Truace
Subscribe
70/100

Moderate evidence

ProblemPublished recordHealthProblem Space

Full story
More
Export record JSONEmailLinkedInX

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

Truvace record
TRV-2026-2856raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
2f431fee905a…check it

Background 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 appro…

The problem

A systematic review of 19 studies of students found wearable AI stress detection reached 85.6% pooled accuracy but averaged only 75.5% sensitivity and 74.4% specificity, which the authors called suboptimal and not ready to use without questionnaires.

What this doesn’t fix

Accuracy estimates in the meta-analyses came from only six studies, many studies were small, and the review excluded non-English, pre-2015 and public-dataset studies, so conclusions are not definitive.

Sources

1 on file
  1. Journal of Medical Internet Researchdoi:10.2196/52622
    Peer-reviewed2023-12-19

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Accuracy of wearable AI in detecting stress among students

    Accuracy 0.856 (95% CI 0.70 to 0.93)

    6 studies · search to June 12, 2023

    The review’s own words
    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).

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

Cite this record

v1

Truvace Impact Record TRV-2026-2856, v1: “The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.” Truvace, 2026-11-12. /record/TRV-2026-2856 (accessed on the day you copy it). sha256 2f431fee905a62ea…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv12f431fee905a…

    Certified into the record

Check this record hasn’t been altered

Check this record hasn’t been altered
How to check it yourself, without trusting this page

Download any version from /api/record/TRV-2026-2856 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.