Application of wearable devices in assessing emotional dynamic factors in adolescents with depressive disorders
10.3760/cma.j.cn113661-20241230-00449
- VernacularTitle:可穿戴设备在青少年抑郁障碍情绪动态因素评估中的应用分析
- Author:
Yuanzhen WU
1
;
Jie LUO
1
;
Jia ZHAO
1
;
Guoxuan ZHANG
1
;
Fan HE
1
Author Information
1. 首都医科大学附属北京安定医院 国家精神心理疾病临床医学研究中心 精神疾病诊断与治疗北京重点试验室，北京100088
- Publication Type:Journal Article
- Keywords:
Depressive disorder;
Adolescents;
Wearable devices;
R-R interval;
Arousal level;
Emotional valence;
Smart wristbands
- From:
Chinese Journal of Psychiatry
2025;58(7):542-548
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To assess the use of R-R interval (RRI) sequence data collected via wearable devices to evaluate emotional dynamic factors in adolescents with depressive disorders, and to analyze their impact on diagnosis and severity assessment.Methods:Clinical data were prospectively collected from 154 adolescent inpatients with depressive disorders (132 females, 22 males; age 12-18, mean: 13.5±1.6 years) treated at the Child Psychiatry Ward of Beijing Anding Hospital, Capital Medical University between January 2023 and January 2024.A control group of 152 healthy adolescents (62 females, 90 males; age 12-18, mean 14.5±1.3 years) was recruited during the same period. RRI data were obtained using the built-in photoplethysmography (PPG) sensor in the HUAWEI Band 7 wearable device. The device′s integrated emotion evaluation system extracted arousal and emotional valence (as indicators of emotional dynamics) from the collected RRIs. A Long Short-Term Memory (LSTM) network was employed to develop a model for depression diagnosis and severity prediction, while a random forest model was applied to generate receiver operating characteristic (ROC) curves to evaluate model performance. Binary Logistic regression was conducted to investigate the influence of arousal and emotional valence on depression diagnosis and severity.Results:A total of 429 records were collected and analyzed from 306 participants. The LSTM-based diagnosis and severity assessment models achieved area under the curve (AUC) of 0.896 9 and 0.715 3, respectively, indicating good model performance. Binary Logistic regression analysis showed that arousal and emotional valence had significant effects on diagnosis and severity. Specially, lower arousal in both the first 4 hours ( β=-8.906, 95%CI:-17.497 to -0.315) and second 4 hours ( β=-3.033, 95%CI:-5.109 to -0.957) significantly predicted positive depression diagnosis ( β=-1.219, 95%CI:-2.205 to -0.233), while emotional valence in the second 4 hours showed a trend toward a positive association ( β=0.675, 95%CI:-0.107-1.457). First 4-hour emotional valence: significantly positive association with severity ( β=0.322, 95%CI: 0.067-0.577), second 4-hour arousal level: negative association with severity ( β=-0.258, 95%CI:-0.527 to 0.011), whereas arousal in the second 4 hours had a marginal negative effect (β=-0.258, 95% CI:-0.527 to 0.011). Conclusion:RRI may serve as a useful auxiliary measure in diagnosing depressive disorders and predicting severity among adolescents. Wearable smart devices offer promising potential for screening emotional dynamic factors related to adolescent depression.
