A Tailored App for the Self-management of Musculoskeletal Conditions: Evidencing a Logic Model of Behavior Change
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal conditions such as joint pain are a growing problem, affecting 18.8 million people in the United Kingdom. Digital health interventions (DHIs) are a potentially effective way of delivering information and supporting self-management. It is vital that the development of such interventions is transparent and can illustrate how individual components work, how they link back to the theoretical constructs they are attempting to change, and how this might influence outcomes. getUBetter is a DHI developed to address the lack of personalized, supported self-management tools available to patients with musculoskeletal conditions by providing knowledge, skills, and confidence to navigate through a self-management journey. OBJECTIVE: The aim of this study was to map a logic model of behavior change for getUBetter to illustrate how the content and functionality of the DHI are aligned with recognized behavioral theory, effective behavior change techniques, and clinical guidelines. METHODS: A range of behavior change models and frameworks were used, including the behavior change wheel and persuasive systems design framework, to map the logic model of behavior change underpinning getUBetter. The three main stages included understanding the behavior the intervention is attempting to change, identifying which elements of the intervention might bring about the desired change in behavior, and describing intervention content and how this can be optimally implemented. RESULTS: The content was mapped to 25 behavior change techniques, including information about health consequences, instruction on how to perform a behavior, reducing negative emotions, and verbal persuasion about capability. Mapping to the persuasive system design framework illustrated the use of a number of persuasive design principles, including tailoring, personalization, simulation, and reminders. CONCLUSIONS: This process enabled the proposed mechanisms of action and theoretical foundations of getUBetter to be comprehensively described, highlighting the key techniques used to support patients to self-manage their condition. These findings provide guidance for the ongoing evaluation of the effectiveness (including quality of engagement) of the intervention and highlight areas that might be strengthened in future iterations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".