Engaging children with cerebral palsy in interactive computer play-based motor therapies: theoretical perspectives
Bibliographic record
Abstract
Purpose To provide a theoretically grounded understanding of engagement in interactive computer play-based motor therapies by children with cerebral palsy in home settings.Methods A motivational framework for engagement and its relationship with three contemporary theories (self-determination theory, expectancy-value theory, social cognitive theory of self-regulation) was overviewed. A scoping review was conducted to understand how engagement is influenced by features of the technology and intervention design that impact intrinsic and extrinsic motivation, child and parent values and expectancies, and the processes of self-regulation. Multiple reviewers screened and extracted data from 26 articles describing home-based clinical trials of interactive computer play-based motor interventions for children with cerebral palsy. A narrative synthesis framework was used for analysis.Results Features of the technology and the intervention influence feelings of autonomy (e.g., personalization), competence (e.g., calibration), and relatedness (e.g., social play, virtual therapist/coach). There may be multiple and differently valued goals in interactive computer play-based interventions (e.g., game- and therapy-focused) that, if disconnected or unmet, negatively impact engagement. Multiplayer interactions, real-time feedback and progress tracking provide information that influences self-regulation and engagement over time.Conclusions Optimizing engagement in interactive computer play-based motor interventions requires closer alignment with client-led values/goals; design of technologies and interventions that sustain intrinsic motivation; and feedback that informs/builds self-efficacy.Implications for rehabilitationThe decision to prescribe an interactive computer play-based motor intervention should be guided by client-led goals and an informed understanding of the capacity of the interactive computer play-based intervention to meet individual client values/expectancies.Sustaining intrinsic motivation in interactive computer play-based motor therapies is greatly influenced by features of the technology (e.g., calibration, feedback, personalization) and the intervention (e.g., interactions with therapists, social play).Increased effort should be directed towards ensuring that interactive computer play-based interventions and technologies reward and reinforce efforts towards therapy goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".