The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement

Abstract

The gold-standards for measuring energy expenditure (EE) under laboratory and free-living settings are whole-room indirect calorimeters and doubly labeled water (DLW), respectively These methods of measuring EE are generally used for quantifying differences in EE within individuals or across populations and can also be used as criterion measures to develop and validate wearable activity monitors for estimating EE Conversely, there can be added benefits of integrating wearable devices in EE studies using room calorimetry and DLW In EE studies aimed at measuring total EE, device-based measures add a dimension of context due to the fine temporal resolution and sensitivity to detect movement intensity which can be used to parse the individual contributors to total EE The focus of this workshop is to introduce the when, why, and how to integrate wearables to EE studies using room calorimeters and DLW For example, wearable monitors can be utilized during room calorimetry to better inform components of EE (resting, thermic effect of feeding, activity, etc ) Doubly labeled water studies give an average estimate of total daily energy expenditure over an assessment period Pairing wearable monitors with DLW, researchers can gain insight into day-to-day, weekday vs weekend, or inter-day variability in physical activity which may influence overall EE 1 Using wearable activity monitors in metabolic and nutritional studies This talk will cover the scope of how activity monitors have been used in different types of applications such as controlled trials and natural histories 2 Adding wearable activity monitors to whole-room indirect calorimetry studies This talk will present the methodology of room calorimetry, and the components of daily EE that wearables can help to quantify (e g , sleep, resting, activity, Detecting hotspots for physical activity using accelerometry, GPS and GIS BACKGROUND AND AIM: Daily physical activity is not one behavior that takes place in one location; it consists of many different behaviors occurring in different locations To get a better understanding of the correlates and determinants of physical activity behavior, knowing in which context it occurs can add valuable additional information With the emerging of methods to combine accelerometer and global positioning system (GPS) The aim of this presentation is to explain how the process of identifying physical activity hotspots works, and demonstrate the method using examples from several studies conducted in Australia and Denmark METHODS: Data were collected among school-children in Denmark and preschool children in Australia using an accelerometer (ActiGraph GT3X or Axivity) and a GPS (Qstarz BT-Q1000X) for 7 days (5 week days, 2 weekend days) to determine their level of activity and movement patterns The GPS position was recorded every 15 seconds and their activity level was recorded and 100Hz and compiled into 15 second epochs Data were merged and processed using HABITUS, an online tool available via the University of Southern Denmark The processed data-points were imported into the geographical information software ArcGISpro, where optimized hot-spot analyses were conducted to identify the statistically significant spatial clusters of GPS points with higher or lower physical activity levels For each hotspot, we identified the type of area, revealing the built environment characteristics of places with a significantly higher level of physical activity RESULTS: Physical activity hotspots were identified in the outdoor areas of early care and education centers (ECEC), schoolyards, as well as neighborhoods In neighborhoods, for schoolchildren, activity hotspots primarily consist of schoolyards, sports facilities and shared backyards between multistory social housing complexes For preschool children, neighborhood activity hotspots in private yards, ECECs, public parks, and shopping areas In schoolyards, activity hotspots were primarily at a ball-game areas, climbing areas, and open spaces For ECECs, activity hotspots were in many different types of areas, but more often in open spaces and areas with large fixed-play-equipment CONCLUSIONS: Collecting and processing accelerometer and GPS data is time-consuming, but in combination with the optimized hot-spot analysis tool in ArcGISpro, the data provides unique possibilities to identify locations where the activity level is significantly higher (or lower) than the average Classifying built environmental characteristics of these locations reveals which type of environments are most important for physical activity, for different age groups and genders, at different geographic scales were used to classify weekly light PA (100-759 cpm) and moderate to vigorous PA (MVPA) (>759 cpm) Two TWSA activity spaces were computed for each participant’s total GPS wear time (kernel density estimation - KDE, and density ranking - DR) TWSA activity spaces were used to measure exposure to three activity-related environments (walkability, recreation opportunities, and greenness) OLS regression measured TWSA exposure associations with PA outcomes, controlling for sex, age, ethnicity, and total device wear time As a comparison, OLS regressions were also run for 1000m buffer from home exposures to the three environments RESULTS: Participants had a weekly average of 26 8 hours of light PA and 12 5 hours of MVPA DR measured exposure to recreation opportunities was associated with decreased MVPA (β=-17 3, 95% CI[-28 1, -6 4]), as was DR measured walkability (β=-2 4, 95% CI[-3 8, -1 1]) and greenness (β=-57 7, 95% CI[-114 5, -0 9]) DR measured exposures were not associated with light PA KDE measured walkability exposure was associated with decreased light PA (β=-23 5, 95% CI[-45 6, -1 3]) No other associations were detected in this sample between exposures and light PA No home buffer measured exposures were associated with PA outcomes CONCLUSION: TWSA exposure results show a counterintuitive, but consistent relationship between increased time spent in green, walkable, and recreation opportune places with reduced PA time In comparison, no relationships were found between PA time and home buffer exposure measures By accounting for both the total exposure of individuals as well as the time they spend in locations, we may be better able to detect relationships between environmental exposures and physical activity through more sensitive and accurate measures of exposure Further work will need to be done to understand the counterintuitive associations in this study 38,792 of accelerometer GPS 15-second individual Multi-scale , play 5 data Identified playspaces play g , play sport , and in-between and (e g , 1,723 play episodes were identified from collected data On average, child’s consisted of five play episodes with a 2 94-minute duration and meters/minute For each play maintained moderate to vigorous intensity physical (MVPA) for 28% of the time Of the 1,723 20% in play areas, 6% in sports pitches, 22% strictly in-between features, and 3% were outside of while 49% were across multiple areas in parks Average time spent across spaces in/around parks varied by individual characteristics Children maintaining an accelerometer average above the MVPA threshold (>573) spent more time in areas designated for play (+6%) and less time in spaces between features (-7%), compared to children less active Girls spent more time in play areas (+5%) and between features (+4%) whereas boys spent more time in sports pitches (+10%) Results characteristics of play episodes and how spaces in parks are used for children’s play Findings that children’s free play occurs across spaces, and not necessarily concentrated in areas designated for play, which implies the importance of spatial BACKGROUND & AIM: Most evidence describing the amount of sleep associated with a lower mortality risk comes from studies that used self-reported measures of sleep and includes limited information about other sleep dimensions like sleep quality and timing This study examined associations between accelerometer-derived sleep duration, quality, timing, and mortality METHODS: Data are from the UK Biobank cohort of adults aged 40-69 years (2006-2010) Approximately 6 years post baseline, 103,712 adults participated in an activity monitoring sub-study and wore an Axivity AX3 wrist-worn triaxial accelerometer over 7-days Monitor data were processed using the R package GGIR to generate sleep duration (hours/day), sleep quality (wake after sleep onset, sleep efficiency), and sleep timing (onset, offset, midpoint) exposures Data were linked to mortality outcomes including all-cause, cardiovascular disease (CVD), and cancer mortality assessed via National Health Service registries in UK with follow-up up to 12/31/19 We first estimated Hazard ratios (HRs, 95% CI) for sleep duration and mortality outcomes using cubic splines Next, we computed HRs for quartiles of the sleep quality and timing exposures in relation to mortality All models were adjusted for age, sex, race-ethnicity, education, Townsend deprivation index, employment status, lifestyle factors, chronic conditions, functional pain, and general health rating Sensitivity analysis included examinations of heterogeneity in our sleep duration-mortality associations by demographic and lifestyle variables RESULTS: Over an average of 5 1 years 1,762 deaths occurred (1,108 cancer, and 338 CVD deaths) Participants slept on average from 23:41 to 7:12, for about 6:42 hours/day, and were awake for 46 minutes When compared to sleeping 7 0 hours/d, sleeping less than 6 hours per day was associated with a 14-33% higher risk for all-cause mortality (p<0 01; e g , HR5 hrs/d: 1 23 [0 95, 1 61]); 28-56% higher risk for CVD mortality (p=0 05; e g , HR5 hrs/d: 1 41 [0 78, 2 56]), with no clear associations for cancer mortality (p>0 05) Sleeping less than 6 hours/day on 3+ nights in a week was associated with a 20% increased risk for all-cause mortality (