Open AccessResearch on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data
Author(s) -
Antanas Bliudzius,
Roma Puronaitė,
Justas Trinkūnas,
Audronė Jakaitienė,
Vytautas Kasiulevičius
Publication year - 2021
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-219006
Subject(s) - prediabetes , activity tracker , wearable computer , physical activity , medicine , wearable technology , computer science , physical therapy , diabetes mellitus , type 2 diabetes , embedded system , endocrinology
BACKGROUND: Monitoring physical activity with consumers wearables is one of the possibilities to control a patient’s self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking. OBJECTIVE: This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine. METHODS: Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices. RESULTS: Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters. CONCLUSIONS: Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.