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A Validation Study of the Eurostat Harmonised European Time Use Study (HETUS) Diary Using Wearable Technology

Overview
Publisher Biomed Central
Specialty Public Health
Date 2019 Jun 5
PMID 31159770
Citations 15
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Abstract

Background: The central aim was to examine the accuracy of the full range of daily activities recorded in self-report time-use diaries against data from two objective passive data collection devices (wearable camera and accelerometer) serving as criterion reference instruments. This enabled systematic checks and comparisons on the timing, sequence and duration of activities recorded from the three data sources.

Methods: Participants (n = 148) were asked to complete a single-day self-report paper time-use diary designed for use in the Harmonised European Time Use Study (HETUS), while simultaneously wearing a camera that continuously recorded images of their activities, and an accelerometer tracking physical movement. In a reconstruction interview shortly after the data collection period, participants viewed the camera images to help researchers interpret the image sequences. Of the initial 148 recruits (multi-seed snowball sample, 59% women, aged 18-91, 43% > 40) 131 returned usable diary and camera records (of whom 124 also provided a usable whole-day accelerometer record. We compare time allocation estimates from the diary and camera records, and also match the diary and camera records to the simultaneously recorded accelerometer vector magnitudes.

Results: The data were examined at three analytic levels: aggregate, individual diarist and timeslot. The most important finding is that the estimates of mean daily time devoted to 8 of the 10 main activities differ by < 10% in the camera and diary records. The single case of major divergence (eating) can be explained by a systematic difference between the procedures followed by the self-reporting diarist and the observer coding the camera records. There are more substantial differences at the respondent level, paired t-tests showing significant differences in time spent in the 4/10 categories. 45% of all variation in the accelerometer vector magnitudes in the timeslots is explained by camera and diary records. Detailed activity classifications perform much better than METs as predictors of actigraphy.

Conclusions: The comparison of the diary with the camera and accelerometer records strongly supports using diary methodology for studying the full range of daily activity, particularly at aggregate levels. Accelerometer data could be combined with diary measures to improve estimation of METs equivalents for various types of active and sedentary behaviour.

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References
1.
McAuley E, Blissmer B, Marquez D, Jerome G, Kramer A, Katula J . Social relations, physical activity, and well-being in older adults. Prev Med. 2000; 31(5):608-17. DOI: 10.1006/pmed.2000.0740. View

2.
Brunner E, Stallone D, Juneja M, Bingham S, Marmot M . Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. Br J Nutr. 2001; 86(3):405-14. DOI: 10.1079/bjn2001414. View

3.
Shephard R . Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med. 2003; 37(3):197-206; discussion 206. PMC: 1724653. DOI: 10.1136/bjsm.37.3.197. View

4.
Craig C, Marshall A, Sjostrom M, Bauman A, Booth M, Ainsworth B . International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003; 35(8):1381-95. DOI: 10.1249/01.MSS.0000078924.61453.FB. View

5.
Williams D, Anderson E, Winett R . A review of the outcome expectancy construct in physical activity research. Ann Behav Med. 2005; 29(1):70-9. DOI: 10.1207/s15324796abm2901_10. View