The Living Lab at Home: Feasibility and Acceptability of Multimodal In-Home Data Collection Among Youth Across the Developmental Spectrum
Bibliographic record
Abstract
OBJECTIVE: Dynamic, real-time, in-home methods of data collection are increasingly common in child health research. However, these methods are rarely cocreated or used with families of youth with developmental disabilities. We aimed to determine the feasibility of codesigned methods for in-home data collection for youth across the developmental spectrum. METHODS: Sixteen youth (14-18 years) with autism spectrum disorder, cerebral palsy, and/or chronic pain completed 14 days of data collection, wearing an accelerometer, answering Ecological Momentary Assessment (EMA) questionnaires, and collecting salivary cortisol samples. Participants completed a poststudy interview regarding their experiences. Data were analyzed for feasibility, quantity, and quality. RESULTS: At least 1 EMA response was provided on 73% of days, with 54% of the total number of administered prompts answered before the next prompt arrived. In total, 77% of participants wore the accelerometer ≥10 hours for at least 7 days. Adherence to 8-day saliva sampling after accounting for protocol violations and dry samples was 28%. No significant adverse events were reported aside from mild emotional distress (25%). Families reported generally high satisfaction, willingness to participate again, and acceptability, with moderate burden and interference. Qualitative interviews described: (1) the research question's value to the family as a motivator of engagement; (2) in-home data collection is not a passive or neutral experience; (3) personalized approaches and context are important to families; and (4) a clear need for continued iteration and engagement. CONCLUSION: In-home multimodal data collection is potentially feasible for families across the developmental spectrum but requires iteration based on family feedback to increase adherence.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".