Deconstructing Persuasive Strategies in Mental Health Apps Based on User Reviews using Natural Language Processing

Text Mining is concerned with extracting interesting and significant patterns or knowledge from unstructured text data. In this paper, we applied the text mining approach using natural language processing (NLP) techniques, especially topic modelling (with automated topic labelling), in deconstructing the persuasive strategies implemented or employed by 100 mental health apps based on user reviews. We focus on the persuasive strategies in the primary task support category of the Persuasive Systems Design (PSD) framework. We used the Latent Dirichlet Allocation (LDA) topic modelling algorithm, in conjunction with semantic attributes, to achieve our goal. Our experimental results revealed that self-monitoring is the most employed persuasive strategy. Finally, we compare our findings with that obtained using manual coding method and found significant similarities.

[1]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[2]  Lukasz Golab,et al.  Social Media Mining to Understand Public Mental Health , 2017, DMAH@VLDB.

[3]  Harri Oinas-Kukkonen,et al.  Native Mobile Applications For Personal Well-Being: A Persuasive Systems Design Evaluation , 2012, PACIS.

[4]  Joanna Lumsden.  Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies , 2011 .

[5]  Akane Sano,et al.  Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study , 2018, Journal of medical Internet research.

[6]  Kiemute Oyibo,et al.  Personalizing health theories in persuasive game interventions to gamer types: an African perspective , 2018, AfriCHI.

[7]  Danushka Bollegala,et al.  Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach , 2018, JMIR public health and surveillance.

[8]  Michael E. Labhard,et al.  Mobile Therapy: Case Study Evaluations of a Cell Phone Application for Emotional Self-Awareness , 2010, Journal of medical Internet research.

[9]  Jun Hu,et al.  DeLight: biofeedback through ambient light for stress intervention and relaxation assistance , 2018, Personal and Ubiquitous Computing.

[10]  Yunlong Wang,et al.  Health Behaviour Change Techniques in Diabetes Management Applications: A Systematic Review , 2019, ArXiv.

[11]  Lena Sanci,et al.  Self-monitoring Using Mobile Phones in the Early Stages of Adolescent Depression: Randomized Controlled Trial , 2012, Journal of medical Internet research.

[12]  Eija Kaasinen,et al.  TECHNOLOGY Acceptance MODEL FOR Mobile Services AS A DESIGN FRAMEWORK , 2011 .

[13]  Kalevi Korpela,et al.  Activity spaces and urban adolescent substance use and emotional health. , 2009, Journal of adolescence.

[14]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[15]  C. Davey,et al.  Youth Codesign of a Mobile Phone App to Facilitate Self-Monitoring and Management of Mood Symptoms in Young People With Major Depression, Suicidal Ideation, and Self-Harm , 2018, JMIR mental health.

[16]  Zhiyong Lu,et al.  Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. , 2016, Advances in experimental medicine and biology.

[17]  G. Andersson,et al.  Mobile technology for mental health assessment , 2016, Dialogues in clinical neuroscience.

[18]  Rita Orji,et al.  Apps for Mental Health: An Evaluation of Behavior Change Strategies and Recommendations for Future Development , 2019, Front. Artif. Intell..

[19]  Julita Vassileva,et al.  Improving the Efficacy of Games for Change Using Personalization Models , 2017, ACM Trans. Comput. Hum. Interact..

[20]  Rita Orji,et al.  Persuasive technology for health and wellness: State-of-the-art and emerging trends , 2018, Health Informatics J..

[21]  Rita Orji,et al.  Persuasive Mobile Apps for Health and Wellness: A Comparative Systematic Review , 2020, PERSUASIVE.

[22]  Lejla Turulja,et al.  Text Mining for Big Data Analysis in Financial Sector: A Literature Review , 2019, Sustainability.

[23]  Kyle Taylor,et al.  Smartphone ownership is growing rapidly around the world, but not always equally , 2019 .

[24]  K. Mason,et al.  Voice of airline passenger: A text mining approach to understand customer satisfaction , 2019, Journal of Air Transport Management.

[25]  Lynette Kvasny Yarger,et al.  Text Mining Mental Health Reports for Issues Impacting Today’s College Students: Qualitative Study , 2018, JMIR mental health.

[26]  James Boit,et al.  Topical Mining of Malaria Using Social Media. A Text Mining Approach , 2020, HICSS.

[27]  Ahmed E. Hassan,et al.  Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews , 2015, Empirical Software Engineering.

[28]  Juan Enrique Ramos,et al.  Using TF-IDF to Determine Word Relevance in Document Queries , 2003 .

[29]  Jun Liu,et al.  Discovering Design Principles for Health Behavioral Change Support Systems , 2017, ACM Trans. Manag. Inf. Syst..

[30]  Sangaralingam Kajanan,et al.  Designing Fitness Apps Using Persuasive Technology: A Text Mining Approach , 2015, PACIS.

[31]  Ahmed E. Hassan,et al.  What Do Mobile App Users Complain About? , 2015, IEEE Software.

[32]  Fusheng Wang,et al.  Data Management and Analytics for Medicine and Healthcare , 2016, Lecture Notes in Computer Science.

[33]  Judith Gebauer,et al.  User requirements of mobile technology: results from a content analysis of user reviews , 2008, Inf. Syst. E Bus. Manag..

[34]  Harri Oinas-Kukkonen,et al.  Persuasive Systems Design: Key Issues, Process Model, and System Features , 2009, Commun. Assoc. Inf. Syst..

[35]  Topology and mental distress: Self-care in the life spaces of home , 2014, Journal of health psychology.

[36]  Vahid Ghasemi,et al.  Factors Influencing Customers' Satisfaction and Dissatisfaction with Hotels: A Text-Mining Approach , 2019, Tourism Analysis.

[37]  Gerard de Melo,et al.  Public opinion matters: mining social media text for environmental management , 2020, LINK.

[38]  David Bakker,et al.  Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism. , 2018, Journal of affective disorders.

[39]  Rita Orji,et al.  Usability Issues in Mental Health Applications , 2019, UMAP.