Agreement of Sedentary Behavior Metrics Derived From Hip- and Thigh-Worn Accelerometers Among Older Adults: With Implications for Studying Physical and Cognitive Health

Abstract

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study

“…The main shortcoming of the cut-point method was that it misclassified approximately 40% of activPAL registered nonsitting time as sitting, while simultaneously overpredicting sit-to-stand transitions such that approximately 70% of the transitions it predicted were not activPAL transitions, resulting in inaccurate measures of sitting patterns. These findings are in line with other studies that support the use of hip-worn accelerometry for measuring motion and movement but suggest thigh-worn devices for measuring posture and postural transitions ( 11 – 13 , 15 – 17 ). Thus, evidence on sitting patterns measured using ActiGraph cut-points should be interpreted with caution.…”

“…The AG cut-point method overestimated true sitting time and failed to capture sit-to-stand transitions that are key to the measurement of sitting patterns ( 15 – 17 , 45 ). This underscores the importance of using methods for their intended use.…”

Association of Accelerometer‐Measured Sedentary Accumulation Patterns With Incident Cardiovascular Disease, Cancer, and All‐Cause Mortality

“…The study population, middle‐ to older‐aged adults, are notably also an appropriate population for targeting messages and interventions regarding SB, given their typically high volumes of SB and poorer adherence to current MVPA guidelines. However, less error in measuring SB accumulation, and potentially less bias of estimates toward the null, might have been achieved with either a thigh‐mounted accelerometer 47 , 48 or a waist‐worn triaxial monitor with algorithms 49 that can better separate sitting from standing posture, and ideally with 24‐hour monitoring to minimize unobserved behaviors. 50 A further strength was the carefully selected conceptual model underpinning the range of statistical models presented.…”

Cohort profile: the Women’s Health Accelerometry Collaboration

“…33 Using the raw data, we can also apply two machine-learnt algorithms developed specifically for older women; one designed to distinguish sitting, riding in a vehicle, standing still, standing moving and walking, 34 while the other was designed to accurately quantify sitting bouts, 35 which, without the algorithm, are measured with substantial error. 36 While studies investigating the associations between less common cancer subtypes and physical activity or sedentary behaviour among older women have been limited due to smaller sample sizes and few cancer events, the combined cohorts provide improvement in statistical power, allowing researchers to be better equipped to investigate these associations. In addition to increasing power for the less common cancer outcomes, by including both cohorts we capture more diversity in the population of women in this age range which allows us to better understand these associations in a more heterogeneous population.…”