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August 2024 - Volume 32 - Issue 4

• Editor-in-Chief: Yeur-Hur Lai, PhD, RN, FAAN Yea-Ing Lotus Shyu, PhD, RN, FAAN
• Online ISSN: 1948-965X
• Ranking: 35/191 Nursing
• Impact Factor: 2.4 5-year Impact Factor: 2.5

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Announcement: The Journal of Nursing Research (JNR) has grown significantly over the past 30 years, becoming more international and having an increasing impact on nursing care. As the second official journal of the Taiwan Nurses' Association, JNR was launched as a Chinese-only publication in 1993. Reflecting the strong commitment of its editorial staff to enhancing nursing science and healthcare worldwide, JNR began publishing in English in June 2001. The dedication of its editorial board and worldwide network of reviewers and researchers earned JNR's indexing in Science Citation Index (SCI) and Social Science Citation Index (SSCI) in 2012 and increasing recognition as an internationally respected nursing journal.

As an SCI- and SSCI-indexed journal, JNR has seen its influence on the international nursing society grow in its rising readership and growing submissions base. To enhance international knowledge dissemination and interactions, JNR has operated as an open-access, article-processing-charge-free academic journal since 2019. Through multifaceted efforts and rigorous review, JNR today has a proven impact, with a readership spanning nearly 200 countries. The success of JNR is also shown in its impact factor, indicating a growing number of JNR citations in the literature. JNR is continuing to grow and improve and is expected to be one of the top journals contributing to nursing science and the healthcare system in the next decade.

Call for Papers: The Taiwan Nurses Association has published  The Journal of Nursing Research ( JNR ) in English since June 2001 as a vehicle to expand the Association’s international perspective and promote academic exchange with nursing professionals overseas. As editor of JNR , I welcome the submission of manuscripts of relevance and interest to those concerned with the conduct or results of nursing-related research. You are encouraged to submit original articles addressing research into the practice, theory, or philosophy of nursing. All articles published in JNR will be peer-reviewed. Please e-mail the journal editorial office: [email protected]

Sincerely, Yeur-Hur Lai Yea-Ing Lotus Shyu

Editor-in-Chief, The Journal of Nursing Research Taiwan Nurses Association www.twna.org.tw

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A practice‐based model to guide nursing science and improve the health and well‐being of patients and caregivers

Sherry s. chesak.

1 Nursing Research Division, Mayo Clinic, Rochester MN, USA

Lori M. Rhudy

Cindy tofthagen.

2 Nursing Research Division, Mayo Clinic, Jacksonville FL, USA

Linda L. Chlan

Associated data.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Aims and Objectives

The purpose of this paper is to describe a model to guide nursing science in a clinical practice‐based setting. Exemplars are provided to highlight the application of this nursing research model, which can be applied to other clinical settings that aim to fill evidence gaps in the literature.

Nurse scientists are well positioned to develop new knowledge aimed at identifying global health solutions to multiple disparities. The generation and application of this knowledge are essential to inform and guide professional nursing practice. While a number of evidence‐based practice models exist to guide the integration of literature findings and other sources of evidence into practice, there is a need for additional models that serve as a guide and focus for the conduct of research in distinct scientific areas in practice‐based settings.

Model development and description.

Mayo Clinic is a large, comprehensive healthcare system with a mission to address unmet patient needs through practice, research and education. PhD‐prepared nurse scientists engage in practice‐based research as an integral component of Mayo Clinic's mission. A practice‐based nursing research model was developed with the intent to advance nursing research in a clinical setting.

The components of the Mayo Clinic Nursing Research model include symptom science, self‐management science and caregiving science. The generation of nursing science is focused on addressing needs of patients with complex health conditions, inclusive of caregivers.

Conclusions

While clinical settings provide rich opportunities for the conduct of research, priorities need to be established in which to focus scientific endeavours. The Mayo Clinic Nursing Research model may be applicable to nurses around the globe who are engaged in the generation of knowledge to guide practice.

Relevance to Clinical Practice

The Mayo Clinic Nursing Research model can be used by nurse scientists embedded in healthcare settings to address clinically relevant questions, advance the generation of new nursing knowledge and ultimately improve the health and well‐being of patients and caregivers.

What does this paper contribute to the wider global clinical community?

• There is a need for additional models to guide the conduct of nursing research in clinical settings.
• The Mayo Clinic Nursing Research Model was developed as a model to guide the generation of new nursing knowledge in a clinical, practice‐based setting.
• The model can be used in a variety of clinical settings for researchers who aim to fill evidence gaps in the literature.

1. INTRODUCTION

Nursing is the largest profession in health care, with continued growth expected over the next several years (Grady & Hinshaw, 2017 ). Nursing science plays a critical role in addressing health challenges, generating new knowledge and translating evidence to practice to improve patient outcomes (Grady, 2017 ; Powell, 2015 ). Furthermore, nursing science integrates biobehavioural approaches to better understand patients' needs and preferences, develop individualised symptom management interventions (Trego, 2017 ), advance interventions to promote self‐management of chronic conditions and thus promote well‐being and quality of life (Grady, 2017 ; Powell, 2015 ). Patients' healthcare needs are becoming increasingly more complex, giving rise to the need for practice‐based research. The clinical practice setting provides an opportunity to conduct research, by which patients' and caregivers needs and outcomes may be addressed and improved.

The purpose of this paper is to present the Mayo Clinic Nursing Research (MCNR) model (Figure ​ (Figure1)—a 1 )—a model developed to guide and focus nursing science generation in a practice‐based setting with an emphasis on promoting the health and well‐being of patients and caregivers with complex needs. The components of the model are described, and exemplars of the generation of practice‐based nursing knowledge are presented.

Mayo clinic nursing research model [Color figure can be viewed at wileyonlinelibrary.com ]

2. BACKGROUND

Over a century ago, Florence Nightingale recognised not only the need for formal training for nurses but also the power of the nurse to improve patient outcomes (Nightingale, 1992 ). This is still true in today's healthcare environment. Nurses can help fill a critical need not only for the education and training of healthcare workers, but also for the design and testing of solutions to common health problems (National Institutes of Health, 2015 ). As noted by Dr. Patricia Grady, director emeritus of the National Institute of Nursing Research (NINR), ‘…nurse scientists can use their expertise in clinical research and their understanding of the relationship between behaviour and biology to further expand the reach and impact of nursing science in the larger community’ (National Institute of Nursing Research, 2016 , p. 6). However, recommended models for the structure and organisation of nursing research in clinical settings are scarce.

PhD‐prepared nurse scientists (sometimes referred to as nurse researchers) design and implement research studies to improve health‐related outcomes. Although most nurse scientists are employed in academic settings such as schools/colleges of nursing, there is an emerging trend for nurse scientists to have full‐time appointments in practice settings (Robichaud‐Ekstrand, 2016 ). The nurse scientist role has wide variability in how it is operationalised but can be described in three ways. First, in academic settings, Boyer's model of scholarship includes discovery, integration, application and teaching to frame the discussion of discovery and practice in nursing (Boyer, 1990 ; Hickey et al., 2019 ). Academic service partnerships have emerged as strategies to close the academic‐practice gap by connecting clinical practice with academia in order to meet mutually beneficial goals (Sadeghnezhad et al., 2018 ). Examples of programmes in academic‐service partnerships include preparation of new graduate nurses, patient safety initiatives, transitions‐in‐care programmes, advancement of evidence‐based nursing and opportunities for clinical research (Sadeghnezhad et al., 2018 ). While such programmes inform the advancement of nursing research as a component of evidence‐based practice in clinical settings, they are less informative in guiding the generation of knowledge among nurse scientists embedded in clinical settings.

In a second approach, a nurse scientist supports evidence‐based practice, quality improvement, the conduct of research by clinical nurses and, if applicable, ANCC Magnet Recognition Program® activities (Kowalski, 2020 ). A third approach similarly involves embedding nurse scientists in clinical practice settings but the role is focused on the conduct and facilitation of nursing‑oriented research, rather than simply providing support for research conducted by others (Chan et al., 2010 ). This third approach is used in the setting in which this model was developed.

Evidence‐based practice models such as the Iowa Model and the Johns Hopkins Nursing Evidence‐Based Practice Model have been adopted to guide translation of evidence to practice but they have limited utility in describing the infrastructure, focus and outcomes of nursing research in a clinical setting. The Iowa Model Revised: Evidence‐Based Practice to Promote Excellence in Health Care uses an algorithm to guide evidence‐based practice processes from identification of a trigger to integrating and sustaining a practice change (Buckwalter et al., 2017 ). The conduct of research is included in the Iowa Model as a strategy to be used when insufficient evidence exists to recommend a practice change. The Johns Hopkins Nursing Evidence‐Based Practice Model (Dang & Dearholt, 2018 ) includes a patient‐centred approach and incorporates a continuum of Inquiry–Practice/Learning–Practice Improvement as a method to ensure that best practices are applied to patient care. However, the model is centred on an evidence‐based practice approach, which differs from research in that research involves systematic investigation of phenomena to discover new information or reach new understandings and conclusions to generate new knowledge (Cohen et al., 2015 ; Hickey et al., 2019 ). The Joanna Briggs Institute (JBI) (Joanna Briggs Institute, 2016 ), based in the Faculty of Health and Medical Sciences at the University of Adelaide, South Australia, aims to promote evidence‐based decision‐making by promoting the use of the best available evidence. JBI, through its JBI Collaboration, works with universities and hospitals around the world to synthesise, transfer and implement evidence that is culturally relevant and applicable across diverse healthcare settings internationally.

The NINR sets strategic funding and training priorities that advance nursing science to enhance the health and well‐being of individuals across diverse populations (National Institute of Nursing Research, 2016 ). Current research priorities established by the NINR include four scientific foci: symptom science, wellness, self‐management of chronic conditions, and end‐of‐life and palliative care (National Institute of Nursing Research, 2016 ). In addition, all areas of NINR's research programmes place an emphasis on promoting innovation and developing the nurse scientists of the 21st century (National Institute of Nursing Research, 2016 ). Recognising that symptoms are the primary reason patients seek care, the NINR developed the symptom science model to advance research. The symptom science model describes an analytical sequence beginning with a sequelae or cluster of symptoms, which are then characterised into a phenotype with biological correlates, followed by the application of research methods that can be used to identify targets for therapeutic and clinical interventions (Cashion & Grady, 2015 ).

Nurse scientists are well positioned to develop new knowledge aimed at identifying global health solutions to social, economic, psychological and biological disparities. The generation and application of this knowledge are essential to provide the best available evidence to inform and guide professional nursing practice. While a number of evidence‐based practice models exist to guide the integration of literature findings and other sources of evidence into practice, there is a need for additional models that serve as a guide and focus for the conduct of research in distinct scientific areas in practice‐based settings. Therefore, the project team identified a need for the development of a model articulating the goals and strategies to advance nursing research within their institution, and which would have broad applicability to other institutions and nurse scientists embedded in the clinical practice.

Mayo Clinic is a large academic medical centre that incorporates practice, education and research into its mission, which has been emulated in the Department of Nursing and the Division of Nursing Research for over three decades. Today, the Mayo Clinic Nursing Research Division is an enterprise‐wide unit providing infrastructure and support for nursing research at its sites in Mayo Clinic. A cadre of PhD‐prepared nurse scientists lead independent programmes of research and provide consultation to all staff in research‐related matters, including scientific review of research protocols. In addition, small cadres of registered nurses providing direct patient care conduct research studies under the mentorship of a nurse scientist. These clinical nurse scholars identify clinically relevant questions that are investigated by an independent research study (Chlan et al., 2019 ). Details of this programme are described elsewhere (Chlan et al., 2019 ; National Institute of Nursing Research, 2016 ).

The project team developed a model of nursing research to guide the foci for nurse scientists' research at the institution and to generate new nursing knowledge based on needs that arise from the practice setting. The model was also intended to encompass strategic priorities established both by the institution and the field of nursing science. No ethics approval was required for this project.

The team started the process of model development by conducting a literature review regarding (1) existing models of nursing research and evidence‐based practice, (2) nursing science, (3) the nurse scientist role, (4) national and international nursing research strategic priorities and (5) research strategies to transform health care. In addition, the team sought input from multidisciplinary stakeholders at the institution regarding their perception of the current and potential future contributions of nursing science to the practice. Finally, organisational resources describing the research environment were used to inform the model. Thus, it is a model rooted in practice, rather than a theory‐based model.

4.1. The Mayo Clinic nursing research model

The MCNR model is focused on three primary areas across multiple diseases, illnesses, and healthcare settings: symptom science, self‐management science and caregiving science. With a focus in these areas, nurse scientists leverage team science, big data, innovation and technology to move knowledge generation quicker along the discovery, translation and application continuum to meet the needs of patients and caregivers.

The following assumptions informed the development of the model. First, nursing research is vital for the generation of new knowledge to improve the health and well‐being of patients and their caregivers. Second, the health and well‐being of individuals with complex conditions are enhanced by developing and testing patient‐centred interventions through research that focuses on the science of symptom assessment and management, self‐management and caregiving. The MCNR model was developed to guide how this vision will be implemented in a clinical setting with programmes of nursing research aligned to inform and transform health care.

4.2. Patients and caregivers as the focal point of the model

At the centre of the model (Figure ​ (Figure1) 1 ) are the patient and caregiver with complex needs—medical, physical or psychosocial—around which all other elements in the model centre. The nurse scientist focuses on a better understanding of those needs and the testing of interventions used to address them, with the definitive goal of improving patients' and caregivers' health and well‐being. For the purposes of this model, health is defined from a holistic, phenomenological perspective of optimal overall physical, mental, spiritual, social and role functioning (Saylor, 2004 ; Watson, 2008 ); and well‐being is designated as individuals' perceptions, judgements and expectations regarding their health (Saylor, 2004 ; Sullivan, 2003 ). These foci are consistent with the patient‐centred model of care in which patients are viewed as a whole and their individual viewpoints and characteristics are taken into consideration when making decisions regarding care (Zhao et al., 2016 ). It is also congruent with the mission and values of Mayo Clinic (Mayo Clinic, 2021 ), as well as the profession of nursing (Spurlock, 2019 ).

4.3. MCNR model scientific foci

The generation of symptom science, self‐management science and caregiving science are the scientific foci that promote the health and well‐being of patients and caregivers in a practice‐based, patient‐centred clinical setting. It is through the conduct of scientific investigation in these three main areas, described below, that nursing research seeks solutions to unmet, complex health needs of patients and caregivers.

Symptom science seeks to transform the practice using biological, clinical and/or behavioural approaches to investigate symptoms aiming to individualise care and assess patient‐reported outcomes such as quality of life and well‐being (Grady, 2017 ). Self‐management science is based on a complex set of cognitive and behavioural self‐regulation responses that individuals engage in to manage chronic illnesses or factors that increase the risk for illness (Araújo‐Soares et al., 2019 ). Research to support self‐management includes developing and evaluating a broad range of interventions often focused on providing education and guidance for managing specific illnesses, partnering with healthcare providers and coping with challenges of living with chronic illness (Allegrante et al., 2019 ).

Caregiving science is research that explores effective approaches to reduce burden on and promote the health and well‐being of professional and lay caregivers (Grady, 2017 ). Research that examines methods to include caregivers in the care process and to design and test interventions that include them has the potential to significantly contribute to improved patient outcomes and patient‐centred care (Littleton‐Kearney & Grady, 2018 ).

4.4. Leveraging team science, big data, innovation, and technology

In addition to cutting‐edge research methods, nurse scientists leverage team science, big data, innovation and technology as tools, resources and methods to seek solutions to unmet health needs of patients and caregivers (Brennan & Bakken, 2015 ; Conn, 2019 ; Grady & Gough, 2018 ). Within the MCNR model, these four resources and methodologies contribute to the advancement of nursing science in the areas of symptom, self‐management, and caregiving. Team science leverages the strengths and expertise of professionals trained in different disciplines or nursing specialties through a collaborative effort to address a scientific challenge (Bennett & Gadlin, 2012 ). Team‐based research initiatives can be uni‐ or multidisciplinary groups, and teams can be large or small (Conn, 2019 ). In team science, multiple stakeholders contribute unique perspectives on the topic at hand and are deeply engaged in the project (Bennett et al., 2018 ). The World Health Organisation has acknowledged the importance of team‐based research through implementation of nursing collaborating centres, which focus on collaborative research of global or regional importance (National Institutes of Health, 2015 ).

Big data science allows researchers to analyse large and complex volumes of information that are newly available at unprecedented rates from sources such as electronic health records, large databases, sensor‐enabled equipment, imaging techniques, smart devices and high‐throughput genetic sequencing methods (Fernandes et al., 2012 ). Through the application of big data research methods, including artificial intelligence, researchers can discover new ways of understanding and addressing the needs of the patient (Fernandes et al., 2012 ). For example, big data methodologies can be implemented to maximise the utility of patient‐reported outcome data in order to capture the patients' perspectives on how their disease, and the treatment of their disease, is impacting their lives. These data can be used to inform clinical decision‐making, predict long‐term outcomes and identify future innovations in health technologies and other interventions (Calvert et al., 2015 ). This patient‐centric approach ultimately allows healthcare providers to have a better understanding of how individuals are living with and managing their illness, and to make more informed decisions regarding personalised interventions that will have a measurable impact on the patient experience (Brennan & Bakken, 2015 ).

Innovation is defined as a creative, fast‐moving endeavour that involves scientific methods and improvisation to design unique solutions that change the world (Mayo Clinic Center for Innovation, 2020 ). Innovative research uses novel theoretical concepts, methodologies and interventions to challenge current clinical practice paradigms. Innovations in health care can be seen in product innovation for the introduction of new types of goods and services, and in process innovation, which is centred on enhancing internal processes for the production of high‐quality care (Arshad et al., 2018 ; Govindasamy & Wattal, 2018 ; Thune & Mina, 2016 ).

Technology in medical research involves innovations that impact health or healthcare delivery (Healthcare News & Insights, 2020 ; Martins & Del Sasso, 2008 ). Biotechnology, machine learning, pharmaceuticals, information technology, remote monitoring and medical devices are examples of technology. Other technologies include software and applications for self‐management and symptom tracking. Technologies can maximise efficiency and access to health care, such as digital solutions to connect patients to the appropriate provider (National Institute of Mental Health, 2020 ).

4.5. Discovery‐translation‐application continuum

Research conducted at Mayo Clinic occurs along a continuum to address unmet patient needs. The process by which new information makes its way into practice along this continuum is through discovery, translation and application, depicted in the outermost ring of the model in Figure ​ Figure1. 1 . Discovery uses scientific methods to seek solutions to improve the health and well‐being of patients with complex conditions; translation is the development and testing of possible solutions; and application is the dissemination, integration, and evaluation of solutions into practice (Ammerman et al., 2014 ).

Nursing research contributes to innovation at all points along the discovery‐translation‐application continuum, continually advancing science, transforming patient care and improving outcomes (Grady, 2017 ). Guided by the MCNR model, nurse scientists discover answers to puzzling clinical questions that can be translated and applied directly to clinical practice to improve patient care as rapidly and as safely as possible. There are at least seven implementation science models or frameworks available to guide translation of findings to practice. Systematic reviews show variability in their scope and application so selection of an implementation framework according to the context of change is key (Dintrans et al., 2019 ; Moullin et al., 2015 ). In our setting, translation is achieved through clinical partnerships where the department's evidence‐based practice model is used to guide implementation. As depicted in the model in circular form (Figure ​ (Figure1), 1 ), this process is iterative rather than linear. Discoveries are made through observation, discussion or other forms of data. These discoveries, seen through the nursing lens, may have broader applications to be considered. Further, empirical evidence is needed prior to implementing new discoveries into practice. During implementation, new discoveries and applications may come to light.

5. EXEMPLARS OF THE MAYO CLINIC NURSING RESEARCH MODEL

The overall purpose of the MCNR model is to provide a coordinated focus and consistent approach that guides and prioritises practice‐based nursing research. Nurse scientists use the model in their own focused areas of research as well as to guide nurses in the conduct of research that arises from their practice. Outlined below are exemplars of how the MCNR model guides the conduct of practice‐based research among nurse scientists at Mayo Clinic. Examples of how the model has informed research are presented. Not all aspects of the model are evident in each exemplar.

The first nursing research exemplar, within the domain of symptom science (second ring of the MCNR model), aims to address unmet needs of critically ill patients (centre of model) related to comfort‐promoting interventions. Under the mentorship of a PhD‐prepared nurse scientist, this descriptive, cross‐sectional study is being conducted by two practising ICU nurses who first identified in their own clinical setting the problems of: (1) numerous sources of discomfort among ICU patients; (2) the absence of objective assessment of these discomforts as distinct from objective assessment of pain; and (3) the inability to intervene appropriately with effective comfort‐promoting interventions. Next, they identified the distinction between discomfort and pain. They are currently assessing, describing and quantifying the contributing sources of discomfort experienced by nonmechanically ventilated ICU patients using the Discomforts Perceived by ICU Patients instrument, a modified version of the French instrument Inconforts des Patients de REAnimation (IPREA) questionnaire (Baumstarck et al., 2019 ). The end‐product of this study will be the discovery of new knowledge (outer ring of model) to inform ICU nursing practice regarding discomfort‐producing stimuli. Future areas of investigation would include developing and testing interventions (translation of possible solutions through clinical trials), of which those that are found to be effective would then be directly applied in the setting of ICU clinical nursing practice contributing to symptom science for critically ill patients.

An exemplar within the domain of caregiving science (second ring of MCNR model) is a multidisciplinary trial co‐led by a nurse scientist and physician (team science—third ring of model). The investigators noted that patients with advanced cancer or those nearing the end‐of‐life experience significant, unique distress related to their disease, treatment and impending mortality. In addition, they noted a lack of evidence on best methods to manage psychosocial distress in patients and caregivers with complex needs (centre of model). Thus, they designed a study to determine the feasibility of a modified version of the Resilient Living Program (The Resilient Option, 2020 ) that is tailored to the needs of patients with advanced cancer and their adult caregivers. Outcomes of the study include feasibility of participant recruitment, acceptability of the intervention and self‐reports of resilience, quality of life, stress, anxiety, sleep, fatigue and caregiver role overload. Findings from this study will lead to the discovery (outer ring of model) of best practices for integrating a resilience training programme within the care of patients with complex needs (centre of model), and their caregivers. Future studies will examine the outcomes of revised training programmes that are more effectively tailored to the unique needs of these populations.

Recognising the emotional distress their patients endure, a group of nurses working on the bone marrow transplant (BMT) unit expressed interest in specific nursing interventions to support their patients' emotional well‐being. Although they knew from their clinical experience that hospitalisation for BMT is quite stressful, they wanted to have a better understanding of when the most distressing times were for the patients, and what aspects of undergoing BMT were the most stressful. A review of the literature did not identify the specific information they were seeking. In collaboration with a nurse scientist and social workers on the unit, they implemented a descriptive study aimed at answering their questions. The study is in progress, and when finished, the results will inform both nursing and social work practice. This is an example of how clinical nurses identified a need centred around the health and well‐being of complex patients (centre of the MCNR model), focused on symptom science (second ring of the model), and used team science (third ring of the model) to discover new information (outer ring of the model) from which nursing interventions can be developed and tested.

The final nursing research exemplar is within the domains of symptom science and self‐management science (second ring of the MCNR model) to address the unmet needs of complex critically ill patients (centre of model). As of this writing, a randomised controlled clinical trial is testing the efficacy of self‐administered versus intensive care unit (ICU) nurse‐administered sedative therapy for anxiety in critically ill patients receiving mechanical ventilatory support (1R01 {"type":"entrez-nucleotide","attrs":{"text":"HL130881","term_id":"1051909465","term_text":"HL130881"}} HL130881 ). Primary outcomes of the study include anxiety, duration of mechanical ventilation, delirium, level of arousal, alertness and sedative exposure. Post‐ICU outcomes are also being examined and include functional status, depression and health‐related quality of life. Findings from this clinical trial will be applied to the practice setting (outer ring of the model) to implement patient‐centred interventions that improve not only ICU outcomes but also quality of life during the trajectory of recovery from critical illness and injury.

6. DISCUSSION

The MCNR model guides nursing research across settings and prioritises inquiry on symptom science, self‐management science and caregiving science. The model is unique in that it specifically focuses on generation of nursing knowledge through the focus and conduct of research in a practice‐based clinical setting. Few such models have been found in the literature; those that are available focus on advancing bedside nurses' involvement in research (Brewer et al., 2009 ; Stutzman et al., 2016 ). Robust programmes of nursing research remain relatively uncommon in clinical settings (Robichaud‐Ekstrand, 2016 ). Availability of time and resources needed to facilitate clinical research are often constrained. Even in large academic medical centres with institutional commitment, the contributions of nursing research often go unrecognised, even from within the nursing profession. The MCNR model can be used to communicate the scope and focus of nursing research, from which studies can be developed to address significant problems impacted by nursing practice.

In creating the MCNR model, we sought to demonstrate the unique contributions of nursing research at our institution and develop a framework to guide the overall direction of nursing research. This model may have limited application in nonclinical settings; however, other institutions may glean information to develop similar models tailored to their settings. Adaptation of the model to fit a specific organisational context and available resources may be necessary. Although the model is implemented in a setting rich in human and other resources to guide nursing science, it could easily be used in settings with more limited resources to help frame the scope and function of nursing science. However, this model was primarily developed for use in clinical settings in which some resources for the conduct of research exist. Unfortunately, there are still many settings where the resources needed to facilitate nursing research are sparse or non‐existent.

The MCNR model can also be integrated with existing models of nursing research. The National Institutes of Health Symptom Science Model is one example of a complementary model that can be used in tandem with the MCNR. The Symptom Science Model provides a guide for researchers to study complex symptoms experienced by individuals and incorporates the components of phenotypic characterisation, biomarker discovery and clinical application, with an overall goal of symptom reduction and improvement (Cashion et al., 2016 ). These methodologic components can be used to advance the care of patients with complex needs in the context of the institutional priorities and infrastructure described in the model. The MCNR model can be applied in several ways to advance scientific knowledge in the areas of symptoms, self‐management and caregiving. The model incorporates advancements in biological sciences, technology and big data methods to meet the needs of patients in a holistic way using nursing's unique body of knowledge (Henly et al., 2015 ). While nurse scientists may not have extensive expertise in all areas, collaborating with other scientists and clinicians who have complementary expertise ensures that investigations incorporate the best science and technology from other fields to inform nursing knowledge and practice.

As nurse scientists are increasingly employed in clinical settings, it will become more important to evaluate and publish outcomes of models, including this one. Nursing research within our institution is evolving to best meet the needs of patients. The MCNR model is a step in the process to define our direction and differentiate our areas of expertise from those of other disciplines.

The model is not without limitations. The MCNR Model was developed by nurse scientists within the Division of Nursing Research to serve as a guide and focus for our conduct of research, and to communicate our work with others. It is a reflection of the current foci of nursing research at a single institution and, as noted earlier, may need to be adapted to meet the needs of other institutions. It is intended to serve as a starting point for the infrastructure needed to generate research ideas and to serve as a guide to focus the conduct of research in distinct scientific areas in practice‐based settings. It is not intended to constrain research foci that are outside of this model. The model may be of lower utility in settings where nurse scientists are not available. It will be revisited periodically by the research team and stakeholders to ensure that it reflects the current focus of nursing research throughout the institution.

7. CONCLUSION

Nurse scientists embedded in healthcare settings are uniquely positioned to inform translation of research findings to practice. As health care evolves and the needs of patients and caregivers become more complex, the importance of studying symptoms, self‐management and caregiving is becoming increasingly critical. Nurse scientists leverage team science, big data, innovation and technology to move knowledge generation along the continuum of discovery, translation and application. The MCNR model can be used to advance generation of new nursing knowledge to improve the health and well‐being of patients and caregivers.

8. RELEVANCE TO CLINICAL PRACTICE

The MCNR model can be used by nurse scientists embedded in healthcare settings to address clinically relevant questions and ultimately improve the overall physical, mental, spiritual, social and role functioning of patients and caregivers, as well as to enhance individuals' perceptions, judgements and expectations regarding their health. The model provides a structure for addressing nursing science priorities through the discovery, translation and application continuum, and advancing the generation of new nursing knowledge.

CONFLICT OF INTEREST

The authors report no conflicts of interest with this manuscript.

AUTHOR CONTRIBUTIONS

Conception and design of the work, drafting of the article, critical revisions of the article and final approval of the version to be published: All authors.

DATA AVAILABILITY STATEMENT

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This paper is in the following e-collection/theme issue:

Published on 16.8.2024 in Vol 26 (2024)

Impact of 25 Years of Mobile Health Tools for Pain Management in Patients With Chronic Musculoskeletal Pain: Systematic Review

Authors of this article:

• Jenny Lin-Hong Shi, MD, PhD   ; 
• Regina Wing-Shan Sit, MD, PhD  

Department of Medicine, Jockey Club School of Public Health and Primary Care, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China

Corresponding Author:

Regina Wing-Shan Sit, MD, PhD

Department of Medicine

Jockey Club School of Public Health and Primary Care

Prince of Wales Hospital, The Chinese University of Hong Kong

4/F, JC School of Public Health and Primary Care Building

Hong Kong, 999077

Phone: 852 25039406

Fax:852 26095998

Email: [email protected]

Background: Mobile technologies are increasingly being used in health care and public health practice for patient communication, monitoring, and education. Mobile health (mHealth) tools have also been used to facilitate adherence to chronic musculoskeletal pain (CMP) management, which is critical to achieving improved pain outcomes, quality of life, and cost-effective health care.

Objective: The aim of this systematic review was to evaluate the 25-year trend of the literature on the adherence, usability, feasibility, and acceptability of mHealth interventions in CMP management among patients and health care providers.

Methods: We searched the PubMed, Cochrane CENTRAL, MEDLINE, EMBASE, and Web of Science databases for studies assessing the role of mHealth in CMP management from January 1999 to December 2023. Outcomes of interest included the effect of mHealth interventions on patient adherence; pain-specific clinical outcomes after the intervention; and the usability, feasibility, and acceptability of mHealth tools and platforms in chronic pain management among target end users.

Results: A total of 89 articles (26,429 participants) were included in the systematic review. Mobile apps were the most commonly used mHealth tools (78/89, 88%) among the included studies, followed by mobile app plus monitor (5/89, 6%), mobile app plus wearable sensor (4/89, 4%), and web-based mobile app plus monitor (1/89, 1%). Usability, feasibility, and acceptability or patient preferences for mHealth interventions were assessed in 26% (23/89) of the studies and observed to be generally high. Overall, 30% (27/89) of the studies used a randomized controlled trial (RCT), cohort, or pilot design to assess the impact of the mHealth intervention on patients’ adherence, with significant improvements (all P <.05) observed in 93% (25/27) of these studies. Significant (judged at P <.05) between-group differences were reported in 27 of the 29 (93%) RCTs that measured the effect of mHealth on CMP-specific clinical outcomes.

Conclusions: There is great potential for mHealth tools to better facilitate adherence to CMP management, and the current evidence supporting their effectiveness is generally high. Further research should focus on the cost-effectiveness of mHealth interventions for better incorporating these tools into health care practices.

Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42024524634; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=524634

Introduction

Chronic musculoskeletal pain (CMP) is defined as musculoskeletal pain that persists or recurs for longer than 3 months [ 1 ]. Interventions to help manage CMP are usually based on multimodal and biopsychosocial models [ 2 - 4 ]. CMP is a global burden, affecting approximately 1 in 5 adults [ 5 ]. One study indicated that over 70% of people 65 years and older have experienced an episode of joint pain [ 6 ]. Given that the percentage of the population 65 years and older is expected to increase from 15% to 24% by 2060, chronic musculoskeletal conditions will definitely become an increasing burden for the health care system [ 7 ].

The long-term nature and frequent need for continuous monitoring in CMP management gave rise to early developments in telemonitoring and telehealth. These innovations designed to improve CMP management and prevent disability and death have been improved by ongoing technological advancements. One such advancement is mobile device–based health care, or mobile health (mHealth). In 2022, the number of mobile users worldwide stood at 7.26 billion, which is projected to reach 7.49 billion by 2025 [ 8 ]. Mobile technologies such as smartphones and wireless monitoring devices are increasingly finding innovative applications and have emerged as potential alternatives to support the self-management of patients with CMP [ 9 ]. These applications include communication, data collection, patient monitoring, and education, as well as to facilitate adherence to CMP management [ 9 ].

The available evidence has pointed to the promising effects of mHealth interventions on CMP. A recent review [ 10 ] evaluated the effectiveness of app-based interventions on several CMP conditions (including general chronic pain, osteoarthritis [OA] pain, chronic neck pain [CNP], chronic low back pain [CLBP], rheumatoid arthritis, menstrual pain, migraine-related pain, and frozen shoulder pain), stating that mobile apps are significantly more effective in reducing pain compared with control conditions. Du et al [ 11 ] analyzed the use of web-based and mHealth interventions in patients with CLBP, showing that mHealth tools had a better effect on both pain and functional outcomes. In a similar vein, Thurnheer et al [ 12 ] analyzed the efficacy of mobile app usage in the management of patients with cancer and noncancer pain (eg, acute pain, general chronic pain, CNP, CLBP, and menstrual pain), reporting beneficial effects on pain relief, particularly in the out-clinic setting.

The evidence of the use of mHealth systems is still emerging, mainly focusing on its effect on clinical outcomes. However, current CMP management often requires a long-term care plan. Adherence to CMP management is critical to achieving improved outcomes, quality of life (QoL), and cost-effective health care [ 13 ]. A review of adherence behaviors from the World Health Organization noted that “increasing adherence may have greater effects on health than improvements with specific medical therapy” [ 14 ]. The true impact of these mHealth tools on adherence to treatment regimens may be overlooked, as mHealth promoters are more eager to show their effects on clinical outcomes (eg, morbidity, mortality, and biometric markers of clinical disease). Adherence to CMP treatment is a critical link that would connect the promise of mHealth to the ultimate goal of improving clinical outcomes.

To the best of our knowledge, no review published to date has examined the effects of using mHealth tools on the adherence to management and clinical outcomes of patients with CMP. Therefore, the aim of this systematic review was to provide an overview of the evidence with respect to a broad range of outcomes, including feasibility, usability, acceptability, and adherence, of mHealth tools to impact CMP-specific clinical outcomes. This approach enabled considering mHealth tools at all stages of development and to gauge the effectiveness of these tools across a range of technologies and CMP subtypes, many of which have overlapping treatment regimens and require similar adherence behaviors.

The protocol of this systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42024524634). The review was performed following the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 15 ].

We undertook a 25-year systematic review of mHealth interventions used to facilitate the self-management of CMP and adherence to treatment and management regimens. For this review, CMP included all types of chronic primary and secondary musculoskeletal pain based on the complete list of CMP conditions in the International Classification of Diseases, 11th edition (ICD-11) foundation layer supplement [ 16 ]. Our definition of mHealth was adopted from the Global Observatory for eHealth definition: “medical and public health practice supported by mobile devices” [ 17 ]. Given the comprehensive nature of CMP, this review goes beyond defining adherence as compliance with a treatment regimen by including a wide range of interventions such as medication reminders, symptom monitoring, educational tools, and facilitated patient-provider communication [ 18 ].

Search Strategy

The search strategy was based on CMP diseases according to the ICD-11 [ 19 ]. The search was conducted from January 1999 through December 2023. Using Boolean phrases, we searched the PubMed, MEDLINE, Cochrane (CENTRAL), EMBASE, and Web of Science databases for studies that assessed the role of mHealth interventions in CMP management. The search strategy was first developed for the PubMed database using Medical Subject Headings (MeSH) and was adapted for other databases. The search was filtered either by English language or by date of publication or publication type as an article. The detailed search strategy for each database is provided in Multimedia Appendix 1 .

Inclusion Criteria

We included original research published in peer-reviewed journals that evaluated the effects of mHealth tools on CMP-related clinical outcomes; adherence to management; and usability, feasibility, and acceptability features. All available study designs (randomized controlled trials [RCTs], cohort studies, cross-sectional studies, case series, case reports, questionnaires, mixed methods studies, and qualitative studies) were eligible for inclusion. Allowing for flexibility in the outcomes measured and in study design was necessary for an inclusive view of mHealth interventions at all stages of design, development, and evaluation. The detailed inclusion criteria for study selection and mHealth tools are provided in Multimedia Appendix 2 .

Exclusion Criteria

Only articles reporting mHealth interventions designed for CMP were included. We excluded articles regarding interventions that were not tested in a sample population with clearly described methods and results. In addition, review articles, editorials, commentaries, dissertations, poster presentations, abstracts, proposals for future studies, study protocols, and descriptive articles describing new tools but not testing them in a sample population were excluded. The publication language was restricted to English.

Data Extraction and Analysis

Publications were initially screened for potential inclusion based on a simultaneous review of titles and abstracts by 2 independent reviewers. Any discrepancies were resolved by consensus among the researchers. Information, including publication year, location, study sample characteristics, types of mobile technology used, intervention and control details (if available for the given study design), outcomes measured, and summary results reported, was extracted and compiled using a Microsoft Excel spreadsheet.

We performed descriptive analyses of the data and summarized the findings from the included studies with an emphasis on statistical results reported in RCTs and cohort studies. We highlighted differences between groups when these results were available. Studies were organized for analysis based on the primary objective of the study and the key outcomes measured. Outcomes were organized into the following categories: (1) usability, feasibility, and acceptability of the mHealth tool; (2) effect of the mHealth intervention on adherence to CMP management; and (3) effect of the mHealth intervention on pain outcomes.

In all, 866 articles were retrieved in full text and assessed for eligibility, among which 383 articles were excluded due to duplication. Based on the search criteria, 337 articles were excluded due to not meeting the study design criteria or not aligning with the definition of mHealth used in our study. A total of 142 articles were included in the full-text retrieval process, of which 53 were excluded due to an inappropriate study design, intervention, or population or no access to the full text. Finally, a total of 89 articles were eligible and met all inclusion criteria; the details of these included studies are provided in Multimedia Appendix 3 [ 20 - 107 ]. Multimedia Appendix 4 illustrates the PRISMA flowchart for the study selection process and Multimedia Appendix 5 provides a list of excluded studies with reasons for exclusion.

Study Characteristics

The publication time period for this 25-year systematic review spanned from 1999 to 2023, with an overall increase in articles published after 2017 and a significant increase during the COVID-19 pandemic from 2019 to 2022 ( Figure 1 ). A total of 26 countries published eligible studies across 5 continents (Asia, Europe, Africa, America, and Oceania) from 1999 to 2023. Most of the eligible studies were published in Europe ( Figure 2 ). The top-3 ranked countries publishing in this field were the United States (22/89, 25%), Germany (11/89, 12%), and Australia (7/89, 8%) ( Figure 3 ). The vast majority of technology development for mHealth has been based in the United States, especially in the earlier days of smartphone introduction.

RCTs that assessed the differences between different mHealth tools or between an mHealth tool and standard or control care were the most common study designs, accounting for 35% (31/89) of the included studies, followed by cohort (23/89, 26%), pilot (14/89, 16%), cross-sectional (8/89, 8%), qualitative (7/89, 8%), and questionnaire (4/89, 4%) study designs; there was only 1 (1%) case series, case report, and cost-effectiveness study each. Study durations ranged from only a few hours to 12 months depending on the study design.

mHealth Users

The CMP populations considered in this review included adult and young patients with nonspecific CMP, specific OA without or with surgery, CLBP, and neck pain. Patients with OA and CLBP were the most commonly investigated groups among the included studies. The characteristics of the target user group were often the key impetus for the development of the mHealth tool. For example, some researchers noted that a certain percentage of patients report suboptimal satisfaction for various reasons after total knee arthroplasty, such as continued pain or stiffness [ 108 - 111 ]. For patients with knee pain 24 months post triple arthrodesis following a crush injury, a specific mobile camera could immediately provide corrective techniques when applying in-clinic equipment to the patient’s shoe [ 102 ]. We identified 10 studies addressing the perceptions or experiences about mHealth tools in adult CMP populations with and without surgery [ 26 , 28 , 30 , 55 , 62 , 85 , 87 , 95 , 112 ] and their practitioners [ 30 , 35 , 87 ], as well as in young people [ 90 ]; the authors of these studies agreed that mHealth interventions were acceptable, useful, and feasible. However, there is still a gap between patients and physicians in understanding and communication about the treatment and management of knee OA [ 30 ]. It is expected that multiple stages of user experience testing could be a template for future mHealth tools aimed at chronic disease management [ 113 ]. However, only 1 eligible article [ 87 ] mentioned an mHealth app prototype that was codeveloped with patients and health care providers (HCPs) in 2021. Ultimately, it appears that diverse individuals can use mHealth tools as long as the tools are tailored to the needs of the population and sufficient training and support are provided.

Types of Tools Used in mHealth for CMP

In most of the reported mHealth interventions, mobile phones or other devices were either provided to users or considered a required intervention for study participation. We classified the studied mHealth tools into 4 main categories: mobile app, mobile app plus monitor, mobile app plus wearable sensor, and web-based mobile app plus monitor. The details of each type of mHealth tool are provided in Multimedia Appendix 6 . Mobile app (78/89, 88%) was the most commonly used tool and the primary platform for patients with CMP. For example, mHealth apps could be installed on the patient’s mobile phone at any time to help remember to check pain symptoms, maintain physical therapy, connect to coaches, or communicate virtually with HCPs in real time. The next most common mHealth tool reported was a mobile app plus monitor, which was used in 6% (5/89) of the included studies, followed by a mobile app plus wearable sensor (4/89, 4%) and web-based mobile app plus monitor (1/89, 1%). One study reported multiple digital technologies. These mHealth programs focused mainly on a combination of mobile and internal or external monitors or an external sensor, thus facilitating the transfer of data automatically without requiring the patient to manually submit the data and providing faster delivery of self-management therapy.

Study Outcomes

Multiple outcome measures were used to evaluate mHealth tools depending on diverse study objectives. For the purposes of this analysis, the outcomes were organized into 3 categories: (1) usability, feasibility, and acceptability; (2) effect of the mHealth intervention on adherence to CMP management protocols; and (3) effect of the mHealth intervention on clinical outcomes.

Impact on Adherence

A total of 30 of the 89 studies (34%) evaluated or reported the effect of an mHealth intervention on adherence to CMP management, including medication adherence, engagement in healthy intervention therapy, and frequency of symptom monitoring. These studies include 2 questionnaire studies, 1 case series, 10 cohorts, 9 pilot studies, and 8 RCTs with or without a control group. Among these 30 studies, 1 (3%) observed mixed results, 25 (83%) showed a significant difference (judged at P <.05), and 1 (3%) found no difference. Multimedia Appendix 7 provides an overview of these studies [ 29 , 35 , 38 , 41 , 42 , 49 , 52 - 58 , 61 , 65 , 68 , 71 , 72 , 75 , 80 , 81 , 84 , 91 , 92 , 98 - 101 , 104 , 106 ].

Mobile apps with a monitor or wearable sensor were mainly investigated in patients with OA, which were used as daily reminders for self-management. Studies in patients with OA [ 80 ] and those undergoing surgery showed significant improvements of the apps with respect to patient adherence rates [ 38 , 49 , 65 , 100 ]. Some studies observed that a mobile app combined with a motion sensor or monitor could support early rehabilitation with good compliance [ 65 ], suggesting that surgeons can consider these two tools as appropriate alternatives to traditional physical therapy programs after joint surgery [ 49 , 100 ]. For younger patients with CMP, leveraging extant digital tools with appropriate user-informed adaptations can also help to build capacity tailored to support young people’s self-management of musculoskeletal pain [ 90 ]. For other CMP groups, such as patients with CLBP and CNP, use of a mobile app alone was the main intervention. Although recognizing the inadequacy of traditional neck pain treatments compared with treating CLBP, a mobile app implemented with a self-classification algorithm was found to be particularly effective in increasing adherence to an exercise program among older and younger office workers with neck pain [ 58 ]. However, another study indicated that the clinical importance of added adherence with use of a mobile app is unclear in a specific population with upper- or lower-limb musculoskeletal conditions [ 56 ]. Often, using an mHealth system as an interface between the patient and the provider was perceived as less burdensome and associated with less judgment compared to face-to-face contact, particularly in situations in which the patients were not fully adherent to the recommended treatment [ 87 , 112 ]. mHealth tools facilitated better management and improved patient confidence to monitor CMP, making the patients feel in control and strengthening their coping mechanisms.

Impact on Clinical Outcomes

A total of 55 of the 89 included studies (62%) assessed or reported the effect of mHealth on disease-specific clinical outcomes, including pain intensity, pain-related function, pain-related disability, pain-related cognition, health-related QoL, and medication use. These studies include 1 case series, 13 cohort studies, 4 cross-sectional studies, 7 pilot studies, 1 questionnaire study, and 29 RCTs. Of the 29 RCTs that measured the effect of mHealth on CMP-specific clinical outcomes, 28 (93%) reported significant differences (judged at P <.05) between groups; no significant differences were found in 1 (3%) study and mixed results were observed in 1 (3%) study. In addition, a significant effect of mHealth tools was observed in 13 cohort studies and 7 pilot studies. Multimedia Appendix 8 provides an overview of these studies [ 20 - 23 , 25 , 27 , 29 , 34 , 37 , 39 - 42 , 44 - 48 , 50 , 51 , 53 , 54 , 56 , 58 , 59 , 61 , 63 , 64 , 67 - 69 , 72 - 74 , 76 , 78 , 79 , 81 - 83 , 86 , 88 , 89 , 94 , 96 - 101 , 103 - 107 ].

A total of 49 interventions (including RCTs, cohort studies, and pilot studies) were related to improving pain intensity outcomes. Among these 49 studies, 46 (94%) reported significant improvements in pain intensity. Both younger and older patients receiving app messages with tailored instructions on pain management experienced statistically significant improvements in their pain intensity levels compared to those of patients receiving usual care or an intervention without an mHealth tool. However, mobile app–based relaxation exercises did not effectively reduce CNP [ 68 ], highlighting the importance of future mHealth tools to include an individualized and tailored program. Another trial did not show a significant improvement in pain perception at 6 months, although the mHealth tool tested in this trial was determined to be feasible and associated with a satisfactory user experience [ 53 ]. Among the outcomes of the mHealth tools evaluated, 26 studies focused on pain-related functional performance, 15 studies focused on pain-related disability, 15 studies focused on pain-related cognitive performance, 14 studies focused on pain-related QoL, and 4 studies focused on pain-related medication use. Generally, mHealth tools were associated with a significant improvement in functional and cognitive performance and QoL, along with a significant decrease in the disability burden and medication use. However, in patients with CLBP, the improvement in pain-related disability was small and of uncertain clinical significance after using a self-management app for 9 months [ 82 ].

Usability, Feasibility, and Acceptability

Among the 89 included studies, 23 (26%) assessed or reported usability, feasibility, and acceptability using qualitative methods and compiled usage data, including 1 case series, 9 cohort studies, 4 cross-sectional studies, 6 pilot studies, 1 qualitative study, 1 questionnaire study, and 1 RCT. These data ranged from patient satisfaction to cost-effectiveness estimations as well as the timing and frequency of engagement with mobile apps and platforms. Multimedia Appendix 9 provides an overview of these studies [ 22 , 24 , 29 , 33 , 38 , 43 , 45 , 49 , 52 , 57 , 61 , 66 , 71 , 72 , 74 , 77 , 80 , 92 , 93 , 95 , 101 , 107 ].

In general, these studies found mHealth tools and platforms to be usable, feasible, acceptable, and appreciated among users compared with traditional measures. For example, both older and younger patients with CMP and those who underwent joint surgery perceived that using an mHealth tool increased their independence and confidence in pain management [ 22 , 38 , 80 ]. One study reported that the prescription of therapeutic exercises via a smartphone app is feasible and well-accepted among patients of all ages [ 112 ]. Seven studies showed the long-term (>3-month follow-up) feasibility and acceptability of mHealth tools [ 24 , 33 , 38 , 49 , 52 , 72 , 101 ]. Specifically, with long-term follow-up, patients with knee or hip OA seemed to have preferences for goals related to physical activity and nutrition rather than for goals related to vitality and education [ 71 ]. Patients with OA undergoing primary hip or knee arthroplasty particularly appreciated the mHealth tools empowering patients, facilitating transitions from specialized hospital care to primary care, reducing unplanned contacts with the health system, and reducing overall health costs, proving to be cost-effective [ 38 ]. Another study performed in a real-world setting with a large (N=10,264) and diverse population experiencing CMP found that mHealth was accepted and considered especially useful for pain reduction [ 62 ]. The majority of studies included in this review focused on the patient as the end user of mHealth tools, although some also evaluated acceptability and perceptions from the perspective of HCPs. Features of mHealth tools such as automated reminders, messages with educational and motivational content, healthy living challenges, and wireless transmission of data contributed to increased self-care awareness and knowledge about CMP.

Main Findings

This review showed that interventions based on mHealth systems have beneficial effects on adherence and clinical outcomes for individuals with CMP. Thus, this scientific evidence suggests that these mHealth systems could be promising alternatives for CMP self-management through multimodal approaches. The evidence presented here indicates that while the potential of mHealth tools is high, their results during implementation and execution are nevertheless mixed.

Mobile apps are the most widely reported mHealth tool for interventions, which have been successfully used to facilitate adherence to CMP management and improve clinical outcomes [ 114 , 115 ]. The freedom and portability of mobile devices combined with the advanced capacity to facilitate 2-way communication and collect and analyze data for a real-time response offer enormous potential to both patients and providers [ 116 , 117 ]. Owing to their abilities for automation, personalization, and easy integration into existing health systems, mobile apps are less operator-dependent and are less reliant on processes to facilitate the active and time-consuming exchange of information compared to traditional tools. However, apps may have a limitation in terms of difficulty of use for the older population with minimal technology experience or familiarity, and there is clearly room for improvement. Moreover, there is a lack of scientific and health professional support in many available mHealth systems, highlighting the need for developing appropriate apps based on the well-recognized guidelines in the management of CMP [ 113 ].

There is a growing recognition of the need for digital technologies to improve access to age-appropriate resources and personalized support for cocare [ 118 - 120 ]. Unlike previous reviews in this field that only included RCTs or cohort studies, this systematic review also included 13 qualitative studies (including questionnaire, interview, and discussion studies). These informative studies used qualitative methods that yielded rich data that can be used to better understand how and why mHealth tools impact adherence behaviors and clinical outcomes. Qualitative data can also enable patient-physician discussions regarding modifiable self-management options based on the perspectives and needs of patients, HCPs, or both groups. Moreover, user feedback can inform hypotheses that can then be tested. Research that seeks to understand how and why mHealth works will deliver on the broader promise of mHealth. Future mHealth tools will be able to draw on the knowledge generated when discrete hypotheses around the relative importance of, for example, patient-provider communication, optimal user interfaces, or targeted motivational messages are tested. These informative studies could lead to better mHealth tools that deliver improved health outcomes.

Implications and Future Directions

This review found that the usability, feasibility, and acceptability of mHealth tools for CMP management and adherence to different programs were generally high among both patients and providers. mHealth offers a way to address different barriers to care and to reduce health disparities from both patient and HCP perspectives. However, only one article published in this field over the last 25 years mentioned that the mHealth app prototype was codeveloped by patients and HCPs [ 87 ]. There will be more opportunities to codesign mHealth tools in the future. Undoubtedly, innovative mHealth tools could unintentionally increase health disparities due to unequal access to technology. There is also recognition that unequal access to, use of, and knowledge of information can influence the uptake and use of mHealth tools. These inequalities and the needs from target user groups should be taken into consideration early in the design and development of future mHealth tools. However, none of the studies included in this review addressed systematic differences in usability between diverse patient groups and geographical areas. Future research can be designed to better understand these differences and to encourage the development of mHealth tools that address the needs of diverse patient groups and populations living in regions with different levels of economic development.

The high prevalence of CMP globally coupled with the advantages of providing help through apps offers opportunities to help countless people who may be looking to the potential of mHealth to lessen the burden of their pain. One key aspect of this potential involves an increase in cost-effectiveness and expanded outreach of pain management. Of note, only one study included in the review specifically focused on the issue of cost-effectiveness [ 60 ]. Furthermore, only one study presented the postmarketing observational data after a follow-up duration of 9 months [ 96 ]. Rigorous cost-effectiveness analyses will be necessary to demonstrate not only the health impact but also the value of investing in these innovations. Future studies evaluating the cost-effectiveness of mHealth tools are indeed needed, since we can see that pockets of mHealth innovations are expanding around the globe ( Figure 3 ). Besides cost, language, and literacy barriers, availability and connectivity issues are also potential barriers to consider when developing these types of mHealth tools. Nevertheless, the strong attachment people have to mobile phones and the tendency to carry them everywhere open up opportunities for continuous symptom monitoring and connecting patients with providers outside of health care facilities.

Limitations and Strengths

There are some limitations of this study. First, we did not perform a meta-analysis and we did not weigh the quality of evidence or study design against reported results. Second, only the English literature was included, and the sample size of the included studies varied substantially from 1 in a case report to 10,264 in a cohort study. Third, the diversity of study designs, objectives, and outcome measures made clear comparisons among studies difficult, and the quality of evidence was deemed to be variable. However, this is the first 25-year systematic review focusing on evidence collected from January 1999 to December 2023 regarding the impact of mHealth on CMP management adherence. The main strengths of this review are that we included a diverse array of study designs; assessed both self-management and clinical outcomes; and incorporated the nascent literature regarding mHealth feasibility, usability, and acceptability.

Conclusions

mHealth is a potential high-impact tool to improve health outcomes among those with CMP through supporting adherence to personalized or tailored self-management programs for pain. Further evaluation of mHealth tools is needed, especially research that informs the cost-effectiveness of these tools. More innovation, optimization, and high-quality research in mHealth has the potential to transform the promise of mHealth technology into the reality of improved health care delivery and outcomes.

Acknowledgments

We would like to express our gratitude for the support from Hong Kong Jockey Club. Funding was provided by the Hong Kong Jockey Club Charities Trust.

Conflicts of Interest

None declared.

Search strategy.

Inclusion criteria for study selection and mHealth tools.

Eligible studies.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow for study selection.

Exluded studies with reasons.

Four types of mobile health tools.

Included studies investigating the impact of mobile health tools on adherence.

Includied studies investigating the impact of mobile health tools on clinical outcomes.

Included studies investigating the usability, feasibility, and acceptability of mobile health tools for patients with chronic musculoskeletal pain.

PRISMA 2020 checklist.

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Abbreviations

Edited by G Eysenbach; submitted 10.04.24; peer-reviewed by E Ross, N Kerckhove; comments to author 04.06.24; revised version received 18.06.24; accepted 16.07.24; published 16.08.24.

©Jenny Lin-Hong Shi, Regina Wing-Shan Sit. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.08.2024.

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