Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.
Paul R. Hibbing, Seth A. Creasy, and Jordan A. Carlson
John Bellettiere, Supun Nakandala, Fatima Tuz-Zahra, Elisabeth A.H. Winkler, Paul R. Hibbing, Genevieve N. Healy, David W. Dunstan, Neville Owen, Mikael Anne Greenwood-Hickman, Dori E. Rosenberg, Jingjing Zou, Jordan A. Carlson, Chongzhi Di, Lindsay W. Dillon, Marta M. Jankowska, Andrea Z. LaCroix, Nicola D. Ridgers, Rong Zablocki, Arun Kumar, and Loki Natarajan
Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.
Ruth F. Hunter and Ione Avila-Palencia
William Bellew, Tracy Nau, Ben J. Smith, Melody Ding, and Adrian Bauman
David A. Wilson, Simon Brown, Paul E. Muckelt, Martin B. Warner, Sandra Agyapong-Badu, Danny Glover, Andrew D. Murray, Roger A. Hawkes, and Maria Stokes
Inactive older adults tend to have decreased strength and balance compared with their more active peers. Playing golf has the potential to improve strength and balance in older adults. The aim of the study was to compare the strength and balance of recreational golfers with non-golfers, aged 65–79 years. Grip strength, single leg balance, and Y Balance Test (YBT) were assessed. Golfers (n = 57) had significantly (right, p = .042; left, p = .047) higher maximal grip strength, than non-golfers (n = 17). Single leg stance times were significantly longer in golfers (right, p = .021; left, p = .001). Normalized YBT reach distances were significantly greater for golfers than non-golfers for composite, posteromedial, and posterolateral directions on both right and left legs. Playing golf appears to be associated with better grip and both static and dynamic balance in 65–79 year olds, indicating that a study of the effects of playing golf is warranted through a larger, fully powered, longitudinal study.
Samuel R. Nyman
Suzanne Portegijs, Sandra van Beek, Lilian H.D. van Tuyl, and Cordula Wagner
This study is conducted in order to gain a better understanding of the relationship between physical activity and agitated behavior among older people with dementia, and physical activity and characteristics of long-term care wards. Data were collected among people with dementia living in long-term care facilities (N = 76) by conducting observations at the wards and distributing questionnaires among professional caregivers. The results show that participants are largely inactive (82.8%) and a significant relation was found between the degree of physical activity and characteristics of the ward such as “taking sufficient time,” which relates to the time caregivers take when interacting with residents. This study supports the existing knowledge about the degree of physical activity among people with dementia in long-term care and adds information about the potential influence of organizational factors that could be valuable for daily practice.
Alexander Ivan B. Posis, John Bellettiere, Rany M. Salem, Michael J. LaMonte, JoAnn E. Manson, Ramon Casanova, Andrea Z. LaCroix, and Aladdin H. Shadyab
The goal of this study was to examine associations between accelerometer-measured physical activity (PA) and sedentary time (ST) with mortality by a genetic risk score (GRS) for longevity. Among 5,446 women, (mean [SD]: age, 78.2 [6.6] years), 1,022 deaths were observed during 33,350 person-years of follow-up. Using multivariable Cox proportional hazards models, higher light PA and moderate to vigorous PA were associated with lower mortality across all GRS for longevity categories (low/medium/high; all p trend < .001). Higher ST was associated with higher mortality (p trend across all GRS categories < .001). Interaction tests for PA and ST with the GRS were not statistically significant. Findings support the importance of higher PA and lower ST for reducing mortality risk in older women, regardless of genetic predisposition for longevity.
Nina Vansweevelt, Filip Boen, Jannique van Uffelen, and Jan Seghers
Background: The retirement transition constitutes both a risk and an opportunity for changes in physical activity (PA) and sedentary behavior (SB). The present systematic review aims to summarize the current evidence regarding the differences between socioeconomic status (SES) groups in changes in PA and SB across the retirement transition. Methods: The authors searched 5 databases. Inclusion criteria were: investigating statutory retirement, measuring PA and/or SB at least once before and once after retirement, and reporting information on SES differences. Results are reported by means of a narrative synthesis, combined with harvest plots based on direction of effect. Results: We included 24 papers from 19 studies. Sixteen papers focused on PA, 3 on SB, and 5 investigated both. For total PA, occupational PA, and total sedentary time, nearly all publications reported more favorable changes for high SES groups. For recreational PA, active transport, and screen time, there seemed to be a tendency toward more favorable changes for high SES groups. Changes in household/caregiving PA did not appear to differ between SES groups. Conclusions: Changes in movement behavior during the retirement transition are potentially more favorable for high SES adults. Nonetheless, the differences between SES groups seem to depend on the domain of movement behavior.