Wearable Activity Trackers That Motivate Women to Increase Physical Activity: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Physical inactivity is a significant public health concern, particularly among women in the United States. Wearable activity trackers (WATs) have been proposed as a potential solution to increase awareness of and engagement in physical activity (PA). However, to be effective, WATs must include features and designs that encourage daily use. OBJECTIVE: This study aims to explore the features and designs of WATs that appeal to women and determine whether devices with these attributes are effective motivators for women to be physically active. METHODS: A mixed methods study guided by the self-determination theory was conducted among 15 women. Participants trialed 3 WATs with influence in their respective accessory domains: Apple Watch for the wrist; Oura Ring for the finger; and Bellabeat Leaf Urban for multiple sites (it can be worn as a bracelet, necklace, or clip). Participants documented their daily PA levels and rated their satisfaction with each device's comfort, features, and motivational effect. Focus groups were also conducted to gather additional feedback and experiences within the a priori areas of comfort, features, and motivation. RESULTS: Behavioral Regulation in Exercise Questionnaire-2 scores indicated that most participants (14/15, 93%) were motivated at baseline (amotivation score: mean 0.13, SD 0.45), but on average, participants did not meet the national minimum PA guidelines according to the self-reported Physical Activity Vital Sign questionnaire (moderate to vigorous PA score: mean 144, SD 97.5 min/wk). Mean WAT wear time was 16.9 (SD 4.4) hours, 19.4 (SD 5.3) hours, and 20.4 (SD 4.7) hours for Apple Watch, Bellabeat Leaf Urban, and Oura Ring, respectively. During focus groups, participants reinforced their quantitative ratings and rankings of the WATs based on personal experiences. Participants shared a variety of both activity-related and non-activity-related features that they look for in a motivating device. When considering what the ideal WAT would be for a woman, participants suggested features of (1) comfort, (2) extended battery life, (3) durability, (4) immediate PA feedback, (5) intuitive PA sensing, and (6) programmability. CONCLUSIONS: This study is the first to specifically address women's experiences with and preferences for different types of WATs. Those who work with women should realize how they view WATs and the role they play in motivation to be active.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".