Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study
Bibliographic record
Abstract
BACKGROUND: Despite the availability of a great variety of consumer-oriented wearable devices, perceived usefulness, user satisfaction, and privacy concerns have not been fully investigated in the field of wearable applications. It is not clear why healthy, active citizens equip themselves with wearable technology for running activities, and what privacy and data sharing features might influence their individual decisions. OBJECTIVE: The primary aim of the study was to shed light on motivational and privacy aspects of wearable technology used by healthy, active citizens. A secondary aim was to reevaluate smart technology adoption within the running community in Germany in 2017 and to compare it with the results of other studies and our own study from 2016. METHODS: A questionnaire was designed to assess what wearable technology is used by runners of different ages and sex. Data on motivational factors were also collected. The survey was conducted at a regional road race event in May 2017, paperless via a self-implemented app. The demographic parameters of the sample cohort were compared with the event's official starter list. In addition, the validation included comparison with demographic parameters of the largest German running events in Berlin, Hamburg, and Frankfurt/Main. Binary logistic regression analysis was used to investigate whether age, sex, or course distance were associated with device use. The same method was applied to analyze whether a runner's age was predictive of privacy concerns, openness to voluntary data sharing, and level of trust in one's own body for runners not using wearables (ie, technological assistance considered unnecessary in this group). RESULTS: A total of 845 questionnaires were collected. Use of technology for activity monitoring during events or training was prevalent (73.0%, 617/845) in this group. Male long-distance runners and runners in younger age groups (30-39 years: odds ratio [OR] 2.357, 95% CI 1.378-4.115; 40-49 years: OR 1.485, 95% CI 0.920-2.403) were more likely to use tracking devices, with ages 16 to 29 years as the reference group (OR 1). Where wearable technology was used, 42.0% (259/617) stated that they were not concerned if data might be shared by a device vendor without their consent. By contrast, 35.0% (216/617) of the participants would not accept this. In the case of voluntary sharing, runners preferred to exchange tracked data with friends (51.7%, 319/617), family members (43.4%, 268/617), or a physician (32.3%, 199/617). A large proportion (68.0%, 155/228) of runners not using technology stated that they preferred to trust what their own body was telling them rather than trust a device or an app (50-59 years: P<.001; 60-69 years: P=.008). CONCLUSIONS: A total of 136 distinct devices by 23 vendors or manufacturers and 17 running apps were identified. Out of 4, 3 runners (76.8%, 474/617) always trusted in the data tracked by their personal device. Data privacy concerns do, however, exist in the German running community, especially for older age groups (30-39 years: OR 1.041, 95% CI 0.371-0.905; 40-49 years: OR 1.421, 95% CI 0.813-2.506; 50-59 years: OR 2.076, 95% CI 1.813-3.686; 60-69 years: OR 2.394, 95% CI 0.957-6.183).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".