Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women.

ObjectivesIndependently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.MethodsA convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.ResultsRest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.ConclusionsIn this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions

Objectively measured sedentary behavior and quality of life among survivors of early stage breast cancer.

PurposeThis study examined relationships between sedentary behavior accumulated in different bout durations and quality of life (QoL) among breast cancer survivors.MethodsPostmenopausal breast cancer survivors completed the Short Form Health Survey to assess QoL and wore an accelerometer to measure sedentary behavior and physical activity between August 2011 and May 2013.ResultsParticipants (n = 134) averaged 509.7 min/day in sedentary time with 285.2 min/day in short bouts (<20 min) and 224.5 min/day long bouts (≥20 min). Linear regression models indicated that greater total sedentary time was significantly associated with worse physical QoL (b = -0.70, p = 0.02) but not mental QoL (p = 0.92). Models that examined the accumulation of sedentary time in short bouts and long bouts together showed that time in long sedentary bouts was significantly related to physical QoL (b = -0.72, p = 0.02), while time in short bouts was not (p = 0.63). Moderate-to-vigorous intensity physical activity (MVPA) was a significant effect modifier of the relation between time spent in long sedentary bouts and physical QoL (p = 0.028) such that greater time in long bouts was associated with worse physical QoL only among women with lower levels of MVPA.ConclusionsFindings indicate that time spent in long sedentary bouts is associated with worse physical QoL among breast cancer survivors who do not engage in high levels of MVPA. Future research should examine reducing sedentary time as a potential strategy to improve physical QoL

Two-Arm Randomized Pilot Intervention Trial to Decrease Sitting Time and Increase Sit-To-Stand Transitions in Working and Non-Working Older Adults.

BACKGROUND: Excessive sitting has been linked to poor health. It is unknown whether reducing total sitting time or increasing brief sit-to-stand transitions is more beneficial. We conducted a randomized pilot study to assess whether it is feasible for working and non-working older adults to reduce these two different behavioral targets. METHODS: Thirty adults (15 workers and 15 non-workers) age 50-70 years were randomized to one of two conditions (a 2-hour reduction in daily sitting or accumulating 30 additional brief sit-to-stand transitions per day). Sitting time, standing time, sit-to-stand transitions and stepping were assessed by a thigh worn inclinometer (activPAL). Participants were assessed for 7 days at baseline and followed while the intervention was delivered (2 weeks). Mixed effects regression analyses adjusted for days within participants, device wear time, and employment status. Time by condition interactions were investigated. RESULTS: Recruitment, assessments, and intervention delivery were feasible. The 'reduce sitting' group reduced their sitting by two hours, the 'increase sit-to-stand' group had no change in sitting time (p < .001). The sit-to-stand transition group increased their sit-to-stand transitions, the sitting group did not (p < .001). CONCLUSIONS: This study was the first to demonstrate the feasibility and preliminary efficacy of specific sedentary behavioral goals. TRIAL REGISTRATION: clinicaltrials.gov NCT02544867.The pilot study was supported by funds provided by the Department of Family Medicine & Public Health, UCSD. The work of Andrew J Atkin was supported by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence (RES-590-28-0002). Funding from the British Heart Foundation, Department of Health, Economic and Social Research Council, Medical Research Council, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It was first available from PLOS via http://dx.doi.org/10.1371/journal.pone.014542

Total Sitting Time and Sitting Pattern in Postmenopausal Women Differ by Hispanic Ethnicity and are Associated With Cardiometabolic Risk Biomarkers.

Background Sedentary behavior is pervasive, especially in older adults, and is associated with cardiometabolic disease and mortality. Relationships between cardiometabolic biomarkers and sitting time are unexplored in older women, as are possible ethnic differences. Methods and Results Ethnic differences in sitting behavior and associations with cardiometabolic risk were explored in overweight/obese postmenopausal women (n=518; mean±SD age 63±6 years; mean body mass index 31.4±4.8 kg/m2). Accelerometer data were processed using validated machine-learned algorithms to measure total daily sitting time and mean sitting bout duration (an indicator of sitting behavior pattern). Multivariable linear regression was used to compare sitting among Hispanic women (n=102) and non-Hispanic women (n=416) and tested associations with cardiometabolic risk biomarkers. Hispanic women sat, on average, 50.3 minutes less/day than non-Hispanic women (P&lt;0.001) and had shorter (3.6 minutes less, P=0.02) mean sitting bout duration. Among all women, longer total sitting time was deleteriously associated with fasting insulin and triglyceride concentrations, insulin resistance, body mass index and waist circumference; longer mean sitting bout duration was deleteriously associated with fasting glucose and insulin concentrations, insulin resistance, body mass index and waist circumference. Exploratory interaction analysis showed that the association between mean sitting bout duration and fasting glucose concentration was significantly stronger among Hispanic women than non-Hispanic women (P-interaction=0.03). Conclusions Ethnic differences in 2 objectively measured parameters of sitting behavior, as well as detrimental associations between parameters and cardiometabolic biomarkers were observed in overweight/obese older women. The detrimental association between mean sitting bout duration and fasting glucose may be greater in Hispanic women than in non-Hispanic women. Corroboration in larger studies is warranted

Multi-sensor physical activity recognition in free-living

Abstract Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions

Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences

Abstract. Life-logging devices are becoming ubiquitous, yet still processing and extracting information from the vast amount of data that is being captured is a very challenging task. We propose a method to find discriminative regions which we define as regions that are salient, consistent, repetitive and discriminative. We explain our fast and novel algorithm to discover the discriminative regions and show different applications for discriminative regions such as summariza-tion, classification and image search. Our experiments show that our algorithm is able to find discriminative regions and discriminative patches in a short time and extracts great results on our life-logging SenseCam dataset.

Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.

PURPOSE:This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data. METHODS:Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit-stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models. RESULTS:Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts. CONCLUSIONS:This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted

Relationship between Objectively Measured Transportation Behaviors and Health Characteristics in Older Adults

This study used objective Global Positioning Systems (GPS) to investigate the relationship between pedestrian and vehicle trips to physical, cognitive, and psychological functioning in older adults living in retirement communities. Older adults (N = 279; mean age = 83 ± 6 years) wore a GPS and accelerometer for 6 days. Participants completed standard health measures. The Personal Activity and Location Measurement System (PALMS) was used to calculate the average daily number of trips, distance, and minutes traveled for pedestrian and vehicle trips from the combined GPS and accelerometer data. Linear mixed effects regression models explored relationships between these transportation variables and physical, psychological and cognitive functioning. Number, distance, and minutes of pedestrian trips were positively associated with physical and psychological functioning but not cognitive functioning. Number of vehicle trips was negatively associated with fear of falls; there were no other associations between the vehicle trip variables and functioning. Vehicle travel did not appear to be related to functioning in older adults in retirement communities except that fear of falling was related to number of vehicle trips. Pedestrian trips had moderate associations with multiple physical and psychological functioning measures, supporting a link between walking and many aspects of health in older adults