Acceptability of Active and Passive Data Collection Methods for Mobile Health Research: Cross-Sectional Survey of an Online Adult Sample in the United States
Bibliographic record
Abstract
Background: Digital health technologies, including wearable devices and app-based cognitive and health assessments, are pervasive and crucial to better understanding important public health problems (eg, Alzheimer's disease and related dementias). Central to understanding mechanisms driving individuals' willingness to share various data streams are concerns regarding data privacy, security, and control over generated data. Objective: This survey was designed to learn more about attitudes and opinions related to digital health technologies and the sharing of associated data. Methods: A total of 1509 adults were recruited from Prolific to complete an online survey via Qualtrics. Of these, 1489 participants provided valid data for analyses. Participants completed a structured survey consisting of multiple modules after informed consent was provided. These included: (1) demographic characteristics; (2) prior research experience; (3) mobility factors (eg, use of mobility aids, driving frequency); (4) technology ownership (eg, smartphones, tablets, home Wi-Fi); (5) social media use (eg, frequency of engagement with platforms such as Facebook, Instagram, and TikTok); (6) willingness to contribute different types of data across categories, including activities, sensors, and metadata; (7) opinions about data control and privacy options (eg, data deletion, stream-specific control); and (8) willingness to interact with assistive technologies such as robots, for Instrumental Activities of Daily Living. Results: The final cohort (N=1489) had a mean age of 35.5 years (SD 12.0), was 44% female (n=652), and predominantly identified as White (76%, n=1134), with high rates of smartphone ownership (99%, n=1479) and home Wi-Fi access (98%, n=1464). Participants were most willing to share data streams with clear health implications and least willing to share data streams with greater privacy or reidentification potential (eg, GPS location, in-vehicle dashcam footage). On average, people were willing to complete ambulatory cognitive assessments for 56.7 (SD 36.2) days, air quality monitoring for 58.1 (SD 37.7) days, and GPS location monitoring for 37 (SD 39.0) days. People expected control over their data, including the ability to delete all or specific streams of the data contributed for research. Most participants prioritized control over their data, with 71% (n=1061) favoring the ability to delete all data contributed for research purposes. Stream-specific data deletion (65%, n=960) and time-specific deletion (44%, n=653) were also valued; interest in sharing data with insurance providers (30%, n=453) or caregivers (26%, n=384) was notably lower. Conclusions: Findings have implications for the design of digital health technologies and education-related to the use and implications of collected data.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".