The effect of a novel wearable feedback device on cardiopulmonary resuscitation training in medical students: a three-arm cluster randomized controlled trial
In brief
Wearable feedback device raises correct compression depth to 90% in students
In a cluster-randomized trial of 618 medical students, real-time wearable feedback increased the proportion of compressions at the target depth from 71% with traditional training to 83%, and adding deliberate practice pushed it to 90%. Rate improvements were modest, and the benefit was seen only in an academic setting, so broader validation is needed.
- Journal
- Frontiers in public health (Q1)
- Published
- 21 July 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Enze Liu, Liyuan Gan, Wenwen Ma, Mo Chen, Chen Zhang, Xintao Huang, et al.
- PMID
- 42553382
- DOI
- 10.3389/fpubh.2026.1883769
Why clinicians should know about it
- Picked for Critical Care and Intensive Care Medicine (top studies of the week, 9 August 2026): High-quality evidence in a top journal
- Picked for Emergency Medicine (top studies of the week, 9 August 2026).
Abstract
BACKGROUND: While real-time feedback devices are proven to improve skill mastery in CPR training, current options are often suboptimal due to high costs, skin irritation, and single-functionality constraints. To overcome these limitations, we engineered a novel wearable feedback device, the accuracy of which was validated in our prior research. Furthermore, we conceptualized a hybrid training paradigm combining real-time feedback with deliberate practice. Given the current lack of efficacy data for this approach, this study aims to assess the device's real-world training performance and investigate whether this hybrid training approach can further optimize resuscitation training outcomes. METHODS: Students from Chongqing Medical University were recruited and randomly assigned to three groups: traditional training (Group A), real-time feedback training (Group B), or real-time feedback plus deliberate practice training (Group C). All participants were required to perform 2 min of chest compressions on CPR manikins. The primary outcomes were the proportion of correct compression depth (50-60 mm) and adequate compression rate (100-120 min-1). The secondary outcomes were chest compression depth (mm), chest compression rate (min-1), and the proportion of correct compression release. RESULTS: A total of 618 participants were randomly assigned to Group A (n = 210), Group B (n = 204), and Group C (n = 204). The proportion of correct compression depth in Group B was higher than that in Group A (mean [SD], 82.55 [12.67] % vs. 71.54 [22.28] %, mean difference [95% CI], -11.0 [-14.5 to -7.5] %, p < 0.001) and lower than that in Group C (90.01 [8.32] %, mean difference, 7.5 [5.4 to 9.6] %, p < 0.001). The proportion of adequate compression rate in Group B was higher than that in Group A (mean [SD], 81.61 [14.59] % vs. 78.15 [13.60] %, mean difference [95% CI], -3.5 [-6.3 to -0.7] %, p = 0.014) and showed no significant difference from that in Group C (83.12 [11.93] %, mean difference [95% CI], 1.5 [-1.1 to 4.1] %). The compression depth in Group B was deeper than that in Group A (mean [SD], 57.02 [5.15] mm vs. 53.47 (4.71) mm, p < 0.001) but did not differ from that in Group C (57.45 [4.36] mm, p = 0.367). No significant differences were observed in chest compression rate or the proportion of correct compression release among the three groups (p > 0.05). CONCLUSION: In conclusion, within the academic setting of a medical university, this novel feedback device improved CPR performance in compression depth and rate among medical students compared to traditional training. Furthermore, the hybrid training method combining real-time feedback and deliberate practice further enhanced training efficacy regarding chest compression depth in this population. However, these findings require independent replication in broader populations and diverse clinical settings before widespread implementation recommendations can be made.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.