise existing evidence on the relevance of health behaviour theories and models in research projects.

This paper reviews health behaviour theories and models commonly underpinning research projects in public health and clinical practices. The electronic databases, such as MEDLINE, CINAHL, and Scopus, as well as the search engines Google and Google Scholar were searched to identify health behaviour theories and models.

Theories and models are essential in a research project. Theories provide the underlying reason for the occurrence of a phenomenon by explaining what the key drivers and outcomes of the target phenomenon are and why, and what underlying processes are responsible for causing that phenomenon. Models on the other hand provide guidance to a research project and assist in visualising the structural framework of causal pathways by showing the different levels of analysis. Health behaviour theories and models in particular offer valuable insights for designing effective and sustainable research projects for improved public health practice.

Conclusions

By employing appropriate health behaviour theory and/or model as a research framework, researchers will be able to identify relevant variables and translate these into clinical and public health practices.

Introduction

Theory is a way to understand reality better and is integral to research ( Reeves et al., 2008 ). Theory includes ideas like a clear hypothesis (which is only applicable in quantitative research), to working models and frameworks of thinking about reality ( Alderson, 1998 ). Meleis (2011 : 29) defined a theory as:

an organised, coherent, and systematic articulation of a set of statements related to significant questions in a discipline and communicated as a meaningful whole. It is a symbolic depiction of those aspects of reality that are discovered or invented for describing, explaining, predicting, or prescribing responses, events, situations, conditions, or relationships.

Models are used to represent theories and to guide research projects. Models typically visualise the structural framework of causal pathways by showing the different levels of analysis ( Meleis, 2011 ). Theory provides the logic behind what, how, and/or why a particular phenomenon might occur ( Kitchel and Ball, 2014 ). A research project that is rooted in theory enhances knowledge ( Leshem and Trafford, 2007 ; Sinclair, 2007 ), and therefore linking the theoretical framework of a research project to established and comprehensive theories is important. The theoretical framework provides a conceptual basis for understanding and designing an appropriate methodology to explore a given problem ( Grant and Osanloo, 2014 ; Lester, 2005 ), serving as a foundation for a research project, and guiding all of the activities related to that particular project ( Fox et al., 2015 ; Lester, 2005 ).

There are many theories and models that have been developed for the purpose of gaining a better understanding of health behaviour and influencing factors. These include, but are not limited to, the theory of planned behaviour (TPB), the theory of reasoned action, social cognitive theory (SCT), the health belief model (HBM), the behavioural model of healthcare utilisation, the trans-theoretical model of change, the ecological model, the biomedical model, and the biopsychosocial model. Theories cannot be described as right or wrong, but they do vary in their relevance to inquiries. Every theory can provide a distinct way of observing a problem, allowing its investigation from different perspectives and a more complete understanding of its facets ( Reeves et al., 2008 ). The selection of a theory that best fits a particular study is about justifying that the chosen theory meets the research questions, the structure of the research, and the research design ( Grant and Osanloo, 2014 ). Theoretical triangulation (combining two or more theories in a given research project), too, has been argued to provide the opportunity to address the issue being studied comprehensively, and to increase the validity of the explanations generated ( Ngulube et al., 2015 ; Rimer and Glanz, 2005 ). Theories and models significantly influence the way evidence is gathered, analysed, interpreted, and used ( Alderson, 1998 ). For this reason, the most-often used theories and models of health behaviour for framing the structure of robust research projects in public health and clinical practices are selected and discussed in this paper.

A search was performed on MEDLINE, CINAHL, and Scopus databases using the keywords ‘health’ AND ‘behaviour’ AND ‘model’ OR ‘theory’, from the start date of each database to January 2020. In addition, Google and Google Scholar were searched. Finally, in framing the structure of a research project in public health and clinical practices, commonly used theories and models of health behaviour are included and discussed. To identify theories and models commonly used to underpin research projects in public health and clinical practices, existing evidence including systematic reviews and meta-analyses, describing a particular theory or model, were reviewed.

Biomedical model

The biomedical model relies on the notion of disease ( Havelka et al., 2009 ), which is characterised by its sequence of aetiology to pathology to manifestation. The assumption behind this model is that every disease has a specific causal factor that physically affects the human body. This factor may be internal (vascular, immunological, and metabolic) or external (physical, chemical, and microbiological) in origin. The biomedical model views disease as a separate entity (i.e., independent of the individual affected), and therefore an individual involved is assumed to undergo medical procedures, such as surgery, radiology, and pharmacology, which physicians will manage in their entirety ( Havelka et al., 2009 ). This means that the biomedical model emphasises the pathology of the disease and generally does not consider personal and other factors that may influence its severity, outcome, treatment, or prevention.

With these notions, the biomedical model helped to enhance the understanding of disease or illness and useful treatment ( Havelka et al., 2009 ), particularly at a time when acute infectious diseases caused by a single agent were the foremost health concern. Nowadays, the view is that disease causation is multifactorial, including individual, social, and environmental factors ( Parascandola, 2011 ). In view of the complexity of health and disease, the biomedical model has been criticised by scholars from a range of disciplines for ignoring the broader social and psychological factors that may affect health behaviour ( Deacon, 2013 ; Havelka et al., 2009 ). The narrow approach of the biomedical model is exclusively organ-oriented and has little to offer prevention and control programs that may play a substantial role in reducing the occurrence of chronic diseases by changing factors such as health beliefs, attitudes, and behaviours ( Havelka et al., 2009 ). In contrast to the approach suggested by the biomedical model, individuals with a health condition should be enabled to become more actively involved in the management of their health ( Stamm et al., 2014 ).

Biopsychosocial model

The biopsychosocial model evolved from the biomedical model by considering disease or illness to be a complex outcome of biological, psychological, social, and cultural factors ( Gatchel and Turk, 2008 ). These factors may operate to ease, worsen, or otherwise alter the course of diseases, though their significance differs from disease to disease, from one person to another, and even between two different episodes of the same disease in one person ( Fava and Sonino, 2008 ). With this perspective, Engel (1977) , the originator of the biopsychosocial model, attempted to offer an understanding of disease and its determinant factors, particularly those medical conditions for which the biomedical model is not a good fit ( Green and Johnson, 2013 ). The principal contribution of the biopsychosocial model to medical healthcare is that the social dimension has been shifted from the patient context to the role of the healthcare system itself in causing and relieving disease ( Álvarez et al., 2012 ). This model has also gained extensive acceptance in guiding the provision of healthcare for various conditions ( Álvarez et al., 2012 ). There is evidence that the biopsychosocial model has made a considerable improvement in the way that chronic pain care for instance is provided ( Weiner, 2008 ). It is the biopsychosocial model that has led to the emergence of an effective multidisciplinary approach to the management of chronic pain ( Gatchel and Turk, 2008 ). In addition, a systematic review of the literature has demonstrated that the biopsychosocial model is effective for the optimal management of chronic diseases in primary care ( Kusnanto et al., 2018 ).

Despite its wide application in epidemiological studies and the clinical care of various health issues, the biopsychosocial model has limitations ( Pilgrim, 2015 ; Weiner, 2008 ). For example, there is a concern that because the biopsychosocial concept was developed as a way of approaching disease with a more humanistic and holistic view than had been customary, it was not intended to account for the limitations of all other theories or models of health, and it was simply an alternative means to understand the interplay between variables that influence population health ( Weiner, 2008 ). A variable is defined as ‘an empirical phenomenon that takes on different values or intensities’ ( Flannelly et al., 2014 : 162). Moreover, Smith et al. (2013) argued that the biopsychosocial model is not definable and therefore not testable as we presently use it. This model also fails to answer the essential question of how the biological, psychological, and social variables interact in the manifestation of the disease ( Weiner, 2008 ).

Social cognitive theory (SCT)

SCT evolved from learning theory and focusses on reciprocal determinism, the dynamic interplay between humans and their environments ( Bandura, 2004 ). Unlike most behavioural and social theories, which emphasise the personal, social, and environmental factors that govern human behaviour, SCT hypothesises that human behaviour is an artefact of the dynamic interaction of individual, behavioural, and environmental factors ( McAlister et al., 2008 ). According to this theory, human motivation and action are broadly determined by three expectancies: situation-outcome, action-outcome, and perceived self-efficacy ( Luszczynska and Schwarzer, 2005 ). Outcome expectations (situation-outcome and action-outcome) are ‘beliefs about the likelihood of various outcomes that might result from the behaviours that a person might choose to perform, and the perceived value of those outcomes’ ( McAlister et al., 2008 : 172). For example, the belief that consulting a healthcare provider and taking the course(s) of action recommended for a disease will lead to recovery from the disease would be an action-outcome expectancy. Perceived self-efficacy expectancy, which is an individual’s beliefs about his/her capability to execute a particular action required to achieve the desired outcome is the other key construct of SCT ( Luszczynska and Schwarzer, 2005 ). Taking an individual with Chronic Obstructive Pulmonary Disease (COPD) as an example, perceived self-efficacy expectancy can be described as the individual’s beliefs about his/her skill or ability to seek the required treatments to lower the chance of complications and associated consequences ( Main et al., 2010 ).

Bandura proposed that in addition to the knowledge of health risks and benefits, self-efficacy is necessary for behaviour change to occur. According to Bandura, self-efficacy is the central construct because it affects behaviour directly, and indirectly, by influencing goals, outcome expectations, as well as barriers and facilitators ( Bandura, 2004 ). Several primary studies ( Janicke and Finney, 2003 ; Rogers et al., 2005 ), systematic reviews and meta-analyses ( Plotnikoff et al., 2013 ; Stacey et al., 2015 ; Young et al., 2014 ) have been undertaken using SCT. These studies provide evidence that SCT is comprehensive in addressing a range of factors affecting health behaviours, such as healthcare utilisation, physical activity, and nutrition. However, this theory is not without criticism. For example, McAlister et al. (2008) criticised that SCT is so broad that it has not been tested comprehensively, unlike other health behaviour theories.

Theory of planned behaviour (TPB)

The TPB is an extension of the theory of reasoned action ( Montaño and Kasprzyk, 2008 ), and emphasises the theoretical concept that individual motivational factors regulate the likelihood of behaving in a particular way ( Ajzen, 1991 ). This theory relies on the underlying assumption that the most important predictors of a particular behaviour, when the individual does not have full control over that behaviour, are the intention to perform the behaviour and perceived behavioural control ( Montaño and Kasprzyk, 2008 ; Rise et al., 2010 ). Moreover, the TPB posits that intention to perform the behaviour is a function of three factors ( Casper, 2007 ):

• 1. Attitude toward the behaviour – refers to beliefs regarding the outcomes of performing a specific behaviour;
• 2. Subjective norm – refers to perceived social pressure to perform or not perform the behaviour; and
• 3. Perceived behavioural control – refers to perception about the extent to which the behaviour is within the individual’s control, measured in terms of his or her capability or skill and opportunity about performing the behaviour.

The TPB has been used successfully to predict and explain a range of health-related and social behaviours, including healthcare seeking and screening programs ( Luzzi and Spencer, 2008 ; Mo and Mak, 2009 ; Sniehotta et al., 2014 ). Further, a meta-analysis study that evaluated the suitability of the TPB provides evidence that the theory is an effective framework for predicting screening intentions and attendance ( Cooke and French, 2008 ). However, critics have noted that the TPB exclusively emphasises rational reasoning (for example, from knowledge about the significance of seeking treatment for COPD to decision to start seeking treatment), and excludes unconscious influences on health behaviour ( Sheeran et al., 2013 ) and the role of emotions beyond anticipated affective outcomes ( Conner et al., 2013 ). According to Sheeran et al. (2013) , modifying conscious cognition, such as behavioural intentions and risk perceptions, does not result in seeking treatment for COPD and adherence to the recommended course(s) of action, mainly due to the influence of non-conscious or implicit processes. This limitation is not only restricted to the TPB, but it also applies to most theories and models of health behaviour. In addition, a meta-analysis study by McEachan et al. (2011) , which attempted to predict health-related behaviours with the TPB criticised the theory for its static explanatory nature which does not assist in understanding the influences of behaviour on cognition and future behaviour.

Ecological model

McLaren and Hawe (2005 : 9) defined the ecological model as ‘a conceptual framework designed to draw attention to individual and environmental determinants of behaviour. The visual metaphor is a series of concentric or nested circles which represents a level of influence on behaviour’. This model of health behaviour integrates social and psychological factors and gives attention to environmental and policy perspectives on behaviour. The model is tailored to consider multiple levels of factors that are constantly interacting to affect health behaviour ( Glass and McAtee, 2006 ). According to Sallis et al. (2008 : 466), the four core principles of the ecological perspective model of health behaviour are that:

• 1. There are multiple influences on specific health behaviours, including factors at the intrapersonal, interpersonal, organizational, community, and public policy levels;
• 2. Influences on behaviours interact across these different levels;
• 3. Ecological model should be behaviour-specific, identifying the most relevant potential influences at each level; and
• 4. Multi-level interventions should be most effective in changing behaviour.

The main strength of the ecological model is that it is unique in considering that multiple levels of factors affect health behaviour, which expands opportunities for appropriate interventions ( Sallis et al., 2008 ). In contrast to those models focussing merely on individual factors, the ecological model’s perspective holds that policy and environmental modifications influence practically the whole population. Based on the ecological model, scholars have argued that the potential factors affecting health behaviour within a population are contemplated within the social context, which can include family, friends, neighbourhoods, and formal and informal organisations such as health institutions ( Stokols, 1996 ). Thus, the model concludes that it takes both personal-level and environmental or policy-level factors to achieve a comprehensive understanding of the diverse groups of correlates of health behaviour ( Sallis et al., 2008 ).

Although the ecological model of health behaviour is more comprehensive, critics have identified the following four limitations:

• 1. Due to its complexity, the model lacks specificity about the most significant posited influencing factors, which places a greater challenge on health professionals to determine critical factors for each health behaviour ( Livingood et al., 2011 ; Sallis et al., 2008 );
• 2. Even in the case of the behaviour-specific ecological model, there is a lack of information about the dynamic interactions of variables across the different levels. Thus, the ecological model broadened its perspective without specifying variables or presenting guidance about how to use the model to enhance research ( Livingood et al., 2011 ; Sallis et al., 2008 );
• 3. The ecological model makes it difficult to formulate testable hypotheses and is demanding to manipulate experimentally. Therefore, it is difficult to operationalise ( Korin, 2016 ); and
• 4. As Green et al. (1996 : 273) argued:

If the ecological credo of everything influences everything else is carried out to its logical extreme, the average health practitioner has little basis on which to set priorities and has good reason to do nothing because the potential influence of or consequences on other parts of an ecological system are beyond comprehension, much less control.

Health belief model (HBM)

The HBM is one of the oldest and most frequently used theoretical models to explain health behaviour ( Rosenstock, 2005 ). It was first designed in the 1950s to describe why people do and do not adopt disease prevention programs or screening strategies for early detection of disease and has been modified subsequently. The HBM argues that health behaviour can best be understood if people’s beliefs or perceptions about health are known. Accordingly, an individual’s belief about the threat of a disease or health problem, along with his/her belief in the effectiveness of the recommended course of action/health behaviour, will determine the probability that he/she will adopt the behaviour ( Nutbeam et al., 2010 ; Strecher and Rosenstock, 1997 ). The key elements, including perceived seriousness, susceptibility, benefits, and barriers comprise the model. The model also incorporates cues to action, modifying factors, and self-efficacy to expand the scope of its application further ( Strecher and Rosenstock, 1997 ).

Strecher and Rosenstock (1997) defined the key constructs of the HBM as follows:

• 1. Perceived susceptibility – an individual’s belief about his/her chance of getting the problem/disease;
• 2. Perceived seriousness – an individual’s belief about how severe the disease and its consequences are;
• 3. Perceived benefits – an individual’s belief as to whether the recommended action reduces the threat or severity of the impact;
• 4. Perceived barriers – an individual’s belief about what could prevent him/her from undertaking the recommended action;
• 5. Modifying factors – personal factors that influence the adoption of new behaviour;
• 6. Cues to action – factors that activate the person towards adopting the new behaviour; and
• 7. Self-efficacy: An individual’s confidence in his/her ability to take action.

These concerns are further influenced by other factors such as past experiences, culture, and sociodemographic factors, which are called modifying factors in general ( Strecher and Rosenstock, 1997 ). In addition, cues to action, which may be events, people, or anything that triggers people to adopt a new behaviour, are an important concept in the HBM ( Abraham and Sheeran, 2005 ).

In general, according to the HBM, modifying factors, cues to action, and self-efficacy influence individuals' perceptions of susceptibility, seriousness, benefits, and barriers, and thus their adoption of new health behaviour. Despite it having been used in a wide range of studies exploring various health behaviours, critics have identified the following limitations. Firstly, the model fails to indicate the significance of intention formation, or the influence that others' approval may have upon people’s behaviour ( Abraham and Sheeran, 2015 ). Secondly, the relationship between risk and severity combining to inform a sense of threat is not explicitly defined ( Champion and Skinner, 2008 ).

Behavioural model of health services utilisation (BMHSU)

The medical sociologist Andersen (1968) developed the BMHSU, which has come to be widely used to study utilisation and access of services. The model was originally designed to improve understanding of why families use health services, to explain and measure equitable access to healthcare, and to assist in developing policies leading to equitable access. According to this model, health services utilisation is a function of three main factors, namely predisposing, enabling, and need factors ( Andersen, 1968 ; Jahangir et al., 2012 ). Predisposing factors include demographic variables, social structure, and health belief, while factors such as income, regular source of care, health insurance, and travelling and waiting times are enabling factors ( Andersen, 1968 ). Need factors include an individual’s perceived healthcare needs (for example, self-perceived health, self-reported number of symptoms, restricted activity, number of bed days, and activities of daily living) and other indicators of their health status ( Jahangir et al., 2012 ).

Andersen’s initial BMHSU has its limitations, such as lack of adequate consideration of organisational factors as enabling factors ( Gilbert et al., 1993 ; Patrick et al., 1988 ), and its failure to account for the extent and quality of social relationships ( Pescosolido, 1992 ) in predicting health services utilisation behaviour. In response to these limitations, and to enhance its applicability further for exploring health services use and access, Andersen revised the model in 1995. According to the updated version, health services use and health status are influenced by several factors, in addition to those in the original version, environmental factors, which include factors related to the healthcare system, and other environmental characteristics are added as predictors of health services utilisation. Thus, the conceptual framework of Andersen’s BMHSU employs a system perspective to integrate an array of factors influencing the decision to seek healthcare.

Andersen’s model has been used in a wide range of studies investigating health services utilisation behaviours of people with varied health problems ( Brown et al., 2009 ; Dhingra et al., 2010 ; Han-Kyoul and Munjae, 2016 ; Salinas et al., 2010 ), suggesting that the model is effective in predicting health-related behaviours. A systematic review of studies conducted using Andersen’s model as their theoretical framework also demonstrated that there are hundreds of primary studies that have effectively applied the model ( Babitsch et al., 2012 ).

Each theory and model of health behaviour has its strengths and weaknesses, and each contributes to our understanding of reality in different ways. For example, the HBM, which emphasises individuals' perception, attitude, and belief, best suits studies that merely seek to investigate individual characteristics that influence health behaviour. However, unlike the HBM and biomedical model, most health issues, are complex, caused by multiple factors, personal, socio-cultural, and environmental. In such cases, the biopsychosocial model, ecological model, and Andersen’s behavioural model are important in examining those wide arrays of factors that influence health behaviour. The selection of a theory or theories that have the best fit to a particular research project must therefore be preceded by the clear justification that the chosen theory fits the research questions, the structure of the research, and the research design ( Grant and Osanloo, 2014 ).

Key points for policy, practice and/or research

• • Theory is a tool for a better understanding of reality.
• • In quantitative research, theory is a blueprint for a research project.
• • Health research established in theory enhances knowledge and provides a strong evidence for clinical and public health practices.
• • Each theory and model of health behaviour has its strengths and weaknesses, and each contributes to our understanding of reality in different ways.
• • Linking the theoretical framework of a research project to established and comprehensive theories and/or models is about justifying that these theories and/or models fit the research questions, the structure of the research, and the research design. This is to ensure a valid and reliable study is produced.

Getahun K Beyera (BSc, MPH, PhD), currently completed his PhD in Public Health.

Jane O’Brien (BExSportSc, GradCert (Mgmt), RN, MApplSci, PhD, EP), is a Lecturer School of Nursing.

Steven Campbell (RN, BNurs, PhD, FRSH, FACN), is a Professor of Clinical Redesign-School of Nursing.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval: This paper is a review of literature; therefore, ethics approval was not needed.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Getahun K Beyera https://orcid.org/0000-0001-9059-6669

Steven Campbell https://orcid.org/0000-0003-4830-8488

Jane O’Brien https://orcid.org/0000-0002-6504-8422

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Mental health care is hard to find, especially for people with Medicare or Medicaid

Rhitu Chatterjee

With rates of suicide and opioid deaths rising in the past decade and children's mental health declared a national emergency , the United States faces an unprecedented mental health crisis. But access to mental health care for a significant portion of Americans — including some of the most vulnerable populations — is extremely limited, according to a new government report released Wednesday.

The report, from the Department of Health and Human Services' Office of Inspector General, finds that Medicare and Medicaid have a dire shortage of mental health care providers.

The report looked at 20 counties with people on Medicaid, traditional Medicare and Medicare Advantage plans, which together serve more than 130 million enrollees — more than 40% of the U.S. population, says Meridith Seife , the deputy regional inspector general and the lead author of the report.

Medicaid serves people on low incomes, and Medicare is mainly for people 65 years or older and those who are younger with chronic disabilities.

The report found fewer than five active mental health care providers for every 1,000 enrollees. On average, Medicare Advantage has 4.7 providers per 1,000 enrollees, whereas traditional Medicare has 2.9 providers and Medicaid has 3.1 providers for the same number of enrollees. Some counties fare even worse, with not even a single provider for every 1,000 enrollees.

"When you have so few providers available to see this many enrollees, patients start running into significant problems finding care," says Seife.

The findings are especially troubling given the level of need for mental health care in this population, she says.

"On Medicare, you have 1 in 4 Medicare enrollees who are living with a mental illness," she says. "Yet less than half of those people are receiving treatment."

Among people on Medicaid, 1 in 3 have a mental illness, and 1 in 5 have a substance use disorder. "So the need is tremendous."

The results are "scary" but "not very surprising," says Deborah Steinberg , senior health policy attorney at the nonprofit Legal Action Center. "We know that people in Medicare and Medicaid are often underserved populations, and this is especially true for mental health and substance use disorder care."

Among those individuals able to find and connect with a provider, many see their provider several times a year, according to the report. And many have to drive a long way for their appointments.

"We have roughly 1 in 4 patients that had to travel more than an hour to their appointments, and 1 in 10 had to travel more than an hour and a half each way," notes Seife. Some patients traveled two hours each way for mental health care, she says.

Mental illnesses and substance use disorders are chronic conditions that people need ongoing care for, says Steinberg. "And when they have to travel an hour, more than an hour, for an appointment throughout the year, that becomes unreasonable. It becomes untenable."

"We know that behavioral health workforce shortages are widespread," says Heather Saunders , a senior research manager on the Medicaid team at KFF, the health policy research organization. "This is across all payers, all populations, with about half of the U.S. population living in a workforce shortage."

But as the report found, that's not the whole story for Medicare and Medicaid. Only about a third of mental health care providers in the counties studied see Medicare and Medicaid patients. That means a majority of the workforce doesn't participate in these programs.

This has been well documented in Medicaid, notes Saunders. "Only a fraction" of providers in provider directories see Medicaid patients, she says. "And when they do see Medicaid patients, they often only see a few."

Lower reimbursement rates and a high administrative burden prevent more providers from participating in Medicaid and Medicare, the report notes.

"In the Medicare program, they set a physician fee rate," explains Steinberg. "Then for certain providers, which includes clinical social workers, mental health counselors and marriage and family therapists, they get reimbursed at 75% of that rate."

Medicaid reimbursements for psychiatric services are even lower when compared with Medicare , says Ellen Weber , senior vice president for health initiatives at the Legal Action Center.

"They're baking in those discriminatory standards when they are setting those rates," says Steinberg.

The new report recommends that the Centers for Medicare & Medicaid Services (CMS) take steps to increase payments to providers and lower administrative requirements. In a statement, CMS said it has responded to those recommendations within the report.

According to research by Saunders and her colleagues at KFF, many states have already started to take action on these fronts to improve participation in Medicaid.

Several have upped their payments to mental health providers. "But the scale of those increases ranged widely across states," says Saunders, "with some states limiting the increase to one provider type or one type of service, but other states having rate increases that were more across the board."

Some states have also tried to simplify and streamline paperwork, she adds. "Making it less complex, making it easier to understand," says Saunders.

But it's too soon to know whether those efforts have made a significant impact on improving access to providers.

CMS has also taken steps to address provider shortages, says Steinberg.

"CMS has tried to increase some of the reimbursement rates without actually fixing that structural problem," says Steinberg. "Trying to add a little bit here and there, but it's not enough, especially when they're only adding a percent to the total rate. It's a really small increase."

The agency has also started covering treatments and providers it didn't use to cover before.

"In 2020, Medicare started covering opioid treatment programs, which is where a lot of folks can go to get medications for their substance use disorder," says Steinberg.

And starting this year, Medicare also covers "mental health counselors, which includes addiction counselors, as well as marriage and family therapists," she adds.

While noteworthy and important, a lot more needs to be done, says Steinberg. "For example, in the substance use disorder space, a lot of addiction counselors do not have a master's degree. And that's one of their requirements to be a counselor in the Medicare program right now."

Removing those stringent requirements and adding other kinds of providers, like peer support specialists, is key to improving access. And the cost of not accessing care is high, she adds.

"Over the past two decades, [in] the older adult population, the number of overdose deaths has increased fourfold — quadrupled," says Steinberg. "So this is affecting people. It is causing deaths. It is causing people to go to the hospital. It increases [health care] costs."

• Centers for Medicare & Medicaid Services
• mental health

• Open access
• Published: 13 April 2024

Changes in long-term care insurance revenue among service providers during the COVID-19 pandemic

• Tomoko Ito 1 , 2 ,
• Xueying Jin 2 , 3 ,
• Makiko Tomita 2 , 4 ,
• Shu Kobayashi 4 &
• Nanako Tamiya 1 , 2  

BMC Health Services Research volume  24 , Article number:  464 ( 2024 ) Cite this article

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The COVID-19 pandemic has impacted peoples’ health-related behaviors, especially those of older adults, who have restricted their activities in order to avoid contact with others. Moreover, the pandemic has caused concerns in long-term care insurance (LTCI) providers regarding management and financial issues. This study aimed to examine the changes in revenues among LTCI service providers in Japan during the pandemic and analyze its impact on different types of services.

In this study, we used anonymized data from “Kaipoke,” a management support platform for older adult care operators provided by SMS Co., Ltd. Kaipoke provides management support services to more than 27,400 care service offices nationwide and has been introduced in many home-care support offices. The data used in this study were extracted from care plans created by care managers on the Kaipoke platform. To examine the impact of the pandemic, an interrupted time-series analysis was conducted in which the date of the beginning of the pandemic was set as the prior independent variable.

The participating providers were care management providers ( n  = 5,767), home-visit care providers ( n  = 3,506), home-visit nursing providers ( n  = 971), and adult day care providers ( n  = 4,650). The results revealed that LTCI revenues decreased significantly for care management providers, home-visit nursing providers, and adult day care providers after the COVID-19 pandemic began. The largest decrease was an average base of USD − 1668.8 in adult day care.

The decrease in revenue among adult day care providers was particularly concerning in terms of the sustainability of their business. This decrease in revenue may have made it difficult to retain personnel, and staff may have needed to be laid off as a result. Although this study has limitations, it may provide useful suggestions for countermeasures in such scenarios, in addition to support conducted measures.

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The COVID-19 pandemic has changed many peoples’ health-related behaviors. In particular older adults, who were the most serious victims of COVID-19 [ 1 , 2 ], had their activities restricted in order to avoid contact with people [ 3 , 4 ]. A previous quasi-experimental study revealed that the number of users of long-term care insurance (LTCI) services was influenced by the pandemic [ 5 ]. A decrease in the number of users of adult day care was particularly evident. At the same time, the decrease in service utilization has simultaneously affected the provider management practices of the service providers [ 6 ].

The LTCI system was launched in Japan in 2000. In 2000, the LTCI system was established to follow the rapid aging. The LTCI system ensures two main types of services: services for convalescent older adults who live at home and services for those who stay in facilities. For those who live at home, there are also two types of services: home-visit services, in which professionals go to the home of the patient to provide services, and day-care services, in which the patient himself/herself goes to the facility during the daytime to receive services at the facility. Most users of LTCI services pay 10% of the total cost of these services out-of-pocket, and the rest is covered by pooled LTCI premiums and taxes. In part, users with sufficient income are asked to pay up to 30% of the total cost.

Under this system, private providers were allowed to provide long-term care services, covered by public insurance [ 7 , 8 ]. This change opened up a market that had previously only been available to providers in a public capacity [ 9 ]. As a result, the number of providers of LTCI services has been increasing every year. For example, the number of facilities providing adult day cares increased from 8,037 in 2000, when the system was first established, to 24,087 in 2020 [ 10 ]. The increase in the number of such providers has also led to an increase in accessibility of use, helping to better maintain the daily lives and activities of those who need care. On the other hand, in the LTCI business, revenue strongly depends on the public’s use of the services. When a user quits using LTCI services, it has a significant impact on the revenue of the provider. In such cases, service management can easily deteriorate, and providers may sometimes close immediately after they have been established.

During the pandemic, some offices were forced to close due to infection control, and there were concerns about managerial difficulties in terms of maintaining the revenues by providing publicly insured LTCI services in Japan. With declining revenues, which lead to unstable finances for establishments and increased risks of bankruptcy [ 11 ], there have been a number of Japanese studies showing the impact [ 12 ] of the pandemic on revenues. That report [ 12 ] revealed about 40% of providers faced a reduction in revenue and also they predicted that reduction continuously. However, there were few studies to show the decline in revenues of LTCI providers with the nationwide scope in Japan. There have also been investigations regarding service scope, in terms of differences in the variety of services offered due to the pandemic’s impact. In a previous study, the decline in number of users was different from that of LTCI services. This present study could suggest the future decline in revenue with the other pandemics or some disasters, and the necessity to consider the preventive measurement for the stopping of services provided. For that future consideration, firstly, the descriptive study to reveal the impact of the pandemic on the LTCI revenue was needed for the current situation with few previous studies. Additionally, time series analysis was effective to capture the impact on the change of revenue compared with the former trend. Therefore, this study aimed to verify the change in revenue experienced by LTCI among service providers in Japan during the COVID-19 pandemic, for a variety of services.

Study subjects

In this study, we used anonymized data from “Kaipoke,” a management support platform for older adult care operators provided by SMS Co., Ltd. Kaipoke provides management support services to more than 27,400 care service offices nationwide and has been introduced in many home-care support offices. Study subjects were the LTCI service providers including care management, home-visit care, home-visit nursing, and adult day care which had provided their services in November 2018. We followed up on the subjects’ monthly revenue from December 2018 to November 2020. Kaipoke supports the office works of LTCI service providers including the requirement of revenue, therefore, we could extract the data.

Each service provider of the LTCI system in Japan was certified by a municipal body established by the providers. The municipal body examined the standards for personnel, facilities, and equipment of providers in accordance with the standards set by the national government. Certified providers can provide services for disabled older adults, and claim the expenses to the city as a public insurer.

The outcome of this study was the revenue of LTCI services claimed by the provider. Revenue was recorded in Japanese yen. The exchange rate was set at an average of 113 USD in November 2018, to show global results. The revenue from LTCI services was discussed and revised by the subcommittee of the Ministry of Health, Labor, and Welfare once every three years. In recent years, against the backdrop of a shortage of LTC personnel, restructuring within LTCI companies has been increasing. Revenue was standardized throughout the country in the form of “units.” The units were determined on a regional basis between 0.09 USD and 0.10 USD, reflecting the differences in prices and other factors in the region. The LTCI service providers in the region received revenues in yen based on the conversion of the determined unit [ 13 ]. For example, the unit for one service of between 30 min and one hour in home-visit care, mainly with physical assistance, was 394 units in 2018. In the 23 wards of Tokyo, the area with the highest billing rate, this would have been billed at 0.10 USD per unit, for a total of 39.7 USD. The lowest billing rate is 0.09 USD per unit, which would have come out to 34.9 USD for the same service, a difference of 4.8 USD. We used the amount reflecting this regional difference as the outcome.

In this study, the outcome was the amount of revenue received by providers for four types of services: care management, home-visit care, home-visit nursing, and adult day care. The revenue was based on the revenues made in the 2018 revision. Care management [ 14 ] was a service done by a “care manager,” who would make the care plan and prepare the LTC schedule, in liaison and coordination with service providers. A care manager was the only official person to make the care plan, except in the cases of families with qualified members who had completed a certain level of experience and training in care management, and had successfully completed an examination [ 10 , 15 ]. The most recent results showed that care management was paid at 125.7 USD per month per user [ 16 ]. Home-visit care was a service in which a trained person visited the user’s home to provide daily living care such as cleaning and laundry, and physical care such as bathing and toileting. This service is the most widely used home-visit service, with approximately one million users nationwide. Per capita usage was 673.5 USD per month in 2018. Home-visit nursing was primarily provided by nurses. This service included the management of chronic disease conditions or the maintenance of medical treatments such as catheterization. The amount of revenue per user for home-visit nursing was 426.5 USD per month on average. Finally, adult day care was a service that assisted in daily care and training to maintain or improve the user’s physical or psychological functions at the facility, rather than at home. Users took shared buses or private cars to the facility, where several users gathered for activities, meals, and bathing. This care was provided to relieve the users’ senses of social isolation, maintain the users’ physical and psychological functions, and reduce the physical and psychological burdens on the users’ families. The number of adult day care users was the largest among the community-based services, at 1.13 million. The amount of monthly revenue for the usage of this service in 2018 was 820.4 USD.

Certified providers were billed for LTCI on a monthly basis. In this study, the outcome was the amount claimed by each provider for each type of service in the 24 months from December 2018 to November 2020.

The exposure in this study was the new coronavirus infection pandemic. In Japan, the first case of infection was recorded on January 16, 2020. The number of infected individuals has gradually increased since then. The first wave in Japan was confirmed in April 2020, and the daily number of positive cases in the first wave was 720. At that time, little was known about the new coronavirus. Japan was in a state of panic and required people to stay at home. This was followed by a second wave in August 2020 with more than 1,500 positive cases per day. Therefore, in this study, we considered the exposure period as beginning in January 2020, when the pandemic started in Japan.

Statistical analysis

The distribution of monthly revenues of LTCI service providers was described in a time series between December 2018 and November 2020. A summary of the distribution is shown as the means and medians of the revenues (unit: USD). To assess this trend, an interrupted time-series analysis was conducted [ 17 ], in which the outcome was the trend in the amount of monthly revenues of LTCI services (Yt). This analysis was established to observe the interruption of the outcome variable level and trend through the equally spaced periods before and after the introduction of an intervention. In the present study, the intervention of analytical framework was defined for the pandemic of COVID-19. Therefore, we tried to extract the interruption by the pandemic on the LTCI revenue trend. In addition, for that analysis, the assumption was needed that the influences on the outcome variable were stable through the observed periods. We also followed that assumption. The periods were divided into pre-COVID-19 (December 2018-December 2019) and post-COVID-19 (January 2020-November 2020) periods. During the pandemic, the Japanese government issued a declaration of emergency across seven prefectures where the infection had spread [ 18 ]. Thereafter, the state of emergency was expanded to all prefectures. However, seven prefectures, including Tokyo, Kanagawa, Saitama, Chiba, Osaka, Hyogo, and Fukuoka, where emergency declarations were issued earlier, had dense populations and were often the subject of intensive emergency declarations. To capture this difference in areas, we grouped the subjects by a risk area, which included the seven mentioned prefectures, and a control area.

The results were expressed by seven coefficients β 0 - β 7 according to this formula.

β 0 : Estimated the base level;

β 1 : Estimated the trend pre-COVID-19;

β 2 : Estimated the change in level post-COVID-19;

β 3 : Estimated the change in trend post-COVID-19;

β 4 : Estimated the trend in the risk area;

β 5 : Estimated the trend pre-COVID-19 in the risk area;

β 6 : Estimated the change in level post-COVID-19 in the risk area;

β 7 : Estimated the change in trend post-COVID-19 in the risk area.

Here, Tt is the time elapsed each month (1 ≤ Tt ≤ 24) from the start (December 2018) to the end of the observation period (November 2020). Xt was a dichotomous dummy variable that included pre-COVID-19 (Xt = 0) and post-COVID-19 (Xt = 1). Z was also a dummy variable that denoted the area difference: seven prefectures (Z = 1) and other prefectures (Z = 0). A generalized linear model was applied using log link and Poisson distribution. STATA version 14.2 (Stata-Corp LP, College Station, TX, USA) was used for the analysis. The results of analysis and figures were output via the STATA syntax “itsa” introduced by Linden [ 19 ]. The statistical significance level was a two-sided p -value of less than 5%.

Ethical issues

This study was conducted following the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board (Ethical Review Board in Institution of Medicine, University of Tsukuba. Approval number: 1301). The present study data was completely anonymous without individual information. No personal information was obtained on the part of the researcher for this study. Therefore, disclosure of this study information to the research subjects was done publicly. Patient informed consent was waived by the review board of Institution of Medicine in University of Tsukuba as the data contains no individually identifying information.

The participating providers were care management providers ( n  = 5,767), home-visit care providers ( n  = 3,506), home-visit nursing providers ( n  = 971), and adult day care providers ( n  = 4,650). The breakdown between the seven risk area prefectures where emergency declarations were issued earlier and the other areas (35 prefectures) was close to 50–50 for all service providers. The risk areas were urban and had a larger number of providers between them. The numbers of providers in the risk areas and control areas are listed in Table  1 . For each of the Pre- and Post-COVID-19 periods, we described the mean and median across insurance provider revenues, in 30-day periods. Comparisons of the values between Pre- and Post-COVID-19 showed an increasing trend in all services and areas, except for adult day care in the risk areas. Among adult day care providers in the risk areas, the mean decreased by approximately 100 USD and the median decreased by approximately 200 USD.

In the Fig.  1 , the plots show the actual observed values (dots) and the predicted (regression) lines between the risk and control areas. The regression lines expressed an increasing trend in both the means and medians of all the services and areas during all periods. However, in the case of adult day care, the intercept of the line in Post-COVID-19 declined markedly. The lines for the risk areas were also below those of the others. During the Post-COVID-19, the space between lines widened.

Changes in the revenues (USD) of long-term care insurance services during the COVID-19 pandemic in Japan

Table  2 presents the results of our interrupted time-series analysis. For care management, there was a decline in both the level and trend Post-COVID-19. For home-visit care and home-visit nursing, only a decline in the mean level of home-visit care was observed. For adult day care, there were big drops in the means and medians, but the trends did not change.

The results showed that the LTCI revenue of care management providers decreased significantly in terms of both level and trend after the COVID-19 pandemic. The revenue level of home-visit nursing providers also decreased. Finally, in adult day care providers, the level of revenue declined markedly, but the trend did not. In our previous study, the number of users of day services decreased in that trend after the pandemic began. Therefore, as one of the possible reasons, they had restoration of revenue in that trend because the amount of use was increased each individual based on their demands. These nationwide results could be the potential evidence to help the prediction and counter-measurement in the future.

The decrease in the level of revenue among adult day care providers was suggested to have had a serious impact on the sustainability of the business. Prior studies have shown that the decline in the number of adult day care users was significant, which also led to a decrease in revenue for the providers. In adult day care, since people gather and engage in group activities, both users and providers have to be cautious during activities in order to prevent infection. However, the reduction in users may also have influenced the revenue of the providers. Among adult day care providers, approximately 1,600 USD worth of revenue was reduced. A decrease in revenue of about 1,600 USD per month can be equivalent to the salary of one employee [ 20 ]. Aramaki [ 20 ] mentioned the ratio of labor costs tends to be high among long-term care providers, and in adult day care providers it has been reported to be over 60%. In home-visit nursing, the labor cost ratio is even larger, at over 80%. Therefore, employment adjustments may be necessary even for decreases in revenue at an average level of 779 USD. Reductions in revenue can make it difficult for service providers to maintain operations, as well as retain personnel and staff.

In Japan, several surveys have shown concerns regarding the reduction in revenue of LTCI providers. They reported continued year-over-year declines in expenses for personal preventive equipment (PPE) maintenance [ 12 , 21 ]. Some reports suggested that the COVID-19 pandemic may have exacerbated the effects of already unstable management practices, resulting in bankruptcies [ 11 , 22 ]. In the US, the American Health Care Association and the National Center for Assisted Living (AHCA/NCAL) reported that 55% of welfare facilities for older adults operate at a loss, and that 89% of facilities operate at a profit margin of less than 3% [ 23 ]. In addition, many facilities have cited increased costs due to the introduction of additional personnel and PPE for infection control [ 23 ]. In a survey of home-visit care providers in Massachusetts, 80.9% of the 94 providers who responded reported a decrease in home-visit care hours, 98.7% experienced cancellation of visits due to infection concerns, and 64.5% reported that family members took over caregiving of their patients [ 24 ]. The threat of the pandemic forced many lifestyle changes in order to save lives. Among older adults in particular, who are the most at-risk of severe COVID-19 outcomes, changes in the use of long-term care services have been significant. This has affected the management of care centers. Smaller providers in rural areas are more unstable and susceptible to such a decrease in revenue. Typically, these providers are indispensable public resources for maintaining long-term care in the community. Although what we have identified in this study is only a partial decrease in revenue, there is concern that this process will lead to the closure of some providers. It is necessary to continue monitoring the supply of long-term care and the influence of the pandemic. In Japan, financial assistance was provided for LTCI providers suffering from the pandemic, but this was intended to provide necessary PPE and comfort to staff and to supplement insurance, not to compensate for loss of revenue [ 25 ]. In the US, the government helps the Medicare service supplier to recoup the claim to support their financial aspects [ 26 ]. In Japan, the most important thing was to ensure infection prevention, and the care providers were not able to catch up with changes in their finances. The reduction in revenue was not anticipated during the progression of the pandemic. During a pandemic, such as the one that occurred in this study, a decrease in the revenue of LTCI service providers can be expected. The results of this study can hopefully provide suggestions for countermeasures, in addition to support measures already enacted. The number of users and the revenue decreased due to infection control measures. However, it was necessary to maintain staffing to work for infection control for those who continue to use. For the imbalance between the revenue and the number of people employed, we consider that funds defined in the amount of the decrease in users would ensure the maintenance of conventional employment. Additionally, it would contribute to maintaining the quality of services after the pandemic.

This study had several limitations. First, it was a descriptive study and could not provide causal inferences. The data source was an online support system for LTCI providers in Japan. Although the data were available on a national scale, there are concerns regarding their national representativeness. In particular, the scale of the business of LTCI providers was not considered, and the interpretation of the results should be viewed with caution.

This study found that after the beginning of COVID-19, there was a marked decline in LTCI revenue at care management, home-visit nursing, and adult day care providers. The downward trend in adult day care was particularly significant, suggesting an impact on the maintenance of operations for these businesses. These nationwide results could be the potential evidence to help the prediction and counter-measurement in the future. Additional research is needed to determine whether these changes in the revenues of LTCI providers will impact the maintenance of their business operations.

Availability of data and materials

The data that support the findings of this study are available from SMS Co., Ltd. but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

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Acknowledgements

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This work was supported by SMS Co., Ltd, grant number CRE30018, at the University of Tsukuba.

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Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan

Tomoko Ito & Nanako Tamiya

Health Services Research and Development Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan

Tomoko Ito, Xueying Jin, Makiko Tomita & Nanako Tamiya

Department of Social Science, National Center for Geriatrics and Gerontology, 7- 430 Morioka-cho, Obu, Aichi, 474-8511, Japan

Xueying Jin

Analysis & Innovation Dept., SMS Co., Ltd., Sumitomo Fudosan Shibakoen Tower, 2-11-1, Shibakoen, Minato-ku, Tokyo, 105-0011, Japan

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All jointly designed the study, TI conducted all data analysis, TI wrote first and all drafts of manuscript. XJ, MT, SK, and NT provided critical feedback and helped improve the research, analysis and manuscript. All authors read and approved the final manuscript.

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Correspondence to Tomoko Ito .

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Ethics approval and consent to participate.

This study was conducted in accordance with the guidelines of the Declaration of Helsinki, and was approved by the review board of Institution of Medicine in University of Tsukuba. No personal information was obtained on the part of the researcher for this study. Therefore, disclosure of this study information to the research subjects was done publicly. Patient informed consent was waived by the review board of Institution of Medicine in University of Tsukuba as the data contains no individually identifying information.

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Competing interests.

This research was supported by SMS Co., Ltd. and was conducted as a part of the joint research between SMS Co., Ltd. and University of Tsukuba. NT received the fund by SMS Co., Ltd. as the principal investigator of the joint research. MT was a researcher at the University of Tsukuba for joint research with SMS Co., Ltd. in the 2023 financial year (i.e., April 2023 to March 2024). MT and SK were employed by SMS Co., Ltd. TI and XJ had no conflict of interest to be disclosed.

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Ito, T., Jin, X., Tomita, M. et al. Changes in long-term care insurance revenue among service providers during the COVID-19 pandemic. BMC Health Serv Res 24 , 464 (2024). https://doi.org/10.1186/s12913-024-10832-4

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Cultural adaptation and validation of the caring behaviors assessment tool into Spanish

• Juan M. Leyva-Moral 1 ,
• Carolina Watson 1 ,
• Nina Granel 1 ,
• Cecilia Raij-Johansen 1 &
• Ricardo A. Ayala 2 , 3  

BMC Nursing volume  23 , Article number:  240 ( 2024 ) Cite this article

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Metrics details

The aim of the research was to translate, culturally adapt and validate the Caring Behaviors Assessment (CBA) tool in Spain, ensuring its appropriateness in the Spanish cultural context.

Three-phase cross-cultural adaptation and validation study. Phase 1 involved the transculturation process, which included translation of the CBA tool from English to Spanish, back-translation, and refinement of the translated tool based on pilot testing and linguistic and cultural adjustments. Phase 2 involved training research assistants to ensure standardized administration of the instrument. Phase 3 involved administering the transculturally-adapted tool to a non-probabilistic sample of 402 adults who had been hospitalized within the previous 6 months. Statistical analyses were conducted to assess the consistency of the item-scale, demographic differences, validity of the tool, and the importance of various caring behaviors within the Spanish cultural context. R statistical software version 4.3.3 and psych package version 2.4.1 were used for statistical analyses.

The overall internal consistency of the CBA tool was high, indicating its reliability for assessing caring behaviors. The subscales within the instrument also demonstrated high internal consistency. Descriptive analysis revealed that Spanish participants prioritized technical and cognitive aspects of care over emotional and existential dimensions.

Conclusions

The new version of the tool proved to be valid, reliable and culturally situated, which will facilitate the provision of objective and reliable data on patients beliefs about what is essential in terms of care behaviors in Spain.

• This paper provides a culturally translated, adapted, and validated version of the Caring Behaviors Assessment tool in the Spanish context, which can be used to obtain reliable and culturally adapted data on essential aspects of patient care.

• The findings of this study contribute to the wider global clinical community by demonstrating the importance of considering cultural factors when assessing and evaluating patient care from patients’ own perspective, and also emphasizes the need for culturally sensitive approaches in healthcare settings.

• This validated instrument facilitates the measurement of caring behaviors in the Spanish context, allowing for objective evaluation and improvement. Use of the Caring Behaviors Assessment tool could thus serve as a valuable resource for both future research and clinical practice.

Peer Review reports

Caring,  as a complex culturally derived phenomenon, encompasses recognition of individuals’ uniqueness and includes moral, emotional, and cognitive dimensions [ 1 ]. Within the field of nursing, the professional act of caring is defined as an interpersonal process characterized by nurses’ expertise, competencies, personal maturity, and interpersonal sensitivity. The ultimate aim is to meet patients’ bio-psycho-social needs, ensuring their protection, emotional support, and overall satisfaction [ 2 ]. Furthermore, caring has been understood as the pivotal element that patients expect and should encounter to feel satisfied with nursing services [ 3 ]. Therefore, the concept of caring is dynamic, requiring adaptation to diverse sociocultural contexts.

Drawing on humanistic, transformative, integrative, and complex ontological and epistemological perspectives, various nursing theories have been developed that focus on promoting human-centred care [ 4 , 5 ]. One such perspective is the theory of human-to-human relationships proposed by Travelbee [ 6 ], which emphasizes the unique and irreplaceable nature of anyone who has lived or will live in this world. In this perspective, therapeutic human relationships evolve through a series of interactive steps, including the emergence of identities and the development of empathy (and later sympathy) until finally establishing rapport with persons receiving care [ 7 ].

Similarly, Watson [ 8 , 9 ] has elaborated a care process consisting of the following ten steps (caritas process): 1) consciously practising kindness and honesty while providing care; 2) being authentically present in a facilitative manner; 3) cultivating spirituality by transcending the self; 4) developing and maintaining a relationship of trust; 5) supporting the expression of both positive and negative feelings; 6) using creativity to obtain information during the care process; 7) engaging in genuine teaching and learning that take a global view of phenomena, while considering the perspective of the other; 8) creating healing environments that enhance integrity, comfort, dignity, and peace; 9) consciously and intentionally assisting with basic needs while enhancing the mind, body, and spirit; 10) remaining open to the experience of life and death, including care of both the professional and the patient’s soul. In short, caring is the essence of nursing and is a fundamental element for establishing effective nurse-patient relationships and achieving high-quality health outcomes.

The quality of nursing care is directly related to patients’ general experience and satisfaction. Evidence shows that patient experience with nursing care is a crucial predictor of patient satisfaction [ 10 , 11 ]. Studies indicate that providing expert and integrated care contributes to patients’ sense of safety and feeling embraced [ 12 ]. Conversely, professional nursing practice based on the biomedical model has been associated with low patient satisfaction and limited professional fulfilment among nurses [ 13 ].

Nevertheless, measuring nursing care plays an essential role in assessing its effectiveness and quality. By measuring nursing care, healthcare organisations and policymakers can identify areas for improvement and make evidence-based decisions to enhance patient outcomes. While caring cannot be reduced to a mere collection of actions and behaviours, this step is crucial in systematising the components of care that impact patients’ experiences [ 14 ] and in determining the contribution of nursing to health systems [ 15 ]. Watson [ 9 ] argues that, without engaging in philosophical contradictions, the use of quantitative instruments to assess care is necessary to provide scientific evidence. Such evidence helps managers and researchers to evaluate the complex and unique role of nursing and its effects on health.

The presence of an adequate number of well-trained nurses is known to reduce the risk of patient mortality, with outcomes similar to those achieved by physicians [ 16 ]. Nevertheless, nursing care extends beyond numerical values and clinical outcomes. It is well-established that discrepancies exist between the perceptions of nurses and patients regarding what constitutes care, primarily due to the uniqueness of each individual; hence the application of individualized care is promoted and takes into account the sociocultural context [ 17 ]. Moreover, humanised care is associated with high levels of patient and family satisfaction in various contexts [ 18 ].

One of the oldest and most widely used tools for assessing nursing care is the Caring Behaviours Assessment (CBA) tool, developed by Cronin and Harrison [ 19 ]. The authors were concerned about the exclusion of patients’ perspective in care settings and sought to identify which behaviours communicated care and how their effectiveness could be evaluated. Consequently, they created and validated the CBA, which comprises 63 items, grouped into seven subscales based on Watson’s ten carative factors. The instrument has been translated and validated in several languages, including Chilean Spanish [ 15 ]. However, the Spanish spoken in Spain exhibits distinct differences to the Chilean variety in word usage, meaning and cultural nuances, influenced by other languages spoken in the country such as Catalan or Galician. Consequently, despite extensive debate in recent years, there are currently no reliable assessment instruments available in the Spanish context that adequately consider cultural nuances in patients’ experiences. Therefore, using the CBA in an apparently similar but different language variety could lead to misinterpretation [ 20 ].

The aim of this study is to report the process of cultural translation, adaptation, and validation of the CBA in Spain, which to the best of our knowledge is the only culturally grounded version available. This new version of the CBA will provide a reliable means to obtain objective, tangible, and culturally adapted data on patients’ perceptions of the elements they deem to be essential in their care.

Approval was obtained from the relevant Ethics Committee on 2020 ( ethics committee name, hidden for blinding purposes ). Then, a study organised in three phases was undertaken on 2021–2022. The phases were as follows: 1. Transculturation. 2. Training. 3. Administration.

A previous publication reported the process of creating a version of the CBA in Latin-American Spanish, namely in Chile. The authors of that publication suggested several steps for obtaining a transculturally adapted version, which we used here. These steps were as follows:

Translating the CBA from English to Spanish : one translation (draft 1) was done by a non-nursing translator, and another one (draft 2) by two bilingual nurses, who were familiar with Watson’s theory. The two drafts were then contrasted, leading to an agreed translation (draft 3).

Back-translation from Spanish into English : A bilingual nurse who was familiar with the subject but unfamiliar with the CBA, back-translated draft 3 into English (draft 4).

Refining the Spanish draft prior to the pilot test : the authors reworked a refined version (draft 5) by contrasting the back-translation with the original CBA in English.

Pilot-testing the translated version : Once satisfactorily refined, the translated version was tested with 36 volunteers. This step included interviewing them to identify their understanding of each item.

Linguistic and cultural adjustment: draft 5 was further adjusted by analyzing the volunteers’ responses and using three linguistic criteria: semantic disambiguation, morpho-syntax, and language. This step aimed to ensure one of the key traits of the CBA: plain language. As in the Latin-American version by Ayala and Calvo [ 15 ], conjugation was adjusted (i.e., use of the subjunctive tense instead of the present tense), so that the items reflected hypothetical situations. Otherwise, it would be all too easy for patients to misconstrue that they were being asked to assess the actual care provided by specific nursing staff. Equally, the order of the Likert-type scale was maintained from 1 to 5, left to right. Lastly, grammatical structures and words that sounded natural in spoken Spanish were double-checked with a linguistic consultant. This process led to the preliminary version of the CBA in Spanish.

A team of research assistants was trained in the application of the instrument to ensure a standardised administration process. The training included, for example, that informed consent had to be obtained from all participants before they were given a copy of the questionnaire, that the instructions had to be read aloud to the participants clearly and calmly, that the instrument had to be completed privately, and that the assistants had to remain nearby and attend to participants’ queries. This phase was crucial to minimize the risk of inducing an observer effect on responses.

We administered the transculturally-adapted version of the CBA to a non-probability sample ( N  = 402). To test its psychometric properties [ 21 ], the preliminary version was applied to a sample of adults (between 5 and 10 per item; with a mean age of 39.5 years [SD = 16.5]), who had been hospitalised within the previous 6 months (mean = 2.75 times). This phase aimed to assess the CBA with users of similar characteristics and under similar conditions to those of the final intended users: the CBA is specifically designed to be used in hospital settings.

The procedure yielded 402 observations, providing a significant amount of data for the analysis of item/scale and subscale/scale consistency, as well as the overall reliability of the CBA in measuring a single construct. Of the 402 observations, 120 were excluded from the analysis as they were from health practitioners. As a result, the final sample size was for the analysis was N  = 282.

Statistical analysis

Our objective was to analyse the single items and item‐scale consistency, as well as explain potential differences in perceptions based on demographic data. In addition to assessing the validity of the scale, we also aimed to determine the relevance of diverse caring behaviours within the particular cultural setting of the study. To achieve this, we used correlation analyses to examine the associations between caring behaviors and relevant cultural factors.

Analyses were performed by examining mean and SD (± 1SD) values per item to identify the highest‐ and the lowest‐ranking behaviours. In addition, a Kaiser–Meyer–Olkin (KMO) factor adequacy and Bartlett’s test for sphericity were used to know if our dataset could be factored. Afterwards, Exploratory Factor Analysis (EFA) was used to find common structure in data. The final number of factors was obtained using a parallel analysis. The factorial method employed was minimum residual with Varimax rotation.

Finally, Cronbach’s alpha as well as McDonald’s omega were used to estimate internal consistency and reliability respectively. All statistical analyses were performed using R statistical software (v4.3.3) [ 22 ] and the package psych (v2.4.1) [ 23 ].

As previously mentioned, 120 out of the 402 participants were health professionals. Our initial intention was to retain them in the sample, but their responses made the items markedly redundant, likely due to their familiarity with philosophies of care or a self-validating effect. Therefore, these participants were excluded from the sample. The paragraphs below report the results of the validation tests.

Scores by items

As per descriptive statistics, we calculated mean scores ± 1SD for each of the 63 items of the CBA. The five highest‐ranking and five lowest‐ranking behaviours are listed below (Tables  1 and 2 ). The means ranged from a maximum of [4.87] (± 0.44) for item 3 “Know what they’re doing” to a minimum of [2.88] (± 1.06) for item 25 “Visit me if I move to another hospital unit.”

Cronbach’s alpha and MacDonald’s omega scores by subscales

To calculate the mean ± 1SD per subscale, the items were grouped into their respective subscales. Table 3 shows the scores by subscales alongside their reliability coefficients (ω). As expected, the subscale “Existential/phenomenological/spiritual forces” was the lowest-ranking subscale (3.76 ± 0.34), while “Human needs assistance” was the highest-ranking subscale (4.49 ± 0.23). Nevertheless, both Cronbach’s alpha and McDonald’s omega were 0.8 or higher in all subscales. Importantly, Cronbach’s alpha for the overall scale was 0.96, indicating that the instrument shows a high internal consistency, while McDonald’s omega showed high reliability (0, 97).

Consideration of scale purification

After running the statistical tests, we were dissatisfied with some of the results and deliberated on the need for scale purification [ 24 ]. We found that the items correlating less highly with the overall scale, typically those carrying some existential meaning, were not automatically associated by the respondents with nursing care, and some even considered they were not pertinent to nurses’ work.

Additionally, numerous participants informed us that some items were confusing or sounded redundant. This result had already been detected during the linguistic phase of the study (phase 1), when participants often pointed out that some questions were being asked twice, although differently, which they found somewhat tiresome or repetitive (see Table  4 ).

The decision to perform scale purification for the sake of simplicity required some debate among the listed researchers, as our aim was to have a very high correlation in all of the items. Naturally, this is not the aim of validating an instrument per se. More problematic still were the items that had relatively lower correlations but were meaningful from a theoretical perspective [ 25 ].

We thus aimed to combine personal judgement and statistical criteria, as keeping those items could allow changes in perception to be assessed across time. Furthermore, when removing the items in question, the overall Cronbach’s alpha increased only minimally (from 0.960 to 0.963). Therefore, we decided to keep all 63 items, as in the original CBA [ 19 ], resulting in the validated version of the CBA questionnaire in Spanish. The final version and the item-by-item translation are provided in the Supplementary material .

Exploratory factor analysis

Interestingly, EFA showed that while subscales 1, 2 and 5 are conceptually linked (Humanism/Faith-hope/Sensitivity, Helping/trust, Supportive/protective/corrective environment), these were also strongly associated in the dataset. Similarly, subscales 4 and 6 (Teaching/learning, Human needs assistance) and 3 and 7 (Expression of positive/negative feelings, Existential/phenomenological/spiritual forces) formed somewhat 4 separate groupings on their own. This was also highlighted by the parallel analysis, which showed that 5 factors were found. The latter was reassuring in terms of how well structured the CBA tool is. Additionally, EFA enabled us to identify that the highest loadings (L, see Table  5 ) were item 17 “Really listen to me when I talk” (L = 0.71); item 36 “Ask me what I want to know about my health/illness” (L = 0.70); item 37 “Help me set realistic goals for my health” (L = 0.69); item 06 “Encourage me to believe in myself” (L = 0.69); item 07 “Point out positive things about me and my condition” (L = 0.67); and item 28 “Encourage me to talk about how I feel” (L = 0.67).

KMO and Bartlett’s sphericity test showed that our data set was able to be factorized. KMO overall was 0.93, while Bartlett’s sphericity test (X 2  = 11126.8, p  < 0.05) also suggested that our dataset could be used in EFA. This analysis was done using 5 factors, as shown by the parallel analysis. Table 5 shows the item loadings higher than 0.5 for each factor, while the results for the EFA are shown on Table  6 . The first 3 factors explain 30% of observed variability, while adding factors 4 and 5, completed the 45% of variability explanation (see Table  6 ).

The variability explained after the EFA clearly demonstrates how complex the observed variability becomes following the application of the CBA tool.

How respondents answered the open‐ended question

Some carefully selected examples of the participants’ responses are shown in Table  7 . Additionally, in Phase 1 participants seemed surprised by the items relating to existential/phenomenological/spiritual dimensions. The participants disagreed that these dimensions pertained to nursing care (i.e., “What have nurses become now? Psychologists?”).

Discussion of cultural adaptation and validity of the CBA

The steps taken to ensure accurate cultural adaptation of the Spanish version of the CBA were essential to creating a version tailored to Spanish users, considering the specific features of a region influenced by several languages. Cronbach’s alpha for overall reliability was high (0.96), and all its subscales were 0.8 or higher. The overall Chronbach’s alpha is reassuring as it mirrors that of the Chilean Spanish CBA validated by Ayala and Calvo in 2017 [ 15 ], although in our study there was more dispersion across the subscales. Equally, McDonald’s omega showed high reliability.

Research studies conducted in different regions have also validated CBA versions for patients in the USA [ 26 ], Saudi Arabia [ 27 ] and Jordan [ 28 ]. These studies consistently reported overall Cronbach’s alpha values above 0.8, adding cumulative evidence in support of the CBA as a valid instrument to measure nurses’ caring behaviours.

Moreover, a descriptive analysis was conducted to identify the caring behaviours receiving the highest and lowest ranking. As expected, some items showed weaker correlations with the overall scale, and some participants even considered them “irrelevant” or unrelated to nurses’ duties. When we compared our study to that performed by Ayala and Calvo [ 15 ] and the original by Cronin and Harrison [ 19 ], similarities were found in the results for most of the items. However, differences were found in the item “consider my spiritual needs”, which was rated lower by the Spanish sample. This discrepancy may be related to cultural and contextual factors influencing perceptions and expectations regarding caring behaviours.

Emergence of a 5-dimensional factorial solution for the CBA scale in the Spanish context

Our study presents evidence for a 5-dimensional factorial solution for the CBA scale in the Spanish healthcare context. The convergence of findings suggests that the identified dimensions capture meaningful variance in the dataset and reflect underlying patterns of caring behaviors within the Spanish healthcare context.

Our findings suggest a strong theoretical coherence among certain dimensions within the CBA (Caring Behavior Assessment) scale, reflecting interconnected clusters of caring behaviors. For instance, subscales 1, 2, and 5 demonstrate conceptual linkage, forming a cohesive first dimension that encompasses ‘Humanism/Faith-hope/Sensitivity, Helping/Trust, and Supportive/Protective/Corrective Environment’. Specifically, our analysis reveals an expanded understanding within the first dimension, encompassing not only the initial three carative factors as in the original version but also incorporating two additional factors. These include the formation of a humanistic-altruistic system of values, the installation of faith-hope, the cultivation of sensitivity to oneself and others, the development of a helping-trust relationship, and the provision for a supportive, protective, and corrective environment. This expanded dimension highlights the interconnectedness of empathy, compassion, trust, and reliability within caregiving relationships, reinforcing the foundational principles outlined in Watson’s Theory of Transpersonal Care [ 8 ] and also supported by established theories of patient-centered care [ 29 ]. Additionally, this dimension highlights the importance of providing a supportive, protective, and corrective mental, physical, sociocultural, and spiritual environment, aligning closely with Watson’s emphasis on creating conducive environments for healing and growth. By recognizing this evolution in our analysis, we underscore the ongoing refinement and adaptation of theoretical frameworks to specific contexts better capture the complexities of caregiving dynamics and promote holistic patient care.

While subscales 1, 2, and 5 form a single cohesive dimension, subscales 3, 4, 6 and 7, form separate groupings, resulting in a total of five dimensions, each representing specific facets of caring behaviors. The second dimension, ‘Teaching/Learning’, focuses on the educational aspects of caregiving and skills training. This dimension aligns with the principles of transpersonal care, emphasizing the importance of nurturing the growth and development of both caregivers and recipients through shared learning experiences. The third dimension, ‘Human Needs Assistance,’ emphasizes the importance of fulfilling the fundamental needs of people receiving care, reflecting the humanistic approach to caregiving that prioritizes the preservation of dignity and autonomy. The subscale ‘Expression of Positive/Negative Feelings’ captures the acknowledgement and validation of the emotional experiences of patients receiving care, resonating with the empathetic and compassionate aspects of transpersonal care. Lastly, the dimension ‘Existential/Phenomenological/Spiritual Forces’ addresses the existential, phenomenological, and spiritual aspects of caregiving. This dimension emphasizes the interconnectedness of mind, body, and spirit, echoing the holistic perspective of transpersonal care, which acknowledges the spiritual essence and interconnectedness of all beings. This comprehensive framework illuminates the multifaceted nature of caregiving, addressing diverse aspects essential for holistic patient care and well-being.

Relevant findings and preferences of Spanish individuals

The highest-ranking items among the Spanish participants mainly related to technical and cognitive components, such as competence in clinical procedures and the handling of equipment. Conversely, the lowest-ranking behaviours related to emotional and existential dimensions, such as talking about life outside the hospital, understanding patients’ experiences, and considering spiritual needs. These results may indicate that, within the Spanish context, these components are perceived by patients as less important than technical competencies, thus highlighting their priorities in terms of their care, even though the respondents were not hospitalised. These results suggest that clinical skills and technical competencies play an important role in patients’ perceptions of the quality of nursing care in Spain [ 30 ]. This finding is supported by a prior study [ 31 ] comparing nursing practice in Spain with that in the UK.

The prioritization of technical competencies over emotional and existential dimensions in nursing care may be explained by people’s prioritizing. Individuals usually prioritize basic needs and gradually move to more complex ones after basic needs are met. The perception of care may follow a similar pattern. The primary focus may thus be on safety and meeting the standard of performance required to guarantee this basic need, with less emphasis on the overall experience of wellbeing and being looked after. This approach also tends to be used in healthcare delivery, where the main focus is usually placed on survival-related outcomes [ 32 ]. However, as healthcare evolves toward value-based and person-focused approaches, there is growing awareness of the need to expand services and prioritize broader aspects of care. Expectations may thus be informed by factors such as recovery and quality of life, and become aligned with patients’ priorities, expectations and desire for comprehensive care and enhanced overall quality of life. By understanding this dynamic, healthcare professionals can better navigate the complexities of patient expectations and ensure the delivery of care in accordance with diverse needs and preferences.

However, to ensure comprehensive nursing care aligned with the expectations of individuals in Spain, it is essential to have a deep understanding of their individual needs and priorities. Validation studies conducted for specific populations may shed light on the elements of healthcare that are highly valued and contribute to humanisation. For example, research focusing on transgender populations has shown that being asked about their preferred form of address is highly valued [ 33 ] but does not seem to be a priority for the general population in our setting. Similarly, individuals in end-of-life processes place great importance on the ability of nurses and clinicians to show compassion and empathise with their feelings, while these qualities were not prioritised in the participants in our sample [ 34 ]. Equally, women going through challenging experiences, such as miscarriage, stressed that a key element of the care they required was being helped to cope with the future and understand their feelings [ 35 ].

In a similar vein, another study focused on how the general population perceived the quality of nursing services. The findings of that study revealed that various dimensions of quality, such as psychological, physical, and communication components, were rated at a moderate level, suggesting that there was room for improvement in meeting patients’ expectations [ 36 ]. This finding emphasises the importance of tailoring nursing care to specific populations to address the complexity of individual preferences, and highlights the need to focus on the multidimensional aspects of care to enhance the overall quality of nursing activity.

An awareness of contemporary nursing training and the scope of nurses’ work in society could fruitfully contribute to shifting such expectations away from a focus on technical and knowledge-related issues. As stated by López-Verdugo et al. [ 37 ], society often relies on misinformation when referring to nursing work, which is also often based on widely disseminated myths and stereotypes. A stereotyped image of nursing work, and of nurses themselves, may well lie beneath the reaction of some of the Spanish participants in our study when asked about the importance of emotional and spiritual needs in nursing care. Participants may not always fully appreciate the importance of integrated care, just as contemporary nursing remains largely unknown in Spain [ 37 ]. Therefore, a change in perspective is needed to foster greater appreciation of the profession for more rewarding experiences during periods of health and illness, both for users and for healthcare providers.

Previous research has emphasised human care as a driving force in nursing practice, highlighting that quality care relies on a holistic view of care that extends beyond technical proficiency [ 38 ]. Several studies have underscored that human care, which encompasses emotional support, effective communication, and attention to patients’ psychosocial needs, is essential for promoting patient satisfaction and achieving favourable health care outcomes [ 39 ].

A drawback of the CBA is its relatively long length, leading to a risk of tiring respondents. This limitation has been acknowledged in previous literature [ 15 ]. In addition, during the cultural adaptation phase of the present study, participants reported that some items were somewhat repetitive. To address this concern, future research could focus on validating abbreviated versions of this and other instruments. This approach would allow more streamlined integration of theoretical perspectives into routine assessments in clinical practice. Similarly, exploring the perspectives of specific population groups could provide a more nuanced understanding of their unique expectations regarding healthcare.

As patient-centered care gains recognition as a fundamental aspect of quality healthcare, understanding and measuring caring behaviors become necessary for healthcare organizations and professionals, highlighting the importance of tools like the CBA scale.

The interplay between theory and practice has gained prominence in nursing care over the past two decades. This dynamic encompasses various dimensions, ranging from abstract concepts like human sensitivity and emotional engagement to more tangible factors such as clinical skills. In this context, the use of tools to assess and translate nursing care into workable data have gained importance in healthcare policy and management. Indeed, such objective data can be useful for decision-makers in higher-level management, as nurses’ work is key to user satisfaction and the transformation of the biomedical paradigm in health care. Adapting and validating instruments can thus contribute to these processes.

Similarly, implementing ‘tooling up’ strategies can be a useful way of rendering nurses’ often invisible work visible, which, in the process, could incentivise a humane approach, which is perceived to have been lost in the evolutionary loop of healthcare in the industrialised world.

To support this endeavour, this article provides a validated version of the CBA for users in Spain. This version remains true to the original CBA but incorporates certain modifications into the Spanish version for respondents’ ease of use. Through a process of translation, cultural adaptation and statistical analysis, this new version has been demonstrated be a valid and culturally-appropriate instrument, which provides reliable, objective, comparable and culturally-sensitive data on patients’ perceptions of the most essential elements of care during hospitalization.

All authors declare that they have no conflicts of interest. The individuals who participated in this study were research participants and were not involved in the design, conduct, or preparation of the manuscript.

Relevance for clinical practice

The study addressed the problem of the lack of a culturally translated, adapted and culturally validated version of the Caring Behaviors Assessment (CBA) tool in the Spanish context. This was a significant issue as it hindered the collection of objective and culturally sensitive data on essential aspects of care.

The research will have an impact on several groups. First, it will benefit healthcare professionals and providers, policymakers and managers by providing them with a reliable instrument to evaluate and improve patient care. This instrument could enhance their understanding of patient needs and preferences, enabling them to identify areas for improvement and promote person-centered care.

Second, the research could directly benefit the Spanish-speaking population. Through the CBA tool, individuals will be able to ask for care that aligns more closely with their personal values and preferences, thus promoting a shift towards person-centered care.

Availability of data and materials

The datasets used and/or analyzsed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Caring Behaviors Assessment

Exploratory Factor Analysis

Standard Deviation

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Acknowledgements

We would like to express our gratitude to all the participants who took part in the study. We also wish to thank Dr Pedro Hervé (U. Magallanes, Chile) for providing statistical support. Lastly, we would like to thank Dr Sherill N. Cronin (Bellarmine University, USA) for giving us permission to use and translate the CBA tool into Spanish.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Nursing Department, Faculty of Medicine, Universitat Autònoma de Barcelona, Av. Can Domènech S/N, Cerdanyola del Vallès, 08193, Spain

Juan M. Leyva-Moral, Carolina Watson, Nina Granel & Cecilia Raij-Johansen

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Ghent University, Ghent, Belgium

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Contributions

JM.LM. and RA.A. made substantial contributions to the conception and design of the project. including the development of survey instruments and strategic planning for project dissemination. C.W. and N.G. played a key role in data acquisition, overseeing survey implementation and managing outreach efforts. JM.LM. and RA.A analyzed and interpreted data. C.W., C.RJ., and N.G. were involved in drafting and revising the manuscript. JM.LM and RA.A critically reviewed it for significant intellectual content. All authors read and approved the final manuscript.

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Correspondence to Carolina Watson .

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All ethical principles of biomedical research advocated in the Declaration of Helsinki were respected. This study has been reviewed and approved by the UAB Research Ethics Committee in accordance with ethical standards and guidelines. Approval reference number: (approval reference number CEEAH 5194). Participants were provided with a thorough explanation of the study procedures before accessing the questionnaire, ensuring their voluntary participation, with a commitment to maintaining the anonymity of the collected data. Informed consent was obtained from each participant before the completion of the questionnaires.

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Leyva-Moral, J.M., Watson, C., Granel, N. et al. Cultural adaptation and validation of the caring behaviors assessment tool into Spanish. BMC Nurs 23 , 240 (2024). https://doi.org/10.1186/s12912-024-01892-2

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Black Patients Feel Dismissed and Skeptical After Their Experiences in the ED

Many other Black patients participating in the study expressed similar experiences with racism in health care, noting how doctors dismissed their concerns, provided inadequate information about medications, and handled their pain poorly. Resulting feelings of mistrust were compounded, they noted, by how few caregivers looked like them.

By amplifying the experiences and voices of Black patients, the study—by LDI Senior Fellows  Anish K. Agarwal ,  David A. Asch ,  Raina M. Merchant ,  Eugenia C. South , and colleagues—provides data on people’s personal experiences of racism in health care. The work was funded by an LDI Bold Solutions Pilot Grant and published in  JAMA Health Forum .

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• David A. Asch, MD, MBA
• Raina Merchant, MD, MSHP, FAHA
• Eugenia (Gina) South, MD, MS

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• UB team receives funding to demonstrate effectiveness of ‘food-is-medicine’ in health care

UB team receives funding to demonstrate effectiveness of ‘food-is-medicine’ in health care

Lucia Leone (left), associate professor in the Department of Community Health and Health Behavior, and Jill Tirabassi, clinical assistant professor in the Department of Family Medicine, are co-principal investigators on a food-is-medicine project funded by the American Heart Association. Photo: Meredith Forrest Kulwicki

By David J. Hill

Release Date: April 16, 2024

BUFFALO, N.Y. – A team of University at Buffalo researchers has received funding from the American Heart Association (AHA) for research that will focus on the implementation of innovative food prescription programs in older adults.

The AHA has awarded UB $400,000 for the 18-month project as part of the organization’s Health Care by Food initiative, which is leveraging research to build the evidence needed to show the clinical and cost-effectiveness of so-called “food-is-medicine” programs.

The AHA funding comes at a key time. The Centers for Medicare & Medicaid Services (CMS) earlier this year approved an amendment to New York State’s Medicaid 1115 waiver that enables the state to modify its Medicaid program to address the health disparities exacerbated by the COVID-19 pandemic. The waiver paves the way for, among other things, investments in supporting strategies for improved access to food and nutrition, such as food-is-medicine programs.

The field of food-is-medicine is going to develop rapidly as other states, not just New York, have submitted Medicaid 1115 waivers, says Lucia Leone, PhD , associate professor in the Department of Community Health and Health Behavior in UB’s School of Public Health and Health Professions.

Leone is a co-principal investigator on the AHA project with Jill Tirabassi, MD ,MPH clinical assistant professor in the Department of Family Medicine in the Jacobs School of Medicine and Biomedical Sciences at UB.

“With the state’s Medicaid waiver approval, we know these programs are going to be coming down the pipeline in the next few years. Our aim is to look at three different types of food programs for adults over 65 in Western New York and develop ways to make them as user-friendly as possible,” Leone says.

“It is essential that we are able to quickly develop best practices for ensuring food prescription programs are successful at reaching the people who need them,” Leone adds. “This research will help practitioners who are looking to design food prescription programs for their communities understand what works and what doesn’t.”

Food-is-medicine may be defined as providing healthy food resources to treat, manage and prevent specific chronic conditions in coordination with the health care sector.

Food-is-medicine programs often feature:

• Medically tailored meals, which are often delivered to patients with diet-related health conditions or among those at high risk.
• Produce prescription programs that integrate healthy food into a patient’s health care plan, enabling patients to better follow their health care team’s dietary advice.
• And medically tailored groceries, which may include a selection of grocery items prescribed by a registered dietitian or nutritionist for patients with diet-related acute and chronic health conditions who can prepare food at home.

A total of 75 participants will be recruited for the study from primary care clinics at Erie County Medical Center (ECMC). Participants will be split into three groups of 25, each of which will receive a different food-related program over 12 weeks.

One group will receive a weekly credit redeemable at Massachusetts Avenue Project’s mobile produce market. Another group will have fresh produce boxes delivered to their homes via FreshFix, a local food delivery company co-founded by Leone. And the third group will have medically-tailored meal kits, with recipes to make several meals, delivered to their homes, each week. The food delivery partners will curate the items that are sent in each box, tailoring the contents to meet dietary restrictions, such as diabetes or food allergies.

The researchers will focus particularly on the successes and challenges of the implementation of the food is medicine programs. Toward that end, participants will receive surveys each week, which will help Leone and Tirabassi gauge whether people used the voucher or food they received. A community advisory board will also be set up to help oversee the project and provide insight into participants’ lived experiences and how those affect their ability to participate in these programs. For example, do they struggle with using smart phone apps and websites to customize the food they receive?

“Older adults face unique barriers with food and nutrition access,” Tirabassi says, explaining the focus on adults over 65. “They often have multiple chronic health conditions, have experienced life course changes — becoming a widower, for example —and are on a fixed income. Many people in this age also have mobility and transportation challenges.”

The AHA project is focused on food prescription program usage and not health outcomes for a very simple reason, Tirabassi says. “We already know that diet-related changes can affect health outcomes.” But, she adds, “Food prescription programs have not had very high utilization rates, and that is what we need to change.”  

The Primary Care Research Institute in the Department of Family Medicine at the Jacobs School, which has expertise in healthy aging research, is also a partner on the project.

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Oral health behaviors associated with mental health disorders

A study that examined oral hygiene self-care behavior among patients with self-reported mental health disorders was presented at the 102nd General Session of the IADR , which was held in conjunction with the 53rd Annual Meeting of the American Association for Dental, Oral, and Craniofacial Research and the 48th Annual Meeting of the Canadian Association for Dental Research, on March 13-16, 2024, in New Orleans, LA, U.S..

The abstract, "Oral Health Behaviors Associated with Mental Health Disorders," was presented during the "Oral Health and Systemic Conditions" Oral Session that took place on Thursday, March 14, 2024, at 2 p.m. Central Standard Time (UTC-6).

The study, by Gracie Groth of the Arizona School of Dentistry and Oral Health, Mesa, U.S., reviewed electronic dental records for patients treated in an academic advanced care dental clinic between 2018 and 2021 to identify the presence of self-reported anxiety, dental anxiety, depression, bipolar disorder, PTSD, and oral hygiene self-care behaviors (OHB).

Specific OHB included self-reported frequency of daily toothbrushing (TB), interdental cleaning (ID), use of fluoride toothpaste (FTP) and mouthwash (MW), and recommended preventive recare interval and frequency of returning for recare visits within a 2-year period.

Descriptive statistics, Mann-Whitney U, and Wilcoxon rank-sum tests were used for data analysis. ATSU Mesa IRB #2023-136 Exempt. 854 charts were reviewed, with 250 records identified with self-reported MHD.

Age of included patients ranged from 18 to 95 years, with mean age = 53.82 ±18.943. Most were females (n=145, 58.2%). Anxiety was the most common MHD (n=156, 62.4%), followed by depression (n=154, 61.6%), dental anxiety (n=64, 25.6%), bipolar disorder (n=37, 14.8%), and PTSD (n=22, 8.8%).

There were no significant differences in OHB, recare intervals or frequency of recare visits by MHDs. Most did not use ID (n=152, 60.8%) or MW (n=183, 73.2%). A Mann-Whitney U test showed there was a statistically significant difference between men and women for TB (W=11546.000, p=0.004) and FTP (W=11599.000, p=0.007), with women showing greater frequency of use.

The mean recommended recare interval was 5 months, with < 2 attended recare visits reported by sex and all types of MHD. Frequency of performing OHB, except for daily brushing with fluoride toothpaste, and returning for recare at recommended intervals was low for patients with self-reported MHD.

Provided by International Association for Dental, Oral, and Craniofacial Research

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.