Three Body-Worn Accelerometers in the French NutriNet-Santé Cohort: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Accurate assessment of physical activity (PA) in large population-based cohorts remains a major methodological challenge. Self-reported questionnaires, although commonly used due to low cost and simplicity, are prone to recall and social desirability biases, causing misclassification and weakened associations with health outcomes. Body-worn accelerometers provide more objective and reliable measurements, but their acceptability and feasibility in large-scale epidemiological studies must be carefully evaluated to ensure compliance, data quality, and scalability. OBJECTIVE: The primary objective was to assess the acceptability of using 3 body-worn accelerometer devices (Fitbit, ActivPAL, and ActiGraph) among healthy middle-aged adults participating in the NutriNet-Santé cohort. The secondary objective was to assess the feasibility of these devices in terms of wear-time compliance under free-living conditions. METHODS: This is an ancillary study of the European WEALTH (WEarable sensor Assessment of physicaL and eaTing beHaviors) project that was conducted between 2023 and 2024 in a subsample of participants of the NutriNet-Santé surveillance in France. This sample included 126 healthy participants (62 men), with a mean age of 46.3 (SD 11.3) years. Participants wore simultaneously 3 body-worn accelerometer devices (Fitbit [wrist], ActivPAL [thigh], and ActiGraph [waist]) for 7 consecutive days. After the wear period, participants completed a specific 22-item web-based questionnaire, regarding their acceptability of using each device. This questionnaire was based on the Technology Acceptance Model, which identifies perceived usefulness and ease of use as key determinants of technology acceptance. Items were rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree). Feasibility was assessed based on the accelerometer wear time data reported in a log diary by participants. A valid day was defined as ≥600 minutes per day of wear time, and a valid week as at least 4 of such days. Acceptability scores were compared between devices using ANOVA, and feasibility outcomes were compared using Kruskal-Wallis tests. RESULTS: The acceptability assessment based on the questionnaire revealed significant differences among the 3 devices (P<.001). The Fitbit achieved the highest acceptability score (mean 80.5/100, SD 8.13) across most criteria such as comfort, ease of use, and social acceptability, while the ActiGraph received the lowest score (mean 71.7, SD 8.68), mainly due to challenges with stability and interference during PA. In terms of feasibility, the 3 accelerometers demonstrated high compliance, with the ActivPAL recording the highest daily wear time, followed by the Fitbit and the ActiGraph (P<.001). CONCLUSIONS: Results from our study showed that the Fitbit watch appears as the most accepted device for measuring PA in free-living conditions in the NutriNet-Santé study. The large-scale use of such a device must now be evaluated in terms of logistics, cost, and data privacy.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".