Are we missing the sitting? Agreement between accelerometer non-wear time validation methods used with older adults’ data

Abstract

We used Bland Altman plots to compare agreement between a self-report diary and five different non-wear time algorithms [an algorithm that uses ≥60 min of consecutive zeroes (Troiano) and four variations of an algorithm that uses ≥90 min of consecutive zeroes to define a non-wear period] for estimating community-dwelling older adults’ (n = 106) sedentary behaviour and wear time (min/day) as measured by accelerometry. We found that the Troiano algorithm may overestimate sedentary behaviour and wear time by ≥30 min/day. Algorithms that use ≥90 min of continuous zeroes more closely approximate participants’ sedentary behaviour and wear time. Across the self-report diary vs. ≥90 min algorithm comparisons, mean differences ranged between −4.4 to 8.1 min/day for estimates of sedentary behaviour and between −10.8 to 1.0 min/day for estimates of wear time; all 95% confidence intervals for mean differences crossed zero. We also found that 95% limits of agreement were wide for all comparisons, highlighting the large variation in estimates of sedentary behaviour and wear time. Given the importance of reducing sedentary behaviour and encouraging physical activity for older adult health, we conclude that it is critical to establish accurate approaches for measurement.

Keywords:

• sedentary behaviour
• physical activity
• Bland Altman
• limits of agreement
• accelerometry

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Erratum

Public Interest Statement

Sedentary behaviour refers to activities that require little energy expenditure. Accelerometers are small devices that measure accelerations caused by body movements. Researchers frequently use accelerometers to measure sedentary behaviour in the everyday life of study participants. We conducted a study that investigated the impact of different accelerometry analysis algorithms on estimates of sedentary behaviour and wear time (how long participants wore accelerometers for) in community-dwelling older adults. We found that the most commonly used algorithm in older adult sedentary behaviour research may overestimate sedentary behaviour and wear time by ≥30 min/day. Variations of algorithms that classify sedentary behaviour as little-to-no movement in ≥90 min intervals provided estimates of sedentary behaviour and wear time that more closely approximated participants’ self-reported behaviour. Given the importance of reducing sedentary behaviour and encouraging physical activity for older adult health, we conclude that it is critical to establish accurate approaches for measurement.

Competing Interests

The authors declare no competing interest.

Acknowledgments

We gratefully acknowledge the generosity of our study participants, and the integral support of our community collaborators: BC Housing, the City of Vancouver, and the BC Ministry of Health.

Authors’ note

This manuscript has not been published elsewhere and has not been submitted simultaneously for publication elsewhere.

Additional information

Notes on contributors

Anna M. Chudyk

Anna M. Chudyk obtained a BHSc Honours Specialization in Health Sciences (2006) and MSc in Epidemiology and Biostatistics (2008) from the University of Western of Western Ontario. She obtained a PhD in Experimental Medicine from the University of British Columbia in 2016. Anna’s research focuses on the association between the built environment (the human-made infrastructure that comprises the areas where we live, work, and play) and older adults’ health and mobility (namely, physical activity and travel behaviour). Anna will apply the findings of this paper to inform the analysis of accelerometry data that she collects from older adult study participants, and hopes that you will too!