Willingness to Share Data From Wearable Health and Activity Trackers: Analysis of the 2019 Health Information National Trends Survey Data
Bibliographic record
Abstract
BACKGROUND: Sharing data from wearable health and activity trackers (wearables) with others may improve the health and behavioral outcomes of wearable users by generating social support and improving their ability to manage their health. Investigating individual factors that influence US adults' willingness to share wearable data with different types of individuals may provide insights about the population subgroups that are most or least likely to benefit from wearable interventions. Specifically, it is necessary to identify digital health behaviors potentially associated with willingness to share wearable data given that the use of and engagement with various technologies may broadly influence web-based health information-sharing behaviors. OBJECTIVE: This study aims to identify sociodemographic, health, and digital health behavior correlates of US adults' willingness to share wearable data with health care providers and family or friends. METHODS: Data for the analytic sample (N=1300) were obtained from the 2019 Health Information National Trends Survey of the National Cancer Institute. Digital health behavior measures included frequency of wearable device use, use of smartphones or tablets to help communicate with providers, use of social networking sites to share health information, and participation in a web-based health community. Multivariable logistic regression analysis of weighted data examined the associations between digital health behaviors and willingness to share wearable device data, controlling for sociodemographics and health-related characteristics. RESULTS: Most US adults reported willingness to share wearable data with providers (81.86%) and with family or friends (69.51%). Those who reported higher health self-efficacy (odds ratio [OR] 1.97, 95% CI 1.11-3.51), higher level of trust in providers as a source of health information (OR 1.98, 95% CI 1.12-3.49), and higher level of physical activity (OR 2.00, 95% CI 1.21-3.31) had greater odds of willingness to share data with providers. In addition, those with a higher frequency of wearable use (OR 2.15, 95% CI 1.35-3.43) and those who reported use of smartphones or tablets to help communicate with providers (OR 1.99, 95% CI 1.09-3.63) had greater odds of willingness to share data with providers. Only higher level of physical activity was associated with greater odds of willingness to share wearable data with family or friends (OR 1.70, 95% CI 1.02-2.84). Sociodemographic factors were not significantly associated with willingness to share wearable data. CONCLUSIONS: The findings of this study suggest that, among US adult wearable users, behavior-related factors, rather than sociodemographic characteristics, are key drivers of willingness to share health information obtained from wearables with others. Moreover, behavioral correlates of willingness to share wearable data are unique to the type of recipient (ie, providers vs family or friends). Future studies could use these findings to inform the development of interventions that aim to improve the use of patient-generated data from wearable devices in health care settings.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".