Reciprocal Reinforcement Between Wearable Activity Trackers and Social Network Services in Influencing Physical Activity Behaviors
Bibliographic record
Abstract
BACKGROUND: Wearable activity trackers (WATs) are emerging consumer electronic devices designed to support physical activities (PAs), which are based on successful behavior change techniques focusing on goal-setting and frequent behavioral feedbacks. Despite their utility, data from both recent academic and market research have indicated high attrition rates of WAT users. Concurrently, evidence shows that social support (SS), delivered/obtained via social network services or sites (SNS), could increase adherence and engagement of PA intervention programs. To date, relatively few studies have looked at how WATs and SS may interact and affect PAs. OBJECTIVE: The purpose of this study was to explore how these two Internet and mobile technologies, WATs and SNS, could work together to foster sustainable PA behavior changes and habits among middle-aged adults (40-60 years old) in Taiwan. METHODS: We used purposive sampling of Executive MBA Students from National Taiwan University of Science and Technology to participate in our qualitative research. In-depth interviews and focus groups were conducted with a total of 15 participants, including 9 WAT users and 6 nonusers. Analysis of the collected materials was done inductively using the thematic approach with no preset categories. Two authors from different professional backgrounds independently annotated and coded the transcripts, and then discussed and debated until consensus was reached on the final themes. RESULTS: The thematic analysis revealed six themes: (1) WATs provided more awareness than motivation in PA with goal-setting and progress monitoring, (2) SS, delivered/obtained via SNS, increased users' adherence and engagement with WATs and vice versa, (3) a broad spectrum of configurations would be needed to deliver WATs with appropriately integrated SS functions, (4) WAT design, style, and appearance mattered even more than those of smartphones, as they are body-worn devices, (5) the user interfaces of WATs left a great deal to be desired, and (6) privacy concerns must be addressed before more mainstream consumers would consider adopting WATs. CONCLUSIONS: Participants perceived WATs as an awareness tool to understand one's PA level. It is evident from our study that SS, derived from SNS and other pertinent vehicles such as the LINE social messaging application (similar to WhatsApp and WeChat), will increase the engagement and adherence of WAT usage. Combining WATs and SNS enables cost-effective, scalable PA intervention programs with end-to-end services and data analytics capabilities, to elevate WATs from one-size-fits-all consumer electronics to personalized PA assistants.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".