Persuasive Mobile Apps for Health and Wellness: A Comparative Systematic Review

Oyebode, Oladapo; Ndulue, Chinenye; Alhasani, Mona; Orji, Rita

doi:10.1007/978-3-030-45712-9_13

Cited by 53 publications

(37 citation statements)

References 17 publications

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“…This paper focuses on the diet aspects of T2D management as part of testing the application of the co-design approach in intervention development. Previous research has shown most digital dietary apps apply a combination of persuasive strategies to promote healthy eating among users [31], and our workshops' findings closely align with most of these. Namely, personalization and suggestion (most dominant persuasive strategies), self-monitoring, reduction, reminders, expertise, trustworthiness, surface credibility, and real-world feel.…”

Section: Comparison With the Literaturesupporting

confidence: 79%

Co-design of a digital dietary intervention for adults at risk of type 2 diabetes

et al. 2021

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Background Co-design has the potential to create interventions that lead to sustainable health behaviour change. Evidence suggests application of co-design in various health domains has been growing; however, few public-facing digital interventions have been co-designed to specifically address the needs of adults at risk of Type 2 diabetes (T2D). This study aims to: (1) co-design, with key stakeholders, a digital dietary intervention to promote health behaviour change among adults at risk of T2D, and (2) evaluate the co-design process involved in developing the intervention prototype. Methods The co-design study was based on a partnership between nutrition researchers and designers experienced in co-design for health. Potential end-users (patients and health professionals) were recruited from an earlier stage of the study. Three online workshops were conducted to develop and review prototypes of an app for people at risk of T2D. Themes were inductively defined and aligned with persuasive design (PD) principles used to inform ideal app features and characteristics. Results Participants were predominantly female (range 58–100%), aged 38 to 63 years (median age = 59 years), consisting of a total of 20 end-users and four experts. Participants expressed the need for information from credible sources and to provide effective strategies to overcome social and environmental influences on eating behaviours. Preferred app features included tailoring to the individual’s unique characteristics, ability to track and monitor dietary behaviour, and tools to facilitate controlled social connectivity. Relevant persuasive design principles included social support, reduction (reducing effort needed to reach target behaviour), tunnelling (guiding users through a process that leads to target behaviour), praise, rewards, and self-monitoring. The most preferred prototype was the Choices concept, which focusses on the users’ journey of health behaviour change and recognises progress, successes, and failures in a supportive and encouraging manner. The workshops were rated successful, and feedback was positive. Conclusions The study’s co-design methods were successful in developing a functionally appealing and relevant digital health promotion intervention. Continuous engagement with stakeholders such as designers and end-users is needed to further develop a working prototype for testing.

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Section: Comparison With the Literaturesupporting

confidence: 79%

Co-design of a digital dietary intervention for adults at risk of type 2 diabetes

et al. 2021

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“…), including mental health conditions, and can effectively prevent or control them. For instance, specialized mHealth apps provide features for tracking symptoms, blood pressure, body mass index, heartbeat rate, sleep rate, nutritional values in foods, and much more [1]. mHealth apps offer users additional opportunities to share collected data with their healthcare professionals for real-time monitoring, as well as personalize contents to suit their physical and behavioural characteristics [2].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews

2020

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The proliferation of smartphones has led to an increase in mobile health (mHealth) apps over the years. Thus, it is imperative to evaluate these apps by identifying shortcomings or barriers hampering effective delivery of intended services. In this paper, we evaluate 104 mental health apps on Google Play and App Store by performing sentiment analysis of 88125 user reviews using machine learning (ML), and then conducting thematic analysis on the reviews. We implement and compare the performance of five classifiers using supervised ML algorithms that are widely used for solving classification problems. The best performing classifier, with F1-score of 89.42%, was then used in predicting the sentiment polarity of reviews. Next, we conduct a thematic analysis of positive and negative reviews to identify themes representing various factors affecting the effectiveness of mental health apps positively and negatively. Our results uncover 21 negative themes and 29 positive themes. The negative themes fall under the following categories: usability issues, content issues, ethical issues, customer support issues, and billing issues. Some of the positive themes include aesthetically pleasing interface, app stability, customizability, high-quality content, content variation/diversity, personalized content, privacy and security, and low-subscription cost. Finally, we offer design recommendations on how the identified negative factors can be tackled to improve the effectiveness of mental health apps.

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“…The accessibility of smartphones has made possible the wider use of many health-related apps and the sharing of such apps with a great number of people who seek to improve their health [26]. Some of these apps are designed to manage chronic diseases or to treat a patient [3], while others are developed to promote healthy behaviors to improve users' wellness [27].…”

Section: Mobile Health and Wellness Appsmentioning

confidence: 99%

“…Affordance theory has been used in various research studies in the IS domain [34][35][36] and in contexts such as software development [37,38], human-computer interaction [39], and social media [40,41]. Regarding the mHealth domain, studies have used affordance theory to explain phenomena related to mHealth apps with a focus on particular settings [42], specific chronic conditions [43], particular diseases [44], and overall wellness [26].…”

Section: Affordance Theory In Is Researchmentioning

confidence: 99%

Investigating the Affordances of Wellness mHealth Apps

Alshawmar¹,

Mombini

Tulu

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Previous research on the effectiveness of wellness mHealth apps focused on the design and features of such apps and paid insufficient attention to how the whole relationship between the apps and users impact use. Using affordance theory, we investigated what wellness mHealth apps afford to users and why these affordances are not actualized by all users. We conducted a qualitative study, collecting data from apps' reviews and from fifteen participants who used multiple wellness mHealth apps. Our grounded theory analysis revealed four shared affordances (promoting goals, comparing oneself to others, coaching, and nurturing) related to the use of wellness mHealth apps and three immediate concrete outcomes (habit formation, self-awareness, and goal attainment) reached after the affordances were actualized. Nonetheless, factors such as information overload, aesthetic appreciation, and users' characteristics may impact users' actualizations of the shared affordances and prevent some users from reaching their immediate concrete outcomes.

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Persuasive Mobile Apps for Health and Wellness: A Comparative Systematic Review

Cited by 53 publications

References 17 publications

Co-design of a digital dietary intervention for adults at risk of type 2 diabetes

Co-design of a digital dietary intervention for adults at risk of type 2 diabetes

Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews

Investigating the Affordances of Wellness mHealth Apps

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