The home environment and toddler physical activity: an ecological momentary assessment study
Bibliographic record
Abstract
Summary Background Physical activity (PA) promotion/obesity prevention in toddlerhood should include home environments. Objective The aim of the study was to determine social/physical home environment factors associated with toddler PA using ecological momentary assessment (EMA, real‐time data collection). Methods Low‐income mother–toddler dyads were recruited and given a handheld EMA device (53 random beeps followed by social/physical environment survey over 8 d). Simultaneously, PA was assessed via accelerometry (data extracted 15 min before/after response, average activity counts per minute). Linear mixed‐effects models were used, adjusting for toddler age, urban/suburban residence and time of day; covariate moderating effects were examined; within‐subjects and between‐subjects findings were reported. PA was hypothesized to be greater when toddlers are outside (vs. inside), children are nearby (vs. alone), toddlers are interacting with their mothers (vs. not) and TV is off (vs. on). Results The final count was 2454 EMA/PA responses for 160 toddlers (mean age 20 months, range 12–31; 55% male, 66% Black and 54% urban). Associations with PA include (within subjects) the following: outside location (212 additional counts min−1), children nearby (153 additional counts min−1) and interacting with mother (321 additional counts min−1), compared with alternatives. Age was moderated by outside location/PA association (within subjects), with 90 additional counts min−1 per 3‐month age group outside vs. inside. No between‐subjects or television/PA associations were found. Conclusions Home environment factors were associated with PA, including outside location, children nearby and mother interaction. EMA is a novel method, allowing identification of contextual factors associated with behaviours in natural environments.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".