Consumer physical activity tracking device ownership and use among a population-based sample of adults
Bibliographic record
Abstract
Consumer physical activity tracking devices (PATs) have gained popularity to support individuals to be more active and less sedentary throughout the day. Wearable PATs provide real-time feedback of various fitness-related metrics such as tracking steps, sedentary time, and distance walked. The purpose of this study was to examine the prevalence and correlates of PAT ownership and use among a population-based sample of adults. A representative sample of adults ≥18 years (N = 1,215) from Alberta, Canada were recruited through random-digit dialing and responded to a questionnaire via computer-assisted telephone interviewing methods in summer 2016. Questionnaires assessed demographic and health behaviour variables, and items were designed to assess PAT ownership and usage. Logistic regression analysis (odds ratios) was used to assess correlates of PAT ownership and use. On average, participants (N = 1,215) were 53.9 (SD 16.7) years and 50.1% were female. Of the sample, 19.6% (n = 238) indicated they currently own and use a PAT. Participants who owned a PAT wore their device on average 23.2 days within the past month. Currently owning a PAT was significantly associated with being female (OR = 1.41, CI: 1.10 to 1.82), being <60 years of age (OR = 1.86, CI: 1.37 to 2.53), having at least some post secondary education (OR = 1.88, CI: 1.36 to 2.60), having a BMI ≥25 (OR = 1.52, CI: 1.16 to 1.99), and meeting physical activity guidelines (OR = 1.45, CI: 1.12 to 1.88). Similar correlates emerged for PAT use. Correlates significantly associated with PAT use and ownership included being female, being less than 60 years of age, having a post-secondary education, meeting physical activity guidelines, and being overweight/obese. This is the first study to examine characteristics of PAT ownership and use among Canadian adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".