The Association of Physical Activity Fragmentation with Physical Function in Older Adults: Analysis from the SITLESS Study

Abstract

The distribution of physical activity bouts through the day may provide useful information for assessing the impacts of interventions on aspects such as physical function. This study aimed to investigate the associations between physical activity fragmentation, tested using different minimum physical activity bout lengths, with physical function in older adults. The SITLESS project recruited 1360 community-dwelling participants from four European countries (≥65 years old). Physical activity fragmentation was represented as the active-to-sedentary transition probability (ASTP), the reciprocal of the average physical activity bout duration measured using ActiGraph wGT3X+ accelerometers. Four minimum bout lengths were utilised to calculate the ASTP: ≥10-s, ≥60-s, ≥120-s and ≥300-s. Physical function was assessed using the 2-min walk test (2MWT) and the composite score from the Short Physical Performance Battery (SPPB) test. Linear regression analyses, after adjusting for relevant covariates, were used to assess cross-sectional associations. After adjustment for relevant covariates, lower ASTP using ≥10-s bouts were associated with longer 2MWT distances and higher SPPB scores. Lower ASTP using ≥120-s bouts and ≥300-s bouts were associated with longer 2MWT distances but not the SPPB. Less fragmented physical activity patterns appeared to be associated with better physical function in community-dwelling older adults.

Cross‐sectional associations between temporal patterns and composition of upright and stepping events with physical function in midlife: Insights from the 1970 British Cohort Study

“…Our findings align with a number of previous studies that have established associations between a higher frequency of short or transient step event durations and poorer physical function performance in older adults. 20,21 These studies employed the ASTP index of fragmentation using the epoch method, where activity (or posture) is classified over a fixed period of time. We employed an eventbased approach for a similar index of upright events.…”

“…19 For example, a more fragmented activity pattern, characterized by short, transient active events, has been associated with various health outcomes, including poorer physical function in older adults. 20,21 However, there are still limitations in data processing that may lead to misclassification. It is common in studies of PA fragmentation and health, commonly employing the activeto-sedentary transition probability (ASTP), to segment accelerometer time series data by fixed epochs.…”

“…Time‐stamped accelerometer data allow analysis of how PA is accumulated each day and over multiple days, with evidence suggesting that patterns of activity, independent of volume, may significantly impact health outcomes 19 . For example, a more fragmented activity pattern, characterized by short, transient active events, has been associated with various health outcomes, including poorer physical function in older adults 20,21 . However, there are still limitations in data processing that may lead to misclassification.…”

Visual Impairment and Real-World Home Physical Activity With Home Environment in an Older Population

“…Average bout duration/length for the period of device wear was calculated as a sum of active minutes divided by the total number of active bouts. 19 Activity fragmentation, defined as the probability of transitioning from an active to a sedentary state, was calculated as the reciprocal of the participant's mean active bout duration (1 per mean active bout duration in minutes), 20 with higher numbers indicating more fragmented activity (ie, divided into more bouts). Only valid days of data were used in the analysis.…”

Cross-sectional associations between patterns and composition of upright and stepping events with physical function: insights from The Maastricht Study

“…In addition, the reliance on single thresholds of acceleration to define physical activity intensities can lead to misclassification, as this approach assumes that any physical activity above the threshold represents the same intensity for all individuals regardless of fitness [ 33 ]. More recent developments in the processing of accelerometer data permit greater insight into the importance of how a given volume of physical activity is accumulated, and different ‘patterns’ of activity are potentially differentially associated with health outcomes, including physical function [ 32 , 34 , 35 ].…”