Evaluation of a Low-Cost Commercial Actigraph and Its Potential Use in Detecting Cultural Variations in Physical Activity and Sleep

Abstract

The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural data including low-income countries. We evaluated physical activity and sleep-related measures and discussed the potential application of such devices for large-scale step and sleep data acquisition. To that end, we conducted two separate studies. In Study 1, we evaluated the performance of MB by comparing it to the GT3X (ActiGraph, wGT3X-BT), a scientific actigraph used in research, as well as subjective sleep reports. In Study 2, we distributed the MB across four countries (Austria, Germany, Cuba, and Ukraine) and investigated physical activity and sleep among these countries. The results of Study 1 indicated that MB step counts correlated highly with the scientific GT3X device, but did display biases. In addition, the MB-derived wake-up and total-sleep-times showed high agreement with subjective reports, but partly deviated from GT3X predictions. Study 2 revealed similar MB step counts across countries, but significant later wake-up and bedtimes for Ukraine than the other countries. We hope that our studies will stimulate future large-scale sensor-based physical activity and sleep research studies, including various cultures.

Is the Xiaomi Mi Band 4 an Accuracy Tool for Measuring Health-Related Parameters in Adults and Older People? An Original Validation Study

“…These results differ from previous studies since the authors concluded that the MB2 underestimated steps at slower walking speeds (<2.88 km/h) [27]. Similar results were also found during the validation of the MB3, since Topalidis et al concluded that the MB3 underestimated the number of steps, especially in a day with few steps [29].…”

“…Nevertheless, our results match with those of the previous versions of the MB (i.e., the MB2, and MB3) since the authors concluded that the MB can be considered as a suitable low-cost tracker for measuring SC in free-living conditions. It should be noted that the MB2 underestimated steps at slower walking speeds (<2.88 km/h) [27], and the MB3 underestimated the step number especially in days with few steps [29]. On the contrary, Pino-Ortega et al [28] found that the SC in the MB4 had a high accuracy when it was compared with a GPS, whereas the total distance measured by the MB4 can be considered questionable.…”

Sleep Time Estimated by an Actigraphy Watch Correlates With CSF Tau in Cognitively Unimpaired Elders: The Modulatory Role of APOE

“…In a validation study with 27 healthy sleepers the overall agreement between PSG and the Mi band 2 was 84.69% to discriminate wake from sleep. However, they found that the Mi band 2 tended to overestimate the total sleep time (on average 69 min), an observation that has been also reported by a posterior study (Topalidis et al, 2021). In this same article they as well criticize the lack of reliability of several wristbands models to assess sleep architecture (Ameen et al, 2019).…”

“…After a week, the device was connected with the researchers’ smartphones via Bluetooth, and a dedicated application (MiFit app) displayed the number of steps, wake up-times, bedtimes, as well as total sleep times for each day. Total sleep time was automatically measured, and it was calculated as the time interval between bedtime and wake-up-time having excluded periods of detected wakefulness ( Topalidis et al, 2021 ). Sleep was also assessed by the Oviedo Sleep Questionnaire, a self-reported test validated for the Spanish population that provides scores for insomnia and sleep quality ( Bobes et al, 2000 ).…”

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

“…Meharabadi et al used a wearable ring and watch to measure sleep quality, and observed that for total sleep time, the correlation of the actigraphy data with the ring data was 0.86 ( p < 0.001); with the watch data, the correlation was much lower, at 0.59 ( p < 0.001) [ 42 ]. Topalidis et al also observed that wrist-worn device data and actigraph reports that derived the wake-up time and sleep time had high correlations (0.96 and 0.84, respectively; p < 0.001) with subjective reports [ 87 ]. In a study conducted by Chen at al., a PPG smartwatch outperformed the polysomnography method for detecting obstructive sleep apnea.…”