Research on the effect of augmented reality based sensory interaction device in rehabilitation training of patients with chronic heart failure

Chronic heart failure (CHF) significantly impacts patient quality of life and presents challenges in healthcare. Innovative rehabilitation strategies, such as augmented reality (AR) technology, offer potential benefits for improving outcomes. This study aims to investigate the effectiveness of an AR-based sensory interaction device in terms of cardiac function, exercise capacity, quality of life, and patient satisfaction in CHF rehabilitation. A retrospective cohort study enrolled 200 patients with CHF between January 2021 and January 2023. Participants were assigned into a conventional rehabilitation group (n = 86) and an intervention group (n = 114) receiving AR-based somatosensory interactive training. Key measures included general characteristics, complete blood count, cardiac function indicators, 6-minute walking distance (6MWD) and maximum activity time (Tmax), General Self-Efficacy Scale (GSES), Minnesota Living with Heart Failure Questionnaire (MLHFQ) scores, and satisfaction questionnaires of nursing care quality and comfort level. The general characteristics, routine blood counts, and cardiac function indicators were comparable prior to the intervention. Post-intervention analysis revealed significant improvements in the intervention group across several parameters, including left ventricular ejection fraction (LVEF: 54.11% vs. 51.85%, P < 0.001) and cardiac index (CI: 2.56 vs. 2.34 L·min⁻¹·m⁻², P < 0.001). Exercise capacity, indicated by 6-minute walking distance (6MWD: 392.14 m vs. 367.89 m, P < 0.001), and GSES scores also improved significantly (25.14 vs. 23.82, P = 0.009). Satisfaction with rehabilitation was higher in the intervention group (70.18% vs. 54.65%, P = 0.024), while MLHFQ scores decreased more substantially, indicating enhanced quality of life. The correlation analysis revealed that AR-based device effectiveness was positively associated with cardiac functionality, exercise capacity, self-efficacy, and patient satisfaction. AR-based rehabilitation offers significant advantages over usual care in improving cardiac function, exercise capacity, quality of life, and patient satisfaction in CHF patients. These findings, while encouraging, should be interpreted with caution given the retrospective, single-centre design and four-week follow-up.

Authors

Institutions

Publication Details

Journal
BMC Cardiovascular Disorders
Published
2026-02-25
DOI
https://doi.org/10.1186/s12872-026-05625-y
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the effect of augmented reality based sensory interaction device in rehabilitation training of patients with chronic heart failure

Jinhe Zhao, Haizhen JIA, Qiuqi Zhang, Li Ma
BMC Cardiovascular Disorders
Stroke Rehabilitation and Recovery
article

Research on the effect of augmented reality based sensory interaction device in rehabilitation training of patients with chronic heart failure

Jinhe Zhao, Haizhen JIA, Qiuqi Zhang, Li Ma
article en

Abstract

Chronic heart failure (CHF) significantly impacts patient quality of life and presents challenges in healthcare. Innovative rehabilitation strategies, such as augmented reality (AR) technology, offer potential benefits for improving outcomes. This study aims to investigate the effectiveness of an AR-based sensory interaction device in terms of cardiac function, exercise capacity, quality of life, and patient satisfaction in CHF rehabilitation. A retrospective cohort study enrolled 200 patients with CHF between January 2021 and January 2023. Participants were assigned into a conventional rehabilitation group (n = 86) and an intervention group (n = 114) receiving AR-based somatosensory interactive training. Key measures included general characteristics, complete blood count, cardiac function indicators, 6-minute walking distance (6MWD) and maximum activity time (Tmax), General Self-Efficacy Scale (GSES), Minnesota Living with Heart Failure Questionnaire (MLHFQ) scores, and satisfaction questionnaires of nursing care quality and comfort level. The general characteristics, routine blood counts, and cardiac function indicators were comparable prior to the intervention. Post-intervention analysis revealed significant improvements in the intervention group across several parameters, including left ventricular ejection fraction (LVEF: 54.11% vs. 51.85%, P < 0.001) and cardiac index (CI: 2.56 vs. 2.34 L·min⁻¹·m⁻², P < 0.001). Exercise capacity, indicated by 6-minute walking distance (6MWD: 392.14 m vs. 367.89 m, P < 0.001), and GSES scores also improved significantly (25.14 vs. 23.82, P = 0.009). Satisfaction with rehabilitation was higher in the intervention group (70.18% vs. 54.65%, P = 0.024), while MLHFQ scores decreased more substantially, indicating enhanced quality of life. The correlation analysis revealed that AR-based device effectiveness was positively associated with cardiac functionality, exercise capacity, self-efficacy, and patient satisfaction. AR-based rehabilitation offers significant advantages over usual care in improving cardiac function, exercise capacity, quality of life, and patient satisfaction in CHF patients. These findings, while encouraging, should be interpreted with caution given the retrospective, single-centre design and four-week follow-up.

BMC Cardiovascular Disorders
Wuhan University of Science and Technology (CN)
Good health and well-being
Openalex Percentile: Top 44%
Stroke Rehabilitation and Recovery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.