Apps For Depression Research Articles

EvalDepApps: How to evaluate depression management based on evidence and end users

Abstract The use of apps is increasing in the field of mental health due to their ease of use and accessibility, although there is not enough evidence on their effectiveness and safety. EvalDepApps aims to develop an evaluation tool for depression management apps. A systematic review with meta-analysis (SRMA) was performed to evaluate the efficacy and safety of apps for depression, and to identify the evaluation criteria used. PRISMA methodology was followed. The MEDLINE, PsycINFO, and Embase databases were consulted. The risk of bias was assessed with the RoB2 scale. An online 2 rounds Delphi was carried out to prioritize the most relevant criteria identified. 44 people (26 professionals, 18 patients) were invited. They were asked to rate the importance of each criterion on a Likert scale (1 - 6). Those that obtained a high consensus were selected; those with a medium were submitted to the 2nd round. Empathization (6) and co-design (6) sessions were held with patients (23) and professionals (33) in Catalonia, the Canary Islands and Andalusia to identify what relevant aspects the tool should have. Twenty-nine studies were included in the SRMA (67% unclear bias), finding a significant effect of mHealth interventions in reducing depressive symptoms compared to non-active control (Hedges g = −0.62, 95% CI: −0.87 to − 0.37, I2 = 87%). In Round 1 of the Delphi (59% participation) 24 criteria obtained a high consensus, 20 a medium and 7 a low. In Round 2 (52% participation), 4 criteria reached high consensus. The empathy sessions showed that the actions most requested by patients were reduce anxiety, and information about their condition; for professionals, suicide prevention. Regarding co-design, it was proposed that the tool provide ranking of the apps, recommendation systems and a very visual format. The RSMA and Delphi guarantee that the tool will be based on scientific evidence and expert judgment, while empathization and co-design that it will fit with the needs of end users. Key messages • It is relevant to evaluate the quality of health apps, especially those addressed to vulnerable populations such as people with depression. • It is crucial that the development of evaluation tools for digital health interventions will be developed based on evidence-based but also includes end users.

Content, Behaviour Change Techniques, and Quality of Postpartum Depression Apps to Be Recommended by Midwives: Systematic Search and Evaluation.

Postpartum depression is a public health problem that affects a considerable percentage of women. Despite the proliferation of related apps, there are limited data available on the best apps to prevent postpartum depression. We identified which apps available in Spanish could be recommended by midwives based on their content, quality, and behaviour change techniques, as a complementary tool for preventing postpartum depression in women. A systematic search was performed to identify apps available on iOS App Store and Google Play, which were used to replicate how patients' access "postpartum depression prevention" apps. Apps' quality, behaviour change potential, and contents were assessed. A total of 1408 apps were identified, of which 7 were retrieved for assessment (0.5%). The mean objective and subject quality were 3.1 (SD = 1.01) and 2.7 (SD = 1.27), respectively. A total of 24 topics were identified. The mean ABACUS score was 6.6 (SD = 3.64), and the mean number of topics addressed by the apps was 9.9 (SD = 5.90). The results of the present study suggest that a specific free app is not available in Spanish for the prevention of postpartum depression, and only a small percentage of free apps should be recommended based on their quality, BCTs, and contents. The systematic review protocol was not registered.

Assessment of App Store Description and Privacy Policy to Explore Ethical and Safety Concerns Associated with the Use of Mental Health Apps for Depression.

Mental Health apps (MH apps) could help address the huge unmet mental health care need of developing countries. This study aimed to explore potential ethical, data safety, and privacy issues associated with using MH apps for depression. A cross-sectional assessment of the top 50 MH apps (by Google Play store search result ordering) for depression available in India was conducted in November 2021. Most apps were listed under the category of health and fitness (54%). The median number of total and dangerous permissions requested at the time of download was nine and three, respectively. Privacy policy in English was available for 76%. The average length of the privacy policy was 2171 words, and the mean Flesch-Kincaid reading grade level was 12 (much higher than the recommended cut-off of eight). Important features relevant to safeguarding consumer confidentiality, including names of third parties with which user data could be shared (42%), explicit consent before sharing data with third parties (16%), and assurance regarding the collection of de-identifiable data (11%), were missing from the majority of privacy policies. There is an urgent need to improve the accessibility and usability of privacy policies by app developers, with the active involvement of other stakeholders.

Mobile Health Applications for Depression in China: A Systematic Review.

Mobile health (mHealth) applications (apps) have the potential to increase access to mental health care. In China, there is growing interest in mHealth apps for depression. Our objective was to systematically review research on mHealth for depression in China to identify benefits and challenges.A systematic literature search was conducted using Chinese and English databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Randomized and nonrandomized clinical studies on mHealth apps and depression in China were included. Study quality was assessed using the Cochrane Risk of Bias tool.Seven studies met the inclusion criteria with three randomized trials, two quasi-randomized trials, one clinical trial with an uncertain grouping method, and one study with a single-group design. All studies used the WeChat platform and included activities such as psychoeducation, self-management, supervised group chats, and/or remote contact with a healthcare team, in comparison to usual care. All studies reported significant and large benefits for outcomes, but the risk of bias was high.There are few rigorous evaluations of mHealth apps for depression in China, with all included studies involving WeChat programs and most using WeChat to extend nursing discharge care for inpatients with depression. While these studies showed significant improvement in health outcomes as compared to usual care, the results remain inconclusive because of the high risk of bias. mHealth holds promise for increasing access to mental health care in China, but issues such as efficacy, scalability, patient and clinician acceptability, and data privacy must be addressed.

Education on Depression in Mental Health Apps: Systematic Assessment of Characteristics and Adherence to Evidence-Based Guidelines.

BackgroundSuboptimal understanding of depression and mental health disorders by the general population is an important contributor to the wide treatment gap in depression. Mental health literacy encompasses knowledge and beliefs about mental disorders and supports their recognition, management, and prevention. Besides knowledge improvement, psychoeducational interventions reduce symptoms of depression, enhance help-seeking behavior, and decrease stigma. Mental health apps often offer educational content, but the trustworthiness of the included information is unclear.ObjectiveThe aim of this study is to systematically evaluate adherence to clinical guidelines on depression of the information offered by mental health apps available in major commercial app stores.MethodsA systematic assessment of the educational content regarding depression in the apps available in the Apple App Store and Google Play was conducted in July 2020. A systematic search for apps published or updated since January 2019 was performed using 42matters. Apps meeting the inclusion criteria were downloaded and assessed using two smartphones: an iPhone 7 (iOS version 14.0.1) and a Sony XPERIA XZs (Android version 8.0.0). The 156-question assessment checklist comprised general characteristics of apps, appraisal of 38 educational topics and their adherence to evidence-based clinical guidelines, as well as technical aspects and quality assurance. The results were tabulated and reported as a narrative review, using descriptive statistics.ResultsThe app search retrieved 2218 apps, of which 58 were included in the analysis (Android apps: n=29, 50%; iOS apps: n=29, 50%). Of the 58 included apps, 37 (64%) apps offered educational content within a more comprehensive depression or mental health management app. Moreover, 21% (12/58) of apps provided non–evidence-based information. Furthermore, 88% (51/58) of apps included up to 20 of the educational topics, the common ones being listing the symptoms of depression (52/58, 90%) and available treatments (48/58, 83%), particularly psychotherapy. Depression-associated stigma was mentioned by 38% (22/58) of the apps, whereas suicide risk was mentioned by 71% (41/58), generally as an item in a list of symptoms. Of the 58 included apps, 44 (76%) highlighted the importance of help seeking, 29 (50%) emphasized the importance of involving the user’s support network. In addition, 52% (30/58) of apps referenced their content, and 17% (10/58) included advertisements.ConclusionsInformation in mental health and depression apps is often brief and incomplete, with 1 in 5 apps providing non–evidence-based information. Given the unmet needs and stigma associated with the disease, it is imperative that apps seize the opportunity to offer quality, evidence-based education or point the users to relevant resources. A multistakeholder consensus on a more stringent development and publication process for mental health apps is essential.

The Quality of Health Apps and Their Potential to Promote Behavior Change in Patients With a Chronic Condition or Multimorbidity: Systematic Search in App Store and Google Play.

BackgroundMobile apps offer an opportunity to improve the lifestyle of patients with chronic conditions or multimorbidity. However, for apps to be recommended in clinical practice, their quality and potential for promoting behavior change must be considered.ObjectiveWe aimed to investigate the quality of health apps for patients with a chronic condition or multimorbidity (defined as 2 or more chronic conditions) and their potential for promoting behavior change.MethodsWe followed the Cochrane Handbook guidelines to conduct and report this study. A systematic search of apps available in English or Danish on App Store (Apple Inc) and Google Play (Google LLC) for patients with 1 or more of the following common and disabling conditions was conducted: osteoarthritis, heart conditions (heart failure and ischemic heart disease), hypertension, type 2 diabetes mellitus, depression, and chronic obstructive pulmonary disease. For the search strategy, keywords related to these conditions were combined. One author screened the titles and content of the identified apps. Subsequently, 3 authors independently downloaded the apps onto a smartphone and assessed the quality of the apps and their potential for promoting behavior change by using the Mobile App Rating Scale (MARS; number of items: 23; score: range 0-5 [higher is better]) and the App Behavior Change Scale (ABACUS; number of items: 21; score: range 0-21 [higher is better]), respectively. We included the five highest-rated apps and the five most downloaded apps but only assessed free content for their quality and potential for promoting behavior change.ResultsWe screened 453 apps and ultimately included 60. Of the 60 apps, 35 (58%) were available in both App Store and Google Play. The overall average quality score of the apps was 3.48 (SD 0.28) on the MARS, and their overall average score for their potential to promote behavior change was 8.07 (SD 2.30) on the ABACUS. Apps for depression and apps for patients with multimorbidity tended to have higher overall MARS and ABACUS scores, respectively. The most common app features for supporting behavior change were the self-monitoring of physiological parameters (eg, blood pressure monitoring; apps: 38/60, 63%), weight and diet (apps: 25/60, 42%), or physical activity (apps: 22/60, 37%) and stress management (apps: 22/60, 37%). Only 8 out of the 60 apps (13%) were completely free.ConclusionsApps for patients with a chronic condition or multimorbidity appear to be of acceptable quality but have low to moderate potential for promoting behavior change. Our results provide a useful overview for patients and clinicians who would like to use apps for managing chronic conditions and indicate the need to improve health apps in terms of their quality and potential for promoting behavior change.

User Reviews of Depression App Features: Sentiment Analysis

BackgroundMental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotions of this population and are increasingly decisive in influencing mHealth app adoption.ObjectiveThis study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews.MethodsWe extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps.ResultsThe analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety.ConclusionsDevelopers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources.

Smartphone Psychological Therapy During COVID-19: A Study on the Effectiveness of Five Popular Mental Health Apps for Anxiety and Depression

The aims of this study were to examine the effectiveness of a range of smartphone apps for managing symptoms of anxiety and depression and to assess the utility of a single-case research design for enhancing the evidence base for this mode of treatment delivery. The study was serendipitously impacted by the COVID-19 pandemic, which allowed for effectiveness to be additionally observed in the context of significant community distress. A pilot study was initially conducted using theSuperBetter app to evaluate the proposed methodology, which proved successful with the four finishing participants. In the main study, 39 participants commenced (27 females and 12 males,MAge = 34.04 years,SD = 12.20), with 29 finishing the intervention phase and completing post-intervention measures. At 6-month follow-up, a further three participants could not be contacted. This study used a digitally enhanced, multiple baseline across-individuals single-case research design. Participants were randomly assigned to the following apps:SuperBetter (n = 8),Smiling Mind (n = 7),MoodMission (n = 8),MindShift (n = 8), andDestressify (n = 8). Symptomatology and life functioning were measured at five different time points: pre-baseline/screening, baseline, intervention, 3-week post-intervention, and 6-month follow-up. Detailed individual perceptions and subjective ratings of the apps were also obtained from participants following the study’s completion. Data were analyzed using visual inspection, time-series analysis, and methods of statistical and clinical significance. Positive results were observed for all apps. Overall, more favorable outcomes were achieved by younger participants, those concurrently undertaking psychotherapy and/or psychotropic medication, those with anxiety and mixed anxiety and depression rather than stand-alone depression, and those with a shorter history of mental illness. Outcomes were generally maintained at 6-month follow-up. It was concluded that a diverse range of evidence-based therapies offered via apps can be effective in managing mental health and improving life functioning even during times of significant global unrest and, like all psychotherapies, are influenced by client features. Additionally, this single-case research design is a low-cost/high value means of assessing the effectiveness of mental health apps.Clinical Trial Registration: The study is registered with the Australian and New Zealand Clinical Trials Registry (ANZCTR), which is a primary registry in the World Health Organization Registry Network, registration number ACTRN12619001302145p (http://www.ANZCTR.org.au/ACTRN12619001302145p.aspx).

Technology use and interest in digital apps for mental health promotion and lifestyle intervention among young adults with serious mental illness

Digital technology holds promise for reaching young adults with serious mental illness. This study seeks to characterize technology use and explore interests in digital health interventions among young adults with serious mental illness. A survey was collected from participants age 18–35 enrolled in a lifestyle intervention trial about their technology ownership and use; technology use for mental health or other health reasons; and interest in health apps. Responses from 150 participants were summarized. Differences in technology use were compared between individuals with psychotic (n = 65) and non-psychotic disorders (n = 85). Most participants owned mobile phones (92%) and used social media (95%). Smartphone ownership was higher among participants with non-psychotic (98%) compared to psychotic (84%) disorders. Many participants searched online for information about their mental health (73%) or general health (79%). More participants with non-psychotic compared to psychotic disorders expressed interest in apps for depression (71% vs. 54%) or anxiety (78% vs. 54%). Interest in apps for lifestyle, behavioral health, and other health needs was similar between diagnostic groups. These findings may not generalize to all young adults with serious mental illness. There is high access, use, and interest in technology among young adults with serious mental illness. This highlights potential for integrated digital interventions for mental and physical health in this high-risk group.

Examining the Effectiveness of Gamification in Mental Health Apps for Depression: Systematic Review and Meta-analysis.

BackgroundPrevious research showed that computerized cognitive behavioral therapy can effectively reduce depressive symptoms. Some mental health apps incorporate gamification into their app design, yet it is unclear whether features differ in their effectiveness to reduce depressive symptoms over and above mental health apps without gamification.ObjectiveThe aim of this study was to determine whether mental health apps with gamification elements differ in their effectiveness to reduce depressive symptoms when compared to those that lack these elements.MethodsA meta-analysis of studies that examined the effect of app-based therapy, including cognitive behavioral therapy, acceptance and commitment therapy, and mindfulness, on depressive symptoms was performed. A total of 5597 articles were identified via five databases. After screening, 38 studies (n=8110 participants) remained for data extraction. From these studies, 50 total comparisons between postintervention mental health app intervention groups and control groups were included in the meta-analysis.ResultsA random effects model was performed to examine the effect of mental health apps on depressive symptoms compared to controls. The number of gamification elements within the apps was included as a moderator. Results indicated a small to moderate effect size across all mental health apps in which the mental health app intervention effectively reduced depressive symptoms compared to controls (Hedges g=–0.27, 95% CI –0.36 to –0.17; P<.001). The gamification moderator was not a significant predictor of depressive symptoms (β=–0.03, SE=0.03; P=.38), demonstrating no significant difference in effectiveness between mental health apps with and without gamification features. A separate meta-regression also did not show an effect of gamification elements on intervention adherence (β=–1.93, SE=2.28; P=.40).ConclusionsThe results show that both mental health apps with and without gamification elements were effective in reducing depressive symptoms. There was no significant difference in the effectiveness of mental health apps with gamification elements on depressive symptoms or adherence. This research has important clinical implications for understanding how gamification elements influence the effectiveness of mental health apps on depressive symptoms.

Quality evaluation of stress, anxiety and depression apps for COVID-19.

BackgroundCOVID-19 has caused increased stress, anxiety and depression with increased barriers to treatment. Mobile apps offer a potential solution, but there is no information on the quality of such apps recommended for COVID-19. This study aims to evaluate the quality of stress, anxiety and depression apps recommended for COVID-19.MethodsA search was conducted to identify relevant apps on the iOS and Android platforms. 44 apps were evaluated using the Mobile App Rating Scale (MARS), and the American Psychiatric Association's app evaluation model for data privacy and security.ResultsOverall quality scores of iOS and Android apps were 3.69 ± 0.43 and 3.66 ± 0.47. Thirty percent had good/excellent overall scores. In general, the iOS and Android versions of the apps scored best for functionality (4.21 ± 0.48, 4.12 ± 0.53), followed by aesthetics (3.84 ± 0.50, 3.78 ± 0.56), information (3.39 ± 0.54, 3.40 ± 0.60), and engagement (3.31 ± 0.81, 3.34 ± 0.84). Over half (59%) shared personal information with third parties and 14% were compliant with data protection standards.LimitationsOnly free apps available in Singapore were evaluated. Our results are time sensitive due to addition, removal, and update of apps in the app stores, thus our results should be extrapolated with caution to apps from other countries and paid apps.ConclusionApps that addressed all three conditions had the highest overall quality scores. The top ranked apps (Sanvello, Woebot, Happify, Youper, Bloom) were of good quality, but majority were of acceptable quality and had room for improvement. App developers are encouraged to use our findings to improve and develop better quality apps.

Is There an App for That? A Review of Popular Apps for Depression, Anxiety, and Well-Being

Smartphone apps for mental health (MH apps) and wellness reach millions of people and have the potential to reduce the public health burden of common mental health problems. Thousands of MH apps are currently available, but real-world consumers generally gravitate toward a very small number of them. Given their widespread use and the lack of empirical data on their effects, understanding the content within MH apps is an important public health priority. An overview of the content within these apps could be an important resource for users, clinicians, researchers, and experts in digital health. Here, we offer summaries of the content within highly popular MH apps. Our aim is not to provide comprehensive coverage of the MH app space. Rather, we sought to describe a small number of highly popular MH apps in three common categories: meditation and mindfulness, journaling and self-monitoring, and AI chatbots. We downloaded the two most popular apps in each of these categories (respectively: Calm, Headspace; Reflectly, Daylio; Replika, Wysa). These six apps accounted for 83% of monthly active users of MH apps. For each app, we summarize information in four domains: intervention content, features that may contribute to engagement, the app’s target audience, and differences between the app’s free version and its premium version. In the years ahead, rigorous evaluations of highly popular MH apps will be needed. Until then, we hope that this overview helps readers stay up-to-date on the content within some of the most widely used digital mental health interventions.

Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.

BackgroundThe number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively.ObjectiveThe aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression.MethodsWe systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed.ResultsNone of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms.ConclusionsPromising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.

Managing depression in India: Opportunities for a targeted smartphone app.

More than forty million people suffer from depression in India. A lack of awareness, stigma related to mental health issues, and limited accessibility to treatment services magnify the profound personal and societal impact of depression. Given the rise of smartphones in India, mobile technology can help alleviate some of these depression-related challenges. The aim of this paper is to investigate the essential features of an India targeted depression smartphone app. We conducted an online survey to profile the needs of individuals with depression in India, which varied based on variables such as socioeconomic background, age, level of awareness toward depression, and the extent of exposure to mental health stigma. We also conducted a systematic evaluation of depression apps currently available to Indian users to investigate the user needs that these apps met and the needs that they failed to meet. Based on our findings, we made a set of recommendations related to the essential features of a future app targeted at managing depression in India. Presently available depression apps fall short in providing some significant features such as local language options, content in audio and video formats, and user location matched resources. These gaps make these apps less than fully relevant to a diverse set of Indian users. It is essential to provide depression-related information in a targeted manner depending upon each user's particular needs and context. Potential customizations, such as offering content in local languages and flexible formats (e.g. audio, video, and text); and providing user-relevant diagnostic tools and location matched treatment resources can help improve the suitability of the app for diverse users.

Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence.

BackgroundMental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specialists, habitually challenging, has worsened because of the COVID-19 pandemic. Internet-based CBT is an effective and feasible strategy to increase access to treatment for people with depression. Mental health apps may further assist in facilitating self-management for people affected by depression; however, accessing the correct app may be cumbersome given the large number and wide variety of apps offered by public app marketplaces.ObjectiveThis study aims to systematically assess the features, functionality, data security, and congruence with evidence of self-guided CBT-based apps targeting users affected by depression that are available in major app stores.MethodsWe conducted a systematic assessment of self-guided CBT-based apps available in Google Play and the Apple App Store. Apps launched or updated since August 2018 were identified through a systematic search in the 42matters database using CBT-related terms. Apps meeting the inclusion criteria were downloaded and assessed using a Samsung Galaxy J7 Pro (Android 9) and iPhone 7 (iOS 13.3.1). Apps were appraised using a 182-question checklist developed by the research team, assessing their general characteristics, technical aspects and quality assurance, and CBT-related features, including 6 evidence-based CBT techniques (ie, psychoeducation, behavioral activation, cognitive restructuring, problem solving, relaxation, and exposure for comorbid anxiety) as informed by a CBT manual, CBT competence framework, and a literature review of internet-based CBT clinical trial protocols. The results were reported as a narrative review using descriptive statistics.ResultsThe initial search yielded 3006 apps, of which 98 met the inclusion criteria and were systematically assessed. There were 20 well-being apps; 65 mental health apps, targeting two or more common mental health disorders, including depression; and 13 depression apps. A total of 28 apps offered at least four evidence-based CBT techniques, particularly depression apps. Cognitive restructuring was the most common technique, offered by 79% (77/98) of the apps. Only one-third of the apps offered suicide risk management resources, whereas 17% (17/98) of the apps offered COVID-19–related information. Although most apps included a privacy policy, only a third of the apps presented it before account creation. In total, 82% (74/90) of privacy policies stated sharing data with third-party service providers. Half of the app development teams included academic institutions or health care providers.ConclusionsOnly a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources. Sharing of users’ data is widespread, highlighting shortcomings in health app market governance. To fulfill their potential, self-guided CBT-based apps should follow evidence-based clinical guidelines, be patient centered, and enhance users’ data security.

Terms and conditions apply: Critical issues for readability and jargon in mental health depression apps

BackgroundMental health services are turning to technology to ease the resource burden, but privacy policies are hard to understand potentially compromising consent for people with mental health problems. The FDA recommends a reading grade of 8. ObjectiveTo investigate and improve the accessibility and acceptability of mental health depression app privacy policies. MethodsA mixed methods study using quantitative and qualitative data to improve the accessibility of app privacy policies. Service users completed assessments and focus groups to provide information on ways to improve privacy policy accessibility, including identifying and rewording jargon. This was supplemented by comparisons of mental health depression apps with social media, music and finance apps using readability analyses and examining whether GDPR affected accessibility. ResultsService users provided a detailed framework for increasing accessibility that emphasised having critical information for consent. Quantitatively, most app privacy policies were too long and complicated for ensuring informed consent (mental health apps mean reading grade = 13.1 (SD = 2.44)). Their reading grades were no different to those for other services. Only 3 mental health apps had a grade 8 or less and 99% contained service user identified jargon. Mental health app privacy policies produced for GDPR weren't more readable and were longer. ConclusionsApps specifically aimed at people with mental health difficulties are not accessible and even those that fulfilled the FDA's recommendation for reading grade contained jargon words. Developers and designers can increase accessibility by following a few rules and should, before launching, check whether the privacy policy can be understood.

A qualitative exploration of the feasibility of incorporating depression apps into integrated primary care clinics.

The use of mobile applications or "apps" is beginning to be identified as a potential cost-effective tool for treating depression. While the use of mobile apps for health management appears promising, little is known on how to incorporate these tools into integrated primary care settings-especially from the viewpoints of patients and the clinic personnel. The purpose of this study was to explore patient- and clinic-level perceptions of the use of depression self-management apps within an integrated primary care setting. Patients (n = 17), healthcare providers, and staff (n = 15) completed focus groups or semi-structured interviews in-person or via Zoom between January and July 2020. Participants were asked about barriers and facilitators to app use, how to best integrate it into care, and reviewed pre-selected mental health apps. Data were analyzed using a directed content analysis approach. From a patient perspective, features within the app such as notifications, the provision of information, easy navigation, and a chat/support function as well as an ability to share data with their doctor were desirable. Providers and staff identified integration of app data into electronic health records to be able to share data with patients and the healthcare team as well as clear evidence of effectiveness as factors that could facilitate implementation. All participants who reviewed apps identified at least one of them they would be interested in continuing to use. Overall, patients, healthcare providers, and staff believed depression apps could be beneficial for both patients and the clinic.

Just-in-time Adaptive Mechanisms of Popular Mobile Applications for Individuals with Depression: Systematic Review

Background: There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. Objective: With this work, we systematically assess the use of JITAI mechanisms in apps for individuals with depression. Methods: We systematically searched for apps addressing depression in the Apple App Store, the Google Play Store, and in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. Relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, two authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, and Google Scholar), publications cited on the app’s website, information on the app’s website, and the app itself. Results: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations or individuals. Three apps did not use any measurements, 20 apps exclusively used self-reports that are insufficient to leverage the full potential of JITAIs, and the five apps employing self-reports and passive measurements used them as progress or task indicators only. While 23 of the 68 reviewed publications investigated the effectiveness and 14 publications investigated the efficacy of the apps, not one publication mentioned or evaluated JITAI mechanisms. Conclusions: Promising JITAI mechanisms have not yet been translated into mainstream depression apps. The lack of publications investigating whether JITAI mechanisms lead to an increase of the apps’ effectiveness or efficacy highlights the need for further research, especially in real-world apps.

A review of mobile chatbot apps for anxiety and depression and their self-care features

BackgroundAnxiety and depression rates are at an all-time high. Smartphone-based mental health chatbots can aid psychiatrists replacing some of the costly human based interaction providing a unique opportunity to expand the availability and quality of mental health intervention whilst providing an alternative approach to fill the much-needed self-care gap. ObjectiveAssess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. MethodsA search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbot apps for anxiety and depression. Eligibility was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and post-installation by two reviewers. Information on quality was assessed by following the mHONcode principles. ResultsOnly a handful (n=11) of chatbots were included from an initial search of 1000 that provide a substitute for human-human based interaction and clearly had a therapeutic human substitute goal in mind. The majority of reviewed apps had a high number of downloads indicating their popularity. The apps were also of a general high quality based on our assessment according to the mHONcode principles. ConclusionThe general popularity of apps reviewed, and results of our quality assessment indicate chatbots have a promising future within the realm of anxiety and depression. Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed low-cost assistance to professionals.

Even ‘Child-Friendly’ Apps Routinely Share Data

Even ‘Child-Friendly’ Apps Routinely Share Data