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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".