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Can the apple watch ECG app be successfully used for measuring stress?
1
University of Waterloo, 162 McGarry Dr, Canada
Publication date: 2023-04-26
Popul. Med. 2023;5(Supplement):A592
ABSTRACT
Background and Objective:
Stress is a major public health issue correlated with severe health conditions, including depression, obesity, and cardiovascular diseases. Public health efforts typically are designed regarding the self-perceived stress of people. Interestingly, new advances in smart technology can support stress quantification, complementing traditional measurement methods. Hence, this study aimed to examine the prevalence of stress among the Canadian population using the Apple Watch ECG app.
Methods:
Forty-one participants were invited to use an iPhone 7 and an Apple Watch 7 for 2 weeks to measure self-perceived stress and ECG metrics six times per day. The iPhone contained an app that collected ECG from Apple Watch and allowed participants to self-report stress through a questionnaire composed of the Depression, Anxiety, and Stress Scale (DASS-21) and a Likert-based scale question about self-perceived stress level. Both scales were dichotomized into non-stress and stress concerning a cut-off score of 14 points for DASS-21 and 2 points for the single item. Statistical analysis was performed using the Statistical Package for Social Sciences (v. 28.0). First, Heart Rate Variability (HRV) data were extracted from the ECG. Next, the data normality and homogeneity were determined using the Kolmogorov-Smirnov and Levene tests. Then, Mann-Whitney was used to compare HRV variables with self-perceived stress. For all analyses, P<.05 was considered significant.
Results:
Several features were statistically significant, including the standard deviation of NN intervals (SDNN), the root mean square of successive differences (RMSSD), the heart’s acceleration (AC) and deacceleration (DC), and several high-frequency features.
Conclusion:
Features widely used in HRV analysis (SDNN, RMSSD) proved statistically significant when measuring stress. In addition, AC and DC are relatively new indicators and lack research with a focus on stress, suggesting new avenues of research. Ultimately, the results support using the Apple Watch ECG to study stress.
Stress is a major public health issue correlated with severe health conditions, including depression, obesity, and cardiovascular diseases. Public health efforts typically are designed regarding the self-perceived stress of people. Interestingly, new advances in smart technology can support stress quantification, complementing traditional measurement methods. Hence, this study aimed to examine the prevalence of stress among the Canadian population using the Apple Watch ECG app.
Methods:
Forty-one participants were invited to use an iPhone 7 and an Apple Watch 7 for 2 weeks to measure self-perceived stress and ECG metrics six times per day. The iPhone contained an app that collected ECG from Apple Watch and allowed participants to self-report stress through a questionnaire composed of the Depression, Anxiety, and Stress Scale (DASS-21) and a Likert-based scale question about self-perceived stress level. Both scales were dichotomized into non-stress and stress concerning a cut-off score of 14 points for DASS-21 and 2 points for the single item. Statistical analysis was performed using the Statistical Package for Social Sciences (v. 28.0). First, Heart Rate Variability (HRV) data were extracted from the ECG. Next, the data normality and homogeneity were determined using the Kolmogorov-Smirnov and Levene tests. Then, Mann-Whitney was used to compare HRV variables with self-perceived stress. For all analyses, P<.05 was considered significant.
Results:
Several features were statistically significant, including the standard deviation of NN intervals (SDNN), the root mean square of successive differences (RMSSD), the heart’s acceleration (AC) and deacceleration (DC), and several high-frequency features.
Conclusion:
Features widely used in HRV analysis (SDNN, RMSSD) proved statistically significant when measuring stress. In addition, AC and DC are relatively new indicators and lack research with a focus on stress, suggesting new avenues of research. Ultimately, the results support using the Apple Watch ECG to study stress.
| ISSN: | 2654-1459 |
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