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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".