Objective Home-Monitoring of Physical Activity, Cardiovascular Parameters, and Sleep in Pediatric Obesity

Introduction: Clinical research and treatment of childhood obesity is challenging, and objective biomarkers obtained in a home-setting are needed. The aim of this study was to determine the potential of novel digital endpoints gathered by a home-monitoring platform in pediatric obesity.Methods: In this prospective observational study, 28 children with obesity aged 6-16 years were included and monitored for 28 days. Patients wore a smartwatch, which measured physical activity (PA), heart rate (HR), and sleep. Furthermore, daily blood pressure (BP) measurements were performed. Data from 128 healthy children were utilized for comparison. Differences between patients and controls were assessed via linear mixed effect models.Results: Data from 28 patients (average age 11.6 years, 46% male, average body mass index 30.9) and 128 controls (average age 11.1 years, 46% male, average body mass index 18.0) were analyzed. Patients were recruited between November 2018 and February 2020. For patients, the median compliance for the measurements ranged from 55% to 100% and the highest median compliance was observed for the smartwatch-related measurements (81-100%). Patients had a lower daily PA level (4,597 steps vs. 6,081 steps, 95% confidence interval [CI] 862-2,108) and peak PA level (1,115 steps vs. 1,392 steps, 95% CI 136-417), a higher nighttime HR (81 bpm vs. 71 bpm, 95% CI 6.3-12.3) and daytime HR (98 bpm vs. 88 bpm, 95% CI 7.6-12.6), a higher systolic BP (115 mm Hg vs. 104 mm Hg, 95% CI 8.1-14.5) and diastolic BP (76 mm Hg vs. 65 mm Hg, 95% CI 8.7-12.7), and a shorter sleep duration (difference 0.5 h, 95% CI 0.2-0.7) compared to controls.Conclusion: Remote monitoring via wearables in pediatric obesity has the potential to objectively measure the disease burden in the home-setting. The novel endpoints demonstrate significant differences in PA level, HR, BP, and sleep duration between patients and controls. Future studies are needed to determine the capacity of the novel digital endpoints to detect effect of interventions.