Improving Adherence to Physical Therapy in the Development of Serious Games: Conceptual Framework Design Study
Bibliographic record
Abstract
BACKGROUND: Insufficient levels of treatment adherence can have adverse effects on the outcomes of physical rehabilitation. To address this issue, alternative approaches to traditional therapies, such as serious games, have been designed to enhance adherence. Nevertheless, there remain gaps in the development of serious games concerning the effective implementation of motivation, engagement, and the enhancement of treatment adherence. OBJECTIVE: This study aims to design a conceptual framework for the development of serious games that incorporate essential adherence factors to enhance patient compliance with physical rehabilitation programs. METHODS: We formulated a conceptual framework using iterative techniques inspired by a conceptual framework analysis. Initially, we conducted a comprehensive literature review, concentrating on the critical adherence factors in physical rehabilitation. Subsequently, we identified, categorized, integrated, and synthesized the concepts derived from the literature review to construct the conceptual framework. RESULTS: The framework resembles a road map, comprising 3 distinct phases. In the initial phase, the patient's characteristics are identified through an initial exploration. The second phase involves the development of a serious game, with a focus on enhancing treatment adherence by integrating the key adherence factors identified. The third phase revolves around the evaluation of the serious game. These phases are underpinned by 2 overarching themes, namely, a user-centered design and the GameFlow model. CONCLUSIONS: The conceptual framework offers a detailed, step-by-step guide for creating serious games that incorporate essential adherence factors, thereby contributing to improved adherence in the physical rehabilitation process. To establish its validity, further evaluations of this framework across various physical rehabilitation programs and user groups are necessary.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".