A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".