A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement

Background Chronic arthritis (CA), an umbrella term for inflammatory rheumatic and other musculoskeletal diseases, is highly prevalent. Effective disease-modifying antirheumatic drugs for CA are available, with the exception of osteoarthritis, but require a long-term commitment of patients to comply with the medication regimen and management program as well as a tight follow-up by the treating physician and health professionals. Additionally, patients are advised to participate in physical exercise programs. Adherence to exercises and physical activity programs is often very low. Patients would benefit from support to increase medication compliance as well as compliance to the physical exercise programs. To address these shortcomings, health apps for CA patients have been created. These mobile apps assist patients in self-management of overall health measures, health prevention, and disease management. By including persuasive principles designed to reinforce, change, or shape attitudes or behaviors, health apps can transform into support tools that motivate and stimulate users to achieve or keep up with target behavior, also called persuasive systems. However, the extent to which health apps for CA patients consciously and successfully employ such persuasive principles remains unknown. Objective The objective of this study was to evaluate the number and type of persuasive principles present in current health apps for CA patients. Methods A review of apps for arthritis patients was conducted across the three major app stores (Google Play, Apple App Store, and Windows Phone Store). Collected apps were coded according to 37 persuasive principles, based on an altered version of the Persuasive System Design taxonomy of Oinas-Kukkonen and Harjuma and the taxonomy of Behavior Change Techniques of Michie and Abraham. In addition, user ratings, number of installs, and price of the apps were also coded. Results We coded 28 apps. On average, 5.8 out of 37 persuasive principles were used in each app. The most used category of persuasive principles was System Credibility with an average of 2.6 principles. Task Support was the second most used, with an average of 2.3 persuasive principles. Next was Dialogue Support with an average of 0.5 principles. Social Support was last with an average of 0.01 persuasive principles only. Conclusions Current health apps for CA patients would benefit from adding Social Support techniques (eg, social media, user fora) and extending Dialogue Support techniques (eg, rewards, praise). The addition of automated tracking of health-related parameters (eg, physical activity, step count) could further reduce the effort for CA patients to manage their disease and thus increase Task Support. Finally, apps for health could benefit from a more evidence-based approach, both in developing the app as well as ensuring that content can be verified as scientifically proven, which will result in enhanced System Credibility.

[1]  Ahmed Habbani,et al.  Emerging wireless technologies in e-health trends, challenges, and framework design issues , 2012, 2012 International Conference on Multimedia Computing and Systems.

[2]  R. Geenen,et al.  EULAR recommendations for the non-pharmacological core management of hip and knee osteoarthritis , 2013, Annals of the rheumatic diseases.

[3]  Rita Azevedo,et al.  Smartphone application for rheumatoid arthritis self-management: cross-sectional study revealed the usefulness, willingness to use and patients’ needs , 2015, Rheumatology International.

[4]  Kazuya Okamoto,et al.  Objective assessment of abnormal gait in patients with rheumatoid arthritis using a smartphone , 2012, Rheumatology International.

[5]  C. Abraham,et al.  A taxonomy of behavior change techniques used in interventions. , 2008, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[6]  Christopher Cunningham,et al.  Gamification by Design - Implementing Game Mechanics in Web and Mobile Apps , 2011 .

[7]  S. Michie,et al.  Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy , 2010, Journal of medical Internet research.

[8]  Nadia Bianchi-Berthouze,et al.  User needs for technology supporting physical activity in chronic pain , 2012, CHI EA '12.

[9]  M. Hochberg,et al.  Effect of Mindfulness-Based Stress Reduction in rheumatoid arthritis patients. , 2007, Arthritis and rheumatism.

[10]  P. Davies The American heritage dictionary of the English language , 1981 .

[11]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[12]  A. Bandura Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[13]  H. Oinas-Kukkonen,et al.  Persuasive Features in Web-Based Alcohol and Smoking Interventions: A Systematic Review of the Literature , 2011, Journal of medical Internet research.

[14]  Elie Karam,et al.  Common chronic pain conditions in developed and developing countries: gender and age differences and comorbidity with depression-anxiety disorders. , 2008, The journal of pain : official journal of the American Pain Society.

[15]  K Rivet Amico,et al.  An information-motivation-behavioral skills model of adherence to antiretroviral therapy. , 2006, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[16]  S. Kelders,et al.  Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions , 2012, Journal of medical Internet research.

[17]  K. Spink,et al.  Physical activity in women with arthritis: examining perceived barriers and self-regulatory efficacy to cope. , 2009, Arthritis and rheumatism.

[18]  Rajesh Balkrishnan,et al.  The importance of medication adherence in improving chronic-disease related outcomes: what we know and what we need to further know. , 2005, Medical care.

[19]  Frederick Wolfe,et al.  Predicting depression in rheumatoid arthritis: the signal importance of pain extent and fatigue, and comorbidity. , 2009, Arthritis and rheumatism.

[20]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[21]  A. Bauman,et al.  Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. , 2007, Circulation.

[22]  Christopher E. Beaudoin,et al.  Apps Seeking Theories: Results of a Study on the Use of Health Behavior Change Theories in Cancer Survivorship Mobile Apps , 2015, JMIR mHealth and uHealth.

[23]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[24]  Ana Rita Pereira Azevedo,et al.  Future perspectives of Smartphone applications for rheumatic diseases self-management , 2015, Rheumatology International.

[25]  Illhoi Yoo,et al.  A Systematic Review of Healthcare Applications for Smartphones , 2012, BMC Medical Informatics and Decision Making.

[26]  Nicol Nijland,et al.  A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies , 2011, Journal of medical Internet research.

[27]  E. Taal,et al.  Group education for patients with rheumatoid arthritis and their partners. , 2003, Arthritis and rheumatism.

[28]  J. Beckham,et al.  Pain coping strategies in rheumatoid arthritis: Relationships to pain, disability, depression and daily hassles , 1991 .

[29]  S le Cessie,et al.  Using internet technology to deliver a home-based physical activity intervention for patients with rheumatoid arthritis: A randomized controlled trial. , 2006, Arthritis and rheumatism.

[30]  Heinz Mandl,et al.  Psychological Perspectives on Motivation through Gamification , 2013, IxD&A.

[31]  S. Maes,et al.  Targeting motivation and self-regulation to increase physical activity among patients with rheumatoid arthritis: a randomised controlled trial , 2015, Clinical Rheumatology.

[32]  R. Zajonc SOCIAL FACILITATION. , 1965, Science.

[33]  J. Hazes,et al.  Dynamic exercise therapy in rheumatoid arthritis: a systematic review. , 1998, British journal of rheumatology.

[34]  B. J. Fogg,et al.  Behavior Wizard: A Method for Matching Target Behaviors with Solutions , 2010, PERSUASIVE.

[35]  J. Brug,et al.  Apps to promote physical activity among adults: a review and content analysis , 2014, International Journal of Behavioral Nutrition and Physical Activity.

[36]  Elizabeth J Lyons,et al.  Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis , 2014, Journal of medical Internet research.

[37]  B. Skinner Operant Behavior , 2021, Encyclopedia of Evolutionary Psychological Science.

[38]  Christina Keller,et al.  A Mobile Internet Service for Self-Management of Physical Activity in People With Rheumatoid Arthritis: Challenges in Advancing the Co-Design Process During the Requirements Specification Phase , 2015, JMIR research protocols.

[39]  M. Fishbein A theory of reasoned action: some applications and implications. , 1980, Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.

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

[41]  K. Lorig,et al.  Evidence suggesting that health education for self-management in patients with chronic arthritis has sustained health benefits while reducing health care costs. , 1993, Arthritis and rheumatism.

[42]  Harri Oinas-Kukkonen,et al.  Persuasive Technology in Mobile Applications Promoting Physical Activity: a Systematic Review , 2016, Journal of Medical Systems.

[43]  P. Nicassio,et al.  Behavioral intervention with and without family support for rheumatoid arthritis , 1992 .

[44]  M. Lombard,et al.  Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability , 2002 .

[45]  Bert N. Uchino,et al.  The relationship between social support and physiological processes: a review with emphasis on underlying mechanisms and implications for health. , 1996, Psychological bulletin.

[46]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[47]  J. Cafazzo,et al.  Design of an mHealth App for the Self-management of Adolescent Type 1 Diabetes: A Pilot Study , 2012, Journal of medical Internet research.

[48]  K. Chakravarty,et al.  Evidence-based recommendations for the role of exercise in the management of osteoarthritis of the hip or knee--the MOVE consensus. , 2005, Rheumatology.

[49]  H. Christensen,et al.  Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review , 2013, Journal of medical Internet research.

[50]  J. Cassel,et al.  Social Support and Health , 1977, Medical care.

[51]  Kazuya Okamoto,et al.  Development of lifelog sharing system for rheumatoid arthritis patients using smartphone , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[52]  Harri Oinas-Kukkonen,et al.  Towards Deeper Understanding of Persuasion in Software and Information Systems , 2008, First International Conference on Advances in Computer-Human Interaction.

[53]  N. Gyurcsik,et al.  General and arthritis-specific barriers to moderate physical activity in women with arthritis. , 2011, Women's health issues : official publication of the Jacobs Institute of Women's Health.

[54]  Ana Tajadura-Jiménez,et al.  Motivating people with chronic pain to do physical activity: opportunities for technology design , 2014, CHI.

[55]  R. Cialdini,et al.  Social influence: Social norms, conformity and compliance. , 1998 .

[56]  L. Swartz,et al.  Scaling Up mHealth: Where Is the Evidence? , 2013, PLoS medicine.

[57]  Sebastian Deterding,et al.  Gamification: designing for motivation , 2012, INTR.

[58]  Irene Jensen,et al.  Coaching patients with early rheumatoid arthritis to healthy physical activity: a multicenter, randomized, controlled study. , 2008, Arthritis and rheumatism.

[59]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[60]  R. Verbrugge,et al.  Impact of Medication Adherence on Hospitalization Risk and Healthcare Cost , 2005, Medical care.