Behaviour change techniques and physical activity using the fitbit flex
Bibliographic record
Abstract
The availability of low-cost accelerometer technology has led to a surge in consumer-based physical activity monitors (Lee et al., 2014). A top leader in this market is Fitbit (Dolan, 2014). Fitbit's activity tracking devices allow for self-monitoring and other features related to behaviour change (e.g., goal setting) called behaviour change techniques (BCTs). It has been determined that one popular wrist-worn device, the Fitbit Flex®, incorporates 20 BCTs (Lyons et al., 2014). The purpose of this study was to explore user's experience with the Fitbit Flex® as it relates to physical activity behaviour and BCTs. Participants (n=28, 82.1% female, 18-71 years old) completed an online survey assessing: (1) demographics and Fitbit acquisition, (2) step volume (number of steps for the past week and the first week of use), and (3) user's perceived importance and/or frequency of use of the 20 BCTs. Participants had used the Fitbit for an average 5 months and there was a significant increase of almost 2000 steps (p = .003) from the first to the past week of use. The BCTs rated among the highest for perceived importance for physical activity behaviour (i.e., step volume) were feedback, self-monitoring, and goal setting. The BCTs related to feedback or reward of outcomes (i.e., weight loss) and social features (e.g., social support) were rated on the lower end of importance/frequency. Overall, the present study contributes to understanding the influence wearing a Fitbit Flex has on physical activity as well as the importance of certain BCTs, which has implications for future physical activity promotion and product development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".