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Top 9 Mental Health Apps in 2022 Supported by Science

• What they are
• How we chose

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Research-informed mental health apps are a great tool for supplementing traditional treatment and promoting mental wellness.

It seems as if there’s an app for everything — from checking your email to watching your favorite shows to even “speaking in dog.”

There are also a seemingly endless number of health apps aimed at helping you track your physical or mental health. But how do you decide which ones are legit?

Just because an app is available doesn’t mean it’s been vetted as being safe and effective for the condition or concern it targets. In fact, most mental health apps are surprisingly not designed in conjunction with a mental health expert. Because of that lack of expertise, some apps may give just plain bad advice.

However, research-informed mental health apps may be different. Some research suggests that they may be a great tool to supplement traditional therapy and to help manage your wellness needs.

Lift of the best research-informed mental health apps

If you want to jump directly to the sections for each app, you can click the links below.

• Best overall: Moodfit
• Best for therapy: Talkspace
• Best for meditation: Headspace
• Best for stress: iBreathe
• Best for anxiety: MindShift
• Best for sleep: CBT-i Coach
• Best for mood boost: Happify
• Best for tracking symptoms: Bearable
• Best for tracking medications: Medisafe

What are mental health apps and what makes them research-informed?

Mental health apps are digital tools to help improve your mental health and wellness. While these apps can’t treat or diagnose any conditions, they’re a convenient way to support your mental health journey in addition to therapy or other forms of treatment.

Research suggests that mental health apps can also help better monitor and manage symptoms or disorders. However, while there are many mental health apps available, not all of them are backed by research and may not be as effective as those that are.

A research-informed app is based on techniques that have been researched and found effective, such as:

• cognitive behavioral therapy (CBT)
• mindfulness
• deep breathing

In some cases, the apps themselves may be backed by scientific research, though it’s important to keep in mind that studies on specific apps are often conducted by researchers with a link to the creators of the app, which may influence results. Also, research on these apps is often preliminary or ongoing, so it may take more time to determine how effective they really are.

Some people use the term “evidence-based” interchangeably with “research-informed,” but generally speaking “evidence-based” refers to rigorous research, and it’s rare that apps have been studied thoroughly and independently. Therefore, it’s often more appropriate to use the phrases “research-informed” or “supported by research.”

Criteria we used to pick

We took several factors into account when making our top app picks, including:

• Supported by research. Each of our selections is backed by scientific research.
• User reviews. We scoured the reviews to ensure each app is well-received by the majority of actual users.
• Price. We’ve included options that suit any budget — from free apps to those requiring a one-time or monthly fee.
• Vetting. All services have been vetted to ensure that they meet Psych Central’s medical , editorial , and business standards.

Our picks of the best research-informed mental health apps

Best overall.

• Price: Free for basic; Premium upgrade available for $8.99/month
• Available for: iPhone, Android

Described as “fitness for your mental health,” Moodfit strives to get your mental health in shape. It does this by offering a variety of science-backed tools and activities to guide you on your mental wellness journey, including:

• mindfulness practices
• guided breathing exercises
• gratitude and mood journals
• a thought diary based on cognitive behavioral therapy (CBT)

The app helps you establish healthy routines so that you can meet your unique needs. It also tracks your habits, so you can identify patterns and areas for improvement.

Why we chose it

Moodfit helps you find what works best to support your unique mental health needs by offering a customizable suite of tools and resources.

What we like

• It’s customizable to meet your unique goals.
• It offers a wide variety of tools to fit your needs and preferences.
• The app provides weekly and monthly summary reports to help you identify patterns and discover what does (and doesn’t) work for you.
• It’s easy to use with an intuitive interface.

What to look out for

• A few users noticed longer loading times, particularly when inputting tracking information.
• Certain features are available only with an additional cost.
• While Moodfit is built on science-backed techniques like CBT, mindfulness, and deep breathing and is often included in review studies on mental health apps, research specifically on the app’s effectiveness is lacking.

Best for therapy

• Price: Varies depending on your location and therapist availabilty; plans start at $65/week

Talkspace makes therapy easy, by providing access to licensed mental health professionals right from your phone. The app gives you direct access to a therapist of your choosing, wherever and whenever you need support.

This is a good option for those with busy schedules or who prefer to speak with a therapist from where they feel most comfortable, instead of going to an office.

You can choose to talk, video, or text chat with your therapist, or even schedule a live session at a mutually convenient time.

Research from 2020 indicates that messaging with a therapist via Talkspace helped reduce symptoms of anxiety and depression. Another study from 2020 suggests that treatment delivered through text, voice, and video messaging via Talkspace was similarly effective in reducing symptoms of post-traumatic stress disorder (PTSD) as traditional forms of treatment.

Still, it’s good to keep in mind that researchers involved in these studies were connected to Talkspace, which may influence results.

Talkspace offers 24/7 direct access to mental health professionals in a variety of specialties, so you can find the support you need when you need it.

• Talkspace provides 24/7 direct access to licensed mental health professionals.
• It offers several ways to connect with therapists, including video, text, and audio chat messaging.
• You can change therapists, so you can find the right one for your needs.
• The relatively high cost may be a barrier for some.
• Communication is not always live, so there may be a wait time for responses from your therapist

If you’d like to learn more about Talkspace, consider reading our in-depth review.

Best for meditation

• Price: $12.99/month; $69.99/year

The Headspace app uses mindfulness and meditation tools and resources to help you live a more mindful life. These practices are designed to support your mental well-being by helping relax your mind and establish positive, healthy habits.

Meditation has both mental and physical health benefits. Research shows that it can help:

• reduce stress and anxiety
• ease symptoms of depression
• lower blood pressure
• soothe certain types of pain

Plus, many studies have researched the effectiveness of Headspace specifically, though scientists conducting the studies were often connected to the company, which may influence results.

The Headspace app offers a variety of scientifically supported meditation and mindfulness exercises that are known to help reduce stress, relax the mind, and boost focus.

• Headspace provides a nice assortment of meditations with a variety of types and lengths.
• It offers exercises for all experience levels — from beginner to advanced.
• Users rate the app very highly.
• A paid subscription is required for most features, including most meditations.
• Some users say the company’s customer service can be slow to respond.

If you’d like to learn more about Headspace, consider reading our in-depth review.

Best for stress

• Price: Free
• Available for: iPhone

Research shows that deep breathing may help reduce stress, making the iBreathe app a good choice for those looking for a simple way to promote calm.

iBreathe doesn’t have any bells or whistles other than providing several deep breathing exercises, so you can focus on establishing a stress-free breathing practice without distraction.

The app may be simple, but it does allow for a lot of personalization. You have complete control over all of your breathing intervals, with customization options for how long you inhale, exhale, hold, or even cycle through in each exercise. You can also set breathing reminders for yourself throughout the day.

iBreathe focuses solely on offering customizable breathing exercises that help ease stress and promote a sense of calm.

• It’s simple and easy to use.
• You can customize breathing intervals within exercises.
• It integrates with Apple Health.
• You can schedule multiple breathing reminders throughout the day.
• The app is available only for iPhone, not Android.
• It doesn’t offer options beyond breathing exercises.

Best for anxiety

MindShift was created by the nonprofit Anxiety Canada, so this app focuses solely on ways to relieve anxiety. It uses scientifically proven strategies rooted in CBT practices that promote mindfulness and relaxation.

Research shows that CBT is effective for easing symptoms of anxiety and anxiety disorders .

These tools target mindset, helping you reorient your thinking and make positive, lasting changes to make anxiety manageable.

MindShift is free and offers quick relief tools, a community forum, and experiments and exercises to challenge limiting beliefs and overcome fears that cause anxiety.

MindShift uses interactive CBT-based tools and strategies to ease anxiety by helping promote relaxation and mindfulness techniques.

• It was deeveloped specifically to provide anxiety relief by Anxiety Canada.
• The app offers a community forum for peer-to-peer support.
• It provides a variety of tools to promote healthy habits, including a thought journal, coping cards, meditations, and exercises to help you build positive habits.
• It’s specifically tailored for anxiety relief, so may not be suited toward tracking other mental health needs.
• Some users find tracking to be too time-consuming.

Best for sleep

Cbt-i coach.

Do you experience insomnia or other sleep problems ? CBT-i Coach may help. This app is for anyone with insomnia or who would like to improve their sleep regimen and habits.

CBT-i Coach was a collaborative effort between:

• the U.S. Department of Veteran Affairs’ National Center for PTSD
• Stanford School of Medicine
• the U.S. Department of Defense’s National Center for Telehealth and Technology

It’s a free app designed to support professional insomnia or sleep disorder treatment (though it can be used on its own!).

Through a structured program, CBT-i Coach helps you learn about sleep while developing your own positive sleep routine .

In a 2016 study , almost 60% of clinicians participating in a survey 2 years after the CBT-i Coach app was released noted that they had used the app with a client and that they felt the app improved homework adherence and treatment outcomes.

Similarly, another 2016 study noted that participants who used the CBT-i Coach app as a supplement to CBT-i treatment experienced significant improvements in sleep and that the app didn’t compromise the benefits of CBT-i therapy. It’s important to know, however, that participants who only received CBT-i treatment without using the app, also reported significant sleep improvements.

CBT-i Coach offers a structured program that uses scientifically proven strategies to help you build better sleep habits to improve and alleviate symptoms of insomnia.

• It’s completely free with no ads.
• The app is especially useful for veterans who face sleep challenges, as it was created based on the manual “Cognitive Behavioral Therapy for Insomnia in Veterans.”
• The app’s interface is simple and easy to navigate, especially for tracking sleep habits.
• Many users find that some features are not easily accessible.
• For those not working with a sleep therapist while using the app, the technical language used to describe sleep habits may be difficult to understand.

Best for improving mood

• Price: $14.99/month; $139.99/year; $449.99/lifetime access

A little fun is almost guaranteed to put a smile on your face. With the Happify app, you participate in games that are not only fun but are backed by science to improve your mood, lower stress levels, and overcome negative thoughts, among other positive benefits.

This app helps you transform your life by allowing you to target a specific mental wellness area that you’d like to improve. Happify focuses on positive emotions to promote meaningful improvement to overall happiness.

Happify offers over 65 tracks to help with:

• coping better with stress
• navigating negative thoughts
• building self-confidence
• fueling your career success
• achieving mindfulness through meditation

Happify uses fun, engaging, and science-backed games and activities to help manage stress, practice mindfulness, build confidence, and boost your overall mood.

• The app offers fun and engaging games tailored to different goals.
• It was developed by mental health professionals.
• It’s easy to use.
• Very limited options are available on the free version, so paid upgrades are required for more variety and choices.
• It’s on the pricier side.
• Some users find that the timed challenges can create stress, rather than reduce it.

Best for tracking symptoms

• Price: Free for basic; Premium subscriptions from $4.49–$15.99/month or $27.99/year

With capabilities for wellness journaling, setting medication reminders, and even recording your food, the Bearable app goes beyond simply tracking symptoms. The app provides a comprehensive outlook on your overall physical and mental health and wellness.

This may give you a deeper insight into your health, and how factors like different medications, treatments, or triggers may be affecting it. Identifying symptoms related to these factors can help you communicate your health with your doctor, allowing for better engagement and diagnosis.

In one 2021 review study of apps to track symptoms in people with cancer, Bearable was one of the top three highest-scoring apps. While this is encouraging, it’s good to keep in mind that research specifically on whether the app is beneficial for people with mental health conditions is lacking.

Bearable makes tracking symptoms easy so that you can better understand patterns, behaviors, and symptoms and share them with your treatment team.

• It’s easy to use and customizable.
• The app sllows you to track your mood, symptoms, daily activities, sleep, medication, exercise, and more.
• Community input is valued and implemented.
• Health integration for Android users isn’t available.
• For some users, it may be overwhelming to have so many tracking options.
• The insights feature isn’t included in the free version.

Best for tracking medications

• Price: Free; offers in-app purchase

Do you feel like you may benefit from reminders when it comes to taking and refilling your medications? Medisafe is an easy-to-use solution.

This app, created by Medisafe Inc., offers a clean and simple interface for managing your medication reminders. It allows you to share reports with others, including your prescription provider and family members.

Research on the Medisafe app indicates that it may help improve medication adherence, though it’s important to keep in mind that these studies are often conducted by researchers with connections to Medisafe Inc., which may influence results.

One of the easiest to use medication tracking apps available, Medisafe not only helps you stay safe and on track with all of your prescription medications, but it also has the unique capability of alerting a designated contact when you’ve missed any dosages.

• It provides alerts whenever any of your medications shouldn’t be taken together.
• It offers integration with GoodRx, a discount prescription drug provider.
• The app has plenty of reminder options, including when it’s time for refills.
• It allows users to easily communicate their past and current medications with their treatment team.
• Some users feel the app has too many features, which may create confusion.
• Some Android users have reported reminder issues when switching time zones.

How the apps compare

What to look for in a mental health app.

Can’t find what you were hoping for here? Not to worry — there are many research-informed mental health apps available for download. Here are a few tips on finding the right one for you:

• Backed by science. While there are many mental health apps, not all are supported by scientific evidence. It’s a good idea to do a little research and see if an app is based on well-researched principles or has been studied independently.
• Positive user feedback. It’s recommended to read reviews to see the experience actual users are having with the app.
• Cost. Many apps are free, but some cost a monthly, annual, or one-time fee. It’s a good idea to consider what will work best with your budget.
• Design and features. You may want to consider making a list of features that are most important to you, so you can look for apps targeted to those needs.
• User security and privacy. It’s important to look for apps that keep your data safe and secure.

Let’s recap

Research-informed mental health apps are backed by scientific research, making them a quality option for your mental health journey. They can be a good tool to add to existing therapy or medication treatment. Some of these apps connect you directly with mental health professionals, while others provide tools and resources that offer support.

Though they aren’t meant to be a replacement for treatment, mental health apps can effectively improve your overall mental wellness, as well as help target specific challenges. From helping you deal with anxiety, sleep issues, stress, meditation, or medication adherence — chances are there’s a science-backed app to suit your needs.

Last medically reviewed on February 22, 2022

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• Drissi N, et al. (2020). An analysis on self-management and treatment-related functionality and characteristics of highly rated anxiety apps. https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7391980/
• Gershkovich M, et al. (2020). Integrating exposure and response prevention with a mobile app to treat obsessive-compulsive disorder: feasibility, acceptability, and preliminary effects.  https://www.sciencedirect.com/science/article/abs/pii/S0005789420300666?via%3Dihub
• Hull TD, et al. (2020). Two-way messaging therapy for depression and anxiety: Longitudinal response trajectories. https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7291694/
• Kaczkurkin AN, et al. (2015). Cognitive-behavioral therapy for anxiety disorders: An update on the empirical evidence. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610618/
• Koffel E, et al. (2016). A randomized controlled pilot study of CBT-I Coach: Feasibility, acceptability, and potential impact of a mobile phone application for patients in cognitive behavioral therapy for insomnia. https://journals.sagepub.com/doi/10.1177/1460458216656472
• Kuhn E, et al. (2016). CBT-I Coach: A description and clinician perceptions of a mobile app for cognitive behavioral therapy for insomnia. https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC4795288/
• Lu DJ, et al. (2021). Evaluation of mobile health applications to track patient-reported outcomes for oncology patients: A systematic review. https://www.sciencedirect.com/science/article/pii/S2452109420302657
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• Malgaroli M, et al. (2020). Message delivery for the treatment of posttraumatic stress disorder: Longitudinal observational study of symptom trajectories. https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7221629/
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Self-help: a Systematic Review of the Efficacy of Mental Health Apps for Low- and Middle-Income Communities

• Open access
• Published: 11 November 2023

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• B. Gama   ORCID: orcid.org/0000-0003-0169-2208 1 &
• S. Laher   ORCID: orcid.org/0000-0002-1298-0769 1  

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Low- and middle-income countries (LMICs) are tasked with providing adequate and accessible mental health care. However, this has been a slow process due to the lack of resources. With the recent advances in technology, mental health apps offer the opportunity to provide mental health care that is accessible and affordable. This study explored the efficacy of mental health apps in LMICs using the AAAQ framework. A systematic review following PRISMA guidelines explored studies published from 2015 to 2021. Seven studies met the inclusion criteria and were analysed using content analysis and thematic synthesis. Themes centred around the availability of mental health care systems in LMICs, some of the barriers to accessing mental health care, the need for mental health apps to be congruent with the communities that they are used in and quality criteria for apps. The study offers valuable insight towards mediating some of the struggles faced in the implementation of appropriate mental health care in LMICs using mental health apps.

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Avoid common mistakes on your manuscript.

Introduction

According to the World Health Organisation, there is an access gap for basic mental health care and treatment of about 35% to 50% in developed countries. This increases to 75% to 80% in developing countries (Wiktorowicz et al., 2020 ). In an attempt to increase access to care, low- and middle-income countries (LMICs) Footnote 1 have opted for community-based interventions (Robertson & Szabo, 2017 ). Thornicroft et al. ( 2016 ) argue that community health care is a practice that caters for access to mental care within the local population and is particularly useful as a form of inclusion to disadvantaged communities. They advocate for an evidence-based and practical application of mental healthcare in which comprehensive and integrated mental health care engages not only the individual but also the family as well as interaction with other providers of health care such as traditional healers. However, increasing mental healthcare needs cannot be solely taken up by community healthcare and require a supporting practice that further improves the training for clinicians and re-envisions the delivery of mental health care (Lake & Turner, 2017 ). Hence, digital technologies are often cited as a means of providing access to mental healthcare. The rapid increase in the daily use of mobile and smartphones facilitates this mode of intervention. Further digital interventions have the potential to change the therapeutic platform and engage with individuals in real-time (Torous et al., 2018 ). Thus, this study focuses on mental health apps as a form of digital intervention.

Mental Health App Review

Mental health apps can be defined as computational mobile software that is downloadable and has been designed to provide support to those with mental illness (Kenny et al., 2016 ). Mobile applications that resonate with mental health assistance have become readily available with a record of about 10 000 to 20 000 mental health apps which are downloadable from Google play store and IOS Apple store (Lecomte et al., 2020 ; Torous et al., 2019a ). Despite a large number of mental health apps, only 3 to 4% recorded estimates are evidence-based apps (Lecomte et al., 2020 ; Nicholas et al., 2015 ). This indicates that much of the apps go clinically untested with minimal expert supervision. This not only exposes the user to further harm, but their information can be further exploited.

However, researchers have begun to apply more clinically based measures towards the provision of appropriate mental health apps to users with a particular focus on commonly diagnosed disorders such as depressive disorders, bipolar disorders, anxiety, posttraumatic stress disorders, schizophrenia, suicidal behaviours, and addictions (Anmella et al., 2023 ; Dworkin et al., 2023 ; Gu et al., 2020 ; Ibrahim et al., 2022 ; Torous et al., 2019b ). Wang et al. ( 2018 ) conducted a systematic review study that looked at the effectiveness of mental health apps in monitoring and managing mental health symptoms. The study draws focus on apps that assisted with anxiety, mood disorders, sleep disorders, and addictions using primarily a game-based design. Overall individuals who used the apps experienced greater stress reduction (Wang et al., 2018 ). An early intervention mHealth app THRIVE for sexual assault survivors was tested for its feasibility in reducing post-traumatic stress and alcohol misuse (Dworkin et al., 2023 ). The feasibility of THRIVE was supported by the app’s core functionality such as the cognitive behavioural modules and the coaching calls. These were not only helpful in encouraging acceptability but they were seen as satisfactory with regards to app user-friendliness (Dworkin et al., 2023 ). Moreover, apps such as Wysa were found to be useful in offering people support when it came to symptoms of anxiety and depression during the COVID-19 pandemic (Sinha et al., 2023 ).

Support Offered by Mental Health Apps

In a survey of 15,000 mental health apps conducted by the World Health Organisation in 2015, it was found that about 29% of them have their focus on mental health diagnosis, treatment, or support (Anthes, 2016 ; Chandrashekar, 2018 ). Mental health apps also include functions such as symptom tracking, diary entries, and appointment or medication reminders as well as motivational quotes (Hollis et al., 2015 ). These apps are aimed at being a form of self-help towards mental health outpatients, extending to those who have not been diagnosed. They can be used in conjunction with other comparative methods of intervention such as internet-based intervention which offers direct contact with a mental health practitioner (Donker et al., 2013 ). Arguably, mental health apps also extend the ability for the patient to track their symptoms through ecological momentary assessment (EMA) (Donker et al., 2013 ; Hollis et al., 2015 ;). There is engagement with the aspect of mental health literacy in which the apps implement psychoeducation and self-assessment (EMA) on top of information on referral allowing the patient to assess what they have and request assistance from the nearest treatment centre (Bautista & Schueller, 2023 ; Mindu et al., 2023 ).

Researchers such as Marshall et al. ( 2019 ) and Mindu et al. ( 2023 ) indicate that mental health apps provide immediate assistance, convenience, and affordability, which is of particular importance to LMICs. Vaidyam et al. ( 2019 ) argue that mental health apps have a potential that can be continually harnessed via technologies like machine learning which can record any digital signals observed and notify the patient of any sudden change concerning disorders like anxiety as well as notifying the clinicians for immediate intervention for at-risk individuals. The intervention can be subsequently offered with immediacy despite the distance. In an Australian study, mental health apps were outlined to be efficient in reinforcing evidence-based monitoring for patients with mild mental health conditions (Donker et al., 2013 ).

According to Kenny et al. ( 2016 ), mental health apps have the potential of challenging some of the difficulties faced by the administration of mental health care in LMICs due to the cost-effectiveness of the interventions and their accessibility. Furthermore, they allow for the engagement of treatment-based intervention within a natural setting (Hollis et al., 2015 ; Kenny et al., 2016 ). Hence, this study aimed to explore the efficacy of mental health apps for LMICs using a systematic review method. The review considered specifically which areas studies have been conducted in, on which populations, the nature of the app (for example, online screening tool, telepsychology, self-care, medication monitoring, AI-based, cognitive behavioural therapy), as well as the affordances and limitations offered by the app for the context. The AAAQ framework was used to structure the exploration of affordances and limitations with regards to mental health apps.

The International Covenant on Economics, Social and Cultural Rights (ICESCR) argues that the value of a well-rounded healthcare intervention can be recognised through the availability, accessibility, acceptability, and quality (AAAQ) that it provides towards the recipients (Priebe & Strang, 2016 ; Schierenbeck et al., 2013 ). Availability considers the existence of services such as hospitals, staff, and sanitation and whether they are sufficient for the community. Accessibility refers to the ability for individuals to be able to effectively use facilities without any discrimination towards physical accessibility, financial accessibility, social accessibility, and information accessibility. Acceptability refers to the equal treatment and provision of health that is culturally and ethically respectful to all members of the community. Quality focuses on the provision of care that is scientifically and medically appropriate in an adequate environment and the guarantee of privacy and confidentiality in the administration of care (Priebe & Strang, 2016 ; Schierenbeck et al., 2013 ).

The following research questions guided the study:

What are mental health apps that are used?

Are there any mental health apps that have been standardised for psychological use?

What are the affordances and limitations identified in the articles with regards to the availability, accessibility, acceptability, and quality of mental health apps in LMICs?

Study Design

A systematic review is defined as a summary of the literature that consists of reproducible methods to “systematically search, critically appraise and synthesise on a particular topic” (Gopalakrishnan & Ganeshkumar, 2013 , p.10). The synthesisation of the results using the strategies allows for the reduction of bias and error (Gopalakrishnan & Ganeshkumar, 2013 ). Thus, the systematic review was suitable for this study because it reviews articles on the effective use and efficacy of mental health apps in low- and middle- income communities. Further, using this method reduces error and allows for the replicability and generalisability of the results found. The process of the systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines as well as the four-phase flow chart which engages with the identification, screening, eligibility, and inclusion of primary research when aiming to answer the proposed research questions (Liberati et al., 2009 ; Moher et al., 2015 ).

Search Strategies and Study Selection

The study followed an eight stage-structured framework proposed by Uman ( 2011 ). The initial stage (stage 1) included the formulation of review questions as indicated earlier in the literature review section.

Stage 2: Defining Inclusion and Exclusion Criteria

The literature review and research questions informed the second stage of defining the inclusion and exclusion criteria. Inclusion criteria were that studies needed to focus specifically on LMICs; samples should have some app experience, app trials with user engagement, and app regular use or user testing to ascertain their viability in the community. Qualitative, quantitative, or mixed methods studies were included. Unpublished grey literature such as research reports was eligible for inclusion in the study. Articles that focussed on (1) high-income communities (HIC), (2) in-app written reviews, and (3) users being diagnosed with or comorbidity with any physical illness were excluded from the study.

Stage 3: Search Strategy

Four databases were explored for this study namely: Cochrane Library, Scopus, Sabinet, and DATAD-R drawing in grey literature within the LMICs. The selection of search terms and phrases that were used to identify and select eligible studies included “Mental health app”, OR “mHealth”, OR “Health app”, OR “Technology mental health”, OR “alternative intervention mental health”, OR “Mental health LMIC” OR “mental health intervention LMIC” AND “LMIC”, AND “disadvantaged communities”, AND “rural communities”. The search terms were inserted in the chosen databases using Boolean operators.

Stage 4: Selecting Studies

To facilitate the search for the articles that meet the inclusion and exclusion criteria, the titles, abstracts, and the full-text articles were reviewed and downloaded to a reference managing software (Zotero) as well as an Excel record sheet to track the selected abstracts and articles. A total of 76 articles were located. After the deletion of duplicate articles, 72 articles were screened. For all 72 articles, the authors, year of publication, sample, age, study design, and the reason for including or excluding it for this study were captured (Liberati et al., 2009 ). Figure  1 summarises the article screening process. Seven articles met the inclusion and exclusion criteria.

Four-phase flow chart

Stage 5: Extracting Data

Data extraction across the seven articles followed the PICOS structure (Population, Intervention, Comparison, Outcome, and Study type) as it assisted in providing contextual detail about the populations where the studies had been conducted (Laher & Hassem, 2020 ).

Stage: 6 Assessing Study Quality

The study utilised the CASP checklist tools (Critical Appraisal Skills Programme) to assess the quality of both qualitative and quantitative research studies (Nadelson & Nadelson, 2014 ; Laher & Hassem, 2020 ). The quality score range for the qualitative tool was 0–10 while the quantitative score range was from 0 - 12. Scores for each of the included studies were above seven which is an indicator that the studies were of high quality.

Stage: 7 Analysing and Interpreting Results

The analysis of the articles used two methods of data analysis (content analysis and thematic synthesis). The first research question (What are mental health apps that are used?) and the second research question (Are there any mental health apps that have been standardised for psychological use?) were analysed using content analysis. Content analysis manages to quantify and analyse the prevalent relationship between the concepts (Allen, 2017 ). Table 1 condenses the information relating to the user-tested apps in LMICs, and a frequency paragraph outlines an account for the use of mental health apps. This process included looking at phrases relating to the effectiveness of mental health apps in low- and middle-income communities as well as the impact that they have made or how they could potentially help in retrospect of the current mental health care. Erlingsson and Brysiewicz ( 2017 ) provide a guide on following this method of data collection which includes (1) condensation, (2) code, (3) category, and (4) the formulation of themes.

Following this, a thematic synthesis using a priori coding (AAAQ framework) was used. Thematic synthesis consists of three stages of analysis (Ryan et al., 2018 ; Thomas & Harden, 2008 ). The first stage involves the coding of data relating to the research questions. The second stage involves the grouping of similar codes into the category of descriptive themes and describing the observed pattern across the studies. The third stage depicts the development of analytic themes. This involves synthesising the obtained findings from the different studies and bringing meaning into the identified patterns concerning the proposed research questions (Ryan et al., 2018 ).

Stage: 8 Dissemination of Findings

The findings of the study were used to compile this journal article and are presented hereunder.

Ethical Consideration

The study received an ethics waiver from the Human Research Ethics Committee (Non-medical) at the University of the Witwatersrand (Protocol number: MASPR/20/05W). As the research involves secondary data collection through selecting articles and no human subjects were involved, no ethical clearance was required.

Content Analysis

A description of the sample of texts as well as research questions 1 and 2 were explored using content analysis. The results are presented below.

Description of Sample

The study included a total of seven articles that met the inclusion criteria—see Table 1 . Within this sample, two studies were conducted on samples from Africa, specifically South Africa and Tanzania; two were conducted in Australia within the indigenous communities; two were conducted in South America specifically in Mexico and the Dominican Republic, and one was conducted in India. Three out of the six included articles employed a mixed methods research which involved interviews and design workshops for the apps to test their suitability.

What Are Mental Health Apps that Are Used?

Two of the apps focused on depression and anxiety (Caplan et al., 2018 ; Gonsalves et al., 2019 ); three looked into suicide prevention (Arenas-Castañeda et al., 2020 ; Brown et al., 2020 ; Povey et al., 2020 ); one focused on autism (Kümm, 2018 ), and one looked at dementia screening (Paddick et al., 2021 ). The majority of the studies focused on the youth, with only one looking into the provision of support for mental health assistants (Brown et al., 2020 ), and one was used for an elderly population by administrators referred to as enumerators (Paddick et al., 2021 ).

Are There Any Mental Health Apps that Have Been Standardised for Psychological Use?

None of the articles mentioned the psychological standardisation of the mental health apps, with two articles mentioning the advice of experts in the co-design process (Caplan et al., 2018 ; Gonsalves et al., 2019 ). There was linguistic consideration in some of them. The POD app implemented in India consisted of English but it also included languages that are commonly spoken in the area, Hindi and Konkani (Gonsalves et al., 2019 ). Within the Dominican community, the app was translated into Spanish as that was the local language used (Caplan et al., 2018 ). In Tanzania, the enumerators were trained to administer the app, and instructions were translated into Kiswahili and other tribal languages like Chagga and Maasai (Paddick et al., 2021 ).

Thematic Synthesis

Research question 3 was explored using thematic synthesis. The themes were guided by the AAAQ framework proposed by the United Nations Committee in ensuring that potential barriers to service delivery are addressed (World Health Organization, 2019 ).

Availability: What Are the Issues Identified in the Articles with Regards to the Availability of Mental Health Apps in LMICs?

Low- and middle-income communities often struggle with the provision of adequate public health care; however, with mental health care, the gap is even larger in secluded rural communities, with a wider disparity in comparison to urban communities (Brown et al., 2020 ; Povey et al., 2020 ; Paddick et al., 2021 ). Rural communities rely on assistance from within the community by the community members due to the minimal mental health practitioners available. Furthermore, there remains a lack of infrastructural development resulting in a lack of facilities that appropriately tackle some of the issues faced when it comes to mental health within low-income communities (Arenas-Castañeda et al., 2020 ; Brown et al., 2020 ; Kümm, 2018 ; Povey et al., 2020 ).

Physical availability also extends to the kind of mental health interventions that are received. Brown et al. ( 2020 ) present the case of the indigenous aboriginal community in Australia where community members act as suicide preventers who assist with the youth. However, this does not guarantee that they possess adequate psychological knowledge to assist. Technological growth and development allows for the use of devices like smartphones to make mental health care more available (Arenas-Castañeda et al., 2020 ; Gonsalves et al., 2019 ; Kümm, 2018 ). This allows for more engagement with different kinds of interventions and could further assist some of the community mental health helpers with more tailored information.

In the Mexican study, researchers had to make some of the screening tools available through a web-based design for the convenience of both elders and those that do not own their mobile phones (Arenas-Castañeda et al., 2020 ). This further extends to the unequal resource distribution such as infrastructural development within LMICs where the network towers may not provide fast internet compared to HICs (Caplan et al., 2018 ). Network connectivity was not something was mentioned; however, a question of sustained connectivity was raised during co-designs and implementation (Arenas-Castañeda et al., 2020 ; Brown et al., 2020 ; Caplan et al., 2018 ; Gonsalves et al., 2019 ; Kümm, 2018 ; Povey et al., 2020 ).

Accessibility: Do Mental Health Apps Provide an Accessible Intervention in the LMIC?

The World Health Organisation has noted the potential value of eHealth which is medical care delivered through mobile phones as a means of reaching communities that are in the outskirts and under-resourced places (World Health Organization, 2019 ). Community members acknowledge the effort that is brought by mental health apps as being an easier option to access for its community and those in need of help (Arenas-Castañeda et al., 2020 ; Caplan et al., 2018 ; Gonsalves et al., 2019 ; Kümm, 2018 ; Povey et al., 2020 ). However, financial accessibility impedes getting any help that is outside of the village more especially when it came to the implementation of mental health care through smartphone applications. The accessibility to phones and apps is limited to those who can afford them. The expense that comes with purchasing data further restricts the use of the apps. Caplan et al. ( 2018 ) reported that data spent on using an app for a week is meant to last for a month. Hence, the use of apps cannot be sustained (Caplan et al., 2018 ).

Mental health apps were introduced as an alternative intervention that considers both technical feasibility and contextual differences. Language is one of the main issues that need to be considered in this regard. The mental health apps that were user-tested within the different communities were taken from high-income countries with many of them being in English, yet English is often not the language of communication for individuals in most LMICs (Brown et al., 2020 ; Gonsalves et al., 2019 ; Kümm, 2018 ; Paddick et al., 2021 ; Povey et al., 2020 ). Gonsalves et al. ( 2019 ) reported that in New Delhi and Goa, the sample being explored required children that were either fluent in English, Hindi, or Konkani in an area that has a variety of languages. Furthermore, in a South African study on an app to assist autistic children, almost everything was in English, yet English was the second or third language for participants (Kümm, 2018 ).

Added to this is the issue of technological literacy in the administration and use of mental health apps. Fear of minimal technological proficiency among community members was raised as an access concern (Arenas-Castañeda et al., 2020 ; Caplan et al., 2018 ; Povey et al., 2020 ). LMICs have a larger youth population in comparison to high-income countries. Being more technologically savvy than the elders, the mental health apps target a population that was able to navigate through some of the requirements of the app. Researchers recognised that the reach of mental health through mental health apps was therefore more effective among youth (Kümm, 2018 ; Povey et al., 2020 ).

Aside from technological literacy, literacy levels were also a concern. Gonsalves et al. ( 2019 ) reported that among the students, those that came from government-run and aided schools experienced literacy difficulties. Text-heavy problem-solving concepts had to be reworded into a point-specific language (Gonsalves et al., 2019 ). Further, the use of pictorial formats and audio recordings for better comprehension was opted for (Caplan et al., 2018 ; Gonsalves et al., 2019 ; Kümm, 2018 ; Povey et al., 2020 ).

Acceptability: Are Mental Health Apps Adequately Acceptable in LMICs?

Culture impacts the experience and the conceptualisation of mental illness (Arenas-Castañeda et al., 2020 ; Brown et al., 2020 ; Caplan et al., 2018 ; Gonsalves et al., 2019 ; Kümm, 2018 ; Povey et al., 2020 ). The studies demonstrated that to ensure appropriate implementation and efficient usability of the mHealth apps, the developed features of the apps need to consider cultural sensitivity and acceptability among the community members (Gonsalves et al., 2019 ). Adolescents from indigenous communities were seen to be less likely to seek mental health help compared to their non-indigenous counterparts due to shame, language difference, intergenerational stigma, and lack of accessible facilities (Brown et al., 2020 ; Povey et al., 2020 ). In a South African study, researchers noted that in an app used to monitor facial expression among children diagnosed with autism, the positive facial expressions of South Africans were not expressed similarly to children from the US group (Kümm, 2018 ). Though there is acceptance of mental health apps as means of self-help for those who have little access to mental health, a study within the Indian community acknowledged that the act of self-help is not something common among their adolescents, as such they are used to being given a directive by their elders (Gonsalves et al., 2019 ). This also concurs with some of the doubt around the “dissatisfaction with western approaches” (Brown et al., 2020 ) as they do not consider some of the traditional and pluralistic understandings of mental illness (Brown et al., 2020 ; Kümm, 2018 ).

Researchers outlined that the relevance of mental health apps should consist of the indigenous study of suicidology in indigenous communities for example to help address cultural sensitivity in their administration (Caplan et al., 2018 ; Kümm, 2018 ). Within the administration of POD adventures , researchers ensured that the characters were culturally relevant to the children with a representation of characters across different gender, ages, social class, and who had common names.

The studies emphasise a further need for culturally appropriate intervention that will improve the sustainability of mental health intervention as well as its effectiveness (Brown et al., 2020 ; Kümm, 2018 ; Povey et al., 2020 ). During the user-testing phase, the aboriginal and islander mental health initiative for youth (AIMhi-Y) app included pictorial selection options, drop-down boxes, and audio recordings for each question to support and encourage completion for those who struggle with language literacy (Povey et al., 2020 ).

Participants expressed other concerns such as the potential of mental health apps being culturally insensitive, dismissive, and disrespectful to emergent gatekeepers and at-risk individuals (Brown et al., 2020 ). Further, the lack of understanding of the intentions of the app, home responsibilities, childcare, and employment issues impacted uptake (Caplan et al., 2018 ). The engagement that the mental health app offered was noted as not being sufficient for individuals who experience severe mental illness, as such, can be deemed unfit (Povey et al., 2020 ).

With a majority of mental health apps developed in high-income countries, researchers conducted co-design workshops with locals (Brown et al., 2020 ; Povey et al., 2020 ). This was a means of enhancing acceptability within the community with its members as well as culturally informing the development of the mobile app (Povey et al., 2020 ). The continuous consultation with the community through co-design assisted in the development of INSIST , a mental health app designed for suicide prevention helpers in indigenous communities (Brown et al., 2020 ). The workshops aimed at understanding the participant’s needs such as the provision of voice-based interfaces and having local language support (Caplan et al., 2018 ; Povey et al., 2020 ).

With the novelty of using smartphone technology as a mode of delivering mental health care, the studies indicated concerns around security and data privacy. Povey et al. ( 2020 ) found that participants enquired where the data was going as well as who was going to see it during the user testing of a suicide prevention tool. However, reassurance was given to the participants on the anonymity provided by the app and confidentiality of the information provided. Due to the lack of resources, some participants indicated that they use their phones with family members which makes their information not entirely protected (Caplan et al., 2018 ; Kümm, 2018 ). There is still a huge ethical challenge when it comes to mental health apps as a form of alternative care in ensuring data privacy to users which requires careful consideration.

Quality: Have Mental Health Apps Been Shown to Offer a Level of Quality Care in LMICs?

The implementation of mental health apps requires the assurance that they are of adequate quality to be used in communities where mental health resources are minimal. The user-centred design of mental health apps has been recognised as a means of improving intervention applicability and usability within a low-literacy population (Caplan et al., 2018 ; Gonsalves et al., 2019 ; Kümm, 2018 ). Various design features have been considered within the different communities; however, the use of gamified intervention brought an appeal especially among the adolescent group (Brown et al., 2020 ; Gonsalves et al., 2019 ; Kümm, 2018 ). The participants revealed a preference for games that were built upon real-life stories which offered them relevance and relatability to their social environment. These included pictures of the schools and local surroundings per the participant’s media preference. To encourage engagement with the mobile app, the feature of user-choice rewards and quizzes was found to be useful (Gonsalves et al., 2019 ). End-user experience being a driver of appropriate mental health design, the Autism & Beyond app included a non-verbal communication means as some children were not able to verbally converse (Kümm, 2018 ). This acts further as assurance in meeting the requirements of cultural consideration (Brown et al., 2020 ).

User-testing expressed the participant’s perspective on the attributes that work in mHealth apps as well as the improvements that can be offered. The development of POD adventures encompassed both users and service providers. In the layout of the mental health app, the users and the service providers advocated for the game-based mental health app as it appeals to the youth (Gonsalves et al., 2019 ).

Despite the advocacy that mental health apps have received in user-testing, the practical implementation of it still takes precedence. The unavailability of smartphones to individuals who require mental health assistance was a hindrance to adequate and privately accessible care (Caplan et al., 2018 ; Povey et al., 2020 ). This limits the scalability of the intervention in remote areas with minimal to no mental health care access as well as appropriate response time.

The evidence from the reviewed studies suggests that mental health apps have the potential to provide mental health care to locations that have limited resources. The articles indicated that the majority of the mental health apps were designed for the youth as they are the ones more likely to interact with them, thus, prompting help-seeking behaviour (Arenas-Castañeda et al., 2020 ; Caplan et al., 2018 ; Gonsalves et al., 2019 ; Povey et al., 2020 ). Due to the low and still growing evidence of efficacy (Torous et al., 2018 ), the implementation of mental health apps requires the recognition of the contextual differences between where the apps are adapted from to where they will be adapted to. Hence, co-designing apps with local experts and community members is vital (Caplan et al., 2018 ; Gonsalves et al., 2019 ; Torous et al., 2019a ). The results showed that within the indigenous communities, individuals who assist with suicide prevention also require some support that would make their work easier (Povey et al., 2020 ).

Mental health apps have varying uses within different categories, with most of them being used as a form of support for those diagnosed with mental illness (Lecomte et al., 2020 ). The review demonstrates further the disparity in mental health apps between HICs and LMICs. Mental health apps prioritise the provision of support to their users while being accessible through the Apple store and Google store (Alqahtani & Orji, 2020 ). They offer a variety of services from meditation, information to symptom tracking, online coaching, and social support (Alqahtani & Orji, 2020 ) with a further expansion on having some apps reviewed by psychological experts (Ibrahim et al., 2022 ; Weber et al., 2018 ). However, as it currently stands, the results demonstrate that communities located in the LMICs face the challenge of accessing appropriate and effective mental health care even through apps. While there might be a growth in smartphone ownership, the lack of network infrastructure combined with the cost of data and the sharing of devices limits access to mental health apps. It is also necessary to acknowledge the existence of free to use apps for mental health. However, their efficacy is still to be tested and formally documented in the literature for LMICs.

Contrary to the anonymity that mental health apps provide to their users, there are concerns about the provision of data privacy and confidentiality. Torous et al. ( 2018 ) allude to the lack of structured ethical guidelines in line with digital health interventions as a form of protection. Marshall et al. ( 2020 ) outline that the development of mental health apps often occurs outside the involvement of academic institutions and with little expert advice. Thus, the regulation of interoperability remains an issue. Some of the articles indicate that they implemented their country’s data regulation laws which can often be left to interpretation as there are not designed for the digital delivery of health and mental health.

In cases where devices are shared, privacy concerns are also tied in with cultural concerns. Gopalkrishnan ( 2018 ) discusses the shame and stigma associated with mental illness. Families would not want family members seeking help. Even though apps offer the space for individuals in such communities to get help, the use of shared devices precludes this. Hence, the results emphasise the need for culturally sensitive intervention due to the shame and stigma associated with mental illness.

Contextual differences influence the uptake and efficacy of mental health apps further. Gopalkrishnan ( 2018 ) outlines elements such as emotional expression, shame, power distance, collectivism as well as spirituality, and religion in the relationship between culture and mental health that play a key role in individuals using mental health apps. An example of this was cited in Kümm ( 2018 ) where an autism app failed to detect some of the facial expressions of South African children because it was standardised using American children. Further, using a dog in the American context was suitable as it is considered a friendly animal, but within the South African context, a dog was not seen as a friendly animal among a large segment of the population. Hence, the app not only biases South African children, but it also included examples that were not culturally acceptable.

Majority of apps are in English, but this excludes those that who are not conversant in English. In LMICs, English is not the first language for a substantial sector of the populations. Linked to language is the literacy level of individuals who need to use the apps. Literacy with regards to both the language used in the app as well as technological literacy. Ganasen et al. ( 2008 ) argued that low literacy in individuals diagnosed with mental illness impacts patient care as well as lack of accuracy and validity in mental health app screening tools. As such, the adaptation of mental health apps to the context of use is recognisably important as a user-oriented intervention. Thus, user-testing and co-design workshops are essential when developing apps for use in LMICs (Brown et al., 2020 ; Povey et al., 2020 ).

The studies also alluded to the lack of regulation in the field with regards to mental health apps. There is therefore a need for both legislation and ethical guidelines governing issues of privacy and data security for users of mental health apps. Laher and Hassem ( 2020 ) discuss the ethical challenges associated with online mental health screening and present some guidelines to be used when for the development of online mental health screening tools. A similar initiative is necessary for mental health apps. The National Institute of Mental Health (U.S. Department of Health and Human Services et al.,  2017 ) advocates for the development of a checklist for the evaluation of mental health apps which will help further in ensuring the quality of apps that are available. Newer research has been targeting these gaps of creating digital health frameworks in LMICs taking into consideration culture (Chelberg et al., 2022 ). This indicates the continuous explorative trail with mental health apps and digital health interventions.

The explosive growth of mental health apps introduces an aspect of healthcare that provides efficiency in the intervention methods being implemented. In their basic functionality, Marshall et al. ( 2019 ) present that mental health apps provide immediate assistance, convenience, and affordability, which is of particular importance to its relevance to low- and middle-income countries. This intervention format brings focus towards the self-care that the user can engage with when using the apps as they can both be used a stand-alone self-care or in support of traditional interventions (Donker et al., 2013 ). Arguably, mental health apps also extend the ability for the patient to track their symptoms through ecological momentary assessment (EMA) (Donker et al., 2013 ; Hollis et al., 2015 ). There is engagement with the aspect of mental health literacy in which the apps implement psychoeducation, self-assessment (EMA), and treatment centres on top of information on referral allowing the patient to assess what they have and request assistance to the nearest centre (Olff, 2015 ). However, as indicated in this review, there has been very little research into the efficacy of mental health apps in LMICs, and where this research exists, it is focussed on younger populations. Hence, there is an urgent need to determine if mental health apps are indeed providing the services needed in resource challenged communities in LMICs. Further, the results highlighted important elements with regards to availability, accessibility, acceptability, and quality that app developers need to consider ensuring the utility and uptake of mental health in LMICs.

Data Availability

Data sharing is not applicable to this article, as no new data were created or analysed in this study.

Low- and middle-income countries (LMICs) are classified by the World Bank as countries that have the gross national income (GNI) per capita of $1.026 and $3.995 compared to high-income countries with a GNI per capita that exceeds $12.056 which is based on the categorisation calculated using the Atlas method (Prydz & Wadhwa, 2019 ; The World Bank, 2019 ). According to Prydz and Wadhwa ( 2019 ), the classification of low- and middle-income countries consists of economic growth, inflation, and exchange rates on the overall population of the country.

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Gama, B., Laher, S. Self-help: a Systematic Review of the Efficacy of Mental Health Apps for Low- and Middle-Income Communities. J. technol. behav. sci. (2023). https://doi.org/10.1007/s41347-023-00360-z

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Camacho E , Cohen A , Torous J. Assessment of Mental Health Services Available Through Smartphone Apps. JAMA Netw Open. 2022;5(12):e2248784. doi:10.1001/jamanetworkopen.2022.48784

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Assessment of Mental Health Services Available Through Smartphone Apps

• 1 Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Question   What do mental health smartphone apps offer patients, how has the app landscape changed, and are app popularity metrics associated with privacy?

Findings   In this cross-sectional study of 578 mental health apps, an app marketplace assessment found that while more apps were collecting passive data, most apps still offered similar foundational features. There was no statistically significant correlation between privacy scores and star ratings, but there was a weak correlation between privacy scores and app downloads.

Meaning   These findings suggest that apps on the marketplace offer overlapping features, and metrics such as star ratings or the number of downloads may not provide adequate information about the privacy or efficacy of mental health apps.

Importance   As more patients and clinicians are turning to mental health smartphone apps to expand access to services, little is known about the current state of the app marketplaces and what these apps are actually offering in terms of features, privacy, price, and services.

Objective   To assess the current state of mental health apps, explore the association between app privacy scores and popularity as measured by star ratings and downloads, and to understand opportunities and challenges facing the commercial app landscape.

Design, Setting, and Participants   This cross-sectional study had trained raters using the public-facing M-Health Index and Navigation Database (MIND) to assess and review 578 mental health apps. The sample of apps used in this analysis were pulled from MIND and include apps across various conditions including schizophrenia, eating disorders, sleep, and more. Analysis of these apps was conducted in June 2022.

Exposures   There were 578 mental health apps rated across 105 dimensions derived from the American Psychiatric Association’s app evaluation framework.

Main Outcomes and Measures   App raters assessed each app across 6 categories: (1) app origin and accessibility, (2) privacy and security, (3) clinical foundation, (4) features and engagement, (5) inputs and outputs, and (6) interoperability. Privacy scores were determined by 5 MIND criteria, including (1) having a privacy policy, (2) reporting security measures in place, (3) declaring data use and purpose, (4) allowing for the deletion of data, and (5) allowing users to opt out of data collection. Correlations between privacy scores and popularity metrics (star ratings and number of downloads) were measured.

Results   This study included 578 mental health apps that were identified, assessed, and analyzed across 105 MIND dimensions. Psychoeducation, goal setting, and mindfulness were among the top app features. Of the 578 apps analyzed, 443 (77%) had a privacy policy. This analysis of apps with a privacy policy revealed that there was no statistically significant correlation between privacy scores and Apple App Store ( r  = 0.058, P  = .29) or Google Play Store star ratings ( r  = 0.041; P  = .48). The number of app downloads on the Google Play Store, however, was weakly correlated with privacy scores (χ 2 5  = 22.1; P  < .001).

Conclusions and Relevance   In this cross-sectional study of mental health apps, findings indicate that the current app marketplaces primarily offered basic features such as psychoeducation, goal tracking, and mindfulness but fewer innovative features such as biofeedback or specialized therapies. Privacy challenges remained common, and app popularity metrics provided little help in identifying apps with more privacy.

COVID-19 has increased demands on the mental health care system and led to increased reliance on digital tools for care delivery. 1 While synchronous telehealth (eg, video visits) has now become commonplace, asynchronous tools such as smartphone apps have also expanded in popularity. With more than 10 000 mental health–related apps on the marketplace today, selecting a safe and effective app has become an urgent priority.

As health care regulators continue to struggle to effectively regulate health apps, clinicians and patients must make decisions today without formal support. 2 Numerous app evaluation systems have arisen as a result and aim to provide direct recommendations or guidance to aid users in their selection of relevant apps. For example, a recent review identified at least 44 frameworks for assessing mental health apps 3 and new ones are continually being introduced. 4 Although each framework offers unique benefits, few offer concrete information about an app and are impractical for clinicians and patients to use.

Appreciating the need for actionable guidance around mental health apps via a scalable digital framework, our team has developed, 5 introduced, 6 and maintained 7 the M-Health Index and Navigation Database (MIND). Derived from the principles behind the American Psychiatric Association’s (APA’s) app evaluation framework, 8 MIND evaluates each app across 105 unique dimensions. These evaluation questions are designed to be clinically relevant, easy to update, as objective as possible, auditable, and searchable in a database to ensure any user can use the data to make an informed decision.

As a large public database of mental health apps, MIND offers a practical and data-driven approach for assessing the commercial app marketplace and trends. Since our team’s last examination of this data, based on March 2021 data, 6 we have more than doubled the number of apps included in the database to now over 650 and maintained all app ratings up to date within 6 months of the last review. Using this unique data set, we can assess how the app marketplaces are responding to public needs.

As there have been more public-facing efforts revealing privacy flaws in many popular mental health apps 9 , 10 and calls for apps to be more evidence-based, 11 it remains unclear if there have been any changes in the mental health apps that most people may be downloading from the Apple App Store and Google Play Store. Thus, we assessed whether there is an association between popularity metrics and privacy scores for mental health apps.

Additionally, both patients and clinicians must understand what apps currently offer so that expectations around apps’ functionality, use cases, cost, and evidence can be realistic. For this reason, we analyzed and reported the origin and accessibility, privacy and security, clinical foundation, features and engagement styles, inputs and outputs, interoperability, apps supporting serious mental illness conditions, and passive data collection offered by all 578 apps.

In this cross-sectional study, the objective was to assess the current state of mental health apps, determine how or if the mental health app landscape has changed, and examine whether there is a correlation between app privacy scores and popularity as measured by star ratings and downloads. We hypothesized that the mental health app landscape would not have changed substantially since our previous analysis in 2021 and that there would be no correlation between app privacy scores and popularity metrics.

This study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cross-sectional studies. As this study did not involve human participation, there is no informed consent nor participant information. Instead, we provide app selection, criteria, and characteristics. Institutional review board review was not sought in accordance with 45 CFR §46.

Informed by the APA’s app evaluation framework, 12 MIND is a publicly available database that involves a comprehensive assessment of mental health-related apps across 6 categories: (1) app origin and accessibility, (2) privacy and security, (3) clinical foundation, (4) features and engagement, (5) inputs and outputs, and (6) interoperability.

All apps in MIND were gathered from the Apple App Store or Google Play Store. Since the last evaluation of the database in 2021, 6 apps supporting conditions for headache (n = 48), pain (n = 47), sleep (n = 106), 7 eating disorders (n = 65), 13 and substance use (smoking and tobacco) (n = 228) 14 were added. Top mental health apps were evaluated using MIND, which involves answering 105 objective app questions. Apps were entered into MIND by 1 of 10 raters. App raters consisted of college students, medical students, and research assistants. Each rater underwent a 4-hour interrater reliability training based on our published methods. 5 The training involved an online informational module, followed by practice rating at least 2 apps to assess initial reliability. Reliability was assessed using Cohen κ statistic, 15 for which raters were required to demonstrate good interrater reliability as measured by a minimum threshold of 0.7. All raters surpassed this minimal threshold.

Following training, raters downloaded, examined, and entered answers to the 105 questions into MIND. Only apps that cost $10 or less to download were evaluated. No in-app purchases or subscriptions were made for evaluation. Raters used the basic app version, app store descriptions, free trials, and detailed list of features available through subscription or in-app purchases to evaluate apps.

Analysis was performed on apps available in the database as of June, 2022. We first determined the count and the proportion of apps in our database satisfying each MIND criterion. We then aimed to determine if pairs of MIND criteria were correlated with a statistically significant degree using Fisher exact test for independence. We were most interested in seeing whether there was a correlation between apps having various privacy settings and apps taking data from more sensitive input data sources, such as the smartphone’s microphone or GPS sensor. Correlational statistics were used since the variables of interest are all categorical. The threshold for statistical significance was set at 2-sided P  < .05.

Additionally, between February and June 2022, app raters updated all 578 apps within 180 days of their last entry into the database. We analyzed the number of updates to each MIND criteria, as well as the total number of updates across the entire database.

To quantify the privacy capabilities of each app in our database, we identified 5 MIND privacy criteria that we believed every mental health app should satisfy. These criteria included (1) having a privacy policy, (2) reporting security measures in place, (3) declaring data use and purpose, (4) allowing for the deletion of data, and (5) allowing users to opt out of data collection. Each app was assigned a score from 0 to 5, as the sum of these criteria.

We then analyzed the potential correlation between these underlying privacy scores based on objective empirical data and more consumer-facing measures of popularity, such as downloads. The Apple App Store does not provide the number of app downloads publicly. We scraped and collected this public data from app vendors using Python 3.8.8. The Google Play Store provides download data into categories: those with less than 50 000 downloads and those with 50 000 downloads or more. We ran a χ 2 analysis on the resulting categorical data to calculate the correlation between downloads and our privacy scores. For the continuous rating data on both the Google Play Store and the Apple App Store, we used the P value associated with a Pearson correlation coefficient to determine statistical significance. Finally, we used a 2-sample t test to see if apps exclusively on the Google Play Store had a lower mean privacy score than apps exclusively on the Apple App Store.

Of the 578 apps included in this study, 160 (27.7%) were available only on iOS, 154 (26.6%) were available only on Android, and 264 (45.7%) were available on both iOS and Android. In addition to iOS and/or Android availability, one hundred of the 578 apps (17%) were also available on the web.

There were 525 apps (91%) developed by for-profit companies. Twenty-six (4%) were created from a government or governmental organization, 25 (4%) from a non-profit organization, 18 (3%) from a health care company, and 17 (3%) from an academic institution.

Although 507 apps (88%) were free to download, only 227 (39%) were completely free. Thus, many apps involved in-app purchases (44% [n = 254]) or subscriptions (34% [n = 194]) to unlock the entire app functionality.

All apps were available in English, and 105 (18%) were also available in Spanish. There were 378 apps (65%) that functioned offline in that they operated without connection to the internet, and 312 apps (54%) were identified as having at least one accessibility feature.

Of the 578 apps in the database, 443 (77%) had a privacy policy. Using the Flesch-Kincaid grade level readability formula, the mean (SD) reading grade level for these privacy policies was 12.5 (2.3). This indicates that the reader needs above a grade 12 reading comprehension level to understand the privacy policy. There were 257 apps (44%) that shared personal health information with third parties.

Eighty-five apps (15%) offered either a feasibility or efficacy study. The quality of these studies was not assessed.

The apps examined offered 22 unique features related to therapeutic features. Of these, the most common feature was psychoeducation with 237 (41%), followed by goal setting/habit at 218 (38%) and mindfulness at 217 (38%), see Figure 1 . Of note, the least common features were apps providing biofeedback with sensor data (1%), Acceptance and Commitment Therapy (2%), and Dialectical Behavioral Therapy (2%).

The most common inputs included surveys (45%), diary entries (34%), and microphone (21%). The most common outputs were notifications (68%), a summary of data (61%), and references and information (50%). Eighty-four apps (15%) collected passive data or data that was not entered into the app, specifically biodata such as step count and heart rate, or geolocation.

There were 172 apps (30%) in the database that allowed users to email or export their data. Only nine apps (2%) offer integration with an electronic medical record.

The 3 most common types of conditions our apps purported to help treat were substance abuse related to smoking or tobacco (33%), stress and anxiety (28%), and nonserious mood disorders (20%). By contrast, only 13 apps (2%) were built to address schizophrenia. Additionally, to a statistically significant degree, for-profit companies were disproportionately less likely to produce apps addressing serious mental illness.

Apps which collected biodata, geolocation data, and accessed users’ cameras and microphones were more likely to implement privacy and security measures. A Fisher exact test found that all associated correlation statistics were statistically significant except for the correlation between collecting geolocation data and declaring data use and purpose ( Table 1 ).

Between February and June 2022, 10 app raters updated all 578 apps within 180 days of their last entry into the database. A total of 371 apps (64%) had at least 1 change among the MIND criteria For detailed results, see the eResults in Supplement 1 .

As compared with the 2021 results, we found that the number of apps collecting passive data, including biofeedback with sensor data, biodata, and geolocation, increased from 71 to 78 apps, despite the overall size of the analyzed sample more than doubling from 278 to 578 apps. We additionally noticed that the top 7 app features in 2021 remained the top features today, namely (1) mood tracking, (2) journaling, (3) mindfulness, (4) psychoeducation, (5) deep breathing, (6) symptom tracking, and (7) goal-setting and habits.

Of the 418 Android apps in MIND, 412 had data that we could successfully scrape regarding downloads on the Google Play Store. The number of app downloads on the Google Play Store, however, was correlated with privacy scores (χ 2 5  = 22.1; P  < .001), see ( Table 2 ). Thus, the more app downloads, the larger the privacy score.

Of the 418 Android apps in our database, 305 had enough reviews from the public for the Google Play Store to display a dedicated ratings and reviews section with summary statistics such as the average star rating out of 5. These average and overall ratings on the Google Play Store were uncorrelated with our privacy scores ( r  = 0.041, P  = .48) ( Figure 2 ).

Apple imposed a policy requiring app developers to disclose privacy settings when they next update their apps. To assess the most up-to-date correlation between privacy scores and App Store ratings, we discarded apps that had not been updated recently enough to incur Apple’s new policy. The remaining 340 iOS apps had a privacy score of at least 1 because having a privacy policy was 1 of our 5 privacy criteria. There was no statistically significant correlation between App Store ratings and privacy scores ( r  = 0.058, P  = .29) ( Figure 3 ).

Of the 314 apps which were either on the Google Play Store or the iOS App Store but not both, there was not a statistically significant difference in mean privacy score ( t  = 1.9586, P  = .051). See the eFigure in Supplement 1 for the distribution of privacy scores between Android and iOS apps.

We examined and assessed 578 mental health-related apps rated from January to July 2022 across 105 criteria per app using MIND. From a longitudinal perspective, we note that, since nearly two-thirds of apps featured at least 1 change in the 105 questions across 6 months, it is clear that the temporal dynamics of the marketplace present a challenge to traditional rating and scoring systems, and instead require consistent updating of mental health app databases.

While the potential of apps to increase the quality of and access to care remains high, the current commercial offerings face challenges. Leveraging new sensor and biological data to drive just-in-time adaptive interventions are often discussed, but we found that, within our sample, less than 5% of apps currently supported this capability. Another challenge seen within the mental health app marketplace is a lack of accessibility. Accessibility features may include adjustable text size, text-to-speech or speech-to-text abilities, and a colorblind color scheme adjuster. Only half of the apps analyzed included an accessibility feature. Related to access, our results echo the literature indicating the limited availability of mental health apps offered in Spanish, 16 with only 18% of apps with Spanish language functionality. While all apps in the database are accessible to anyone, only 13 (2%) were designed to support individuals experiencing schizophrenia. These findings suggest that while the app marketplace offers a plethora of apps, marketplace forces alone are not creating an ecosystem of apps that are accessible to all or increasing access in patient groups that may need the most support.

Digital privacy, especially surrounding mental health apps, is a frequent topic of concern. Our results suggest ongoing privacy concerns among popular apps, with app marketplace star ratings providing no correlation with actual privacy features in place. But it is encouraging to see that 77% of apps reviewed possessed a privacy policy. However, with the mean (SD) grade reading level of these privacy policies at 12.5 (2.3), it remains unclear if these policies are accessible by most users. This result echoes many other results in the literature concerning barriers to privacy in digital mental health. 17 - 19 Compared with our prior results in the 2021 analysis of MIND, we note that the grade reading level has not changed in more than a year 6 and remains a barrier to understanding data privacy.

The lack of correlation between privacy scores and consumer ratings on both the iOS App Store and the Google Play Store also suggests consumers may not be aware of or seeking apps based on privacy features—underscoring an opportunity for further education or even regulation. A 2020 survey study conducted on data sharing revealed that only 9% of US consumers surveyed were inclined to share their personal health information with a tech company aiming to improve health care. 20 Despite this, mobile mental health apps with data privacy issues remain popular, as seen in high star ratings and the number of downloads. Recently, the Mozilla Foundation assessed the privacy and security of 32 popular mental health and prayer apps and found that 87.5% of these apps posed substantial privacy concerns for users surrounding their data usage and management. 10 The consistent privacy concerns among these apps suggest that new regulations may be necessary to enforce higher standards, 21 alongside efforts to educate both patients and clinicians about the risks and benefits of these apps.

Overall, there is a lack of clinically supported mental health apps. A 2020 systematic search for wellness and stress-related apps available in app stores found that only 2 percent of their more than 1000 app samples had research studies to support app claims. 22 Leong et al 23 identified only 3.4% (6/179) of apps supporting anxiety and depression found in app stores with scientific evidence in the form of randomized clinical trials or real-world evidence. 23 This situation on the app marketplaces parallels concerns regarding the lack of high-quality research studies on mental health apps 24 and highlights an opportunity for industry and academic collaboration.

Our findings also offer positive interpretations. With so many apps on the commercial marketplaces, clinicians and patients can now be more demanding in what they seek from any given app. Since many apps share similar features ( Figure 1 ), searching for apps that offer that feature for free while also protecting privacy is reasonable. With 39% of apps examined still completely free, and free apps offering similar privacy protections and levels of evidence as paid apps, turning to free apps first may be recommended.

As with all studies, our methods have limitations. Our data are derived from app self-declaration and examination of apps based on the MIND questions. We did not evaluate the quality of different app features or the quality of the science underlying these apps. This is a nearly impossible task given the rapid turnover and changes of these apps compared with the time, effort, and expense of conducting a technical audit of each app. We also only examined apps that cost $10 or less. Thus, our sample may not be indicative of the entire app landscape as these more costly apps may offer different properties. However, since we analyzed the top apps across various conditions from both app stores, our findings may be indicative of the landscape for the most popular and widely used mental health apps. Lastly, in this study, the content type was collected and analyzed, whereas content quality was not. Recent research highlights the difficulty in assessing study quality for mobile mental health apps due to heterogeneity. 24

The findings of this cross-sectional study suggest that the current app marketplaces lack diversity in their offerings and fail to implement potentially high-impact features. Another challenge to the app space is that easily accessible metrics like star ratings fail to consider privacy capabilities. Thus, clinicians and patients must discern apps beyond such measures to ensure the discovery of apps that both fit their unique needs and protect their privacy. Publicly available app libraries 25 and validated app evaluation frameworks like MIND are innovative tools to support users in their app selection.

Accepted for Publication: November 10, 2022.

Published: December 28, 2022. doi:10.1001/jamanetworkopen.2022.48784

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Camacho E et al. JAMA Network Open .

Corresponding Author: John Torous, MD, MBI, Department of Psychiatry, Beth Israel Deaconess Medical Center, 75 Fenwood Rd, Boston, MA 02446 ( [email protected] ).

Author Contributions: Dr Torous had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Camacho, Torous.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Cohen, Torous.

Obtained funding: Torous.

Administrative, technical, or material support: Camacho, Torous.

Supervision: Torous.

Conflict of Interest Disclosures: Dr Torous reported being a Web editor for JAMA Psychiatry and a stockholder in Precision Mental Wellness. No other disclosures were reported.

Funding/Support: This study received funding from the Argosy Foundation.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Not all mental health apps are helpful. Experts explain the risks, and how to choose one wisely

Professor of Law, The University of Melbourne

Associate Professor, School of Psychological Sciences, The University of Melbourne

Postdoctoral Research Fellow, Disability Research Initiative, The University of Melbourne

Disclosure statement

Jeannie Marie Paterson receives funding from the Australian Research Council and has taken part in industry led roundtable discussions about digital mental health.

Nicholas T. Van Dam receives funding from the Three Springs Foundation Pty Ltd to establish the Contemplative Studies Centre at the University of Melbourne.

Piers Gooding receives funding from the Australian Research Council to examine the regulation of digital technologies in mental health care.

University of Melbourne provides funding as a founding partner of The Conversation AU.

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There are thousands of mental health apps available on the app market, offering services including meditation, mood tracking and counselling, among others. You would think such “health” and “wellbeing” apps – which often present as solutions for conditions such as anxiety and sleeplessness – would have been rigorously tested and verified. But this isn’t necessarily the case.

In fact, many may be taking your money and data in return for a service that does nothing for your mental health – at least, not in a way that’s backed by scientific evidence.

Bringing AI to mental health apps

Although some mental health apps connect users with a registered therapist , most provide a fully automated service that bypasses the human element. This means they’re not subject to the same standards of care and confidentiality as a registered mental health professional. Some aren’t even designed by mental health professionals.

These apps also increasingly claim to be incorporating artificial intelligence into their design to make personalised recommendations (such as for meditation or mindfulness) to users. However, they give little detail about this process. It’s possible the recommendations are based on a user’s previous activities, similar to Netflix’s recommendation algorithm .

Some apps such as Wysa , Youper and Woebot use AI-driven chatbots to deliver support, or even established therapeutic interventions such as cognitive behavioural therapy. But these apps usually don’t reveal what kinds of algorithms they use.

It’s likely most of these AI chatbots use rules-based systems that respond to users in accordance with predetermined rules (rather than learning on the go as adaptive models do). These rules would ideally prevent the unexpected (and often harmful and inappropriate ) outputs AI chatbots have become known for – but there’s no guarantee.

The use of AI in this context comes with risks of biased, discriminatory or completely inapplicable information being provided to users. And these risks haven’t been adequately investigated.

Misleading marketing and a lack of supporting evidence

Mental health apps might be able to provide certain benefits to users if they are well designed and properly vetted and deployed. But even then they can’t be considered a substitute for professional therapy targeted towards conditions such as anxiety or depression.

The clinical value of automated mental health and mindfulness apps is still being assessed . Evidence of their efficacy is generally lacking .

Some apps make ambitious claims regarding their effectiveness and refer to studies that supposedly support their benefits. In many cases these claims are based on less-than-robust findings. For instance, they may be based on:

• user testimonials
• short-term studies with narrow or homogeneous cohorts
• studies involving researchers or funding from the very group promoting the app
• or evidence of the benefits of a practice delivered face to face (rather than via an app).

Moreover, any claims about reducing symptoms of poor mental health aren’t carried through in contract terms. The fine print will typically state the app does not claim to provide any physical, therapeutic or medical benefit (along with a host of other disclaimers). In other words, it isn’t obliged to successfully provide the service it promotes.

For some users, mental health apps may even cause harm, and lead to increases in the very symptoms people so often use them to address. The may happen, in part, as a result of creating more awareness of problems, without providing the tools needed to address them.

In the case of most mental health apps, research on their effectiveness won’t have considered individual differences such as socioeconomic status, age and other factors that can influence engagement. Most apps also will not indicate whether they’re an inclusive space for marginalised people, such as those from culturally and linguistically diverse, LGBTQ+ or neurodiverse communities.

Read more: How effective is mindfulness for treating mental ill-health? And what about the apps?

Inadequate privacy protections

Mental health apps are subject to standard consumer protection and privacy laws. While data protection and cybersecurity practices vary between apps, an investigation by research foundation Mozilla concluded that most rank poorly.

For example, the mindfulness app Headspace collects data about users from a range of sources , and uses those data to advertise to users. Chatbot-based apps also commonly repurpose conversations to predict users’ moods , and use anonymised user data to train the language models underpinning the bots .

Many apps share so-called anonymised data with third parties , such as employers , that sponsor their use. Re-identification of these data can be relatively easy in some cases.

Australia’s Therapeutic Goods Administration (TGA) doesn’t require most mental health and wellbeing apps to go through the same testing and monitoring as other medical products. In most cases, they are lightly regulated as health and lifestyle products or tools for managing mental health that are excluded from TGA regulations (provided they meet certain criteria).

How can you choose an app?

Although consumers can access third-party rankings for various mental health apps, these often focus on just a few elements, such as usability or privacy . Different guides may also be inconsistent with each other.

Nonetheless, there are some steps you can take to figure out whether a particular mental health or mindfulness app might be useful for you.

consult your doctor, as they may have a better understanding of the efficacy of particular apps and/or how they might benefit you as an individual

check whether a mental health professional or trusted institution was involved in developing the app

check if the app has been rated by a third party, and compare different ratings

make use of free trials, but be careful of them shifting to paid subscriptions, and be wary about trials that require payment information upfront

stop using the app if you experience any adverse effects.

Overall, and most importantly, remember that an app is never a substitute for real help from a human professional.

Read more: AI chatbots are still far from replacing human therapists

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AI-powered apps working to detect mental health problems

Researchers say future is bright for treating substance abuse through mobile health technologies

Despite the high prevalence of substance abuse and its often devastating outcomes, especially among disadvantaged populations, few Americans receive treatment for substance use disorders. However, the rise of mobile health technologies can make treatments more accessible.

Researchers at the University of Oklahoma are creating and studying health interventions delivered via smartphones to make effective, evidence-based treatments available to those who cannot or don't want to enter traditional in-person treatment. Michael Businelle, Ph.D., co-director of the TSET Health Promotion Center, a program of OU Health Stephenson Cancer Center, recently published a paper in the Annual Review of Clinical Psychology that details the current landscape of mobile health technology for substance use disorders and suggests a roadmap for the future.

The Health Promotion Research Center (HPRC) is at the forefront of mobile health technologies worldwide, having attracted $65 million in grants and supporting nearly 100 mobile health studies. Within HPRC, Businelle leads the mHealth Shared Resource, which launched the Insight™ mHealth Platform in 2015 to create and test technology-based interventions. A multitude of health apps are available commercially, but few have undergone the research necessary to determine if they are effective. Businelle sees the promise of rigorously tested smartphone apps to fill gaps in substance abuse treatment.

"According to the Substance Abuse and Mental Health Services Administration, only 6% of people with substance use disorders receive any form of treatment," Businelle said. "There are many reasons -- we have a shortage of care providers, people may not have reliable transportation, may not be able to get away from work, or they may not be able to afford treatment. However, 90% of all U.S. adults own smartphones, and technology now allows us to create highly tailored interventions delivered at the time that people need them."

Businelle and his team have many mobile health studies underway for substance abuse, and the Insight™ mHealth Platform is used by other research institutions across the United States. The mobile health field is large and growing, not only for substance abuse but for mental health disorders like depression and anxiety. In his publication, Businelle makes several recommendations for research going forward.

Re-randomize clinical trial participants

Thus far, most clinical trials for mobile health interventions have mirrored traditional clinical trials studying new drugs, in which participants are randomly assigned to receive a new drug or a placebo and stay in those groups for the duration of the trial. But that approach doesn't work well for substance abuse trials, Businelle said. For example, if people don't quit smoking on their targeted quit date, they are unlikely to quit during the trial. Unlike traditional trials, mobile health apps can be programmed to re-randomize participants, or move them to a different intervention that might work better for them, he said.

"Instead of being stuck receiving a treatment that we know isn't likely to be effective for an individual, the app can easily re-randomize participants to different treatments," he said. "Just because they weren't successful with one type of intervention doesn't mean that another one won't work."

Objectively verify self-reports

Most substance abuse interventions have historically relied on people to report their own relapses. Unfortunately, because of stigma, people don't always report their usage truthfully, Businelle said. However, technology can now be used to biochemically verify self-reported substance use. In six of his smoking cessation trials, Businelle verifies whether participants have smoked by asking them to blow into a small device connected to a smartphone that detects the presence of carbon monoxide. Facial recognition software confirms the participant is the one testing.

"It is really important for the accuracy of our studies to objectively verify what people report," he said. "We are also developing similar noninvasive technologies that can detect the use of other types of substances without collecting urine or blood samples."

What is a successful outcome?

In mobile health substance abuse trials, success is often measured by whether a person is still using a substance at the end of the trial. But reality isn't usually so straightforward. Businelle said people may stop and start using a substance several times during a six-month trial. Instead of emphasizing the end result, he recommends using technology to assess the effectiveness of an intervention at daily, weekly and monthly intervals. By understanding the number of days of abstinence or number of days until a relapse, for example, the intervention can be more accurately assessed and improved.

Mobile health technology has disadvantages, such as the potential lack of a therapeutic relationship that can develop between patient and therapist, and because some people may need more intensive treatments than mobile health can provide. However, mobile health is still in its infancy.

"Mobile health interventions may reduce stigma because people do not have to attend treatment in person," Businelle said. "Because there is a severe shortage of qualified therapists, always-available behavior change apps could become a first line of treatment for substance abuse, with traditional counseling being reserved for those who do not respond to mobile health interventions."

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Materials provided by University of Oklahoma . Note: Content may be edited for style and length.

Journal Reference :

• Michael S. Businelle, Olga Perski, Emily T. Hébert, Darla E. Kendzor. Mobile Health Interventions for Substance Use Disorders . Annual Review of Clinical Psychology , 2024; 20 (1) DOI: 10.1146/annurev-clinpsy-080822-042337

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• Open access
• Published: 22 March 2019

Using science to sell apps: Evaluation of mental health app store quality claims

• Mark Erik Larsen 1 ,
• Kit Huckvale 1 ,
• Jennifer Nicholas 1 , 2 ,
• John Torous 3 ,
• Louise Birrell 4 ,
• Emily Li 1 &
• Bill Reda 1  

npj Digital Medicine volume  2 , Article number:  18 ( 2019 ) Cite this article

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Despite the emergence of curated app libraries for mental health apps, personal searches by consumers remain a common method for discovering apps. App store descriptions therefore represent a key channel to inform consumer choice. This study examined the claims invoked through these app store descriptions, the extent to which scientific language is used to support such claims, and the corresponding evidence in the literature. Google Play and iTunes were searched for apps related to depression, self-harm, substance use, anxiety, and schizophrenia. The descriptions of the top-ranking, consumer-focused apps were coded to identify claims of acceptability and effectiveness, and forms of supporting statement. For apps which invoked ostensibly scientific principles, a literature search was conducted to assess their credibility. Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries. Scientific language was the most frequently invoked form of support for use of mental health apps; however, high-quality evidence is not commonly described. Improved knowledge translation strategies may improve the adoption of other strategies, such as certification or lived experience co-design.

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Introduction.

Recent reviews have found mobile health (mHealth) apps to be effective in reducing symptoms of depression 1 and anxiety; 2 however, authors acknowledge the disparity between apps with research evidence and the apps currently available to – and used by – consumers. Reviews of the quality of the content within publicly available health apps 3 , 4 and specifically mental health apps 5 , 6 , 7 support this disparity, reporting that the majority of consumer-available apps are not evidence-based and can contain harmful content.

Although there is an increasing interest in accreditation processes, 8 app libraries 9 , 10 and frameworks to support clinicians in recommending mental health apps, 11 personal searches on commercial app stores operated by the major smartphone platform providers remain a common method for discovering mental health apps. 12 In this setting, marketing materials provided by developers are a principal source of information to inform consumer or clinician choice. The format of this material is standardised for commercial app stores, consisting of a written app description and, optionally, screenshots or videos of app functions.

Within this restricted context, the extent to which scientific evidence is presented as a potential marker of quality for health apps is unclear. A preliminary investigation by the authors previously reported that, for apps clinically relevant for depression, 38% of app store descriptions included wording related to claims of effectiveness, whereas only 2.6% provided evidence to substantiate such claims. 13

This study aims to extend this preliminary analysis to further understand how scientific evidence is currently used to market and sell mental health apps by (i) examining the types of claims made by mental health apps and, specifically, estimating the proportion of apps that invoke claims of effectiveness; (ii) describing the types of supporting statements used to justify claims and, specifically, estimating the proportion of apps which invoke scientific principles; and (iii) assessing the credibility of scientific principles that are used as supporting statements. Insight into methods used to present apps on commercial stores has the potential to inform government and professional efforts to establish curated libraries for health apps, as well as develop our understanding of translational gaps between mHealth research and developer practices.

Search and screening

A total of 1435 apps were identified through searches of the app stores (see Table 1 ). Three hundred and fifty apps were screened for eligibility – representing the top 40 ranked apps in each search, except where fewer iOS apps were returned for schizophrenia, self-harm and substance use. Inter-rater reliability for the binary choice to include or exclude each app was measured using Cohen’s kappa at 0.78, suggesting moderate agreement. Following screening for eligibility and removal of duplicates across search terms and platforms, 76 platform-independent apps were retained for coding. During the coding process, an additional three apps were identified as being targeted at clinicians or health professionals; excluding these apps resulted in 73 apps being retained for full coding.

App functionality

The majority of apps (59/73, 81%) described a single mental health-related functionality; fewer apps described two (8/73, 11%) or three (3/73, 4.1%) discrete functions. Three apps did not clearly describe any specific functionality (3/73, 4.1%). The types of functionality described by the apps are summarised in Table 2 .

Claims and disclaimers

Just over four-fifths of apps (§3, 59/73, 81%) made a positive claim in their online app store description, including claims related to effectiveness (§3.a, 47/73, 64%) or acceptability (§3b, 33/73, 45%) – see Table 3 . Twenty-one of these apps claimed both effectiveness and acceptability. The most common form of effectiveness claim was related to improvements in knowledge or skills to support self-management (§3.a.iii, 26/73, 36%), closely followed by improvements in symptoms or mood (§3.a.ii, 22/73, 30%), with fewer apps claiming the ability to diagnose or detect a mental health condition (§3.a.i, 7/73, 10%). A subset of eight apps (8/73, 11%) claimed both improvements in self-management and symptoms. Just under one-third of apps (§5, 22/73, 30%) included some form of disclaimer – either a medical disclaimer (§5.a, 20/73, 27%) or less commonly a legal disclaimer (§5.b, 8/73, 11%).

Supporting statements

Forty-seven apps (§4, 47/73, 64%) also provided some form of statement supporting use of the app (although this is the same number as provided claims of effectiveness, this represents a different, but overlapping, set of apps). The most common form of support was the use of scientific language (§4.a, 32/73, 44%), although eight of these apps used general terms (e.g. “evidence-based treatment”); specific scientific methods or techniques were identified for 24 apps (§4.a.i, 24/73, 33%) – full details of the annotated techniques are described later. Notably only two apps (§4.a.ii, 2/73, 2.7%) described direct evidence associated with the app (a description of a pilot study reducing symptoms of anxiety and depression, and data indicating users regularly report feeling better after using the app), and only one app (§4.a.iii, 1/73, 1.4%) provided citation details to scientific literature (a validation paper associated with a self-report questionnaire). A post-hoc analysis identified that five apps (5/73, 6.8%) mentioned research or clinical trials underway.

The second most common type of support was the description of technical expertise (§4.b, 23/73, 32%). This was predominantly through descriptions of the credibility of the app developer (§4.b.iii, 18/73, 25%), and less commonly through inclusion of expert endorsements (§4.b.iv, 3/73, 4.1%) or awards and prizes (§4.b.ii, 2/73, 2.7%). No apps referred to formal accreditation or certification schemes (§4.b.i).

Ten apps (§4.c, 10/73, 14%) referred to lived experience perspectives, either in their design or development process (§4.c.i, 6/73, 8.2%) or in the development team itself (§4.c.ii, 5/73, 6.8%). App descriptions invoked the “wisdom of the crowd” in just under one-fifth of cases (§4.d, 14/73, 19%), referring to download, usage, or popularity metrics (§4.d.i, 11/73, 15%), user testimonials and reviews ($4.d.ii, 8/73, 11%), or press endorsements (§4.d.iii, 6/73, 8.2%).

Effectiveness claims and their supporting statements

Apps were grouped together based on the type of effectiveness claims made, and the associated supporting statements were examined – see Fig. 1 . The largest single category was apps that did not make a claim of effectiveness ( n  = 26), of which just over half (14/26, 54%) also did not include supporting statements. However, where supporting statements were included, these were evenly distributed across the categories. The small number of apps which made claims related to diagnosis or detection of a mental health condition exclusively invoked supporting statements related to scientific language ( n  = 5/7, 71%).

Histograms showing the frequency of specific categories of supporting statements based on the type of effectiveness claim made by an app. Each app can contain multiple types of supporting statements

Approximately half of the apps included a single type of claim related to improvements in symptoms or self-monitoring. In this set of apps, scientific language and descriptions of technical expertise were invoked equally. For the set of apps that claimed improvements in both symptoms and self-management, supporting statements were predominantly related to scientific statements ( n  = 6/8, 75%) and to the exclusion of statements about lived experience involvement.

App functionality and supporting statements

Apps were also grouped together based on the functionality of the app, and the types of supporting statements invoked were examined – see Fig. 2 . The most common app functionality was to provide information or psychoeducational content, and half ( n  = 13/26, 50%) of these apps provided no supporting statements. Scientific language was frequently used in apps for treatment or therapy ( n  = 18/23, 78%) or self-assessment ( n  = 7/9, 78%). Apps involving peer-support or community support included the highest proportion of support involving technical expertise ( n  = 4/8, 50%), lived experience perspectives ( n  = 3/8, 38%) and the wisdom of the crowd ( n  = 3/8, 38%).

Histograms showing the frequency of specific categories of supporting statements based on the app functionality. Each app can contain multiple functionalities, and multiple types of supporting statements

Evidence search

From the descriptions of the 24 apps which mentioned a specific scientific technique, 11 unique conditions (§1) and 38 unique methods (§3.a.i) were identified, resulting in 49 unique literature searches being conducted – the results of which are presented in Supplementary Information 1 . The most frequent combination found was the mention of cognitive behavioural therapy in relation to depression and anxiety ( n  = 7 and n  = 6, respectively), for which positive evidence was found. 14 The second most common combination was the use of “binaural beats” in relation to depression and anxiety ( n  = 4 and n  = 3, respectively), for which no evidence could be found in the scientific literature. Other combinations described more than once were generally associated with positive evidence, including dialectical behaviour therapy for self-harm ( n  = 3), 15 the use of the Patient Health Questionnaire (PHQ-9) for the assessment of depression ( n  = 3), 16 the Generalised Anxiety Disorder (GAD-7) questionnaire for the assessment of anxiety ( n  = 2), 17 and a harm reduction approach in substance use ( n  = 2). 18 Active listening was also mentioned in reference to a range of conditions, and although no specific evidence could be identified in the literature searches, it is acknowledged that this is considered a key clinical skill. 19 From the remaining combinations of techniques and conditions which were described once, the majority were also associated with positive evidence ( n  = 20), and the remainder with unclear evidence ( n  = 9) or no found evidence ( n  = 8).

Overall, from the 49 combinations of conditions and methods, 26 (53%) were associated with positive evidence, 13 (27%) were associated with unclear evidence, and evidence could not be found for 10 (20%). Aggregating at an app-level, a third of the apps described at least one technique for which evidence could not be found (8/24, 33%).

Seventy-three mental health apps, representing the most highly ranked apps from the two major app stores, were examined in this study. Sixty-four percent of these apps made positive claims about their effectiveness, and 45% claimed acceptability. Statements supporting the use of the apps were presented through scientific descriptions (44%), technical expertise (32%), appeals to the “wisdom of the crowd” (19%), or lived experience involvement (14%). Of the scientific methods described, just over a half (53%) were associated with evidence in academic literature; of the apps describing specific scientific techniques, a third referred to techniques for which no evidence could be found (33%).

From a research perspective, it is perhaps reassuring that scientific language was the leading form of support employed by developers; however, this was present in fewer than half of the apps. Importantly, only two apps (2.7%) provided direct evidence associated with app use – results from a pilot study, and user-reported changes in mood after app use. One app description (1.3%) cited a validation paper for a self-report questionnaire. While these cases represent the best evidence provided by apps in this study, they still fall short of high-quality evidence obtained, for example, from randomised controlled trials.

Although there may be a lack of published evidence directly supporting the use of the mental health apps examined in this review, when apps described scientific techniques more broadly, in just over half the cases these techniques were associated with good evidence from the literature. This raises the hope that apps are evidence-informed, if not necessarily evidence-based. Caution, however, is still required as apps claiming to deliver, for example, cognitive behavioural therapy for depression may have minimal concordance with the actual principles of CBT. 20 Furthermore, a third of apps whose descriptions included scientific techniques referred to principles that had no evidence available in the scientific literature. Together with those apps which cited principles with conflicting evidence, and those which used general scientific language without reference to specific methods, this suggests that developers are using scientific language to appeal to consumers, regardless of the accuracy of the claims. Sector engagement with app developers and consumers may help improve the reporting and understanding of the science associated with mental health apps.

These results are also important in the context of new efforts to regulate health apps. The United States Food and Drug Administration (FDA) is exploring a Software Precertification (Pre-Cert) Pilot Program that will shift regulation towards the app manufacturers themselves and rely on “monitoring real-world performance” of apps in the wild. 8 Given the variable quality of evidence identified in this study, this suggests there may be an opportunity for researchers to work with developers to identify how high-quality evidence and real-world performance data could best be captured.

Of the categories of supporting statements identified in this study, the least frequently described was the involvement of those with lived experience (14%). It is acknowledged that consumer involvement and co-design of interventions can be a key factor for their success, 21 , 22 and conversely a lack of involvement is often associated with poor uptake and engagement of digital interventions. 23 These factors highlight the potential for increased lived experience involvement in the development of mental health apps.

It was also noted that despite increasing interest in app accreditation frameworks and curated libraries, no apps described these in their app store descriptions. For the apps in this study, which already have good visibility through high search result rankings, this may reflect a lack of perceived need for such processes. It may alternatively reflect a lack of awareness of these schemes outside academic or clinical communities, or that accreditation could be used as a marker of credibility in a commercial marketplace. Regardless of the underlying reason, further knowledge translation activities appear to be warranted to increase the profile of such accreditation schemes. One such scheme attempting to identify quality apps for clinical and individual use is the American Psychiatric Association (APA) app evaluation scheme. 11 Although the APA app evaluation framework does not offer direct recommendations or marks of approval, it focuses on informed decision making and helps clinicians and individuals consider the risks and benefits of app use on a case-by-case basis. Such an approach supports selection of an app based upon the individual needs of a user, with clinician consideration of the scientific claims and evidence associated with the app. Future reviews may be warranted to examine whether references to accreditation schemes increase with the adoption of schemes such as the APA framework and the implementation of FDA pre-cert.

In the future, app stores could include standardised data fields allowing developers to provide additional details to support their apps. There has been progress towards mandating that apps include a privacy policy, and this could be extended for health apps by allowing developers to include a PubMed identifier, offering users the opportunity to click through to published articles related to the app, 13 as well as other indicators such as compliance with quality frameworks and lived experience involvement.

It is acknowledged that this study provides only a snapshot of a subset of mental health apps, and that the app stores represent a rapidly evolving ecosystem for distribution of health apps. 13 Nevertheless, these results provide a broad indication of the nature and credibility of claims associated with mental health apps. The study did not examine either the content of ancillary marketing material presented alongside app descriptions, such as screenshots or user comments. These elements were excluded partly for reasons of standardisation (as all apps include a structured textual description but may not include other elements) and partly because it was considered unlikely that either imagery or user comments would reference scientific principles, which was a key purpose of this study. Previous research indicates that while users provide a range of positive or negative ratings, there is only minimal mention of scientific quality or evidence. 24

At the outset of this study, we initially aimed to differentiate between claims related to improvements in mood and improvements in symptoms, as a means of differentiating, for example, feelings of depression vs symptoms of clinical depression. However, it became apparent that such distinctions were not clearly articulated within the app store descriptions, so these coding categories were combined.

This study included appeals to the “wisdom of the crowd” and lived experience involvement as markers of credibility which can be used to support claims made in app store descriptions. It should be noted, however, that user ratings do not necessarily correlate well with clinical utility or quality. 6 , 25

Scientific methods are reported in this study using a three-point evidence scale. More rigorous evidence evaluation schemes exist, for example, through formal systematic review, meta-analysis and the OCEBM Levels of Evidence 26 – however, such a rigorous approach was not possible here due to the number of literature searches required ( n  = 49). Nevertheless, the three-point scale incorporated existing systematic reviews, where available, to differentiate techniques for a particular mental health condition for which there is clear evidence in the literature, mixed or unclear evidence, or no evidence found. Further inspection of in-app content, by multiple stakeholders including those with lived experience and clinical expertise, would be required to obtain a complete understanding of the quality of an app. This would also include an assessment of whether the scientific principles cited are actually used within the app, and to what degree of fidelity.

This review has examined a set of markers of quality which can be derived from app store descriptions, with a particular focus on the description of scientific techniques and evidence. However, these are not the only important markers of quality. Additional factors such as usability, data privacy and security, and integration with clinical workflows and systems are also of importance, and may not be discernible from just the app store description. These domains are included in guidelines for the relaunched NHS Apps Library 27 and the APA framework, 11 amongst others, and serve as a best-practice guide in terms of app development standards and important information to be provided to allow individuals, clinicians or app library providers to make informed decisions about app adoption.

This study examined 73 of the top ranked mental health apps publicly available to map the nature of claims and the type of supporting statements employed in app descriptions presented in app stores. Scientific language was the most frequently employed strategy for supporting effectiveness claims. However, direct evidence from app-specific studies was lacking, and many apps described techniques for which there was not clear evidence in the literature. Lived experience involvement and engagement with formal accreditation processes were limited, suggesting further knowledge translation activities may be required to raise the awareness of these critical aspects of mental health app development.

Search strategy

Apps were selected for mental health conditions based upon the greatest global burden of disease. Based upon estimates of disability-adjusted life years (DALYs) provided by Vigo et al., the five greatest burdens of disease were considered to be depression, self-harm, substance use disorders (combining drug use disorders and alcohol use disorders), anxiety disorders and schizophrenia. 28 Chronic pain syndrome was not included due to the uncertainty associated with the allocation of DALYs between mental health and musculoskeletal conditions. Searches for these five conditions (“depression”, “self harm”, “substance use”, “anxiety” and “schizophrenia”) were performed on 21 November 2017. Searches for Android apps were performed on the US Google Play store website, and for iOS apps through the iTunes search application programming interface (API) set to the US store. For each search term on each platform, the app title and description were extracted (manually for Android, and programmatically for iOS) for the top 40 search results.

After extracting the search result data, the title and descriptions of each app were reviewed to assess eligibility, using the criteria in Table 4 . Apps were not screened at this stage based upon their content nor any claims of effectiveness. Apps were reviewed independently by two coders, with disagreements resolved by discussion to achieve consensus. The consensus set of the top 10 ranked apps for each search term on each platform (according to the order returned by each app store) were retained. Apps which were identified by multiple search terms or across both platforms were de-duplicated.

Identification of claims and supporting statements

Two coders annotated each app description using the coding scheme described below. Disagreements were resolved by a third coder. The broad coding categories were defined in advance, with iterative refinements to sub-categories following pilot testing with a subset of the apps. Screenshots and other materials presented on the app stores (e.g. user comments) were not reviewed and are not included in this analysis.

§1. Target condition(s) : any mood state or mental health condition identified in the description text was annotated as a target condition for the app. If no conditions were explicitly mentioned, the search term (or terms) which identified the app was used.

§2. App functionality : the described function of the app was coded as providing (i) self-assessment; (ii) symptom or mood monitoring; (iii) information or psychoeducation; (iv) therapy or treatment; or (v) peer-support or community support. Zero, one, or more functionalities could be coded.

§3. Positive claims : two broad, non-mutually exclusive, categories of positive claims were identified from the app store descriptions:

Claims of effectiveness . Specifically, text was coded as a claim if it linked the use of the app to any of the following outcomes: (i) the detection or diagnosis of a condition; (ii) improvement in symptoms or mood; or (iii) improvement in the individual’s ability to self-manage their condition (for example, through the acquisition of knowledge or skills).

Claims of acceptability , such as statements focusing on the usability or acceptability of the app, rather than the app’s impact on health and wellbeing.

§4. Supporting statements : to identify the types of statements used to support the use of the app or the claims made, the following categories were identified:

Support invoking scientific language , specifically: (i) mentions or use of a specific scientific technique, method, or principle; (ii) evidence from a study evaluating use of the app; or (iii) citations to scientific literature. Specific scientific techniques were coded and the perceived credibility or evidence associated with these methods was later evaluated (see §6 – Evidence base, below).

Support based on technical expertise , specifically: (i) any formal quality assessment framework, or certification or accreditation programmes related to the developer or app; (ii) prizes or awards for the developer or the app; (iii) the credibility of the app developer or other professionals associated with the app; or (iv) endorsements from credible or trustworthy professionals or organisations.

Support based on design informed by lived experience, specifically: (i) involvement of individuals with lived experience in the design or development of the app (including focus group feedback); or (ii) developers with lived experience.

Support based on “the wisdom of the crowd” , specifically: (i) download, usage, or popularity statistics; (ii) testimonials from users; or (iii) endorsements from the press or media.

§5 . Negative claims : within app store descriptions two types of disclaimers were identified (a) medical disclaimers, such as not being a replacement for medical care, and (b) legal disclaimers.

§6. Evidence base : coded as either: (a) positive evidence from at least one systematic review or randomised controlled trial, with consensus amongst the reviewers; (b) unclear evidence, where some evidence was found but there was also contradictory evidence identified, concerns about the quality of the evidence, or there was not a clear consensus; or (c) no evidence found, where evidence from a systematic review or randomised controlled trial could not be found. For details of the method used to identify evidence, see the Evidence search section, below.

After initial coding, each combination of identified target condition (§1) and scientific technique (§4.a.i) were enumerated. A literature search was conducted to try to establish the state of the evidence, if any, supporting the application of each technique to each identified condition. Given the large number of combinations of techniques and conditions, it was not feasible to conduct a full systematic review or meta-analysis for each. Therefore, for pragmatic reasons, searches were conducted using the MEDLINE, Embase, and PsycINFO databases for articles including the combination of technique and condition, limited to either systematic reviews or randomised controlled trials. Two researchers independently performed each search, and reviewed the titles and abstracts, and full-texts where necessary, to determine whether there was evidence found from at least one systematic review or randomised control trial to support the application of a method for a specific condition. Coding disagreements were resolved by a third reviewer. As this may result in a permissive stance, where a positive single randomised controlled trial could be coded as evidence supporting a technique, the resolved coding decisions were then reviewed by an additional two expert coders to identify any relevant literature supporting, or contradicting, the coding. Evidence was summarised using the three-point coding scale described previously in the coding schema.

Data analysis

Descriptive statistics were used to summarise the results of coding. Sub-group analyses were performed to examine the types of supporting statements invoked for different categories of effectiveness claims, and for different app functionalities.

Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information files.

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Mark Erik Larsen, Kit Huckvale, Jennifer Nicholas, Emily Li & Bill Reda

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Contributions

M.E.L., K.H., and J.N. planned the study, developed the coding framework and reviewed the app descriptions. B.R. conducted the searches and extracted the app descriptions. M.E.L., K.H., J.N., J.T., L.B. and E.L. conducted literature review searches, and contributed to the development of the manuscript. M.E.L. acts as the guarantor for this manuscript.

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Larsen, M.E., Huckvale, K., Nicholas, J. et al. Using science to sell apps: Evaluation of mental health app store quality claims. npj Digit. Med. 2 , 18 (2019). https://doi.org/10.1038/s41746-019-0093-1

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Meditation: a simple, fast way to reduce stress.

Meditation can wipe away the day's stress, bringing with it inner peace. See how you can easily learn to practice meditation whenever you need it most.

If stress has you anxious, tense and worried, you might try meditation. Spending even a few minutes in meditation can help restore your calm and inner peace.

Anyone can practice meditation. It's simple and doesn't cost much. And you don't need any special equipment.

You can practice meditation wherever you are. You can meditate when you're out for a walk, riding the bus, waiting at the doctor's office or even in the middle of a business meeting.

Understanding meditation

Meditation has been around for thousands of years. Early meditation was meant to help deepen understanding of the sacred and mystical forces of life. These days, meditation is most often used to relax and lower stress.

Meditation is a type of mind-body complementary medicine. Meditation can help you relax deeply and calm your mind.

During meditation, you focus on one thing. You get rid of the stream of thoughts that may be crowding your mind and causing stress. This process can lead to better physical and emotional well-being.

Benefits of meditation

Meditation can give you a sense of calm, peace and balance that can benefit your emotional well-being and your overall health. You also can use it to relax and cope with stress by focusing on something that calms you. Meditation can help you learn to stay centered and keep inner peace.

These benefits don't end when your meditation session ends. Meditation can help take you more calmly through your day. And meditation may help you manage symptoms of some medical conditions.

Meditation and emotional and physical well-being

When you meditate, you may clear away the information overload that builds up every day and contributes to your stress.

The emotional and physical benefits of meditation can include:

• Giving you a new way to look at things that cause stress.
• Building skills to manage your stress.
• Making you more self-aware.
• Focusing on the present.
• Reducing negative feelings.
• Helping you be more creative.
• Helping you be more patient.
• Lowering resting heart rate.
• Lowering resting blood pressure.
• Helping you sleep better.

Meditation and illness

Meditation also might help if you have a medical condition. This is most often true if you have a condition that stress makes worse.

A lot of research shows that meditation is good for health. But some experts believe there's not enough research to prove that meditation helps.

With that in mind, some research suggests that meditation may help people manage symptoms of conditions such as:

• Chronic pain.
• Depression.
• Heart disease.
• High blood pressure.
• Irritable bowel syndrome.
• Sleep problems.
• Tension headaches.

Be sure to talk to your healthcare professional about the pros and cons of using meditation if you have any of these or other health conditions. Sometimes, meditation might worsen symptoms linked to some mental health conditions.

Meditation doesn't replace medical treatment. But it may help to add it to other treatments.

Types of meditation

Meditation is an umbrella term for the many ways to get to a relaxed state. There are many types of meditation and ways to relax that use parts of meditation. All share the same goal of gaining inner peace.

Ways to meditate can include:

Guided meditation. This is sometimes called guided imagery or visualization. With this method of meditation, you form mental images of places or things that help you relax.

You try to use as many senses as you can. These include things you can smell, see, hear and feel. You may be led through this process by a guide or teacher.

• Mantra meditation. In this type of meditation, you repeat a calming word, thought or phrase to keep out unwanted thoughts.

Mindfulness meditation. This type of meditation is based on being mindful. This means being more aware of the present.

In mindfulness meditation, you focus on one thing, such as the flow of your breath. You can notice your thoughts and feelings. But let them pass without judging them.

• Qigong. This practice most often combines meditation, relaxation, movement and breathing exercises to restore and maintain balance. Qigong (CHEE-gung) is part of Chinese medicine.
• Tai chi. This is a form of gentle Chinese martial arts training. In tai chi (TIE-CHEE), you do a series of postures or movements in a slow, graceful way. And you do deep breathing with the movements.
• Yoga. You do a series of postures with controlled breathing. This helps give you a more flexible body and a calm mind. To do the poses, you need to balance and focus. That helps you to focus less on your busy day and more on the moment.

Parts of meditation

Each type of meditation may include certain features to help you meditate. These may vary depending on whose guidance you follow or who's teaching a class. Some of the most common features in meditation include:

Focused attention. Focusing your attention is one of the most important elements of meditation.

Focusing your attention is what helps free your mind from the many things that cause stress and worry. You can focus your attention on things such as a certain object, an image, a mantra or even your breathing.

• Relaxed breathing. This technique involves deep, even-paced breathing using the muscle between your chest and your belly, called the diaphragm muscle, to expand your lungs. The purpose is to slow your breathing, take in more oxygen, and reduce the use of shoulder, neck and upper chest muscles while breathing so that you breathe better.

A quiet setting. If you're a beginner, meditation may be easier if you're in a quiet spot. Aim to have fewer things that can distract you, including no television, computers or cellphones.

As you get more skilled at meditation, you may be able to do it anywhere. This includes high-stress places, such as a traffic jam, a stressful work meeting or a long line at the grocery store. This is when you can get the most out of meditation.

• A comfortable position. You can practice meditation whether you're sitting, lying down, walking, or in other positions or activities. Just try to be comfortable so that you can get the most out of your meditation. Aim to keep good posture during meditation.
• Open attitude. Let thoughts pass through your mind without judging them.

Everyday ways to practice meditation

Don't let the thought of meditating the "right" way add to your stress. If you choose to, you can attend special meditation centers or group classes led by trained instructors. But you also can practice meditation easily on your own. There are apps to use too.

And you can make meditation as formal or informal as you like. Some people build meditation into their daily routine. For example, they may start and end each day with an hour of meditation. But all you really need is a few minutes a day for meditation.

Here are some ways you can practice meditation on your own, whenever you choose:

Breathe deeply. This is good for beginners because breathing is a natural function.

Focus all your attention on your breathing. Feel your breath and listen to it as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your mind wanders, gently return your focus to your breathing.

Scan your body. When using this technique, focus attention on each part of your body. Become aware of how your body feels. That might be pain, tension, warmth or relaxation.

Mix body scanning with breathing exercises and think about breathing heat or relaxation into and out of the parts of your body.

• Repeat a mantra. You can create your own mantra. It can be religious or not. Examples of religious mantras include the Jesus Prayer in the Christian tradition, the holy name of God in Judaism, or the om mantra of Hinduism, Buddhism and other Eastern religions.

Walk and meditate. Meditating while walking is a good and healthy way to relax. You can use this technique anywhere you're walking, such as in a forest, on a city sidewalk or at the mall.

When you use this method, slow your walking pace so that you can focus on each movement of your legs or feet. Don't focus on where you're going. Focus on your legs and feet. Repeat action words in your mind such as "lifting," "moving" and "placing" as you lift each foot, move your leg forward and place your foot on the ground. Focus on the sights, sounds and smells around you.

Pray. Prayer is the best known and most widely used type of meditation. Spoken and written prayers are found in most faith traditions.

You can pray using your own words or read prayers written by others. Check the self-help section of your local bookstore for examples. Talk with your rabbi, priest, pastor or other spiritual leader about possible resources.

Read and reflect. Many people report that they benefit from reading poems or sacred texts and taking a few moments to think about their meaning.

You also can listen to sacred music, spoken words, or any music that relaxes or inspires you. You may want to write your thoughts in a journal or discuss them with a friend or spiritual leader.

• Focus your love and kindness. In this type of meditation, you think of others with feelings of love, compassion and kindness. This can help increase how connected you feel to others.

Building your meditation skills

Don't judge how you meditate. That can increase your stress. Meditation takes practice.

It's common for your mind to wander during meditation, no matter how long you've been practicing meditation. If you're meditating to calm your mind and your mind wanders, slowly return to what you're focusing on.

Try out ways to meditate to find out what types of meditation work best for you and what you enjoy doing. Adapt meditation to your needs as you go. Remember, there's no right way or wrong way to meditate. What matters is that meditation helps you reduce your stress and feel better overall.

Related information

• Relaxation techniques: Try these steps to lower stress - Related information Relaxation techniques: Try these steps to lower stress
• Stress relievers: Tips to tame stress - Related information Stress relievers: Tips to tame stress
• Video: Need to relax? Take a break for meditation - Related information Video: Need to relax? Take a break for meditation

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• Mindfulness meditation: A research-proven way to reduce stress. American Psychological Association. https://www.apa.org/topics/mindfulness/meditation. Accessed Dec. 23, 2021.
• AskMayoExpert. Meditation. Mayo Clinic. 2021.
• Papadakis MA, et al., eds. Meditation. In: Current Medical Diagnosis & Treatment 2022. 61st ed. McGraw Hill; 2022. https://accessmedicine.mhmedical.com. Accessed Dec. 23, 2021.
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• Seaward BL. Managing Stress: Principles and Strategies for Health and Well-Being. 9th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.

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• Open access
• Published: 14 May 2024

Prevalence of depression and associated symptoms among patients attending primary healthcare facilities: a cross-sectional study in Nepal

• Nagendra P. Luitel 1 , 2 , 3 ,
• Bishnu Lamichhane 2 ,
• Pooja Pokhrel 2 ,
• Rudrayani Upadhyay 2 ,
• Tatiana Taylor Salisbury 4 ,
• Makhmud Akerke 4 ,
• Kamal Gautam 2 , 3 ,
• Mark J. D. Jordans 2 , 4 ,
• Graham Thornicroft 4 , 5 &
• Brandon A. Kohrt 2 , 3  

BMC Psychiatry volume  24 , Article number:  356 ( 2024 ) Cite this article

Metrics details

Depression is a prevalent mental health condition worldwide but there is limited data on its presentation and associated symptoms in primary care settings in low- and middle-income countries like Nepal. This study aims to assess the prevalence of depression, its hallmark and other associated symptoms that meet the Diagnostic and Statistical Manual (DSM-5) criteria in primary healthcare facilities in Nepal. The collected information will be used to determine the content of a mobile app-based clinical guidelines for better detection and management of depression in primary care.

A total of 1,897 adult patients aged 18–91 (63.1% women) attending ten primary healthcare facilities in Jhapa, a district in eastern Nepal, were recruited for the study between August 2, 2021, and March 25, 2022. Trained research assistants conducted face-to-face interviews in private spaces before the consultation with healthcare providers. Depression symptoms, including hallmark symptoms, was assessed using the validated Nepali version of the Patient Health Questionnaire (PHQ-9).

One in seven (14.5%) individuals attending primary health care facilities in Jhapa met the threshold for depression based on a validated cut-off score ( > = 10) on the PHQ-9. The most commonly reported depressive symptoms were loss of energy and sleep difficulties. Approximately 25.4% of women and 18.9% of men endorsed at least one of the two hallmark symptoms on the PHQ-9. Using a DSM-5 algorithm (at least one hallmark symptom and five or more total symptoms) to score the PHQ-9, 6.3% of women and 4.3% of men met the criteria for depression. The intra-class correlation coefficient for PHQ-9 total scores by health facility as the unit of clustering was 0.01 (95% confidence interval, 0.00-0.04).

Depression symptoms are common among people attending primary healthcare facilities in Nepal. However, the most common symptoms are not the two hallmark criteria. Use of total scores on a screening tool such as the PHQ-9 risks overestimating the prevalence and generating false positive diagnoses. Compared to using cut off scores on screening tools, training health workers to first screen for hallmark criteria may increase the accuracy of identification and lead to better allocation of treatment resources.

Peer Review reports

Introduction

Depression is a significant global health issue, particularly in low- and middle-income countries (LMICs) where the majority of people with depression live [ 1 ]. However, it often goes unnoticed in these countries. To address this, the task-sharing approach has been proposed [ 2 ], which involves training non-specialist healthcare providers to deliver mental health interventions in community settings. The World Health Organization (WHO) has developed the mental health gap action program (mhGAP) and implementation guide [ 3 ] to support this approach, which has been successfully implemented in over 90 countries [ 4 ]. Despite this, the detection rate of mental disorders by trained primary healthcare providers remains low, both in LMICs [ 5 ] and high-income countries [ 6 ].

In Nepal, only 24% of depression cases were detected by trained primary healthcare workers immediately after mhGAP-based training [ 7 ]. This raises concerns about the effectiveness of integrating mental health services into primary healthcare systems, especially considering that depression is a common condition in primary care [ 8 ]. To improve detection rates, routine screening for depression in primary care has been shown to be effective [ 9 ]. Various screening tools, such as the Patient Health Questionnaires (PHQ-9, PHQ-2) and the WHO Well-Being Index (WHO-5), have been recommended for use in primary care. However, their sensitivity in cross-cultural settings has not been widely evaluated [ 10 ]. Additionally, using PHQ-9 as a universal screener may not be feasible in LMICs due to limited resources. Using a mobile app-based clinical guide could be a potential strategy to enhance the detection of depression in primary care. Moreover, the severity of the individual item of PHQ-9 could help to determine the content of the mobile application because the DSM-PHQ algorithm closely aligns with the functionality of the app.

The purpose of this paper is to examine the prevalence of depression, its hallmark symptoms (depressed mood and anhedonia), and other related symptoms (e.g., fatigue, worthlessness, sleep disturbances) that meet Diagnostic and Statistical Manual (DSM-5) criteria in primary healthcare facilities in Nepal. The paper also seeks to identify factors associated with depression in order to estimate the target population in need of clinical services. Furthermore, the paper will investigate the most frequently reported symptoms of depression to inform the development of a mobile app-based clinical guideline for improved detection and management of depression in primary care.

This study was conducted as part of the Emilia (E-mhGAP Intervention guide in Low and middle-income countries: proof-of-concept for Impact and Acceptability) project, funded by UK Medical Research Council. The project aims to develop and test the feasibility and acceptability of a mobile-app-based clinical guide to improve the detection of depression in primary care [ 11 ]. The mobile app provides healthcare providers with the necessary information to assess, treat, and follow- up with individuals with depression. It follows the same protocol and decision trees as the paper version of the WHO mhGAP-IG V2 [ 12 ].

This study was a population-based cross-sectional health facility survey conducted prior to training primary health care workers in mobile app-based clinical guidelines. It was conducted between August 2, 2021 and March 25, 2022 in Jhapa, a district in eastern Nepal. The total population of Jhapa district is 998,054, with females accounting for more than half (52.1%) [ 13 ]. Nepal is one of the poorest countries in South-Asia, ranking 143rd out of 191 countries on the United Nations’ Human Development Index [ 14 ]. The country has a total population of approximately 29.1 million with 6, 666, 937 households.

In Nepal, Community Health Units (CHUs), Basic Health Service Centers (BHCs) in rural areas and Urban Health Centers (UHCs) in urban areas serve as the initial point of contact for basic health services. Health Posts (HPs) are the next level in the health care system. The third tier of health care consists of Primary Health Care Centers (PHCCs), which are higher- level facilities established in each electoral area as the first referral point. The municipal and district hospital are the highest-level healthcare institution within a district. The District Public Health Office (DPHO) or District Health Office (DHO) is responsible for coordinating health care activities in a specific district area [ 15 ]. There are 6 hospitals, 4 PHCCs, 42 HPs, 5 CHUs, 18 UHCs, 61 BHSCs in Jhapa district [ 16 ]. The study was conducted in two municipal hospitals, three PHCCs and five HPs. These health facilities offer primary healthcare services under the local government’s control. These health facilities were selected based on factors such as patient flow, accessibility, reasonable travel distance and availability of internet connectivity and electricity supply.

Sample size and sampling

The study was conducted with randomly selected adults who attended primary health care facilities during the data collection period. The sample size was determined to allow the detection of change in diagnosis of depression in the primary health facilities between the baseline and subsequent follow-up studies. The sample size was determined based on previous data regarding primary care service utilization and depression screening rates [ 7 ]. We aim to screen approximately 50% of adult patients in primary care, with the potential to increase this percentage depending on patient flow. Our plan is to screen around 200 patients per arm per month, totaling 400 patients in the 1-month pre-training enrollment period and 1200 patients per country in the 3-month post-training enrollment period. This sample size will allow us to detect a 43% increase in the clinical case identification rate within each arm using the e-mhGAP-IG, with 90% power at a 5% significance level, assuming an intra-class correlation coefficient of 0.02 [ 11 ].

The inclusion criteria for participation in the study were: 18 years of age or above, fluent in Nepali language, time and availability to complete full survey which was administered orally by research assistants, and willingness to provide informed consent. Those who were incapable of providing informed consent because of an acute medical cause were excluded from the study.

We invited all eligible individuals at the health facility to participate in the study. The inclusion criteria for participation were being 18 years or older, fluent in Nepali, residents of selected municipalities/rural municipalities, and able to provide informed consent. We interviewed all eligible adults who entered into the health facilities and randomly selected one participant when multiple individuals were present simultaneously. Field research assistants created a list of eligible participants upon entering the clinic and then randomly selected a participant by drawing a name from the list using a piece of paper. Interviews were conducted with the selected participant before their consultation. Due to low client flow caused by COVID-19 restrictions, with only one participant visiting at a time, most participants were recruited individually without the need for randomization. Exclusion criteria included the inability to provide informed consent or currently experiencing an acute medical issue. Field research assistants conducted interviews with the consenting participants while they were waiting for health-care services.

Instruments

The nine-item Patient Health Questionnaire (PHQ-9), a widely used tool for assessing depression, was used to assess depression [ 17 ]. Participants score nine common symptoms of depression based on their experience over the previous 2 weeks. It has a 4-point rating scale that ranges from 0 ‘not at all’ to 3 ‘always’. The first two items are the depression hallmark symptoms (depressed mood and anhedonia). At least one of these symptoms is required according to the DSM-5 to make a diagnosis of major depressive episode. The remaining seven items on the PHQ-9 are associated symptoms (e.g., fatigue, worthlessness, sleep disturbances). To meet DSM-5 criteria on the PHQ-9, at least one hallmark symptoms is required and 5 of the 9 total symptoms are required. The PHQ-9 has been culturally adapted, translated, and validated in Nepal [ 18 ]. The validation study determined that sum score cutoff of ≥ 10 had sensitivity = 0.94, specificity = 0.80, positive predictive value (PPV) = 0.42, negative predictive value (NPV) = 0.99, positive likelihood ratio = 4.62 and negative likelihood ratio = 0.07 when compared with a diagnosis of depression made using the Composite International Diagnostic Interview (CIDI) [ 18 ].

Data collection

A two-and-a-half-week training was provided to nine field research assistants for data collection. The training focused on the basics of structured interviewing, study population, sample size and sampling procedure. The training also focused on instruments, scoring, referral system and inclusion/exclusion criteria. Various pre-tests and mock interviews were conducted during the training period to assess the confidence level of the research assistants and whether the instruments correctly measured the symptoms of depression and impact in daily functioning. The research assistants visited each health facility, gauged inclusion/exclusion criteria, obtained written informed consent, and conducted the interviews in a confidential space, either in a spare room within the health facilities or an open ground. Android tablet with a questionnaire application was used for data collection.

Data was collected using an Android tablet with a system in place to minimize missing data and outliers. As a result, there were no missing data points in the dataset. Descriptive statistics were used to report on the socio-demographic characteristics such as age, sex, education, caste/ethnicity, occupation, marital status, religion, number of family members in the household and sufficiency of foods. We presented percentages of the patients who met threshold level for depression based on the Nepali validated cut-off score of PHQ-9 [ 18 ], DSM hallmark symptoms (depressed mood or anhedonia) on the PHQ-9 and DSM algorithm. We tested associations between depression with pre-defined risk factors such as age, sex, education, occupation, caste/ethnicity, marital status, number of family members in the household and food sufficiency in the family. We performed bi-variate and multivariate logistic regression to assess the association between depression and socio-demographic and economic characteristics of the participants. The statistical analysis was performed using the Statistical Package for Social Science IBM SPSS-28 [ 19 ].

In total, 1,914 people were approached for participation in the study. 1,897 participants consented to participate and completed the assessments. The majority were female (63.1%). The age of the participants ranged from 18 to 91 years with a mean age of 48.8 years. Most of the participants were between the age of 25 to 59 (58.3%), having secondary or higher level of education (29.5%), currently married (79.6%), and were Brahman/Chhetri (60.8%).

Table  1 shows that the prevalence of depression was higher among female (16.5%), illiterate (17.1%), unemployed (22.6%) and widow/widower/separated (24.5%) participants, as well as those from Janajati (ethnic minority groups, 18.2%); and smaller household size (participants having 1 to 4 members in the family (17.6%).

Prevalence of depression

Figure  1 presents the percentage of participants who met threshold for depression based on the locally validated PHQ-9 cut-off sum score, DSM major depressive disorder (MDD) hallmark symptoms and DSM MDD criteria (anhedonia symptoms). The result shows that 14.5% of the participants met threshold for depression based on the PHQ-9 cut-of scores. Hallmark symptoms of depression (depressed mood or anhedonia) were reported by 25.4% of women and 18.9% of men. The prevalence of depression was higher among women in all three measurements i.e. PHQ-9 cut-off (16.5%), hallmark symptoms (25.4%) and DSM-algorithm scoring of PHQ-9 (6.3%).

Table  2 presents item analysis of each PHQ-9 item and DSM hallmark symptoms for male and female participants. The most commonly experienced symptoms (most of the time or always) of depression reported by both male and female patients were little energy (female, 34.1%, and male, 29.3%), sleep difficulties (female, 20.7% and male, 16.6%), and little interest or pleasure/anhedonia (female, 15.1%, and male, 11.7%). These symptoms were significantly more frequent among females. Similarly, DSM hallmark symptoms were also frequent among female patients.

Table  3 presents the variables associated with depression in bivariate and multivariate logistic regression models. The prevalence rate of depression varied based on sex, level of education, caste/ethnicity, marital status and number of family members in the household in the bivariate model. Level of education lost its significance level in the multivariate model. Females (OR 1.65) and people from Janajati ethnic minority groups (OR 1.48) had significantly higher risk of depression compared to males and Brahman/Chhetri, respectively. On the other hand, participants who were married (OR 0.57), had 5 to 7 members in the family (OR 0.70) or had more than 7 members in the family (OR 0.54) had a reduced risk for depression (Table  3 ).

Intra-class correlation

The intra-class correlation coefficient (ICC) for the PHQ-9 was calculated with the health facility as the unit of clustering. The ICC was calculated to inform sample size calculations for determining the number of health facilities and number of participants for evaluating the effectiveness of the e-mhGAP app in a future fully-powered trial. The ICC for PHQ-9 total scores across the ten health facilities with the participants collected at baseline (n=537) was 0.01 (95% CI, 0.00-0.04). 

The results of this study indicate that one in seven people attending primary health care facilities in Jhapa met threshold for depression when using a total sum approach with all items of the PHQ-9 based on a locally validated cut-off. The prevalence of depression using sum scores was significantly higher among females compared to males. The most commonly reported symptoms of depression were low energy, sleep difficulties and lack of interest or pleasure. There was a significant difference in the reported symptoms of depression between males and females with females reporting depressive symptoms more frequently. At least one DSM-5 hallmark symptom (depressed mood or anhedonia) was reported by one out of four women and one out of five men. When using the DSM-5 algorithm for scoring the PHQ-9, the prevalence of depression was approximately one out of 20 patients in primary care. This raises a concern that using a total sum score on a screening tool to make a diagnosis could lead to a three-fold overestimation of the prevalence of depression in primary care.

The prevalence rate of depression reported in our study (14.5%) is consistent with or slightly higher than the rates reported in a recent systematic review of studies conducted with patients in primary care settings in low- and middle-income countries using the same PHQ-9 cut-off score [ 5 ]. However the prevalence rate in our study is much lower than the prevalence reported among people attending primary healthcare services in Saudi Arabia [ 20 ], Malawi [ 21 ], India [ 22 , 23 ], Nigeria [ 24 ] and Sri Lanka [ 25 ], all of which use a PHQ-9 sum score approach.

The prevalence of depression in our study is comparable to the prevalence of depression identified among people attending primary care [ 26 ] and general adults in Chitwan, Nepal [ 27 ]. However, it is much lower than the prevalence reported among populations in Nepal affected by natural disasters [ 28 ] and conflict [ 29 , 30 , 31 ]. Similarly, the prevalence rate reported in our study is slightly lower or comparable to the prevalence rate reported among a nationally representative sample of the adult population in Nepal [ 32 ]. However, our prevalence rate is higher than the prevalence of depression reported in the national mental health survey in Nepal, which was only 2.9%; this national prevalence study used the Mini International Neuropsychiatric Interview (MINI) which was not culturally adapted or clinically validated in Nepal [ 33 ]. The discrepancy in reported prevalence rates of depression between our study and the national mental health survey may be attributed to the use of a non-validated tool in the national survey and the study setting differences. Our study is facility-based, whereas the national mental health survey is community-based. Additionally, factors such as sample size, sampling strategies, and cultural sensitivity of the instruments used to assess depression may have contributed to the wide variation in the prevalence of depression in Nepal [ 34 ].

There were no significant associations between age, occupation, religion and food sufficiency in the family and the prevalence of depression. The Janajati caste/ethnic group had a significantly higher prevalence of depression compared to Brahman/Chhetri. Similarly, married participants and those with more than five members in the family had a lower prevalence of depression. Female participants (16.5%) had a significantly greater risk of depression than males (11.1%) which is consistent with previous studies conducted with the general population [ 29 , 30 , 35 ] and the population seeking care from primary healthcare facilities in Nepal [ 26 ]. The higher prevalence of depression among females could be due to the nature and amount of work females perform. In Nepal, males often do not involve themselves in domestic work while women are expected to look after the family and perform household chores even if they are employed [ 36 ]. Our results are consistent with studies conducted among primary healthcare attendees in Delhi and Haryana, India [ 22 , 23 ], Nigeria [ 24 ] and Sri Lanka [ 37 ].

Other factors associated with depression were the number of family members, marital status and caste/ethnicity. Married participants had a lower risk for depression which is consistent with a previous study conducted in Chitwan, Tanahu and Dang [ 29 ]. Our findings are consistent with the study in Saudi Arabia [ 20 ]. There was no significant association between depression and age, occupation and religion of the participants which is consistent with the study conducted among the help-seeking population in Chitwan [ 26 ].

The results of this study have several implications for improving the detection and management of depression in primary healthcare facilities in Nepal.

First, the results of this study can be used as baseline data for evaluating the services provided by trained primary health care workers. Similarly, the intra-class correlation coefficient reported in this study can be used to estimate the sample size (i.e., number of health facilities, number of patients) for future randomized controlled trial to evaluate the effectiveness of mobile app-based clinical guides.

Second, the results show that some symptoms of depression included in the PHQ-9 are highly prevalent among participants, and there was a significant difference in reporting those symptoms between males and females. If the mobile app-based clinical guide includes the commonly reported symptoms, this could help to increase patient engagement, overall detection, and the accuracy of detection of depression across all primary healthcare facilities in Nepal. The mobile app should also ensure that primary care workers screen for the hallmark symptoms to avoid over-diagnosis of depression.

Third, prior evidence shows that people with depression are more likely to contact primary healthcare workers rather than mental health specialists [ 8 ]. The low detection rate of depression by the trained primary healthcare workers in Nepal could be because of the words used by the healthcare workers during consultations. In our previous study, we found a significant increase in the prevalence of depression after changing the wording in the consent form (i.e. using heart-mind problems instead of mental health problems or mental illness) [ 38 ]. The idioms related to mental illness (manasik rog or manasik samasya) are understood as problems associated with the brain-mind, and are often perceived as incurable. Therefore, individuals may be less likely to endorse symptoms out of fear of stigma. On the other hand, the idioms related to the heart-mind (man ko samasya) are understood as something that can be healed and are generally socially acceptable to discuss [ 39 ]. Detection of depression might be increased if more culturally acceptable idioms are included in the mobile application.

Finally, the results of this study can be helpful to policy makers responsible for planning and implementing mental health services in primary care. The prevalence rate reported in this study can be used to allocate resources for training and supervision of healthcare workers and procurement of psychotropic medications in different municipalities.

There are several limitations to our study that should be acknowledged. First, the study was conducted in 10 primary healthcare facilities in Jhapa district with high patient flow; therefore, the results may not be generalizable to the entire population of Nepal. Second, the PHQ-9 which was used to screen patients for depression, has been found to have a high rate of false positives (6 false positives for every 10 patients screening positive for depression) [ 18 ]. Therefore, the prevalence rate reported in our study may be higher than the actual prevalence in the population. To minimize false positive cases, it is recommended to use tiered algorithms and provide regular clinical supervision to trained primary healthcare workers [ 18 ]. Third, our study was conducted during the COVID-19 pandemic, which may have influenced the prevalence of depression. Finally, we relied on self-report measures which may have increased the likelihood of bias. Self-report measures have been shown to predict inflated rates of mental health problems [ 34 ].

Depression symptoms are common among people attending primary healthcare facilities in Nepal. However, the most common symptoms do not always align with the two hallmark criteria. Relying solely on total scores from screening tools like the PHQ-9 may lead to an overestimation of prevalence and false positive diagnoses. Training health workers to first screen for hallmark criteria could improve accuracy and help allocate treatment resources more effectively. Additionally, enhancing the capacity of healthcare providers to identify and manage depression in primary healthcare facilities using a mobile app-based clinical guide may increase the detection rate of depression if the app includes the most commonly reported symptoms of depression by the participants in this study.

Data availability

Interested parties may notify the EMILIA (E-mhGAP Intervention guide in Low and middle-income countries: proof-of-concept for Impact and Acceptability) investigators of their interest in collaboration, including access to the data-set analyzed here, through the following email: [email protected].

Abbreviations

Basic Health Service Center

Community Health Unit

District health office

Diagnostic and Statistical Manual

Health Post

Low and Middle Income Countries

Major Depressive Disorder

mental health Gap Action Program

Primary Health Care Center

Patient Health Questionnaire

Urban Health Center

United Kingdom

World Health Organization

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Luitel NP. Treatment coverage, barriers to care and factors associated with help-seeking behaviour of adults with depression and alcohol use disorder in Chitwan district, Nepal. South Africa: Faculty of Health Sciences, Department of Psychiatry and Mental Health Cape Town University; 2020. http://hdl.handle.net/11427/32404 .

Kohrt BA, Harper I. Navigating diagnoses: understanding mind–body relations, Mental Health, and Stigma in Nepal. Cult Med Psychiatry. 2008;32(4):462–91.

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Acknowledgements

We want to thank Mr. Gobinda Koirala, and research assistants of TPO Nepal for their support in data collection. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. NPL is supported by National Institute for Health Research (NIHR) and Wellcome Trust under the NIHR-Wellcome Partnership for Global Health Research [grant reference 222001/Z/20/Z]. GT is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. TTS is funded by UK Research and Innovation [MR/T019662/1]. GT and TTS are also supported by the UK Medical Research Council (UKRI) for the Indigo Partnership (MR/R023697/1) awards. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence (where permitted by UKRI, ‘Open Government Licence’ or ‘Creative Commons Attribution No-derivatives (CC BY-ND) licence’ may be stated instead) to any Author Accepted Manuscript version arising.

This study is funded by the UK Medical Research Council in relation to the Emilia Project (MR/S001255/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors had full control of all primary data.

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Nagendra P. Luitel

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NPL, GT, BAK, MJD and TTS were responsible for the study design. NPL, PP and BL were responsible for supervision of data collection. NPL and BL performed data analysis. NPL, BL and RU drafted the first version of the manuscript; all authors reviewed and revised the manuscript. All authors read and approved the final manuscript.

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Luitel, N.P., Lamichhane, B., Pokhrel, P. et al. Prevalence of depression and associated symptoms among patients attending primary healthcare facilities: a cross-sectional study in Nepal. BMC Psychiatry 24 , 356 (2024). https://doi.org/10.1186/s12888-024-05794-0

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DOI : https://doi.org/10.1186/s12888-024-05794-0

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Mental Health Journaling: The Benefits of Writing for Wellness

• May 08, 2024

Home » Day One Blog » Mental Health Journaling: The Benefits of Writing for Wellness

As the benefits of mental health journaling continue to be researched, we’re finding more about how this simple practice can improve various aspects of well-being and overall quality of life. Studies have shown that engaging in regular journaling can help reduce stress, manage anxiety and depression symptoms, enhance self-awareness, promote emotional regulation, and even strengthen resilience in the face of challenges.

In this article, we’ll explore the many compelling benefits of mental health journaling. We’ll also offer some prompts and techniques for expanding your journaling practice.

Key Takeaways

• Just 15 to 20 minutes a day of journaling is enough to see the mental health benefits.
• Journaling can be a healthy way of coping with everyday stress and challenges.
• Journaling helps clarify thoughts and regulate emotions.
• Expressive writing facilitates self-reflection, personal growth, and problem-solving.

The Compelling Benefits of Mental Health Journaling

Whatever your reason to start, journaling is a powerful tool that will help you manage and maintain your mental health. 

1. Establish Healthy Coping Skills

Creating healthy coping skills is foundational to maintaining mental wellness. Coping methods are the habits that help you get through stressful or painful experiences. From working out frustrations at the gym to biting fingernails, everyone has big and small, healthy and not-so-healthy ways to cope with life’s challenges. You may not even be aware of the ways you cope with stress, but daily journaling can help you recognize the positive and negative (adaptive and maladaptive) habits you have for dealing with distress. 

Journaling itself is a healthy coping method that anyone can add to their personal toolbox. You don’t need to be a skilled writer or invest in a lot of special equipment. If you can carve out 15 to 20 minutes of quiet time each day, you can instantly start enjoying how journaling benefits mental health. 

A pen and a simple plain notebook or your fingers and a laptop are all you need to get started. Using journal prompts makes writing even easier. Journaling can become your go-to method of dealing with stressful or painful situations.

2. Reduce Stress

Journaling as  expressive writing can also relieve stress . In fact, journaling is scientifically proven to help reduce stress levels and the symptoms of anxiety.

The many  benefits of journaling  are backed by research, with some of the health benefits of expressive writing including lowered blood pressure, improved mood, and a reduction in the number of stress-related doctor’s visits.

3. Manage Anxiety and Depression Symptoms

People diagnosed with anxiety, depression, and other mental health concerns are often  encouraged to include journaling  in their self-care routine. Studies have shown that journaling can  help people manage anxiety and reduce symptoms , especially as part of a mental health treatment program.

Journaling for anxiety , in particular, can increase your self-awareness and help you recognize patterns in your behavior that might be adding to your stress level. Through journaling, you can better track patterns in your behavior that can lead to positive change. Your journal can also reveal how you’ve changed over time and which coping strategies were the most successful.

4. Release and Process Emotions

In childhood, we all learn how to handle our emotions. The first lessons in coping skills come from watching others, mainly parents or other authority figures. Unfortunately, those lessons are sometimes maladaptive. An inability to release and process emotions in a healthy way can affect your self-esteem, relationships, and mental and physical wellness. 

Journaling can help you better understand your emotions. Journaling about feelings offers an opportunity to express your emotions in a safe, private space without judgment. Instead of pushing down uncomfortable feelings or following dysfunctional family patterns because they are familiar, you can pour your thoughts and feelings onto the pages of your journal. 

Expressing emotions is the first step to understanding them better. If, like many people, you’ve been taught to avoid “negative” emotions, you may have difficulty distinguishing between anger and hurt or shame and regret. Journaling is a way to get to know your feelings without sharing them with anyone else. There’s no need to worry about what others might think or if you’re expressing yourself in the “right” way. Your journal is a private space where you can explore your feelings in a way that works for you. 

5. Identify Patterns and Triggers

Journaling benefits mental health by giving you a platform to examine patterns in your moods, reactions, behavior, and thought processes. You may learn you’re unhappy with some of the ways you react and take steps to change. 

Looking through the pages of your journal, you’ll likely recognize your personal triggers—the things that make you feel strong emotions or act out in maladaptive ways. Once you understand these patterns and triggers, you can take steps to change them or change the way you react to them. 

Reflecting on your thoughts and behaviors also encourages you to look at yourself through many different lenses. The point of self-awareness isn’t only to become aware of your more negative traits. Seeing your strengths and your successes is just as important. Recognizing your accomplishments helps you have confidence when things are difficult. 

6. Deepen Self-Awareness

Regular self-reflection is like having a deep conversation with yourself. You can learn things about yourself through journaling that you may not have realized before. If you’re like most people, many of your habits, behaviors, and ways of thinking are automatic. They are learned reactions that you perform without thinking. 

Consider the benefits of self-awareness:

• Self-reflection helps to focus your energy on a specific goal. Gaining insight can help you be more productive and let go of self-doubt. In the business of everyday life, people don’t often have the time to stop and contemplate why they think or react a certain way, especially in circumstances that are emotionally charged. One of the benefits of journaling is that it’s a way to stop, mentally relax, and take time each day to reflect on the most important things.  
• Discover your values, beliefs, and core identity. Part of self-growth is understanding how your core identity forms. Many people grow into adulthood without questioning the values they learned at home, through peer groups, or in their culture. 
• Improve problem-solving and decision-making skills . Self-doubt is an obstacle to making decisions, but journaling can help. Instead of overthinking and making a problem more complicated, writing allows you to empty your mind of confusing and conflicting thoughts. Whatever the issue, journaling is a beneficial way to organize your thoughts so you can see a challenge and its solutions with more clarity. (More on that next.)

7. Clarify Thoughts and Find Solutions

Have you ever said, “I don’t know what to think,” and really meant it? Sometimes there are so many ideas and options that it feels almost impossible to sort them out. Writing in your journal is a way to sort out a multitude of confusing thoughts and get some clarity. 

You can take an analytic approach to journaling when you need to solve a problem or make a big decision. Make a pros and cons list, or write about the possible scenarios related to a difficult decision. Once you’ve written out all the “what ifs,” you can take your time and see how you feel about the potential outcomes. 

Looking back at previous journal entries can also help you get perspective. Reviewing how you’ve handled problems in the past and what you might like to do differently this time can give you the confidence you need to make a change.

8. Strengthen Resilience in the Face of Challenges

When faced with difficult situations or setbacks, journaling provides a way to reflect on challenges and explore different perspectives. Through writing, you can gain new insights, identify patterns, and recognize your own strengths and resources. This process can enable you to reframe your experiences and find new meaning or opportunities for growth within difficult circumstances. By shifting your perspective through journaling, you can build resilience by cultivating a more positive and adaptive mindset.

For example, journaling provides a space for exploring strategies to overcome challenges. By engaging in reflective writing, you can explore different options and develop action plans. Journaling can help build resilience by fostering a sense of control, agency, and proactive engagement with your challenges.

How to Start Mental Health Journaling

Mental health journaling is often used in conjunction with therapy, especially if you are supporting your mental health needs and seeking professional guidance. A trained therapist can provide valuable insights, help you navigate difficult emotions that may arise during the journaling process, and offer personalized strategies for self-reflection and growth. Their expertise can enhance the benefits of therapeutic journaling, ensuring that you receive the necessary support and guidance to address your specific mental health concerns effectively.

However, if you’re starting on your own, here are some steps to guide you in establishing a mental health journaling practice:

1. Choose Your Favorite Way to Write

Decide whether you prefer to journal in a traditional notebook, a digital journaling app like Day One , or even an audio journal. Each medium offers unique advantages: notebooks provide a tactile experience that many find therapeutic; digital apps offer convenience and features like password protection and searchability; audio journals can be particularly helpful if you find speaking more natural than writing. Choose a format that feels most comfortable and accessible for you.

2. Ensure What You Write is Private

Ensure your journal is a private space where you can be completely honest. This confidentiality can make it easier to express thoughts and feelings you might not be ready to share with others. Consider keeping your journal in a locked drawer or using apps with strong end-to-end encryption to maintain your privacy.

3. Try a Structured Approach

If you’re unsure what to write about, consider using journal prompts tailored to your needs. Prompts such as “What am I feeling right now?” or “What did I do today that made me feel good?” can be a great starting point Having a structure can help ease you into the writing process by reducing the pressure to come up with topics on your own. For even more structure, you may want to try a journal template that leads you through the same reflective prompts or questions each time you have a journaling session.

4. Express Your Feelings

Allow yourself to express whatever comes to mind without judgment. The goal is to acknowledge your emotions and thoughts freely. Writing about your feelings can provide a safe outlet for expression and can lead to deeper insights over time. Expressive writing taps into your deepest thoughts and feelings, offering a release that can be both healing and enlightening.

5. Explore Fresh Insights

As you write, focus on uncovering new understandings about yourself and your experiences. Use your journal to explore questions that go deeper, like, “Why does this matter?” or “What can I learn from this?” Encourage yourself to dig beneath the surface of your initial thoughts and feelings, which can lead to transformative insights and personal growth.

6. Consider What Actions You Can Take

After identifying and exploring your feelings and thoughts, think about practical steps you can take to address issues or enhance your well-being. This might include setting small, manageable goals, planning changes in your behavior or environment, or identifying resources for further support such as books, websites, or professionals.

7. Be Patient and Kind with Yourself

Journaling is a personal process that can evoke strong emotions. Approach your experience with kindness and patience, understanding that growth and insight are gradual. Allow yourself the space and time to explore your thoughts and feelings without expectation for quick fixes or immediate clarity.

8. Regularly Review and Reflect to Understand Patterns

Periodically, look back on your journal entries. This can help you see patterns or changes in your thoughts and feelings over time, providing further insights into your mental health journey. This practice can also reinforce positive changes you’ve made and help you recognize areas where you may want to focus more attention moving forward.

By incorporating these steps into your journaling practice, you can create a powerful tool for managing your mental health, enhancing self-awareness, and fostering emotional resilience. Whether used alone or with professional support, mental health journaling can be a transformative and therapeutic practice.

Mental Health Journaling Prompts

The ways journaling benefits mental health are far-reaching, but sometimes staring at the blank page or screen can be intimidating. You may feel stuck when brainstorming an idea to write about or have so much to express you don’t know where to start. Journal prompts can enrich the journaling experience and help you get “unstuck.” 

Journaling prompts are questions, suggestions, or fill-in-the-blank sentences meant to inspire your journaling writing. Journaling prompts for mental health , specifically, may ask pointed questions about your emotions, challenges, or things you are doing to protect your wellness. 

Here are some prompts that can benefit your goal for better mental health:

What feelings am I struggling with right now?

What are my biggest stressors currently, what is one fear or anxiety that i currently have, what am i avoiding by staying busy or distracted, what am i holding onto that i need to let go of, what are some ways i can take better care of myself this week, how have i changed in the last year, what personal strengths have helped me overcome challenges in the past, what situation recently made me feel happy or content, what are three things i am grateful for today.

When using journaling prompts, do your best to find a balance between staying on topic and allowing your mind to take you where it needs to go. Even a prompt that seems simple on the surface can lead to deep insights if you follow your instincts while writing. 

Experience the Benefits of Mental Health Journaling

Writing is a simple but effective way to support your mental health, no matter what challenges you’re facing. Whether you use journaling to complement other forms of mental health care or as a stand-alone practice, it can add meaning to your life on many levels.

Journaling has the potential to bring clarity, healing, and a sense of agency in navigating your emotions and experiences. So, grab a pen and paper, or open the Day One app , and allow the words to flow. Embrace the therapeutic benefits of writing and witness the positive impact it can have on your mental health and overall well-being.

Download the Day One Journal App Today

The Day One journaling app makes it easy to build and maintain a daily journaling habit. Daily journaling reminders , daily writing prompts , and journal streaks are designed to help keep you motivated and consistently journaling.

About the Author

Kristen Webb Wright is the author of three books on journaling. With a passion for writing and self-reflection, Kristen uses her experience with journaling to help others discover the benefits of documenting their thoughts, feelings, and experiences. In her role at Day One, she helps to promote the power of journaling so people from all walks of life can experience the transformative power of journaling.

This content is not professional advice, diagnosis, or treatment.  You understand and agree that the services, products, and any other information you learn from Day One are not intended, designed, or implied to diagnose, prevent, or treat any condition or to be a substitute for professional medical care . Always seek the advice of your mental health professional or other qualified health provider with any questions you may have. If you are in crisis or you think you may have an emergency, call your doctor or 911 immediately. If you’re having suicidal thoughts, call 1-800-273-TALK (8255) to talk to a skilled, trained counselor at a crisis center in your area at any time (National Suicide Prevention Lifeline). If you are located outside the United States, call your local emergency line immediately.

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