Collecting at-Home Biometric Measures for Longitudinal Research From the i3C: Feasibility and Acceptability Study
Bibliographic record
Abstract
Background: The use of individual wearable devices or internet-based applications to collect biometric data from research participants is popular, but several devices may be needed to replace a full set of research measurements. Objective: In this study, we assessed the feasibility of a "Virtual Home Clinic" within the context of long-term epidemiologic studies. Methods: Participants from 3 study cohorts were recruited. Devices were sent to the home to measure anthropometrics, resting metabolic rate, blood pressure (BP), heart rate (HR), heart rhythm, oxygen saturation, glucose, total cholesterol, physical activity, diet, sleep duration or quality, and arterial stiffness over the course of 1 week. Stool and saliva were also self-collected for microbiome, DNA, and cotinine. Feasibility and acceptability of collecting measurements using home devices were assessed. Results: A total of 134 participants were enrolled (87% female, 31% Black; mean age 54.2, SD 8.4 years). Furthermore, 91% (N=122) performed at least one of the home tests. At least two-thirds of participants were able to complete all of the requested readings for glucose, electrocardiogram, BP, diet record, and resting metabolic rate. The scale that measured weight, body composition, and pulse wave velocity (PWV) was more difficult to use (113/134, 84% participants recorded at least one weight and 84/134, 63% recorded a PWV). The device to measure total cholesterol was least successful (32/134, 24% participants completed all readings, 72/134, 54% provided at least one result). Return of biospecimens was highly successful (115/134, 86% returned saliva and 113/134, 84% returned stool). Of 95 who responded to the user acceptability survey, 38 (40%) participants preferred home assessment, 36 (38%) preferred clinic, and 21 (22%) did not have a preference. The mean user acceptability score across devices for ease of use was 4.3 (SD 1.0), for instructions was 4.5 (SD 0.7), and for time to use was 3.9 (SD 1.1; scale of 1-5, with higher scores indicating greater acceptability). The study team documented several regulatory or IT, connectivity or account, data retrieval, and logistical issues encountered during the study. Conclusions: Despite several complications involved with managing multiple devices and applications, most of the components of the virtual home clinic were reasonably feasible and acceptable to participants.
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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 | 0.039 | 0.047 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".