Wearable activity monitors and goals: Perceptions on physical activity, attitudes and motivational outcomes
Bibliographic record
Abstract
Evidence attesting to the benefits of wearable activity monitors for increasing PA has been reported (USDHHS, 2018). Goal setting is one behavior change technique that often accompanies wearable activity monitors and has been deemed an essential component to any health behavior change intervention (National Institute for Health and Care Excellence, 2014). Specific to PA behavior, goal setting has been deemed effective regardless of age, sex, and activity status (McEwan et al., 2016). Therefore, the purpose of this study was to determine if affective goals confer unique benefits on physical activity (PA), attitudes and behavioral regulations consistent with the Organismic Integration Theory (OIT; Ryan & Deci, 2017) among users of wearable activity monitors. Affective goals were compared with instrumental goals, step count and a no goal condition. Adopting a randomized experimental post-test only design, undergraduate students (N = 153) were assigned to one of eight conditions. Participants read a scenario then completed a battery of questionnaires housed on a secure online interface. Differences by condition were not found for short- or long-term PA or attitudes (p’s >.05). Differences were noted for extrinsic regulation (p = 0.025; ηp2 = .105). Results indicated that extrinsic regulation was higher in the no goal condition when compared to most other conditions. These findings imply that goal setting, regardless of type, may offset increases in extrinsic motivation associated with the use of wearable activity monitors. Users of wearable activity monitors looking to improve PA, positive attitudes and motivation associated with PA may benefit by utilizing goal setting in combination with other commonly used BCTs. A further investigation upon goal setting and users of wearable activity monitors is warranted.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".