Abstract
Background: For detailed, large-scale data on 24-hour movement behaviors, we designed a system “Motus” using state-of-the-art wearable and cloud technology, and tested its feasibility on randomly chosen Danish adults in a 2-stage evaluation. Methods: Stage 1: We invited 7735 adults, responding to a national occupational health surveillance-2021. Consented participants received a wearable (SENSmotion Plus) and downloaded the Motus app, which provided instructions for wearable attachment on the thigh and for self-reporting work and sleep hours. Following the 7-day measurement, participants completed a feasibility questionnaire. Administrators recorded time spent on Motus-related tasks (eg, postal package preparation). Identified feasibility issues led to revisions of protocol and Motus elements. Stage 2: We invited 6993 adults from a national public health surveillance-2023. Participants used the revised Motus version. We evaluated Motus on the key issues identified from stage 1. Results: Stage 1: Feasibility ranged from 77% for social acceptability to 98% for adherence to the measurement protocol. Participants reported spending 73 minutes per week (eg, attaching the sensors) on Motus, while administrators reported 15 minutes per participant. We identified 3 issues: 6% consent rate, 20% lost wearables (but not the data), and 10% wearable patches becoming loose. We addressed these issues by sending reminders, using stronger return envelopes, and replacing patch adhesive with higher quality alternatives, respectively. At stage 2, we observed a higher consent rate (23%) and lower patch complaints (<3%) but higher wearables loss (25%). Conclusion: Motus displays promising feasibility for collecting large-scale 24-hour movement behavior data. However, the low participation rate and high sensor loss require improvement before broader implementation, especially in surveillance.
| Original language | English |
|---|---|
| Pages (from-to) | 1244-1255 |
| Number of pages | 12 |
| Journal | Journal of Physical Activity and Health |
| Volume | 22 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Oct 2025 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- accelerometry
- domain-specific physical activity
- SurPASS (Surveillance of Physical Activity, Sedentary Behavior, and Sleep
- work
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver