Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions.

GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Weather and children's physical activity; how and why do relationships vary between countries?

BACKGROUND: Globally most children do not engage in enough physical activity. Day length and weather conditions have been identified as determinants of physical activity, although how they may be overcome as barriers is not clear. We aim to examine if and how relationships between children's physical activity and weather and day length vary between countries and identify settings in which children were better able to maintain activity levels given the weather conditions they experienced. METHODS: In this repeated measures study, we used data from 23,451 participants in the International Children's Accelerometry Database (ICAD). Daily accelerometer-measured physical activity (counts per minute; cpm) was matched to local weather conditions and the relationships assessed using multilevel regression models. Multilevel models accounted for clustering of days within occasions within children within study-cities, and allowed us to explore if and how the relationships between weather variables and physical activity differ by setting. RESULTS: Increased precipitation and wind speed were associated with decreased cpm while better visibility and more hours of daylight were associated with increased cpm. Models indicated that increases in these variables resulted in average changes in mean cpm of 7.6/h of day length, -13.2/cm precipitation, 10.3/10 km visibility and -10.3/10kph wind speed (all p < 0.01). Temperature showed a cubic relationship with cpm, although between 0 and 20 degrees C the relationship was broadly linear. Age showed interactions with temperature and precipitation, with the associations larger among younger children. In terms of geographic trends, participants from Northern European countries and Melbourne, Australia were the most active, and also better maintained their activity levels given the weather conditions they experienced compared to those in the US and Western Europe. CONCLUSIONS: We found variation in the relationship between weather conditions and physical activity between ICAD studies and settings. Children in Northern Europe and Melbourne, Australia were not only more active on average, but also more active given the weather conditions they experienced. Future work should consider strategies to mitigate the impacts of weather conditions, especially among young children, and interventions involving changes to the physical environment should consider how they will operate in different weather conditions.

Process evaluation of a preschool physical activity intervention using web-based delivery.

BACKGROUND AND PURPOSE: Preschool/childcare settings offer a practical target for physical activity interventions. Online learning programs have the potential for greater public health reach and impact. The SHAPES-Dissemination (SHAPES-D) project adapted the original SHAPES in-person intervention for online delivery to teachers. The purpose of this paper is to describe the implementation monitoring and process evaluation for the SHAPES-D project. METHODS: Nine preschools with 26 classrooms participated. A total of 41 teachers were trained via online learning to implement the SHAPES-D program in their classrooms. The dose received, completeness, and fidelity of implementation were assessed through website metrics, teacher surveys and interviews, and classroom observations. RESULTS: Dose received was adequate (73%). Observed completeness and physical activity enjoyment fidelity were high (100%), although moderate-to-vigorous physical activity fidelity and social environment fidelity were low (25% each). Overall implementation was high (91%). DISCUSSION: Results indicate that the online method of delivery is viable for dissemination. The online delivery system provides an easy method of monitoring dose received. This may be the first structural intervention to monitor dose received through web metrics. CONCLUSION: The adaptation of an in-person intervention to an online delivery system increases the potential for dissemination of a successful program to increase physical activity in preschool settings.

Objectively measured physical activity and sedentary time in youth: the International children's accelerometry database (ICAD).

BACKGROUND: Physical activity and sedentary behaviour in youth have been reported to vary by sex, age, weight status and country. However, supporting data are often self-reported and/or do not encompass a wide range of ages or geographical locations. This study aimed to describe objectively-measured physical activity and sedentary time patterns in youth. METHODS: The International Children's Accelerometry Database (ICAD) consists of ActiGraph accelerometer data from 20 studies in ten countries, processed using common data reduction procedures. Analyses were conducted on 27,637 participants (2.8-18.4 years) who provided at least three days of valid accelerometer data. Linear regression was used to examine associations between age, sex, weight status, country and physical activity outcomes. RESULTS: Boys were less sedentary and more active than girls at all ages. After 5 years of age there was an average cross-sectional decrease of 4.2% in total physical activity with each additional year of age, due mainly to lower levels of light-intensity physical activity and greater time spent sedentary. Physical activity did not differ by weight status in the youngest children, but from age seven onwards, overweight/obese participants were less active than their normal weight counterparts. Physical activity varied between samples from different countries, with a 15-20% difference between the highest and lowest countries at age 9-10 and a 26-28% difference at age 12-13. CONCLUSIONS: Physical activity differed between samples from different countries, but the associations between demographic characteristics and physical activity were consistently observed. Further research is needed to explore environmental and sociocultural explanations for these differences.

Relationships among fitness, body composition, and physical activity.

PURPOSE: This study was designed to examine the associations of physical activity and body composition with cardiorespiratory fitness in eighth grade girls. METHODS: A random sample of 1440 eighth grade girls at 36 schools participated in this cross-sectional investigation, which represented an ethnically and geographically diverse group. Cardiorespiratory fitness was assessed using a modified physical work capacity test on a cycle ergometer that predicted workload at a heart rate of 170 beats.min. Physical activity was assessed over 6 d in each girl using an accelerometer and body composition was estimated from body mass index and triceps skinfolds using a previously validated equation. Pearson correlations and multiple regression analyses were used to determine the relationships among fitness, physical activity, and body composition. RESULTS: Significant linear relationships among cardiorespiratory fitness, body composition, and physical activity were found. The combination of fat and fat-free mass along with racial group and a race by fat-free-mass interaction accounted for 18% (R) of the variation in physical fitness. Adding moderate-to-vigorous physical activity to the regression model increased the R to 22%. Black girls had somewhat lower fitness levels (P < 0.05) especially at higher levels of fat and fat-free mass than other racial/ethnic groups. CONCLUSIONS: Physical activity, fat-free mass, and the interaction between fat-free mass and racial group are significantly associated with cardiorespiratory fitness in adolescent girls.

Goals and intentions mediate efficacy beliefs and declining physical activity in high school girls.

BACKGROUND: According to theory, girls who set goals about increasing their physical activity and who are dissatisfied with their current activity level are likely to form intentions to be active and to carry out those intentions, especially if they have high efficacy and control beliefs about being physically active. We tested those ideas while observing naturally occurring change during high school. METHODS: A cohort of 431 black and white girls was tested at the end of their 9th- and 12th-grade academic years. Confirmatory factor analysis established the structural invariance of the measures across the 3-year study period. Structural equation modeling and panel analysis were used to determine whether changes in goal setting and satisfaction would mediate relations of self-efficacy and perceived behavioral control with changes in intention and physical activity. Testing occurred between February and May in 1999 and 2004. Data were analyzed in 2006. RESULTS: Goal setting and intention mediated the indirect relation between self-efficacy and change in physical activity. Perceived behavioral control and physical activity change were related directly and also indirectly by a path mediated through satisfaction and intention. Black girls had lower self-efficacy, but changes in other variables were unrelated to race. CONCLUSIONS: These observations of longitudinal relations elaborate application of self-efficacy theory and the theory of planned behavior to physical activity by showing that goal setting and satisfaction mediate the relations of self-efficacy and perceived behavioral control with changes in intention and physical activity. The results encourage additional research to identify the sources and development of physical activity goals, and their attainment, among girls, and whether experimental manipulation of goals and intentions can mitigate the decline in girls' physical activity during high school.

Predicting energy expenditure from accelerometry counts in adolescent girls.

PURPOSE: Calibration of accelerometer counts against oxygen consumption to predict energy expenditure has not been conducted in middle school girls. We concurrently assessed energy expenditure and accelerometer counts during physical activities on adolescent girls to develop an equation to predict energy expenditure. METHODS: Seventy-four girls aged 13-14 yr performed 10 activities while wearing an Actigraph accelerometer and a portable metabolic measurement unit (Cosmed K4b2). The activities were resting, watching television, playing a computer game, sweeping, walking 2.5 and 3.5 mph, performing step aerobics, shooting a basketball, climbing stairs, and running 5 mph. Height and weight were also assessed. Mixed-model regression was used to develop an equation to predict energy expenditure (EE) (kJ.min(-1)) from accelerometer counts. RESULTS: Age (mean [SD] = 14 yr [0.34]) and body-weight-adjusted correlations of accelerometer counts with EE (kJ.min(-1)) for individual activities ranged from -0.14 to 0.59. Higher intensity activities with vertical motion were best correlated. A regression model that explained 85% of the variance of EE was developed: [EE (kJ.min(-1)) = 7.6628 + 0.1462 [(Actigraph counts per minute - 3000)/100] + 0.2371 (body weight in kilograms) - 0.00216 [(Actigraph counts per minute - 3000)/100](2) + 0.004077 [((Actigraph counts per minute - 3000)/100) x (body weight in kilograms)]. The MCCC = 0.85, with a standard error of estimate = 5.61 kJ.min(-1). CONCLUSIONS: We developed a prediction equation for kilojoules per minute of energy expenditure from Actigraph accelerometer counts. This equation may be most useful for predicting energy expenditure in groups of adolescent girls over a period of time that will include activities of broad-ranging intensity, and may be useful to intervention researchers interested in objective measures of physical activity.