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. 2020 Nov 20;28(5):552–560. doi: 10.1007/s12529-020-09944-y

Stay Present with Your Phone: A Systematic Review and Standardized Rating of Mindfulness Apps in European App Stores

Dana Schultchen 1,, Yannik Terhorst 2,3, Tanja Holderied 1, Michael Stach 4, Eva-Maria Messner 3, Harald Baumeister 3, Lasse B Sander 5
PMCID: PMC8384800  PMID: 33215348

Abstract

Background

Mindfulness-based interventions show positive effects on physical and mental health. For a better integration of mindfulness techniques in daily life, the use of apps may be promising. However, only a few studies have examined the quality of mindfulness apps using a validated standardized instrument. This review aims to evaluate the content, quality, and privacy features of mindfulness-focused apps from European commercial app stores.

Methods

An automated search engine (webcrawler) was used to identify mindfulness-focused apps in the European Apple App- and Google Play store. Content, quality, and privacy features were evaluated by two independent reviewers using the Mobile Application Rating Scale (MARS). The MARS assesses the subscales engagement, functionality, aesthetics, and information quality.

Results

Out of 605 identified apps, 192 met the inclusion criteria. The overall quality was moderate (M = 3.66, SD = 0.48). Seven apps were tested in a randomized controlled trial (RCT). Most of the apps showed a lack of data security and no privacy policy. The five apps with the highest ratings are from a credible source, include a privacy policy, and are also based on standardized mindfulness and behavior change techniques.

Conclusions

The plethora of often low-quality apps in commercial app stores makes it difficult for users to identify a suitable app. Above that, the lack of scientific verification of effectiveness and shortcomings in privacy protection and security poses potential risks. So far, the potential of mindfulness-focused apps is not exploited in commercial app stores.

Keywords: Mindfulness, Apps, MARS, Mobile health, mHealth, Systematic review

Introduction

Mindfulness is described as an approach to be aware and attentive of the present moment in an open and accepting way without any judgment or criticism [13]. In recent years, research interest in mindfulness techniques is growing [46]. Previous studies investigating the efficacy of mindfulness interventions showed positive effects on physical and mental health (e.g., depression, anxiety, well-being) in clinical and non-clinical populations [713]. Various mindfulness techniques are used in health promotion and clinical practice, including standardized programs like mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT; e.g., [1417]). The techniques are instructed by an experienced trainer and practiced in a weekly group session [3, 18]. Furthermore, individual elements of mindfulness techniques like meditations (sitting/movement), body scan, mindfulness yoga or breathing exercises, and visualization exercises have found their way into a variety of different treatments [3, 19]. Studies repeatedly pointed out that it is important for the success of interventions to integrate techniques into daily life [20]. However, this involves the challenge of health behavior change [20, 21]. The use of apps has repeatedly been discussed to overcome these challenges [2124]. Apps can be used flexibly in terms of time and place, are readily available to a broad audience, and offer reminder functions, and supporting material (e.g., pictures and videos) can have a positive impact on behavioral change [2528].

Besides these advantages, there are also some limitations in the use of health apps, which include inadequate protection of data and privacy features and the lack of an informed consent [2935]. Furthermore, only a few studies investigating the efficacy of mindfulness apps [3641] or other health-related conditions such as depression, anxiety, pain, and posttraumatic stress disorders have been published [4247]. Another disadvantage are missing international quality standards for the development of apps, as well as the lack of integration of healthcare providers in the development process [26, 4851]. In a previous review, Mani and colleagues [36] reviewed 23 mindfulness apps from an Australian app stores. The results of this study indicated that the median score exceeds an acceptable score and that quality could be improved. However, this review is limited through the small number of included apps, the inclusion of apps only from the Apple app store, and the use of a single search term (“mindfulness”) focusing on the Australian app store.

Hence, the aim in this study was to conduct a systematic review and quality rating of mindfulness apps in the European commercial app stores and expand the search to different app stores and to use a comprehensive search term and engine. Moreover, this review also included a rating on general app characteristics such as privacy policy, techniques used, and content features, as well as the specific examination of the relationship between user and quality ratings. The five best-rated apps are described in detail to give users and healthcare providers an overview of possible applications of mindfulness apps in practice.

Method

Search Strategy and Procedure

Using an automated search engine (webcrawler), a search in the European app stores from Apple App and Google Play store on the 24th of October 2018 was conducted. Therefore, the following keywords were used: “mindfulness,” “meditation,” “yoga,” and “body scan.”

All apps were listed in a central database, and duplicates were automatically removed. Apps from the Apple App and Google Play store were screened against the following inclusion criteria: (1) sufficient content and conceptualized for mindfulness (i.e., exclusion of fitness and yoga apps or in case that apps only provide timer, reminder, music or quotes), (2) provided in German or English language (in accordance with the reviewers’ language skills), and (3) officially available in the European Apple App or Google Play store. In a second step, apps were downloaded and checked regarding the aforementioned criteria and, if the app was fully functional for the review (e.g., no device problems, development/testing phase).

Data Extraction, Evaluation Criteria, and Instruments

Two independent reviewers evaluated each app using the German version of the Mobile Application Rating Scale (MARS) [52]. All reviewers had a psychology degree and scientific background. Before starting with the evaluating process, every reviewer received a free online training [https://www.youtube.com/watch?v=5vwMiCWC0Sc]. To evaluate the apps, every reviewer had to test each app for at least 15–20 min with the focus on the different subscales of MARS: user engagement, functionality, visual aesthetics, and information quality.

The interrater reliability (IRR) between the two reviewers was calculated. The excellent intra-class correlation of ≥ 0.75 indicates a satisfactory score [53]. In case that the score of an app-rating was below this score, a third rater (DS or LS) was consulted. To combine ratings of each app, averaged mean scores of both reviewers were used for each subscale as well as an overall score.

App Characteristic and Quality Rating: MARS-German

To describe the app characteristics, different descriptive data was obtained, including (1) app name, (2) platform (Apple App or Google Play store), (3) content-related subcategories, (4) price, (5) goals, (6) methods, (7) data protection and privacy, (8) user rating from the Apple App and Google Play store, and (9) number of conducted randomized controlled trials. It should be noted that this part was slightly reduced to focus on the crucial mindfulness information.

Additionally, privacy and security features were reviewed according to the MARS classification system on a 5-point scale (1 = inadequate; 2 = poor; 3 = acceptable; 4 = good; and 5 = excellent). The MARS quality rating comprises the four main subscales: (1) user engagement (5 items: entertainment, interest, customization, interactivity, target group), (2) functionality (4 items: performance, usability, navigation, gestural design), (3) aesthetics (3 items: layout, graphics, visual appeal), (4) information quality (7 items: accuracy of app description, goals, quality of information, quantity of information, quality of visual information, credibility, evidence base). For each subscale, a mean score was calculated [54]. Both review ratings averaged this score. Previous research evaluating psychometric criteria showed that reliability and objectivity of the MARS and the German version of the MARS were good to excellent [55, 56]. Additionally, Terhorst and colleagues [55] confirmed a good construct validity for the MARS.

Results

Search and App Characteristics

The search resulted in 605 apps (Apple App store = 434, Google Play store = 171), of which 192 apps (123 English, 69 German) could be included (see Fig. 1).

Fig. 1.

Fig. 1

Flowchart of the inclusion process

General Characteristics

Most of the apps were free of charge (n = 157, 82%). The prices for the remaining apps ranged between 0.99 EUR and 9.99 EUR (M = 3.49 EUR; SD = 2.03). Most of the apps aimed to improve well-being (n = 175, 91%) and reduce stress (n = 144, 75%), followed by improvement of physical health (n = 58, 30%), reduction of anxiety (n = 55, 29%) and depressive symptoms (n = 23, 12%), emotion regulation (n = 26, 13%), support for behavioral change (n = 19, 10%), entertainment (n = 12, 6%), improvement of social behavior (n = 12, 6%), as well as reduction of addictive behavior (n = 2, 1%). Sixty apps (31%) were classified with other goals (e.g., information about mindfulness, improving sleep quality, self-awareness and concentration).

Data Security and Privacy Policy

Data security and privacy features showed that 15 apps (8%) were password-protected, 14 apps (7%) offered the possibility to use a login-area, and 44 apps (23%) included a privacy policy (7 apps (4%) active confirmation, 12 apps (6%) passive privacy policy). Sixty-four apps (24%) gave information about contact details and the imprint. Four apps (2%) offered an emergency function, including helpline numbers or addresses for medical assistance.

App Quality Rating

The intra-class correlation, indicating agreement of both raters, was excellent (ICC = 0.87, 95% CI: 0.86 to 0.88). The average overall quality rating for mindfulness apps was M = 3.66 (SD = 0.48, range 2.47–4.75), demonstrating a moderate quality. For the different subscales, the following average ratings were found: engagement (M = 3.45, SD = 0.72, range 1.60–5.00), functionality (M = 4.18, SD = 0.48, range 3.00–5.00), aesthetics (M = 3.79, SD = 0.65, range 1.66–5.00) and information (M = 3.24, SD = 0.5, range 2.00–4.58). All data are summarized in Fig. 2.

Fig. 2.

Fig. 2

Quality of included mindfulness-focused apps rated with the Mobile Application Rating Scale (1, inadequate; 2, poor; 3, acceptable; 4, good; and 5, excellent)

Evidence-Based Ratings and Methods

Only seven apps (4%) were tested in randomized controlled trials (RCTs), including “Mindfulness Coach,” “Pacifica for Stress & Anxiety,” “Headspace,” “Calm: Meditation and Sleep,” “7Mind Meditation & Mindfulness,” “Smiling Mind,” and the German app “Die Achtsamkeit App” (translation: “The mindfulness app”; e.g., 36–41, 57–59). Fourteen apps were investigated regarding different other variables such as usability and satisfaction in non-RCT or qualitative assessments [6065]. The mindfulness applications offered various standardized methods, including relaxation (n = 153, 80%), breathing (n = 118, 62%) and body exercises (n = 59, 31%), mindfulness (n = 117, 61%), acceptance (n = 28, 15%), and resource management (n = 4, 2%). Furthermore, different apps focused on monitoring and tracking (n = 55, 29%), feedback (n = 19, 10%), information and education (n = 74, 39%), advices (n = 46, 24%), and training (n = 42, 22%).

User vs. Expert Rating

For 161 apps a user-star-rating (M = 4.31, SD = 0.60) could identified, while 31 apps were not rated by the users. Analyses resulted in correlations between user-star-ratings and the MARS subscales engagement (r = 0.221, p = 0.005), functionality (r = 0.158, p = 0.046) as well as aesthetics (r = 0.202, p = 0.010). No correlation could be found between user rating and the overall score as well as information quality (ps > 0.31).

Features of the Five Top-Rated Apps

In the following, the five best-rated apps (range of the overall quality score 4.61–4.75) will be presented. Besides the high overall score (> 4.61), all apps are from a credible source (i.e., government, university, non-profit organization, or specialized company) and contained a data privacy statement. Four of them also provided contact information on the imprint, and three of them had an emergency function. Two apps are asking for challenges in daily life (e.g., anxiety, motivation, exam stress, loss, loneliness) to create an individual program. Further, three apps offered guided support, whereas the app “Wysa Anxiety & Depression Bot” also provided professional guidance by a therapist for 29.99 EUR per week. This guidance includes two live chat sessions per week and unlimited messaging support. All apps presented psychoeducation and information (e.g., emotion perception and regulation, coping skills, problem solving, goals, gratitude, mindfulness techniques) via text and/or audio. Two apps included reminder functions for daily practice. Four apps were complemented by tracker or journal functions with the focus on daily practice, but also mood and symptoms could be tracked. Not surprisingly, breathing and relaxation exercises (e.g., PMR, meditation, body scan) were integrated into all apps. All features are displayed in Table 1.

Table 1.

Features of the five best-rated apps

App name Credible source Privacy policy Contact information Emergency functions Individual program Guided support Psychoeducation/information Reminder Tracker MBSR/MBCT-based practicesa
Wysa Anxiety and Depression Bot
Youper—AI Assistant x x
Relax Now: Hypnosis Meditation x x x x
Mind the Bump—A Mindfulness Meditation tool for new and expecting parents x x
Mindfulness Coach x x x x

aBody scan meditations, breathing meditations, walking meditations, sitting meditations

Discussion

In this study, the quality of 192 mindfulness-focused apps in the European commercial app stores using a standardized rating instrument was systematically reviewed. Additionally, the current evidence base as well as privacy and security features on the mindfulness-focused apps were evaluated. Furthermore, the content and features of the five best-rated apps were displayed. Given the plethora of available apps, users and healthcare providers might have problems to identify an app that suits their needs. Shortcomings in privacy and security, the mostly lacking scientific evidence-base, lack in the information quality pose additional constraints.

Only seven apps (4%) provided evidence on the effectiveness of the reviewed apps [39, 5759]. This shortcoming in the scientific evaluation of apps is in accordance with the review of Mani and colleagues [36] and other health-related app reviews [26, 4348]. This might partly be explained by the fast development cycle of apps and continuous improvement through user feedback, which does not fit with the time requirements of current scientific studies [6668]. Consequently, scientific- and technology-based developed interventions may be out of date at the time when they are validated. Mohr and colleagues [69] suggest a solution to this problem by creating a continuous evaluation of behavioral intervention technologies (CEEBIT) through systematic prospective analyses.

Another concern was that the MARS subscale “information quality” implies the most deficits given the lowest average score of all subscales. Only 18 of the 192 included mindfulness-focused apps had a score higher than 4.0, which would define the app as a high-quality app on the “information quality” subscale. To prevent misinformation and adverse effects of mindfulness app use in the future, information quality must be improved. Furthermore, the user rating did not correlate with the subscale “information quality,” but with engagement, functionality, and aesthetics. However, wrong or misleading information could result in a decrease in users’ safety [46]. The involvement of experts in the development process (e.g., psychotherapists, researchers, and users) might help to overcome this problem. Moreover, a better description of the app on the app store website, a definition of the specific goals and better content (i.e., general information to mindfulness and how to use the different techniques) would improve information quality.

In addition to the risk of misinformation, users are also at risk from the described deficits in terms of data protection and data security. This is in accordance with prior studies [45, 46, 70]. In the case of violated security approaches, careful use of these apps is proposed [71]. Another issue is that some apps are also submitted private data to commercial entities without any permission [34, 35]. Only a few apps (2%) are offering helpline numbers or points of assistance in case of an emergency. However, there should be at least a disclaimer that in case of unexpected symptoms, users should be searching for professional help or how to access other treatment options, mainly because most mindfulness app users suffer from stress [7274] as well as anxious and depressive symptoms [9].

Despite the named issues, the average MARS score indicated an average overall quality in line with the results of Mani and colleagues [36] who rated 23 apps in the Australian Apple App store in 2015. However, whereas only one app could be identified as a high-quality app (overall mean score > 4.0) in the study of Mani and colleagues [36], the present study identified 50 apps above the MARS overall mean score of 4.0. The five top-rated apps described in detail contained a range of high-quality content and features that can facilitate the application of mindfulness techniques in practice. Furthermore, these apps also include additional functions such as monitoring and tracking, feedback and (guided) support, and education as well as advice, which might foster behavior change.

Based on the positive and critical aspects of the different mindfulness-focused apps, it can be hard to find a suitable app. Consequently, there is a need for independent information platforms (e.g., German Mobile Health App Database: http://mhad.science/; PsyberGuide: https://psyberguide.org/) offering reliable information for health care providers and health seekers about the app content and quality, which could be combined by user and health expert ratings. Moreover, to protect customers and improve app quality, there is an urgent need for universal guidelines regarding (mental health) app control [75, 76]. Therefore, the focus should be on app evaluation, validation, and quality assessment in which health care professionals, policy makers, user, and developers should be involved before launching an app.

This study has several limitations. Firstly, the app market is rapidly growing [69, 77], and the rated apps of this review can be revised. Consequently, a new search and rating process could lead to different apps as well as other evaluations. Secondly, the apps could no longer be available, or due to the focus on the Apple App and Google Play store, apps from other stores could be overlooked due to the selection bias. However, it should be noted that both app stores cover 99% of the total market [78]. Consequently, the missed number of apps should be low. Thirdly, the rated mindfulness apps are limited to specific searches. Fourthly, only German and English apps were included in the rating process due to the language skills of the reviewer. Accordingly, it is not guaranteed that these apps are also available in other countries. Lastly, this review is only based on the MARS rating, which could be extended by different quality rating scales such as ENLIGHT [79] and the APA App Evaluation [80, 81].

Conclusion

In this comprehensive investigation of 192 apps focusing on mindfulness, an average overall quality was found, resulting in possible risks for the users due to a lack of information and content quality, missing privacy policy, and data security as well as lack of evidence base. To offer reliable information for healthcare providers and health seekers, independent platforms, such as the German Mobile Health App Database or PsyberGuide, are needed. In general, apps have a high potential to reach a broad audience who aims to engage in mindfulness practice in daily life and to improve different mental health variables (e.g., stress, depressive and anxiety symptoms). However, at the moment, the commercial distribution channels fail to unfold this potential.

Acknowledgments

The authors would like to thank Rüdiger Pryss, Robin Kraft, Pascal Damasch, and Philipp Dörzenbach for their support in the development of the search engine and their support in the MHAD project.

Author Contributions

Dana Schultchen, Yannik Terhorst, Eva-Maria Messner, and Lasse B. Sander initiated this study. Dana Schultchen, Yannik Terhorst, Eva-Maria Messner, Harald Baumeister, and Lasse B. Sander contributed to the study design and conceptualized the current research question. Dana Schultchen, Tanja Holderied, and Lasse B. Sander rated MHA. Throughout the assessment, all raters were supervised by Dana Schultchen or Lasse B. Sander (licensed psychotherapist). Dana Schultchen, Yannik Terhorst, Tanja Holderied, and Lasse B. Sander conducted the data analyses. Dana Schultchen wrote the first draft of the manuscript. All authors revised and approved the final version of the manuscript for submission.

Funding

Open Access funding enabled and organized by Projekt DEAL. Self-funded.

Compliance with Ethical Standards

Conflict of Interest

Yannik Terhorst, Eva-Maria Messner, Harald Baumeister, and Lasse B. Sander developed and run the German Mobile Health App Database project. The MHAD is a self-funded project at Ulm University with no commercial interests. Harald Baumeister, Lasse B. Sander, and Eva-Maria Messner received payments for talks and workshops in the context of e-mental-health.

Informed Consent

For this type of study, formal consent is not required.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Kabat-Zinn J. Mindfulness-based interventions in context: Past, present, and future. Clinical Psychology. 2003;10:144–156. doi: 10.1093/clipsy/bpg016. [DOI] [Google Scholar]
  • 2.Bishop SR. What do we really know about mindfulness-based stress reduction? Psychosom Med. 2002;64:71–83. doi: 10.1097/00006842-200201000-00010. [DOI] [PubMed] [Google Scholar]
  • 3.Kabat-Zinn J. Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. 2013. New York: Bantam Books; 2013. [Google Scholar]
  • 4.Brown KW, Ryan RM. The benefits of being present: Mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84:822–848. doi: 10.1037/0022-3514.84.4.822. [DOI] [PubMed] [Google Scholar]
  • 5.Gu J, Strauss C, Bond R, Cavanagh K. How do mindfulness-based cognitive therapy and mindfulness-based stress reduction improve mental health and wellbeing? A systematic review and meta-analysis of mediation studies. Clin Psychol Rev. 2015;37:1–12. doi: 10.1016/j.cpr.2015.01.006. [DOI] [PubMed] [Google Scholar]
  • 6.Keng S-L, Smoski MJ, Robins CJ. Effects of mindfulness on psychological health: A review of empirical studies. Clin Psychol Rev. 2011;31:1041–1056. doi: 10.1016/j.cpr.2011.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Shapiro SL, Astin JA, Bishop SR, Cordova M. Mindfulness-based stress reduction for health care professionals: Results from a randomized trial. International Journal of Stress Management. 2005;12:164–176. doi: 10.1037/1072-5245.12.2.164. [DOI] [Google Scholar]
  • 8.Cavanagh K, Strauss C, Forder L, Jones F. Can mindfulness and acceptance be learnt by self-help?: A systematic review and meta-analysis of mindfulness and acceptance-based self-help interventions. Clin Psychol Rev. 2014;34:118–129. doi: 10.1016/j.cpr.2014.01.001. [DOI] [PubMed] [Google Scholar]
  • 9.Hofmann SG, Sawyer AT, Witt AA, Oh D. The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. J Consult Clin Psychol. 2010;78:169–183. doi: 10.1037/a0018555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chiesa A, Serretti A. A systematic review of neurobiological and clinical features of mindfulness meditations. Psychol Med. 2010;40:1239–1252. doi: 10.1017/S0033291709991747. [DOI] [PubMed] [Google Scholar]
  • 11.Godfrin KA, van Heeringen C. The effects of mindfulness-based cognitive therapy on recurrence of depressive episodes, mental health and quality of life: A randomized controlled study. Behav Res Ther. 2010;48:738–746. doi: 10.1016/j.brat.2010.04.006. [DOI] [PubMed] [Google Scholar]
  • 12.Strauss C, Cavanagh K, Oliver A, Pettman D. Mindfulness-based interventions for people diagnosed with a current episode of an anxiety or depressive disorder: A meta-analysis of randomised controlled trials. PLoS ONE. 2014;9:e96110. doi: 10.1371/journal.pone.0096110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Segal ZV, Dimidjian S, Beck A, Boggs JM, Vanderkruik R, Metcalf CA, et al. Outcomes of online mindfulness-based cognitive therapy for patients with residual depressive symptoms: A randomized clinical trial. JAMA Psychiatry. 2020 doi: 10.1001/jamapsychiatry.2019.4693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Michalak J, Steinhaus K, Heidenreich T. (How) do therapists use mindfulness in their clinical work? A study on the implementation of mindfulness interventions. Mindfulness. 2020;11:401–410. doi: 10.1007/s12671-018-0929-9. [DOI] [Google Scholar]
  • 15.Dobkin PL, Zhao Q, Monshat K. Who experiences depressive symptoms following mindfulness-based stress reduction and why? IJWPC. 2017 doi: 10.26443/ijwpc.v4i1.123. [DOI] [Google Scholar]
  • 16.Alsubaie M, Abbott R, Dunn B, Dickens C, Keil TF, Henley W, Kuyken W. Mechanisms of action in mindfulness-based cognitive therapy (MBCT) and mindfulness-based stress reduction (MBSR) in people with physical and/or psychological conditions: A systematic review. Clin Psychol Rev. 2017;55:74–91. doi: 10.1016/j.cpr.2017.04.008. [DOI] [PubMed] [Google Scholar]
  • 17.Parsons CE, Crane C, Parsons LJ, Fjorback LO, Kuyken W. Home practice in mindfulness-based cognitive therapy and mindfulness-based stress reduction: A systematic review and meta-analysis of participants’ mindfulness practice and its association with outcomes. Behav Res Ther. 2017;95:29–41. doi: 10.1016/j.brat.2017.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Mander J, Blanck P. Achtsamkeit in der Psychotherapie [Mindfulness in Psychotherapy] Psychotherapeut. 2018;63:251–264. doi: 10.1007/s00278-018-0286-0. [DOI] [Google Scholar]
  • 19.Fjorback LO, Arendt M, Ornbøl E, Fink P, Walach H. Mindfulness-based stress reduction and mindfulness-based cognitive therapy: A systematic review of randomized controlled trials. Acta Psychiatr Scand. 2011;124:102–119. doi: 10.1111/j.1600-0447.2011.01704.x. [DOI] [PubMed] [Google Scholar]
  • 20.Niemiec R, Rashid T, Spinella M. Strong mindfulness: Integrating mindfulness and character strengths. Journal of Mental Health Counseling. 2012;34:240–253. doi: 10.17744/mehc.34.3.34p6328x2v204v21. [DOI] [Google Scholar]
  • 21.Dennison L, Morrison L, Conway G, Yardley L. Opportunities and challenges for smartphone applications in supporting health behavior change: Qualitative study. J Med Internet Res. 2013;15:e86. doi: 10.2196/jmir.2583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Conroy DE, Yang C-H, Maher JP. Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med. 2014;46:649–652. doi: 10.1016/j.amepre.2014.01.010. [DOI] [PubMed] [Google Scholar]
  • 23.Zhao J, Freeman B, Li M. Can mobile phone apps influence people’s health behavior change? An evidence review. J Med Internet Res. 2016;18:e287. doi: 10.2196/jmir.5692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: A systematic review of the literature. JMIR Mhealth Uhealth. 2015;3:e20. doi: 10.2196/mhealth.3335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kuhn B, Amelung V. Gesundheits-Apps und besondere Herausforderungen. In: Albrecht U-V, editor. Chancen und Risiken von Gesundheits-Apps (CHARISMHA) Hannover: Medizinische Hochschule Hannover; 2016. pp. 100–114. [Google Scholar]
  • 26.Donker T, Petrie K, Proudfoot J, Clarke J, Birch M-R, Christensen H. Smartphones for smarter delivery of mental health programs: A systematic review. J Med Internet Res. 2013;15:e247. doi: 10.2196/jmir.2791. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Hussain M, Al-Haiqi A, Zaidan AA, Zaidan BB, Kiah MLM, Anuar NB, Abdulnabi M. The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Comput Methods Programs Biomed. 2015;122:393–408. doi: 10.1016/j.cmpb.2015.08.015. [DOI] [PubMed] [Google Scholar]
  • 28.Bakker D, Kazantzis N, Rickwood D, Rickard N. mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Ment Health. 2016;3:e7. doi: 10.2196/mental.4984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Prentice JL, Dobson KS. A review of the risks and benefits associated with mobile phone applications for psychological interventions. Can Psychol. 2014;55:282–290. doi: 10.1037/a0038113. [DOI] [Google Scholar]
  • 30.Luxton DD, Hansen RN, Stanfill K. Mobile app self-care versus in-office care for stress reduction: A cost minimization analysis. J Telemed Telecare. 2014;20:431–435. doi: 10.1177/1357633X14555616. [DOI] [PubMed] [Google Scholar]
  • 31.Giota KG, Kleftaras G. Mental health apps: Innovations. Risks and Ethical Considerations ETSN. 2014;03:19–23. doi: 10.4236/etsn.2014.33003. [DOI] [Google Scholar]
  • 32.Fernandez-Luque L, Staccini P. All that glitters is not gold: Consumer health informatics and education in the era of social media and health apps. Findings from the Yearbook, Section on Consumer Health Informatics. Yearb Med Inform. 2016;2016:188–193. doi: 10.15265/IY-2016-045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Grundy Q, Held FP, Bero LA. Tracing the potential flow of consumer data: A network analysis of prominent health and fitness apps. J Med Internet Res. 2017;19:e233. doi: 10.2196/jmir.7347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Grundy Q, Chiu K, Held F, Continella A, Bero L, Holz R. Data sharing practices of medicines related apps and the mobile ecosystem: Traffic, content, and network analysis. BMJ. 2019;364:l920. doi: 10.1136/bmj.l920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Huckvale K, Torous J, Larsen ME. Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation. JAMA Netw Open. 2019;2:e192542. doi: 10.1001/jamanetworkopen.2019.2542. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR Mhealth Uhealth. 2015;3:e82. doi: 10.2196/mhealth.4328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.van Emmerik AAP, Berings F, Lancee J. Efficacy of a mindfulness-based mobile application: A randomized waiting-list controlled trial. Mindfulness. 2018;9:187–198. doi: 10.1007/s12671-017-0761-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Moberg C, Niles A, Beermann D. Guided self-help works: randomized waitlist controlled trial of pacifica, a mobile app integrating cognitive behavioral therapy and mindfulness for stress, anxiety, and depression. J Med Internet Res. 2019;21:e12556. doi: 10.2196/12556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Bennike IH, Wieghorst A, Kirk U. Online-based mindfulness training reduces behavioral markers of mind wandering. J Cogn Enhanc. 2017;1:172–181. doi: 10.1007/s41465-017-0020-9. [DOI] [Google Scholar]
  • 40.Möltner H, Leve J, Esch T. Burnout-Prävention und mobile Achtsamkeit: Evaluation eines appbasierten Gesundheitstrainings bei Berufstätigen. Gesundheitswesen. 2018;80:295–300. doi: 10.1055/s-0043-114004. [DOI] [PubMed] [Google Scholar]
  • 41.Ly KH, Trüschel A, Jarl L, Magnusson S, Windahl T, Johansson R, et al. Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: A randomised controlled trial. BMJ Open. 2014;4:e003440. doi: 10.1136/bmjopen-2013-003440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, Sarris J. The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry. 2017;16:287–298. doi: 10.1002/wps.20472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord. 2017;218:15–22. doi: 10.1016/j.jad.2017.04.046. [DOI] [PubMed] [Google Scholar]
  • 44.Knitza J, Tascilar K, Messner E-M, Meyer M, Vossen D, Pulla A, et al. German mobile apps in rheumatology: Review and analysis using the Mobile Application Rating Scale (MARS) JMIR Mhealth Uhealth. 2019;7:e14991. doi: 10.2196/14991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Sander LB, Schorndanner J, Terhorst Y, Spanhel K, Pryss R, Baumeister H, Messner E-M. ‘Help for trauma from the app stores?’ A systematic review and standardised rating of apps for Post-Traumatic Stress Disorder (PTSD) European Journal of Psychotraumatology. 2020;11:1701788. doi: 10.1080/20008198.2019.1701788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Terhorst Y, Rathner E-M, Baumeister H, Sander L. «Hilfe aus dem App-Store?»: Eine systematische Übersichtsarbeit und Evaluation von Apps zur Anwendung bei Depressionen [‘Help from the App Store?’: A Systematic Review of Depression Apps in German App Stores] Verhaltenstherapie. 2018;28:101–12. doi: 10.1159/000481692. [DOI] [Google Scholar]
  • 47.Thurnheer SE, Gravestock I, Pichierri G, Steurer J, Burgstaller JM. Benefits of mobile apps in pain management: Systematic review. JMIR Mhealth Uhealth. 2018;6:e11231. doi: 10.2196/11231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Byambasuren O, Sanders S, Beller E, Glasziou P. Prescribable mHealth apps identified from an overview of systematic reviews. NPJ Digit Med. 2018;1:12. doi: 10.1038/s41746-018-0021-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Olff M. Mobile mental health: A challenging research agenda. European Journal of Psychotraumatology. 2015;6:27882. doi: 10.3402/ejpt.v6.27882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Weisel KK, Fuhrmann LM, Berking M, Baumeister H, Cuijpers P, Ebert DD. Standalone smartphone apps for mental health-a systematic review and meta-analysis. NPJ Digit Med. 2019;2:118. doi: 10.1038/s41746-019-0188-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Lalloo C, Jibb LA, Rivera J, Agarwal A, Stinson JN. There’s a pain app for that: Review of patient-targeted smartphone applications for pain management. Clin J Pain. 2015;31:557–63. doi: 10.1097/AJP.0000000000000171. [DOI] [PubMed] [Google Scholar]
  • 52.Messner E-M, Terhorst Y, Barke A, Baumeister H, Stoyanov S, Hides L, et al. Development and validation of the German version of the Mobile Application Rating Scale (MARS-G) JMIR Mhealth Uhealth. 2020;8(3):e14479. doi: 10.2196/14479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Fleiss JL. Design and Analysis of Clinical Experiments. Hoboken: John Wiley & Sons; 2011. [Google Scholar]
  • 54.Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: A new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3:e27. doi: 10.2196/mhealth.3422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M, et al. Validation of the Mobile Application Rating Scale (MARS) PLoS ONE. 2020;15:e0241480. doi: 10.1371/journal.pone.0241480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Messner E-M, Terhorst Y, Barke A, Baumeister H, Stoyanov S, Hides L, et al. Development and validation of the German version of the Mobile Application Rating Scale (MARS-G) JMIR Mhealth Uhealth. 2020;8(3):e14479. doi: 10.2196/14479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Economides M, Martman J, Bell MJ, Sanderson B. Improvements in stress, affect, and irritability following brief use of a mindfulness-based smartphone app: A randomized controlled trial. Mindfulness. 2018;9:1584–1593. doi: 10.1007/s12671-018-0905-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Howells A, Ivtzan I, Eiroa-Orosa FJ. Putting the ‘app’ in happiness: A randomised controlled trial of a smartphone-based mindfulness intervention to enhance wellbeing. J Happiness Stud. 2016;17:163–185. doi: 10.1007/s10902-014-9589-1. [DOI] [Google Scholar]
  • 59.Yang E, Schamber E, Meyer RML, Gold JI. Happier healers: randomized controlled trial of mobile mindfulness for stress management. J Altern Complement Med. 2018;24:505–513. doi: 10.1089/acm.2015.0301. [DOI] [PubMed] [Google Scholar]
  • 60.Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018;6:e12106. doi: 10.2196/12106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Moffitt-Carney KM, Duncan AB. Evaluation of a mindfulness-based mobile application with college students: A pilot study. J Am Coll Health. 2019:1–7. 10.1080/07448481.2019.1661420. [DOI] [PubMed]
  • 62.Athanas AJ, McCorrison JM, Smalley S, Price J, Grady J, Campistron J, Schork NJ. Association between improvement in baseline mood and long-term use of a mindfulness and meditation app: observational study. JMIR Ment Health. 2019;6:e12617. doi: 10.2196/12617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Nagel A, John D, Scheder A, Kohls N. Klassisches oder digitales Stressmanagement im Setting Hochschule? [Conventional or digitalized stress management in a university setting] Präv Gesundheitsf. 2019;14:138–145. doi: 10.1007/s11553-018-0670-1. [DOI] [Google Scholar]
  • 64.Huberty J, Green J, Glissmann C, Larkey L, Puzia M, Lee C. Efficacy of the mindfulness meditation mobile app “calm” to reduce stress among college students: Randomized controlled trial. JMIR Mhealth Uhealth. 2019;7:e14273. doi: 10.2196/14273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Tunney C, Cooney P, Coyle D, O'Reilly G. Comparing young people’s experience of technology-delivered v. face-to-face mindfulness and relaxation: Two-armed qualitative focus group study. Br J Psychiatry. 2017;210:284–9. 10.1192/bjp.bp.115.172783. [DOI] [PubMed]
  • 66.Balas EA, Boren SA. Managing clinical knowledge for health care improvement. In: van Bemmeln JH, McCray AT, editors. Yearbook of medical informatics. Stuttgart: Schattauer; 2000. pp. 65–70. [PubMed] [Google Scholar]
  • 67.Brown CH, Kellam SG, Kaupert S, Muthén BO, Wang W, Muthén LK, et al. Partnerships for the design, conduct, and analysis of effectiveness, and implementation research: Experiences of the Prevention Science and Methodology Group. Adm Policy Ment Health. 2012;39:301–316. doi: 10.1007/s10488-011-0387-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Glasgow RE, Lichtenstein E, Marcus AC. Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health. 2003;93:1261–1267. doi: 10.2105/ajph.93.8.1261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Mohr DC, Cheung K, Schueller SM, Hendricks Brown C, Duan N. Continuous evaluation of evolving behavioral intervention technologies. Am J Prev Med. 2013;45:517–523. doi: 10.1016/j.amepre.2013.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Sucala M, Cuijpers P, Muench F, Cardoș R, Soflau R, Dobrean A, et al. Anxiety: There is an app for that. A systematic review of anxiety apps. Depress Anxiety. 2017;34:518–25. 10.1002/da.22654. [DOI] [PubMed]
  • 71.Armontrout J, Torous J, Fisher M, Drogin E, Gutheil T. Mobile mental health: Navigating new rules and regulations for digital tools. Curr Psychiatry Rep. 2016;18:91. doi: 10.1007/s11920-016-0726-x. [DOI] [PubMed] [Google Scholar]
  • 72.Bostock S, Crosswell AD, Prather AA, Steptoe A. Mindfulness on-the-go: Effects of a mindfulness meditation app on work stress and well-being. J Occup Health Psychol. 2019;24:127–138. doi: 10.1037/ocp0000118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.White LS. Reducing stress in school-age girls through mindful yoga. J Pediatr Health Care. 2012;26:45–56. doi: 10.1016/j.pedhc.2011.01.002. [DOI] [PubMed] [Google Scholar]
  • 74.Shapiro SL, Schwartz GE, Bonner G. Effects of mindfulness-based stress reduction on medical and premedical students. J Behav Med. 1998;21:581–599. doi: 10.1023/a:1018700829825. [DOI] [PubMed] [Google Scholar]
  • 75.Torous J, Andersson G, Bertagnoli A, Christensen H, Cuijpers P, Firth J, et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry. 2019;18:97–98. doi: 10.1002/wps.20592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Rodriguez-Villa E, Torous J. Regulating digital health technologies with transparency: The case for dynamic and multi-stakeholder evaluation. BMC Med. 2019;17:226. doi: 10.1186/s12916-019-1447-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Larsen ME, Nicholas J, Christensen H. Quantifying app store dynamics: Longitudinal tracking of mental health apps. JMIR Mhealth Uhealth. 2016;4:e96. doi: 10.2196/mhealth.6020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.StatCounter. Mobile Operating System Market Share Worldwide. 2019. https://gs.statcounter.com/os-market-share/mobile/worldwide. Accessed 28 Mar 2020.
  • 79.Baumel A, Faber K, Mathur N, Kane JM, Muench F. Enlight: A comprehensive quality and therapeutic potential evaluation tool for mobile and Web-based eHealth interventions. J Med Internet Res. 2017;19:e82. doi: 10.2196/jmir.7270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Torous JB, Chan SR, Gipson SY-MT, Kim JW, Nguyen T-Q, Luo J, Wang P. A Hierarchical framework for evaluation and informed decision making regarding smartphone apps for clinical care. Psychiatr Serv. 2018;69:498–500. 10.1176/appi.ps.201700423. [DOI] [PubMed]
  • 81.Henson P, David G, Albright K, Torous J. Deriving a practical framework for the evaluation of health apps. The Lancet Digital Health. 2019;1:e52–e54. doi: 10.1016/S2589-7500(19)30013-5. [DOI] [PubMed] [Google Scholar]

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