Feasibility and Efficacy of the “FUNPALs Playgroup” Intervention to Improve Toddler Dietary and Activity Behaviors: A Pilot Randomized Controlled Trial

This study evaluated the feasibility and effects of the Families Understanding Nutrition and Physically Active Lifestyles (FUNPALs) Playgroup on toddler (12–36-month-old) diet and activity behaviors. Parent–toddler dyads were recruited from disadvantaged communities and randomly assigned to receive 10-weekly sessions of the FUNPALs Playgroup (n = 24) or dose-matched health education control group (n = 26). FUNPALs Playgroups involved physical and snack activities, delivery of health information, and positive parenting coaching. The control group involved group health education for parents only. Process outcomes (e.g., retention rate, fidelity) and focus groups determined feasibility and perceived effects. To evaluate preliminary effects, validated measures of toddler diet (food frequency questionnaire and a carotenoid biomarker), physical activity (PA; accelerometers), general and feeding parenting (self-report surveys), and home environment (phone interview) were collected pre and post. The sample comprised parents (84% female) who self-identified as Hispanic/Latino (38%) and/or African American (32%). Retention was high (78%). Parents from both groups enjoyed the program and perceived improvements in their children’s health behaviors. Objective measures demonstrated improvement with large effects (η2 = 0.29) in toddler diet (p < 0.001) but not PA (p = 0.099). In conclusion, the FUNPALs Playgroup is feasible and may improve toddler eating behaviors

DETERMINING PULSE-RATE FROM WRIST PLACED ACCELEROMETRY IN CHILDREN IN ORDER TO IMPROVE ESTIMATES OF SLEEP.

BACKGROUND: Children’s free-living sleep is most commonly measured via wrist-placed accelerometry. Similar to taking someone’s pulse, wrist-placed accelerometry may be sensitive enough to detect pulse-rate. This may be critical because current sleep detection methods using accelerometry rely on movement alone to detect sleep. This leads to poor detection of wake and the inability to detect sleep stages. Adding a physiological signal like pulse rate to sleep detection addresses both these limitations. The objective of this study was to estimate children’s pulse-rate via wrist placed accelerometry and compare these estimates to electrocardiogram (ECG) as a gold standard. METHODS: Participating children wore a consumer wearable (Apple Watch Series 7) and a wrist-placed research grade accelerometer (Actigraph GT9X) while undergoing an overnight laboratory-based polysomnography (PSG), including a 3-lead ECG. Raw accelerometry data from the Apple device was extracted using SensorLog, a freely available user-written application that leverages the devices’ application programming interface. Actigraph data was extracted via Actilife Software. All subsequent processing was performed in MATLAB. Pulse-rate estimates from the raw accelerometry data were calculated from peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from the criterion ECG were estimated from R-R spacings using R-pulse detection in normalized ECG traces. Mean absolute error (MAE) and mean absolute percent error (MAPE) were calculated to assess agreement between the accelerometry estimated pulse-rate from Actigraph and Apple and the ECG estimated heartrate. RESULTS: Eighty-four 5-12-year-old children (63% male, 72% White, 66% with mild/moderate obstructive sleep apnea) participated. One child was excluded as the ECG data stream was corrupted during collection. For Actigraph MAE and MAPE were high at 39(SD=20) beats/minute and 49.0%(SD=27.4%). For Apple MAE and MAPE were much lower at 8.9(SD=6.2) beats/minute and 10.2%(SD=6.5%) CONCLUSIONS: Raw accelerometry data extracted from Apple but not Actigraph can be used to estimate pulse-rate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of Actigraph’s relatively poor performance

MACHINE LEARNING MODELS FOR PREDICTING LABORATORY-BASED PHYSICAL ACTIVITY TYPE FROM CONSUMER WEARABLES IN CHILDREN

BACKGROUND: Accurate classification of physical activity type could improve estimates of free-living (i.e., occurring in everyday environments) physical activity energy expenditure (PAEE) in children. Studies have shown pattern recognition approaches trained on accelerometer data from research-grade devices can predict children’s free-living activities. However, there has been limited examination of the potential of using accelerometry (accel) and heart rate (HR) data from consumer wearables for increasing the accuracy of children’s activity type prediction. Thus, this study evaluated underlying accel and HR features from consumer wearables to predict children’s activities compared to accel and HR data from research-grade devices. METHODS: One hundred ninety-one children (5-12 years, 53% male, 57% White) completed a 60-minute protocol consisting of simulated free-living activities (e.g., walking, running, soccer, etc.). These activities were directly observed and categorized into four activity classes: Lying Down, Enrichment, Fundamental Movement Skills (FMS), and Sports/Games. Using the underlying accel and HR data from wrist-placed consumer wearables (i.e., Apple Watch Series 7 and Garmin Vivoactive 4S) and the combined accel and HR data (i.e., ActiGraph accel+Actiheart HR) from a wrist-placed, research-grade accelerometer (i.e., ActiGraph GT9X) and a chest-placed research-grade device (i.e., Actiheart 5), 13 time and frequency domain features were extracted at each second and 21 additional features were extracted using 60-second sliding windows for Random Forest model training. Leave-one-subject-out cross validation was used to evaluate the performance of each model. RESULTS: Underlying data from Apple exhibited the highest accuracy (90.8%, 95%CI: 90.7%, 90.9%) followed by ActiGraph+Actiheart (87.6%, 95%CI: 87.6%, 87.7%), and Garmin (85.5%, 95%CI: 85.4%, 85.6%). Apple also exhibited the highest sensitivity (87%, 91%, 92%, 78%) and specificity (98%, 95%, 94%, 97%) across the four activity classes, respectively. CONCLUSIONS: These results demonstrate the potential of consumer wearables to predict children’s activities with similar (or better) precision than research-grade devices. Future studies should evaluate whether features from these devices can accurately predict children’s activities in free-living environments. Grant or funding information: Research reported in this abstract was supported by the National Institute of Diabetes and Digestive and Kidney Diseases under Award Numbers F31DK136205 and R01DK129215 of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health