I got 99 apps but can’t stick with 1
How many apps do you have on your phone…and how many do you actually use?
There is no shortage of digital behavior change interventions (DBCI) such as mobile apps that are designed to promote behaviour change, whether it is a dieting app that helps users track food intake, encourage daily physical activity by reminding users to move every hour or decrease stress with guided relaxation. Increasingly, these interventions are being developed to promote treatment adherence in long term physical health conditions (LTCs) such as diabetes, 1 hypertension 2 or asthma.3 Compared to face-to-face interventions, DBCIs can be a cost-effective option, offering accessible opportunities to influence behavior in real time. However, some form of engagement with the app is thought to be key to its effectiveness 4 and yet research has shown that less than 3% of users engage in the continuous use expected to improve clinical outcomes. 5 How can we change behavior if we can’t engage the user?
Engagement is a multidimensional construct, measured through self-report questionnaires, verbal reports, automatic recording of app use or psychophysical signs and symptoms. 6
Identifying ways to enhance engagement is key to effective behavior change and maintenance
Engagement is a multidimensional construct; the extent of the user’s app usage is underpinned by their interest in the app content and other subjective experiences (e.g. usability of the app).6 It has been argued that effective engagement, defined as ‘sufficient engagement with the intervention to achieve intended outcomes’, is most beneficial in changing behaviour.7
Given this broad definition and lack of clarity on what makes DBCIs engaging to users, we scoped the literature to understand:
(i) how researchers capture engagement in apps designed to target treatment adherence (i.e., medication, self-management) and
(ii) what factors enhance users’ engagement with such apps.
How to get engaged
We synthesized the findings from randomized control trials, qualitative studies, pilot and feasibility studies assessing behavior change apps targeting treatment adherence in adults with LTCs. Engagement was measured differently across studies, however, there were key components which enhanced engagement in treatment adherence apps.
Studies captured engagement via objective (i.e., data analytics) and subjective (i.e., surveys, diaries, and interviews) measures. Feedback on features that enhanced user engagement was gathered via post intervention interviews, surveys, app usage and trends. We organized these findings into seven components and features found to enhance user engagement to app content. 8-23
Example of use
Tailored and personalised reminders and messages
Provide customization in schedule and cadence of reminders and messages. Messages personalized according to user input.
App allows users to set reminders for their medication at the time of day they are most likely to forget to take their medication.
Easy to understand messages and instructions
Terminology is appropriate for target audience and overly complicated questions are avoided.
An app targeted to teens with juvenile diabetes discusses concerns around going out with friends.
Visual summaries of data
Disseminate data in easy-to-understand graphics and provide visual representation of progress and health status.
Blood glucose levels are provided in a chart as feedback for medication adherence effectiveness.
Monitoring behavior (past and future)
Users are prompted to monitor their behavior and reflect on past and future behaviors.
App users log their food intake and blood glucose and monitor patterns (past).
App users write down their intended behaviors and review whether they met them (future).
App offers opportunities for the user to create a social network to support adherence to the intended behavior.
Users app is linked with corresponding app on their spouse’s device. Spouse is alerted when the user misses 3 doses in a row.
Limited burden of use
Limit the amount of time required by user to gain benefits from the app.
Secondary data sources inform app content as opposed to prompting user to input clinical data.
Rewards at behavioral benchmarks
Use of rewards when certain behavioral benchmarks are achieved.
Users earn badges for reaching exercise milestones.
These findings point to key app components important for user engagement in terms of increased usage and a positive user experience. However, further investigation is needed on the effect of these components on treatment adherence and clinical outcomes. Consideration for the measurement and enhancement of user engagement should be of top priority for developers of DBCIs as a first step to target behavior change. Making this investment could help enhance the effectiveness of treatment adherence apps and improve outcomes for patients.
Keller R, Hartmann S, Teepe GW, et al. Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis. J Med Internet Res. 2022;24(1):e33348.
Etminani K, Tao Engström A, Göransson C, et al. How Behavior Change Strategies are Used to Design Digital Interventions to Improve Medication Adherence and Blood Pressure Among Patients With Hypertension: Systematic Review. J Med Internet Res. 2020;22(4):e17201.
Morrison D, Wyke S, Agur K, et al. Digital Asthma Self-Management Interventions: A Systematic Review. J Med Internet Res. 2014;16(2):e51.
Donkin L, Christensen H, Naismith SL, et al. A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies. J Med Internet Res. 2011;13(3):e52.
Helander E, Kaipainen K, Korhonen I, et al. Factors Related to Sustained Use of a Free Mobile App for Dietary Self-Monitoring With Photography and Peer Feedback: Retrospective Cohort Study. J Med Internet Res. 2014;16(4):e109.
Perski O, Blandford A, West R, et al. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017;7(2):254-267.
Yardley L, Spring BJ, Riper H, et al. Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. American Journal of Preventive Medicine. 2016;51(5):833-842.
Hauser-Ulrich S, Künzli H, Meier-Peterhans D, et al. A Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020;8(4):e15806.
Huang Z, Tan E, Lum E, et al. Correction: A Smartphone App to Improve Medication Adherence in Patients With Type 2 Diabetes in Asia: Feasibility Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020;8(4):e18411.
Kooij L, Vos PJE, Dijkstra A, et al. Effectiveness of a Mobile Health and Self-Management App for High-Risk Patients With Chronic Obstructive Pulmonary Disease in Daily Clinical Practice: Mixed Methods Evaluation Study. JMIR Mhealth Uhealth. 2021;9(2):e21977.
Hochstenbach LM, Zwakhalen SM, Courtens AM, et al. Feasibility of a mobile and web-based intervention to support self-management in outpatients with cancer pain. Eur J Oncol Nurs. 2016;23:97-105.
Wei KS, Ibrahim NE, Kumar AA, et al. Habits Heart App for Patient Engagement in Heart Failure Management: Pilot Feasibility Randomized Trial. JMIR Mhealth Uhealth. 2021;9(1):e19465.
McBride CM, Morrissey EC, Molloy GJ. Patients’ Experiences of Using Smartphone Apps to Support Self-Management and Improve Medication Adherence in Hypertension: Qualitative Study. JMIR Mhealth Uhealth. 2020;8(10):e17470.
Stephens JD, Yager AM, Allen J. Smartphone Technology and Text Messaging for Weight Loss in Young Adults: A Randomized Controlled Trial. J Cardiovasc Nurs. 2017;32(1):39-46.
Chew S, Lai PSM, Ng CJ. Usability and Utility of a Mobile App to Improve Medication Adherence Among Ambulatory Care Patients in Malaysia: Qualitative Study. JMIR Mhealth Uhealth. 2020;8(1):e15146.
Adu MD, Malabu UH, Malau-Aduli AE, et al. User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study. JMIR Mhealth Uhealth. 2020;8(6):e17802.
Torbjørnsen A, Ribu L, Rønnevig M, et al. Users’ acceptability of a mobile application for persons with type 2 diabetes: a qualitative study. BMC Health Services Research. 2019;19(1):641.
Duan H, Wang Z, Ji Y, et al. Using Goal-Directed Design to Create a Mobile Health App to Improve Patient Compliance With Hypertension Self-Management: Development and Deployment. JMIR Mhealth Uhealth. 2020;8(2):e14466.
Heiney SP, Donevant SB, Arp Adams S, et al. A Smartphone App for Self-Management of Heart Failure in Older African Americans: Feasibility and Usability Study. JMIR Aging. 2020;3(1):e17142.
Kassavou A, A’Court CE, Chauhan J, et al. Assessing the acceptability of a text messaging service and smartphone app to support patient adherence to medications prescribed for high blood pressure: a pilot study. Pilot Feasibility Stud. 2020;6:134.
Sittig S, Wang J, Iyengar S, et al. Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes. JMIR Mhealth Uhealth. 2020;8(3):e15927.
Steinberg DM, Kay MC, Svetkey LP, et al. Feasibility of a Digital Health Intervention to Improve Diet Quality Among Women With High Blood Pressure: Randomized Controlled Feasibility Trial. JMIR Mhealth Uhealth. 2020;8(12):e17536.
Westergaard RP, Genz, A., Panico, K., Surkan, P. J., Keruly, J., Hutton, H. E., Chang, L. W., & Kirk, G. D. Acceptability of a mobile health intervention to enhance HIV care coordination for patients with substance use disorders. Addiction Science and Clinical Practice. 2017;12(1).