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Exploring the challenges in obtaining physical activity data from women using hip-worn accelerometers.
Wendy J O'Brien1, Sarah P Shultz2, Ridvan T Firestone3
1a School of Food and Nutrition , Massey University , Auckland , New Zealand.
European Journal of Sport Science
|May 16, 2017
Summary
Collecting physical activity data using accelerometers in women is challenging. Discomfort during sleep, social embarrassment, and ethnicity impact data quality and compliance.
Area of Science:
- Public Health
- Epidemiology
- Biomedical Engineering
Background:
- High-quality physical activity data are crucial for health initiatives.
- Accelerometry is a key method for objective physical activity measurement.
- Challenges exist in collecting accurate accelerometry data, particularly from diverse female populations.
Purpose of the Study:
- To evaluate the efficacy and challenges of a hip-worn accelerometry protocol in New Zealand women.
- To determine accelerometer wear-compliance rates.
- To understand barriers and acceptability of wearing accelerometers among different ethnic groups.
Main Methods:
- A 7-day, 24-hour hip-worn accelerometry protocol was implemented in 406 healthy New Zealand women (Māori, Pacific, European) aged 16-45.
- Wear-compliance was defined as ≥10 hours/day for ≥4 days.
- A sub-sample (n=45) participated in post-hoc interviews to explore comfort, convenience, and burdens.
Main Results:
- Overall wear-compliance was 86%.
- European women provided more valid data (92.7%) compared to Pacific (73.0%) and Māori (82.1%) women.
- Key burdens included discomfort during sleep (66.7%) and social embarrassment due to device visibility (45.2%).
Conclusions:
- Participant burden, including sleep discomfort and social embarrassment, alongside ethnicity, significantly affects the quality and compliance of accelerometry data collection in women.
- Improving accelerometer design to reduce size and participant burden is recommended.
- Enhancing participant understanding of study aims and relevance can improve compliance and data quality.

