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JMIR Public Health Surveill ; 7(4): e23806, 2021 04 23.
Article in English | MEDLINE | ID: covidwho-2141288

ABSTRACT

BACKGROUND: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. OBJECTIVE: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. METHODS: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. RESULTS: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. CONCLUSIONS: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period.


Subject(s)
COVID-19 , Electronic Data Processing/methods , Epidemiological Monitoring , Fitness Trackers/statistics & numerical data , Software , Adult , Exercise , Feasibility Studies , Female , Humans , Male , Norway , Quarantine/statistics & numerical data , SARS-CoV-2
2.
Am J Public Health ; 111(7): 1348-1351, 2021 07.
Article in English | MEDLINE | ID: covidwho-1360669

ABSTRACT

Objectives. To examine prevalence and predictors of digital health engagement among the US population. Methods. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698-3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Results. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one's health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09-4.21; P value range < .001-.03). Conclusions. Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention.


Subject(s)
Consumer Health Information/methods , Digital Technology/statistics & numerical data , Health Behavior , Adult , Age Factors , Aged , Aged, 80 and over , Cross-Sectional Studies , Female , Fitness Trackers/statistics & numerical data , Humans , Male , Middle Aged , Mobile Applications/statistics & numerical data , Public Health , Sex Factors , Socioeconomic Factors
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