Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

Abstract

This study combines wearable sensors, weather data, and self-reported mood surveys to assess mental stress on older adults from heat experience. It is designed as a pilot and feasibility study in preparation for a large-scale experiment of older adults' mental wellbeing during extreme heat events. Results show that on-body temperatures from two i-Button sensors coupled with heart rate monitored from a smart watch are important indicators to evaluate individualized heat stress given a relatively uniform outdoor temperature. Furthermore, assessing their mood in their own environment demonstrates potential for understanding mental wellbeing that can change with varying time and location.

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

“…The methods of statistical analysis of wearables’ data and correlation to climate or weather data were primarily regression, linear mixed effect models, correlation, ANOVA, and 1- or 2-tailed t tests ( Table 5 ). Linear regression models or linear mixed effect models, for example, were often used to correlate IETs and area-level temperature data [ 35 , 40 , 42 , 48 , 75 ], but t tests were also used for the comparison between both methods [ 47 , 54 ]. Data sources differed between group-level data and participant-level data [ 42 , 54 ].…”

“…Linear regression models or linear mixed effect models, for example, were often used to correlate IETs and area-level temperature data [ 35 , 40 , 42 , 48 , 75 ], but t tests were also used for the comparison between both methods [ 47 , 54 ]. Data sources differed between group-level data and participant-level data [ 42 , 54 ]. The associations of heat exposure and wearables-measured parameters were mostly examined with linear mixed effect models or different regression models (linear, logistic, or Cox), adjusted for age, sex, and education [ 33 , 34 , 39 , 41 , 42 , 45 , 63 , 65 , 68 , 84 - 86 ].…”

“…Data sources differed between group-level data and participant-level data [ 42 , 54 ]. The associations of heat exposure and wearables-measured parameters were mostly examined with linear mixed effect models or different regression models (linear, logistic, or Cox), adjusted for age, sex, and education [ 33 , 34 , 39 , 41 , 42 , 45 , 63 , 65 , 68 , 84 - 86 ]. For the comparison of the effect of heat between groups with different characteristics such as sex or age and for the comparison of heat-stress and non–heat-stress days, t tests, Chi-square tests, and ANOVAs were used [ 35 , 37 , 43 , 50 , 55 - 57 , 59 , 61 , 63 , 65 , 84 ].…”

MEMO Box: Health Assistant for Depression With Medicine Carrier and Exercise Adjustment Driven by Edge Computing

“…The emotion recognition model is based on EEG signals of the patient. The emotional characteristics [41] of signals in each EEG channel are utilized in this paper, in addition, the emotional correlation between the signals in different channels is also taken into consideration. First, it is assumed that the number of channels for EEG signals of a patient is N .…”

“…With the development of society, we are facing a serious exercise deficit disorder which can cause health crisis. According to sports psychology [41], sports behavior can exert a huge impact on psychology state of humans. The schemes of recommended traditional systems focus on users emotions, however, sports behaviors are also important due to the emotional sensitivity of depression patients [42].…”