Effect of Wearable Activity Trackers and Social Media Use on Day-Level Physical Activity Motivation and Behaviours
Bibliographic record
Abstract
Background. Physical activity (PA) is integral to maintaining good health yet physical inactivity remains a concern. Wearable activity trackers (WATs) have grown in popularity and research recognizes their potential impact on motivation and PA behaviours, specifically when combined with additional intervention strategies. Research has also shown positive associations between health-related social media use and PA. While both WATs and social media are potentially effective tools for behaviour change, research in this field has focused on between-person associations. Currently, less is known about within-person associations between WAT use and daily PA as well as potential interaction effects with daily health-related social media use. Objectives. 1) Examine differences in day-level situational motivation for PA between WAT users and non-users, 2) Examine within-person associations of day-level situational motivation for PA with same-day health-related social media use, 3) Examine differences in day-level PA intensity, duration, and moderate-to-vigorous physical activity (MVPA) in WAT users and non-users, and 4) Examine within-person associations of daily PA with same-day health-related social media use. Methods. English-speaking Canadian adults (≥ 18 years) were recruited. Eligible participants completed a baseline survey assessing social media use, WAT status and demographic information. Participants then completed up to three (3) daily surveys assessing daily situational motivation for PA, daily social media use and self-reported PA behaviours for 14 days. Multi-level modelling was conducted. Results. 328 participants were included. Mean age of participants was 27.2 (9.1) years, 67% (n=220) of participants were female and 71.3% (n=234) of participants identified as WAT users. WAT use was associated with greater intrinsic and identified situational motivation before engaging in daily PA. Daily health-related social media use was not found to be associated with greater autonomous situational motivation. The only significant interaction effect for WAT use and health-related social media was found for external regulation (b=0.23, SE 0.11, p = .03). WAT use was not associated with greater daily PA; however, daily social media use was significantly associated with PA intensity (b=0.29, SE 0.10, p < .01) and MVPA (b=3.38, SE 1.52, p = .026). No significant interaction effects were observed between health-related social media and WAT use for any PA outcome. Conclusions. Greater autonomous (intrinsic motivation, identified regulation) situational motivation for PA in WAT users did not translate to increases in PA behaviours. While daily social media use had no association with daily motivation for PA, results showed a significant association between health-related social media use and PA intensity as well as with MVPA. Alone, WATs and health-related social media use may influence situational motivation for PA and behaviours but no additional benefits on motivation or PA were observed when used in combination. Although WATs should be not discounted as an effective tool, health-related social media platforms could exert a more direct influence on actual PA engagement and is a potential positive addition to PA interventions. Future research should continue to examine the type and timing of health-related social media use to have an optimal effect on PA behaviours.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".