A Systematic Review of Mobile Applications to Support Individuals With Cerebral Palsy and Their Caregivers
Bibliographic record
Abstract
This study aims to systematically review the effect of mobile applications (apps) in supporting individuals with cerebral palsy (CP) and their caregivers. Five databases were searched for articles published between 2013 and 2023. Included studies were original with full available text that assessed the effectiveness of mobile apps to support the daily life of individuals with CP and their caregivers. The Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool was used to assess the risk of bias, and quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Six studies, encompassing 91 individuals with CP, were included. Included studies were mostly experimental (3/6, 50.0%). Predominantly focused on children with CP, the studies covered various areas, such as assistance with speech impairment, intervention mapping with gamification, athletics, relaxation, and educational apps for individuals with CP. Among all, one study focused on caregivers. Out of six studies, two were found to be serious (33.3%), and four (66.7%) had a moderate risk of bias. Quality assessments revealed that grades were low (4/6, 66.7%) and very low quality (2/6, 33.3%). The limited available studies indicate the need for future research on the potential of integrating technological solutions, such as mobile apps, in addressing various facets of management and care of individuals with CP.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".