AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar.
Institute of Digital Healthcare, WMG, University of Warwick, Warwick, United Kingdom.
J Med Internet Res. 2024 Jan 31;26:e52622. doi: 10.2196/52622.
Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial. Wearable artificial intelligence (AI) has emerged as a valuable tool for this purpose. It offers an objective, noninvasive, nonobtrusive, automated approach to continuously monitor biomarkers in real time, thereby addressing the limitations of traditional approaches such as self-reported questionnaires.
This systematic review and meta-analysis aim to assess the performance of wearable AI in detecting and predicting stress among students.
Search sources in this review included 7 electronic databases (MEDLINE, Embase, PsycINFO, ACM Digital Library, Scopus, IEEE Xplore, and Google Scholar). We also checked the reference lists of the included studies and checked studies that cited the included studies. The search was conducted on June 12, 2023. This review included research articles centered on the creation or application of AI algorithms for the detection or prediction of stress among students using data from wearable devices. In total, 2 independent reviewers performed study selection, data extraction, and risk-of-bias assessment. The Quality Assessment of Diagnostic Accuracy Studies-Revised tool was adapted and used to examine the risk of bias in the included studies. Evidence synthesis was conducted using narrative and statistical techniques.
This review included 5.8% (19/327) of the studies retrieved from the search sources. A meta-analysis of 37 accuracy estimates derived from 32% (6/19) of the studies revealed a pooled mean accuracy of 0.856 (95% CI 0.70-0.93). Subgroup analyses demonstrated that the accuracy of wearable AI was moderated by the number of stress classes (P=.02), type of wearable device (P=.049), location of the wearable device (P=.02), data set size (P=.009), and ground truth (P=.001). The average estimates of sensitivity, specificity, and F-score were 0.755 (SD 0.181), 0.744 (SD 0.147), and 0.759 (SD 0.139), respectively.
Wearable AI shows promise in detecting student stress but currently has suboptimal performance. The results of the subgroup analyses should be carefully interpreted given that many of these findings may be due to other confounding factors rather than the underlying grouping characteristics. Thus, wearable AI should be used alongside other assessments (eg, clinical questionnaires) until further evidence is available. Future research should explore the ability of wearable AI to differentiate types of stress, distinguish stress from other mental health issues, predict future occurrences of stress, consider factors such as the placement of the wearable device and the methods used to assess the ground truth, and report detailed results to facilitate the conduct of meta-analyses.
PROSPERO CRD42023435051; http://tinyurl.com/3fzb5rnp.
学生在整个学业生涯中通常会遇到压力。持续的压力源可能导致慢性压力，对他们的身心健康产生不利影响。因此，早期发现和监测学生的压力至关重要。可穿戴式人工智能（AI）已成为一种有价值的工具。它提供了一种客观、非侵入性、非干扰性、自动化的方法，可以实时连续监测生物标志物，从而解决了传统方法（如自我报告问卷）的局限性。
本系统评价和荟萃分析旨在评估可穿戴式 AI 在检测和预测学生压力方面的性能。
本综述的搜索源包括 7 个电子数据库（MEDLINE、Embase、PsycINFO、ACM 数字图书馆、Scopus、IEEE Xplore 和 Google Scholar）。我们还检查了纳入研究的参考文献列表，并检查了引用纳入研究的研究。搜索于 2023 年 6 月 12 日进行。本综述纳入了以使用可穿戴设备中的数据创建或应用 AI 算法来检测或预测学生压力为中心的研究文章。共有 2 名独立评审员进行了研究选择、数据提取和偏倚风险评估。采用改良的诊断准确性研究质量评估工具（Quality Assessment of Diagnostic Accuracy Studies-Revised tool）评估纳入研究的偏倚风险。使用叙述性和统计技术进行证据综合。
本综述纳入了从搜索源中检索到的研究的 5.8%（19/327）。对 32%（6/19）的研究中得出的 37 个准确性估计值进行荟萃分析，结果显示合并后的平均准确性为 0.856（95%CI 0.70-0.93）。亚组分析表明，可穿戴式 AI 的准确性受到压力分类数量（P=.02）、可穿戴设备类型（P=.049）、可穿戴设备位置（P=.02）、数据集大小（P=.009）和基准真实情况（P=.001）的调节。敏感性、特异性和 F 分数的平均估计值分别为 0.755（SD 0.181）、0.744（SD 0.147）和 0.759（SD 0.139）。
可穿戴式 AI 在检测学生压力方面具有一定的应用前景，但目前性能欠佳。鉴于许多这些发现可能是由于其他混杂因素，而不是潜在的分组特征所致，因此应谨慎解释亚组分析的结果。因此，在获得更多证据之前，可穿戴式 AI 应与其他评估方法（如临床问卷）一起使用。未来的研究应探索可穿戴式 AI 区分不同类型压力的能力、区分压力与其他心理健康问题的能力、预测未来压力的发生、考虑可穿戴设备的位置和用于评估基准真实情况的方法等因素，并报告详细结果，以便进行荟萃分析。
PROSPERO CRD42023435051；http://tinyurl.com/3fzb5rnp。
