Feasibility Of Qualitative Geospatial Methods To Explore Physical Activity In Children With Developmental Disabilities
Bibliographic record
Abstract
One in 20 children live with a developmental disability (DD). These children experience significant barriers to physical activity (PA) participation. Greater contextual information regarding their PA behaviours and preferences is needed for effective PA promotion. PURPOSE: To investigate the utility of Geographic Positioning Systems (GPS) and Fitbits with follow up map-based interviews to describe and understand PA behaviours and context in children with DD. METHODS: In this mixed methods study, 15 children with DD were recruited from across British Columbia, Canada, in summer 2022. Participants wore a Fitbit (Charge 4) and a GPS device (QStarz) for 7 days to assess PA (steps/day) and PA location. Intraday Fitbit data was extracted to REDCap via API. Fitbit and GPS data were processed through a custom in-house algorithm to time-align device data, to validate wear time (≥600 min/d), and to identify PA locations and trip mode (walk, car). Geographic Information Systems (GIS ArcMap) was used to create maps from each child’s device data to guide follow up virtual semi-structured interviews with child and parent participants to understand PA contexts and perceptions of daily PA. Statistical analyses were performed in R, with significance set at p < 0.05. Interviews were analyzed using thematic analysis. RESULTS: Six children had autism, 3 had attention deficit disorder, and 6 had both (age 10 ± 2 yrs; 100% boys). Mean steps/d were 8680 ± 4267 across 5 ± 1 days. Compared with older boys (10-12 yrs), younger boys (7-9 yrs) had significantly higher step counts at home (2795 ± 3271 vs. 1671 ± 1958, p = 0.03) and on weekdays (10585 ± 4244 vs.7404 ± 3587, p = 0.005). Overall, the most common trip mode was by car (23 ± 51 min/d). Children provided insight on their preferences for PA locations using the maps. Parents also gave context to their child’s PA by expressing how disability type affects PA and identifying key environmental factors in PA locations (e.g., proximity to busy roads). CONCLUSION: Utilizing Fitbits and GPS with follow up map-based interviews are feasible to describe PA behaviours and contexts in children with DD. This methodology has the potential to provide novel insight on daily PA in children with DD, which can inform how future interventions and programs can support these children to be active.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".