Increasing engagement with smartphone apps for anxiety and depression

Smartphone apps can reduce symptoms of depression and anxiety, but real-world users rarely use them for more than a few days. Our review aimed to identify features that make these apps more engaging and effective, which is especially crucial amidst the stress and isolation of the COVID-19 crisis.

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Mental health apps have shown tremendous promise for delivering efficacious mental health interventions1–4 and are especially needed amidst the stress and physical isolation of the ongoing COVID-19 pandemic. A co-occurring psychiatric epidemic5 has underscored the pressing need for effective digital mental health interventions.6 Despite the abundance of currently available mental health apps, in practice, users rarely engage with them for a meaningful period of time. More than 95% of users stop using a mental health app by day 15, which may curtail any beneficial effects.7 In our recently published review in npj Digital Medicine,8 we aimed to better understand the relationship between user engagement and symptom reduction in smartphone apps for depression and anxiety, with the hope of informing future app development.

In order to study this relationship, we first identified what app features might increase engagement and used the human-computer interaction literature describing the persuasive system design (PSD) framework.9 This framework identifies four mechanisms through which app features increase user engagement: primary task support, dialogue support, social support, and credibility support.9 Table 1 defines each mechanism and lists the individual features in each category.

Table 1. Persuasive system design framework categories, definitions, and features (adapted from Kelders et al. 20129).

Mechanism

Definition

Features

Primary Task Support

Supports app’s primary purpose

Reduction, Tunneling, Tailoring, Personalization, Self-monitoring, Simulation, Rehearsal

Dialogue Support

Improves app-user interaction

Praise, Rewards, Reminders, Suggestion, Similarity, Liking, Social role

Social Support

Leverages social relationships

Social learning, Social comparison, Normative influence, Social facilitation, Cooperation, Competition, Recognition

Credibility Support

Establishes app credibility

Trustworthiness, Expertise, Surface Credibility, Real-world feel, Authority, Third Party Endorsements, Verifiability

 

These categories were useful because they allowed us to comprehensively and systematically consider each app we reviewed. They have also been used previously to evaluate health technology outside of the mental health space. Specifically, PSD has been used to evaluate internet-based health and lifestyle interventions, and individual PSD features were found to predict how well participants stuck to the interventions.9 Thus, we used PSD to evaluate smartphone apps for depression and anxiety with the aim of evaluating the impact of persuasive design engagement features on both user engagement and mood symptoms.

Our systematic search of multiple scientific literature databases yielded 4143 unique articles relevant to smartphone apps and depression or anxiety, and we identified 25 independent studies that were appropriate for our review. Across the 25 studies, there were 4159 participants total, with 2905 participants using 29 unique smartphone apps.

Consistent with past studies,1–4 our meta-analysis found that standalone smartphone apps for depression and anxiety are efficacious in reducing depression and/or anxiety effects, with modest overall effects in randomized controlled trials. We also found that apps with a greater number of engagement features better reduced depression or anxiety symptoms. Our meta-analysis is the first to demonstrate that apps that use a greater number of engagement features have larger clinical effects—an exciting finding that may improve future apps by encouraging mental health app developers to include more of these engagement features.

We were surprised to find that PSD features are negatively associated with engagement, as measured by proportion of participants who complete the study. This finding contradicted our hypothesis that PSD features would be associated with increased study completion and thereby increase efficacy. We posit that participants using apps with more PSD features may use these apps more frequently, benefit more quickly, and then lose motivation to continue using these app once their symptoms have improved, compared to participants using less engaging apps. Because our review was not able to analyze the rate or timing of symptom change, we were not able to assess if users of more engaging apps benefited more quickly.

While completion rate was a convenient measure of engagement, other engagement outcomes such as minutes spent using the app or self-reported user experience of the usefulness, usability, and satisfaction with the app10 might be more informative engagement metrics. Furthermore, the quality of attention paid may affect how much the participant benefits as much as or even more than the duration of engagement. Given the known challenges with real-world engagement with mental health apps, the issue of defining meaningful engagement has been gaining attention and seems likely to influence the next steps in measuring app engagement.11

Overall, our meta-analysis confirmed prior findings that smartphone apps have efficacy in decreasing anxiety and depression symptoms—a welcome conclusion when social distancing measures make digital health interventions crucial. The use of persuasive design features is associated with greater reductions in symptoms, and we hope to see future apps incorporate more PSD features, especially those that are uncommonly used but have shown potential (e.g. social support9). As mental health apps become increasingly relevant, PSD features offer promising guidance on how to better address anxiety and depression symptoms and may also be useful in further exploration of meaningful user engagement.

References

  1. 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 Digital Med. 2019;2:118.
  2. 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.
  3. Firth J, Torous J, Nicholas J, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry 2017;16(3):287-298.
  4. Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 2019;18(3):325-336.
  5. Hossain MM, Tasnim S, Sultana A, et al. Epidemiology of mental health problems in COVID-19: a review. F1000Res. 2020;9:636.
  6. Figueroa CA, Aguilera A. The Need for a Mental Health Technology Revolution in the COVID-19 Pandemic. Front. Psychiatry 2020;11:523.
  7. Baumel A, Muench F, Edan S, Kane JM. Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. J. Med. Internet Res. 2019;21(9):e14567.
  8. Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. npj Digital Med. 2021;4(1):20.
  9. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JEWC. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J. Med. Internet Res. 2012;14(6):e152.
  10. Graham AK, Lattie EG, Mohr DC. Experimental therapeutics for digital mental health. JAMA Psychiatry 2019.
  11. Torous J, Michalak EE, O’Brien HL. Digital Health and Engagement-Looking Behind the Measures and Methods. JAMA Netw. Open 2020;3(7):e2010918.

Ashley Wu

Medical Student, Weill Cornell Medicine