3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study
Bibliographic record
Abstract
BACKGROUND: Chronic conditions are the leading cause of death in the world. Major improvements in acute care and diagnostics have created a tendency toward the chronification of formerly terminal conditions, requiring people with these conditions to learn how to self-manage. Mobile technologies hold promise as self-management tools due to their ubiquity and cost-effectiveness. The delivery of health-related services through mobile technologies (mobile health, mHealth) has grown exponentially in recent years. However, only a fraction of these solutions take into consideration the views of relevant stakeholders such as health care professionals or even patients. The use of behavioral change models (BCMs) has proven important in developing successful health solutions, yet engaging patients remains a challenge. There is a trend in mHealth solutions called gamification that attempts to use game elements to drive user behavior and increase engagement. As it stands, designers of mHealth solutions for behavioral change in chronic conditions have no clear way of deciding what factors are relevant to consider. OBJECTIVE: The goal of this work is to discover factors for the design of mHealth solutions for chronic patients using negotiations between medical knowledge, BCMs, and gamification. METHODS: This study uses an embedded case study research methodology consisting of 4 embedded units: 1) cross-sectional studies of mHealth applications; 2) statistical analysis of gamification presence; 3) focus groups and interviews to relevant stakeholders; and 4) research through design of an mHealth solution. The data obtained was thematically analyzed to create a conceptual model for the design of mHealth solutions. RESULTS: The Model for Motivational Mobile-health Design (3MD) for chronic conditions guides the design of condition-oriented gamified behavioral change mHealth solutions. The main components are (1) condition specific, which describe factors that need to be adjusted and adapted for each particular chronic condition; (2) motivation related, which are factors that address how to influence behaviors in an engaging manner; and (3) technology based, which are factors that are directly connected to the technical capabilities of mobile technologies. The 3MD also provides a series of high-level illustrative design questions for designers to use and consider during the design process. CONCLUSIONS: This work addresses a recognized gap in research and practice, and proposes a unique model that could be of use in the generation of new solutions to help chronic patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".