Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan
Author | Ahmed, Arfan |
Author | Aziz, Sarah |
Author | Abd-Alrazaq, Alaa |
Author | Qidwai, Uvais |
Author | Farooq, Faisal |
Author | Sheikh, Javaid |
Available date | 2024-05-07T05:39:55Z |
Publication Date | 2023 |
Publication Name | Studies in Health Technology and Informatics |
Resource | Scopus |
Identifier | http://dx.doi.org/10.3233/SHTI230486 |
ISSN | 9269630 |
Abstract | Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children. The objective of this study is to examine the impact of intermittent fasting during Ramadan on stress levels in school children as measured using wearable artificial intelligence (AI). Twenty-nine school children (aged 13-17 years and 12M / 17F ratio) were given Fitbit devices and their stress, activity and sleep patterns analyzed 2 weeks before, 4 weeks during Ramadan fasting and 2 weeks after. This study revealed no statistically significant difference on stress scores during fasting, despite changes in stress levels being observed for 12 of the participants. Our study may imply intermittent fasting during Ramadan poses no direct risks in terms of stress, suggesting rather it may be linked to dietary habits, furthermore as stress score calculations are based on heart rate variability, this study implies fasting does not interfere the cardiac autonomic nervous system. |
Language | en |
Publisher | IOS Press BV |
Subject | artificial intelligence circadian fitbit Intermittent fasting Ramadan School children stress wearable devices |
Type | Conference Paper |
Pagination | 291-294 |
Volume Number | 305 |
Files in this item
This item appears in the following Collection(s)
-
Computer Science & Engineering [2402 items ]