Consumer Wearable Usage to Collect Health Data Among Adults Living in Germany: Nationwide Observational Survey Study
Bibliographic record
Abstract
Background: The usage of consumer wearables (CWs; eg, fitness trackers and smartwatches) in the population has increased enormously within the last decade. This has resulted in a large amount of digital person-generated health data that could be used to answer vital research questions. However, little is currently known about the usage of CWs to collect health data from the population living in Germany. Objective: This study aimed to describe the ownership of consumer wearables and their usage for the collection of health data from the adult population living in Germany, as well as the motives for the collection of health data and the average wear times. In addition, this study also aimed to investigate sociodemographic and health- and behavior-related differences between the group of CW users and the group of nonusers. Methods: We used data from the nationally representative survey "German Health Update," which was conducted through telephone interviews in 2021 and 2022. The final sample comprised 4464 adults aged 18 years and older. We derived weighted prevalences for the usage of CWs, as well as adjusted odds ratios for the ownership and the usage of CWs and their association with sociodemographic and health- and behavior-related variables. Results: Of the adult population, 19.3% (843/4459) owned a CW, of whom 77.8% (650/842) used their CW to collect health data (which corresponds to 650/4458, 15.0% of the adult population). Older people, people with a low income, and people with a lower level of physical activity (PA) were less likely to own a CW and were less likely to use it for the collection of health data. Of the CW users who collected health data, 47.2% (321/650) wore their CW during nocturnal sleep. The most frequently named motives for the collection of health data with a CW were "to observe my PA" (544/647, 85.0%), "for fun" (508/644, 79.0%), and "for support during exercising" (423/647, 66.3%). Women chose the motive "to observe my PA" and "to increase my PA" more often than men, whereas men chose the motive "to observe health issues" more often than women. Conclusions: Adults living in Germany owning a CW are younger, have a higher income, and are more physically active than individuals who do not use a CW. This means that the population groups that would be in particular need of health care are not sufficiently represented in these health datasets. Researchers should consider the selectivity of CW users when planning to use CW health data to answer research questions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".