Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
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.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-026-05625-y.
Keywords: AR, Sensory interaction device, Rehabilitation training, Chronic heart failure
Introduction
CHF represents a pervasive challenge in modern healthcare, affecting millions worldwide and imposing substantial burdens on individuals and healthcare systems [1]. Characterized by the heart’s inability to sustain adequate cardiac output to fulfill physiological demands, congestive heart failure (CHF) presents a diverse array of clinical manifestations. These range from fatigue and dyspnea to severe physical incapacitation and a significant decline in quality of life [2]. Despite advancements in pharmacological and device-based therapies, optimizing rehabilitation strategies for patients with CHF remains critical to improve clinical outcomes and reduce hospital readmissions [3]. In this context, tailored rehabilitation interventions aimed at improving physical capacity, optimizing cardiac function, and enhancing psychosocial well-being are central to comprehensive CHF management [4].
Traditional rehabilitation programs for CHF often emphasize physical exercises to improve cardiovascular conditioning [5]. However, those programs might be perceived as monotonous or physically taxing by patients, which can compromise adherence and long-term engagement [6]. Moreover, traditional rehabilitation environments frequently fall short in delivering the personalized feedback and adaptive modalities essential for sustaining patients’ motivation and addressing their individualized therapeutic needs [7]. To address these limitations, innovative approaches are needed that integrate physical training cognitive and emotional engagement, thereby fostering long-term adherence and optimizing therapeutic benefits [8].
In recent years, AR technology has emerged as a promising tool in the rehabilitation landscape, providing immersive and interactive environments that can significantly enhance patient engagement [9]. AR utilizes digital overlays on the real world, offering dynamic feedback and an enriched sensory experience, which can be tailored to individual therapeutic goals [10]. The technology’s capacity to simulate real-world activities while providing real-time feedback offers unique opportunities to enhance rehabilitation programs, especially for chronic conditions such as CHF [11].
AR has been reported to enhance the assessment of patients’ mental and physical status, potentially improving early detection of deteriorating vital signs in patients in Intensive Care Unit (ICU) [12]. Despite growing interest in AR’s potential in medical rehabilitation, empirical evidence specifically examining its impact on CHF rehabilitation remains sparse [13]. This study seeks to fill this gap by investigating the efficacy of an AR-based sensory interaction device in terms of cardiac function, exercise capacity, quality of life, and patient satisfaction in CHF rehabilitation, as well as the correlations between the therapeutic effect of augmented reality (AR) technology and these variables.
Materials and methods
Case selection
This retrospective cohort study enrolled 200 patients with confirmed diagnosis of CHF who received treatment at our hospital between January 2021 and January 2023. Demographic information such as general characteristics, laboratory parameters (including complete blood count), cardiac function indices, exercise capacity measurements, and psychosocial assessment scores (GSES and the MLHFQ) and patients’ satisfaction about cardiac rehabilitation were systematically extracted from patient medical records.
This study was approved by the Human Investigation Review Board of Tianyou Hospital Affiliated to Wuhan University of Science and Technology (approval number: XXX). As this retrospective analysis utilized anonymized clinical data without affecting patient management, the Ethics Committee of our hospital granted a waiver of informed consent in accordance with applicable ethical guidelines and relevant regulatory requirements.
Inclusion and exclusion criteria
Inclusion criteria included the following: patients aged over 35 years with the ability to comply with all study procedures, including normal visual acuity and auditory function; meeting the diagnostic criteria for CHF as previously outlined [14], with a disease duration over one year; and patient consent to participate after counseling. The ability of patients to comply with study procedures was assessed through a standardized pre-enrollment evaluation conducted by trained nursing staff, which included assessment of cognitive function using the Mini-Mental State Examination (MMSE, score ≥ 24 required), visual acuity testing using a standard Snellen chart (corrected visual acuity ≥ 20/40 required), and auditory function assessment through whisper test and pure tone audiometry.
Exclusion criteria included the following: individuals at risk for epileptic seizures, which was determined through comprehensive medical history review focusing on prior seizure episodes, family history of epilepsy, and current use of medications known to lower seizure threshold, along with electroencephalogram (EEG) screening for patients with suspicious history; individuals diagnosed with uncontrolled hypertension or anemia; patients with unstable angina pectoris; and patients with concurrent fractures.
Grouping and treatment methods
Based on the rehabilitation training methods selected by the patients, the participants were categorized into a conventional group (n = 86) and an intervention group (n = 114).
The conventional group underwent standard rehabilitation training, guided by cardiovascular nursing staff, following established cardiac rehabilitation guidelines [15, 16]. During hospitalization, patients engaged in bedside stretching exercises during the initial phase. Once their condition stabilized, walking training commenced according to a structured protocol. Patients were required to stand independently for over one minute with their knees straight, and were instructed to relax their arms naturally at their sides before beginning walking exercises. During these exercises, nursing staff adopted a forward positioning relative to patients and were always prepared to provide manual support for their shoulder as a preventive measure against potential loss of balance and fall risk mitigation. During walking, patients were instructed to avoid using upper extremity for stability or grasping external support during walking, with caregivers being advised to strictly comply with prescribed assistive device restrictions, especially prohibiting unsupervised use of crutches outside of supervised treatment. Walking training was conducted for 20 min per day for the initial two-week phase of hospitalization after admission. The routine rehabilitation training incorporated hemodynamic monitoring, including blood flow, heart rate, and blood pressure, with real-time dynamic adjustment of exercise intensity and frequency.
Upon discharge, patients in the conventional group were provided with a rehabilitation manual developed by our hospital. Additionally, our hospital provided short instructional videos demonstrating rehabilitation exercises, including walking training, jogging, and Tai Chi. Their routine rehabilitation progress was monitored, and feedback was provided through telephone follow-ups conducted twice weekly during the post-discharge period. The telephone follow-up calls, each lasting approximately 10–15 min, focused on assessing patient compliance with the prescribed exercise regimen, inquiring about any symptoms or adverse events experienced during home-based exercise, providing guidance on adjusting exercise intensity based on patient-reported tolerance, and offering motivational support to encourage continued adherence. The follow-up duration lasted for an additional two weeks after discharge, resulting in a total intervention span of four weeks for all patients.
In addition to standard rehabilitation training as described above, the intervention group received an AR technology-based somatosensory interactive intervention using a device developed by Guangzhou Fengdian Computer Technology Co., Ltd. (China) in conjunction with a Microsoft Xbox One Console and Kinect 2 peripherals, both of which were produced by Microsoft’s Chinese subsidiary. The system is equipped with an ATI Xenos graphics processing unit (GPU) for real-time rendering, infrared sensors for motion tracking, an RGB camera, and a multi-array microphone. The AR motion sensing device constructs 3-dimensional (3D) spatial maps by reconstructing the surrounding environment within its field of view and then generates digital skeleton models or virtual avatar representations, enabling patients to interact in real-time with the technical interface, while allowing for precise tracking and recording of comprehensive 3D movements. Patients use their bodies as remote controls for engaging in interactive activities. Installation and operation of the AR device were conducted according to the manufacturer’s instructions under professional guidance to ensure optimized operational safety and tracking accuracy.
The specific AR interventions contained the following contents: Both the bicycle and the treadmill were physical objects and were fixedly installed on the floor of the hospital’s rehabilitation training room. Various medical testing instruments were placed beside the sports equipment, including electrocardiogram machines, echocardiography machines, ambulatory electrocardiogram monitors, and blood pressure detectors. The patient wore AR glasses and underwent AR treatment using bicycles and treadmills under the accompaniment of professional cardiologists and nurses. AR glasses provided patients with immersive simulations of various environments, which enabled them to experience various outdoor and spatial environments as if they were there. Respiratory Muscle Training consisted of the AR device simulating an immersive natural visual audio environment to encourage diaphragmatic breathing and pursed-lip breathing, initiating in the first week of admission, with sessions following a structured schedule of 20 min at a frequency of 2–3 times per week. Bicycle and Treadmill Training involved patients using a stationary bicycle dynamometer with an immersive virtual navigation system to project roadway and city landscapes, which promotes perceptual adaptation from static positioning transitioning to dynamic spatial environments, while treadmill sessions were adapted to mimic outdoor environments allowing patients to perform controlled jogging kinematics. Training started in the second week of admission, with sessions lasting 20–30 min once a week. Interactive Gaming involved mixed reality integrations of physical and virtual aspects to achieve the motion simulation adapted to motion tasks engaging patients to perform motor patterns akin to real-life sports activities, such as swinging a tennis racket or bowling, and was commenced in the second week of admission following a structured schedule of 20–30 min twice a week.
Patient tolerance was continuously monitored during the AR-mediated somatosensory interactive sessions. The intervention incorporated predefined termination criteria as follows: significant respiratory difficulty, chest contraction, dizziness or fatigue, or cardiovascular instabilities manifested as sustained tachycardia (> 160 bpm) or systolic hypertension (> 180 mm Hg/24 kPa). Once the aforementioned conditions occurred, the intervention was immediately halted. This ensured that the interventions remained effective while ensuring patient safety. Concurrently, patients in the intervention group returned to the hospital regularly to participate in AR technology-mediated somatosensory interactive game interventions during the post-discharge period. A possible future target could be the use of portable AR devices during home care, which would enable continuous engagement without requiring hospital visits.
Routine blood test
Prior to the intervention, 5 ml of venous blood was drawn from patients in a fasting state before 8 a.m. A complete blood count was conducted with use of DxH800 blood analyzer (Beckman Coulter, Inc., Brea, CA, USA) to assess levels of red blood cells, white blood cells, neutrophils, lymphocytes, eosinophils, basophils, hemoglobin, and platelets. The BECKMAN Synchronx20 fully automated biochemical analyzer (Beckman Coulter, Inc., Brea, CA, USA) was employed to analyze the levels of C-reactive protein by means of rate scattering turbidimetry. Additionally, complete blood samples were anticoagulated with ethylenediaminetetraacetic acid (EDTA) and the erythrocyte sedimentation rate (ESR) was measured with use of the TEST 1 automated ESR analyzer (ALIFAX, Inc., Italy).
Cardiac functional indices
Cardiac function indicators were assessed for patients both prior to and post the intervention, including left ventricular ejection fraction (LVEF), cardiac index (CI), left ventricular end diastolic diameter (LVEDD), serum heart fatty acid binding protein (H-FABP), and N-terminal pro-brain natriuretic peptide (NT-proBNP).
LVEF, CI, and LVEDD were measured by means of echocardiography (Siemens Acuson Sequoia 512). Measurements were obtained over three cardiac cycles and averaged to ensure accuracy. Venous blood samples were collected at random times, centrifuged at 3000 rpm for 5 min, with the isolation of supernatant. Serum H-FABP levels were determined by use of a bidirectional flow immunoassay (Ruilai Bioengineering Co., Ltd., Shenzhen, China), while NT-proBNP levels were measured by electrochemiluminescence immunoassay (Shanghai Biyuntian Biotechnology Co., Ltd., China).
Sports function indicators
6MWD and Tmax of the patients were measured at baseline and post intervention. The 6MWD was recorded according to American Thoracic Society (ATS) guidelines [17]. Tmax, defined as the total treadmill exercise time (minutes) to volitional fatigue during cardiopulmonary exercise testing (CPET), was recorded automatically by the CPET software [18, 19].
Device effectiveness assessment
Device effectiveness was operationalized as a composite z-score integrating post-intervention percentage change in LVEF, CI, 6MWD and GSES (weights = equal). A higher score indicates greater overall benefit from the AR-based intervention.
GSES
The validated Chinese version of the 10-item General Self-Efficacy Scale was used to assess patients’ self-efficacy at baseline and post intervention [20]. This questionnaire assesses the confidence of subjects in handling diverse life challenges using a 4-point Likert scale with a Cronbach’s alpha coefficient of 0.836, indicating good internal consistency. Raw scores (10–40) were summed then divided by 10 to obtain a 1–4 average item score; higher values indicate stronger self-efficacy. See Supplementary File 1 for the complete Chinese version of GSES.
MLHFQ
We used a strict forward and backward translation process to carefully translate MLHFQ from English to Chinese, and then proofread the Chinese version and determined the final Chinese version [21]. The MLHFQ was utilized to assess the quality of life of patients prior to and post the intervention. The MLHFQ is a disease-specific quality-of-life assessment instrument for patients with heart failure, with score ranging from 0 to 105 and higher scores indicating a worse quality of life. This 21-item tool evaluates three domains, including physical symptoms, daily activity limitations, and psychological effects. Items are scored on a 5-point Likert scale (0–4). The MLHFQ showed good reliability (Cronbach’s α > 0.80) [22].
Satisfaction
A patient satisfaction questionnaire was developed based on the quality of care and comfort of the patients during their hospitalization. The questionnaire was developed following the framework described by Merkouris et al. [23] for assessing patient satisfaction in cardiac care settings, and items were reviewed by a panel of three cardiologists and two cardiac rehabilitation nurses for content validity. The questionnaire demonstrated acceptable internal consistency (Cronbach’s α = 0.78) in a pilot sample of 30 patients prior to the main study. Each patient’s total score ranged from 0 to 100, with classification as follows: 0–79 points = Not satisfied; 80–89 points = Generally satisfied; 90–100 points = Satisfied. See Supplementary File 2 for the complete satisfaction questionnaire.
Statistical method
Measurement data were presented as mean ± standard deviation, depending on whether they follow a normal distribution. Categorical data were reported as frequencies and percentages. T-tests were used to compare continuous variables between two groups. For all categorical comparisons, Pearson’s χ² was used; when expected counts < 5, Fisher’s exact test was applied. A p-value of less than 0.05 was considered statistically significant. All data statistics were performed by SPSS 19 software (SPSS Inc., Chicago, IL, USA) and the pictures were made by the R software package version 3.0.2 (Free Software Foundation, Inc., Boston, MA, USA).
Sample size was estimated using G*Power software (version 3.1.9.7). Based on a two-tailed independent samples t-test with α = 0.05 and power = 0.80, and pilot data showing a mean 6MWD difference of 25 m (SD = 60), the required sample size was 200 with an allocation ratio of 1.3:1 (intervention:control) to reflect real-world clinical adoption patterns.
Results
General information
In the analysis of general characteristics between the intervention group (n = 114) and the conventional group (n = 86), no statistically significant differences were observed across all measured parameters (Table 1). The mean age was similar between groups, with the conventional group at 63.25 ± 8.12 years and the intervention group at 62.88 ± 8.34 years (P = 0.754). Body mass index (BMI) was also comparable, with the conventional group with a mean of 27.45 ± 3.21 kg/m² and the intervention group with a mean of 27.33 ± 3.18 kg/m² (P = 0.799). Education level was practically identical between groups, averaging 12.52 ± 3.45 years of education in the conventional group and 12.45 ± 3.48 years of education in the intervention group (P = 0.895). Gender distribution, smoking and drinking histories and histories of hypertension and diabetes showed no significant differences, as did the duration of disease (P > 0.05). New York Heart Association (NYHA) class distribution and the primary disease diagnoses also did not differ significantly between groups (P > 0.05). All these findings indicated that the baseline characteristics of the two groups were well matched prior to the intervention.
Table 1.
Comparison of general information between two groups
| Parameters | Conventional Group (n = 86) | Intervention Group (n = 114) | t/χ² | P-value |
|---|---|---|---|---|
| Age (years) | 63.25 ± 8.12 | 62.88 ± 8.34 | 0.313 | 0.754 |
| BMI (kg/m²) | 27.45 ± 3.21 | 27.33 ± 3.18 | 0.255 | 0.799 |
| Education Level (years of education) | 12.52 ± 3.45 | 12.45 ± 3.48 | 0.132 | 0.895 |
| Gender (Male/Female) | 46/40 | 62/52 | 0.016 | 0.900 |
| Smoking history [n (%)] | 32 (37.21%) | 43 (37.72%) | 0.005 | 0.941 |
| Drinking history [n (%)] | 21 (24.42%) | 29 (25.44%) | 0.027 | 0.869 |
| History of hypertension [n (%)] | 34 (39.53%) | 45 (39.47%) | 0.000 | 0.993 |
| History of diabetes [n (%)] | 25 (29.07%) | 34 (29.82%) | 0.013 | 0.908 |
| Disease duration (years) | 8.76 ± 2.34 | 8.53 ± 2.42 | 0.668 | 0.505 |
| NYHA Class [n (%)] | 0.313 | 0.855 | ||
| Class II | 40 (46.51%) | 51 (44.74%) | ||
| Class III | 37 (42.91%) | 53 (46.49%) | ||
| Class IV | 9 (10.47%) | 10 (8.77%) | ||
| Primary disease [n (%)] | 3.547 | 0.471 | ||
| Dilated cardiomyopathy | 11 (12.79%) | 16 (14.04%) | ||
| Senile degenerative valvular heart disease | 19 (22.09%) | 20 (17.54%) | ||
| Coronary artery disease | 46 (53.49%) | 54 (47.37%) | ||
| Rheumatic heart disease | 5 (5.81%) | 13 (11.40%) | ||
| Acute myocarditis | 5 (5.81%) | 11 (9.65%) |
BMI body mass index, NYHA New York Heart Association
Preoperative routine blood
The ESR values were comparable between two groups, with the conventional group showing a value of 23.45 ± 7.81 mm/h and the intervention group 22.98 ± 7.65 mm/h (P = 0.671) (Table 2). Similarly, red blood cell counts did not differ significantly (conventional: 4.72 ± 0.41 × 10⁶/µL vs. intervention: 4.75 ± 0.42 × 10⁶/µL, P = 0.662). White blood cell, neutrophil, lymphocyte, eosinophil, and basophil counts showed no significant differences, demonstrating comparable values across the two groups (P > 0.05). Hemoglobin levels showed no significant difference between two groups (conventional: 134.5 ± 12.7 g/L vs. intervention: 134.9 ± 12.9 g/L; P = 0.827). Similarly, platelet counts and C-reactive protein levels demonstrated comparable values between two groups, confirming no significant differences (P values of 0.814 and 0.837, respectively). These results confirmed that baseline hematological characteristics were well matched between the conventional and intervention groups.
Table 2.
Comparison of preoperative blood routine and cardiac function indicators between two groups of patients before intervention
| Parameters | Conventional Group (n = 86) | Intervention Group (n = 114) | t-value | P-value |
|---|---|---|---|---|
| ESR (mm/h) | 23.45 ± 7.81 | 22.98 ± 7.65 | 0.425 | 0.671 |
| Red blood cell (1 × 10⁶/µL) | 4.72 ± 0.41 | 4.75 ± 0.42 | 0.438 | 0.662 |
| White blood cell (1 × 10³/µL) | 7.15 ± 1.53 | 7.18 ± 1.56 | 0.125 | 0.900 |
| Neutrophil (1 × 10³/µL) | 4.11 ± 1.17 | 4.15 ± 1.20 | 0.196 | 0.845 |
| Lymphocyte (1 × 10³/µL) | 2.03 ± 0.64 | 2.05 ± 0.66 | 0.224 | 0.823 |
| Eosinophil (1 × 10²/µL) | 0.33 ± 0.08 | 0.33 ± 0.09 | 0.255 | 0.799 |
| Basophil (1 × 10³/µL) | 0.09 ± 0.03 | 0.09 ± 0.03 | 0.060 | 0.952 |
| Hemoglobin (g/L) | 134.5 ± 12.7 | 134.9 ± 12.9 | 0.219 | 0.827 |
| Platelet (1 × 10³/µL) | 245.3 ± 53.2 | 247.1 ± 54.1 | 0.235 | 0.814 |
| CRP (mg/L) | 5.27 ± 1.34 | 5.31 ± 1.41 | 0.206 | 0.837 |
| LVEF (%) | 44.21 ± 4.56 | 44.56 ± 4.61 | 0.532 | 0.595 |
| CI [L·min⁻¹·m⁻²] | 2.14 ± 0.34 | 2.16 ± 0.36 | 0.270 | 0.787 |
| LVEDD (mm) | 54.32 ± 5.46 | 54.67 ± 5.51 | 0.446 | 0.656 |
| H-FABP (ng/L) | 6.51 ± 1.39 | 6.49 ± 1.41 | 0.107 | 0.915 |
| NT-proBNP (ng/L) | 4.16 ± 0.81 | 4.17 ± 0.82 | 0.098 | 0.922 |
ESR Erythrocyte sedimentation rate, CRP C-reactive protein, LVEF left ventricular ejection fraction, CI cardiac index, LVEDD left ventricular end diastolic diameter, H-FABP serum heart fatty acid binding protein, NT-proBNP N-terminal pro brain natriuretic peptide
Cardiac function indicators prior to intervention
The LVEF was comparable between two groups, with the conventional group demonstrating a value of 44.21 ± 4.56% versus 44.56 ± 4.61% in the intervention group (P = 0.595) (Table 2). Similarly, the CI showed no significant difference, measuring 2.14 ± 0.34 L·min⁻¹·m⁻² in the conventional group and 2.16 ± 0.36 L·min⁻¹·m⁻² in the intervention group (P = 0.787). LVEDD values were comparable between groups, being 54.32 ± 5.46 mm and 54.67 ± 5.51 mm for the conventional and intervention groups, respectively (P = 0.656). The levels of H-FABP and NT-proBNP also showed insignificant differences, with P values of 0.915 and 0.922, respectively. These findings indicate that cardiac function indicators were well balanced between the two groups prior to the intervention.
Cardiac function indicators post-intervention
The LVEF notably increased to 54.11 ± 4.56% in the intervention group versus 51.85 ± 4.61% in the conventional group, with a highly significant P value of < 0.001. Similarly, the CI markedly improved in the intervention group, measuring 2.56 ± 0.34 L·min⁻¹·m⁻² compared to 2.34 ± 0.36 L·min⁻¹·m⁻² in the conventional group (P < 0.001). LVEDD was significantly reduced in the intervention group, showing 49.35 ± 5.36 mm compared to 51.44 ± 5.41 mm in the conventional group (P = 0.007). Additionally, H-FABP levels were significantly decreased in the intervention group, with values of 3.97 ± 1.13 ng/L versus 4.36 ± 1.22 ng/L in the conventional group (P = 0.022). NT-proBNP levels showed a substantial reduction in the intervention group, registering values of 2.94 ± 0.65 ng/L versus 3.38 ± 0.81 ng/L in the conventional group (P < 0.001). These findings indicated that the AR-based sensory interaction device significantly enhanced cardiac function in patients with CHF, as shown in Fig. 1.
Fig. 1.
Comparison of cardiac function indices of patients in two groups post intervention. Note: LVEF: left ventricular ejection fraction; CI: cardiac index; LVEDD: left ventricular end diastolic diameter; H-FABP: serum heart fatty acid binding protein; NT-proBNP: N-terminal pro brain natriuretic peptide. *, p < 0.05; **, p < 0.01; ***, p < 0.001
6MWD prior to and post intervention
At baseline, the 6MWD was comparable between groups, with the conventional group demonstrating a value of 322.14 ± 45.67 m and 325.45 ± 46.12 m in the intervention group, showing no significant difference (P = 0.615) (Table 3). Following intervention, both groups exhibited significant improvements, but the increase was substantially higher in the intervention group, which recorded a 6MWD of 392.14 ± 48.67 m compared to 367.89 ± 47.34 m in the conventional group (P < 0.001). The within-group differences pre- and post-intervention were significant for both groups (P < 0.001), underscoring the efficacy of the AR-based sensory interaction device in enhancing walking capacity in patients with CHF.
Table 3.
Comparison of 6MWD, Tmax, GSES and MLHFQ scores between two groups prior to and post intervention
| Outcome | Conventional Group Pre | Conventional Group Post | Intervention Group Pre | Intervention Group Post | Within-group Conventional | Within-group Intervention | Between-Group Pre | Between-Group Post |
|---|---|---|---|---|---|---|---|---|
| 6MWD (m) | 322.14 ± 45.67 | 367.89 ± 47.34 | 325.45 ± 46.12 | 392.14 ± 48.67 | t = 6.450/P < 0.001 | t = 10.620/P < 0.001 | t = 0.504/P = 0.615 | t = 3.529/P < 0.001 |
| Tmax (min) | 8.32 ± 2.14 | 10.47 ± 2.34 | 8.36 ± 2.34 | 11.51 ± 2.45 | t = 6.288/P < 0.001 | t = 9.927/P < 0.001 | t = 0.111/P = 0.912 | t = 3.031/P = 0.003 |
| GSES | 21.45 ± 3.21 | 23.82 ± 3.45 | 21.78 ± 3.45 | 25.14 ± 3.56 | t = 4.664/P < 0.001 | t = 7.237/P < 0.001 | t = 0.702/P = 0.484 | t = 2.649/P = 0.009 |
| MLHFQ | 47.67 ± 8.45 | 42.14 ± 8.34 | 47.98 ± 8.67 | 39.45 ± 8.56 | t = 4.319/P < 0.001 | t = 7.475/P < 0.001 | t = 0.257/P = 0.797 | t = 2.226/P = 0.027 |
6MWD 6-minute walking distance, Tmax maximum activity time, GSES General Self-Efficacy Scale, MLHFQ Minnesota Living with Heart Failure Questionnaire
Data are presented as mean ± SD
Tmax
Initially, Tmax values were comparable between the conventional group (8.32 ± 2.14 min) and the intervention group (8.36 ± 2.34 min) (P = 0.912) (Table 3). Post-intervention, both groups demonstrated significant increases in Tmax; however, the intervention group showed a more pronounced improvement, achieving a value of 11.51 ± 2.45 min versus 10.47 ± 2.34 min in conventional group (P = 0.003). The change in Tmax from pre- to post-intervention was significant within both groups (P < 0.001), indicating that the AR-based sensory interaction device effectively enhanced exercise tolerance in patients with CHF.
GSES scores
Initial GSES scores were comparable between the conventional group (21.45 ± 3.21) and the intervention group (21.78 ± 3.45), with no statistically significant difference (P = 0.484) (Table 3). Following the intervention, both groups experienced statistically significant elevation in GSES scores. Moreover, the intervention group exhibited a significantly higher improvement, with a post-intervention scores of 25.14 ± 3.56 versus 23.82 ± 3.45 in the conventional group (P = 0.009). The within-group changes from before to after the intervention were significant for both groups (P < 0.001). These findings suggest that the AR-based sensory interaction device positively impacts patients’ self-efficacy in managing CHF.
MLHFQ scores
Pre-intervention MLHFQ scores were comparable between the conventional group (47.67 ± 8.45) and the intervention group (47.98 ± 8.67), with no significant difference (P = 0.797) (Table 3). Post-intervention, both groups showed statistically significant reductions in MLHFQ scores, indicating improved quality of life. The intervention group, however, showed a greater reduction, with scores decreasing to 39.45 ± 8.56 versus 42.14 ± 8.34 in the conventional group (P = 0.027). Within-group analysis revealed significant changes from before to after the intervention for both groups (P < 0.001). These results suggested that the AR-based sensory interaction device effectively enhances the quality of life in patients undergoing rehabilitation for CHF.
Satisfaction with rehabilitation training
In the intervention group, 32 patients (28.07%) reported being highly satisfied, and 48 patients (42.11%) were generally satisfied, resulting in an overall satisfaction rate of 70.18% (Table 4). In contrast, the conventional group reported lower satisfaction, with 19 patients (22.09%) highly satisfied and 28 patients (32.56%) generally satisfied, culminating in an overall satisfaction rate of 54.65%. The difference in overall satisfaction between the groups was statistically significant (P = 0.024), indicating that the AR-based sensory interaction device significantly enhances patient satisfaction in rehabilitation training for CHF.
Table 4.
Comparison of satisfaction with rehabilitation training between two groups of patients [n (%)]
| Outcome | Conventional Group (n = 86) | Intervention Group (n = 114) | χ²-value | P-value |
|---|---|---|---|---|
| Satisfied | 19 (22.09%) | 32 (28.07%) | ||
| Generally satisfied | 28 (32.56%) | 48 (42.11%) | ||
| Dissatisfied | 39 (45.35%) | 34 (29.82%) | ||
| Overall Satisfaction | 47 (54.65%) | 80 (70.18%) | 5.097 | 0.024 |
Correlation analysis
Post-intervention LVEF displayed a positive correlation with device effectiveness (rho = 0.260, P < 0.001), as did the CI (rho = 0.310, P < 0.001) (Fig. 2). A negative correlation was observed between device usage and LVEDD (rho = -0.193, P = 0.006), H-FABP (rho = -0.164, P = 0.020), and NT-proBNP (rho = -0.283, P < 0.001), indicating potential reductions in indices of cardiac stress. Additionally, the 6MWD (rho = 0.223, P = 0.001) and Tmax (rho = 0.200, P = 0.005) were positively correlated with device effectiveness. Elevation of GSES scores (rho = 0.162, P = 0.022), as well as reduction of MLHFQ scores (rho = -0.143, P = 0.044), were noted, alongside increased patient satisfaction with rehabilitation (rho = 0.160, P = 0.024). These findings suggested that the AR-based device effectiveness (as defined in Methods section "Device effectiveness assessment") is positively associated with cardiac functionality, exercise capacity, self-efficacy, and patient satisfaction.
Fig. 2.
Correlation analysis of the effectiveness of somatosensory interactive device based on AR technology in rehabilitation training for patients with CHF
Discussion
The primary rationale behind using AR technology in rehabilitation settings is its ability to provide immersive and engaging environments, which can enhance patient motivation and adherence to rehabilitation programs [24]. The positive outcomes observed in our study, where the intervention group demonstrated superior enhancements in cardiac function, physical capacity, quality of life and satisfaction compared to the conventional group, supporting the notion that integrating technology into rehabilitation can offer substantial benefits [25]. These improvements seem to be driven by several mechanisms inherent to the AR-based modality. This AR system combines integrated technologies such as augmented reality, body-sensing interaction, and image recognition. It has powerful application functions and extensibility, and therefore has extremely high application value [26].
AR-based rehabilitation significantly improved cardiac and psychosocial outcomes in our cohort. The marked improvement in cardiac function indicators, such as LVEF and CI, with concomitant reductions in LVEDD and biomarkers like serum H-FABP and NT-proBNP, suggests that AR interventions may induce favorable hemodynamic changes. The potential mechanism could involve improved cardiovascular efficiency facilitated by more engaging and varied physical activity regimens achievable through AR [27]. The immersive environment created by the AR device might evoke greater physiological responses by simulating real-world activities that encourage higher levels of activity and engagement, fostering beneficial cardiac remodeling and reducing myocardial stress [28].
The significant increases in exercise capacity indicators such as the 6MWD and Tmax in the intervention group further underscore the efficacy of AR technology in extending physical boundaries. The personalized and gamified approach inherent in AR-based interventions could lead to a higher motivation level, which, in turn, translates to enhanced physical performance [29]. By presenting exercise as an engaging and challenging game, AR technology may increase patients’ intrinsic motivation to participate and sustain higher levels of physical activity, compared to traditional rehabilitation exercises, which can often be monotonous and repetitive [30].
The AR-mediated intervention was associated with more significant improvements in GSES scores and reductions in MLHFQ scores, indicating enhanced self-efficacy and improved quality of life among the AR-intervened cohort. This enhancement in outcomes may relate to the increased autonomy and control allowed by the interactive nature of AR systems, which foster a proactive rather than a passive approach to rehabilitation [31]. Engaging patients in this interactive setup could empower them to take charge of their rehabilitation, leading to a stronger belief in their abilities to manage their conditions effectively [32].
Satisfaction scores also showed an appreciable increase in the intervention group, highlighting the potential of AR technology to improve patient experience during rehabilitation. The novel and interactive nature of AR-based exercises likely contributed to the observed satisfaction, as patients may have perceived the intervention as more enjoyable and less burdensome compared to standard rehabilitation methods [33]. Increased satisfaction is not merely ancillary; it plays a critical role in ensuring continued adherence to rehabilitation programs, which is crucial for sustaining long-term health benefits [34].
An interesting aspect of the findings is the significant correlations between the device effectiveness (as defined in Methods section "Device effectiveness assessment") and several positive outcomes, such as improved LVEF, CI, exercise capacity, self-efficacy, and satisfaction. These associations suggest that the AR device may facilitate an integrated improvement across physical, cardiac, and psychological parameters. This multifaceted enhancement underscores the potential of AR-based rehabilitation to provide holistic benefits to patients with CHF, addressing both the physical and mental challenges they face.
The main advantage of AR-based rehabilitation lies in its ability to provide real-time sensory feedback and adjust the difficulty level as per the patient’s capability [35]. This aligns with contemporary rehabilitation paradigms that emphasize personalized care and adaptive exercise protocols tailored to individual patients’ capacities [36]. By offering a platform for such adaptive training, AR technologies can potentially enhance the safety and efficiency of rehabilitation programs [37].
In addition, remote rehabilitation via AR-based interventions offers cost-effective, high-quality care, while reducing the need for face-to-face supervision by the therapists, as well as frequent hospital visits [38]. However, advanced AR systems might be high-cost currently, potentially limiting their accessibility. With the advancement and evolvement of technology, these expenses will be predominantly reduced [39].
While our study provides promising insights into the benefits of an AR-based sensory interaction device in the rehabilitation of CHF patients, several limitations must be acknowledged. First, as a retrospective analysis, patient assignment to either the AR intervention group or conventional rehabilitation group was not randomized, which may introduce selection bias. The unequal group sizes (intervention group: n = 114 vs. conventional group: n = 86) reflect real-world clinical practice patterns where novel interventions are often adopted more gradually [40]. This study only statistically compared the percentage of LVEF. In subsequent studies, we will subdivide and compare Heart Failure with Reduced Ejection Fraction (HFrEF) and Heart Failure with Preserved Ejection Fraction (HFpEF). Second, the single-center design and relative sample size may limit the generalizability of the findings. The disease severity profiles and access to advanced technologies (e.g., AR equipment) may differ systematically from those in community hospitals or underserved populations. Although the study duration was sufficient to observe immediate outcomes, long-term effects and sustainability of the improvements post-intervention remain unclear, warranting further investigation. Additionally, our study did not incorporate a diverse participant demographic, potentially affecting the external validity across different age groups, ethnicities, and socioeconomic statuses. Moreover, potential biases could arise from participants’ varying familiarity and comfort with technology, which were not uniformly assessed prior to the intervention. Third, this study lacked blinding procedures at multiple levels, including intervention allocation, outcome assessment, data collection, database management, and statistical analysis, which may have introduced performance bias and detection bias. Fourth, the use of self-reported questionnaires such as the MLHFQ, GSES, and satisfaction questionnaire may be subject to the Hawthorne effect, whereby participants may have responded more favorably simply due to the awareness of being observed or participating in a study, rather than due to the intervention itself. Finally, while patient-reported outcomes such as self-efficacy and satisfaction were measured, subjective perceptions could influence these areas, suggesting the need for more objective measures in future studies.
Conclusion
In conclusion, our study indicates that employing an AR-based sensory interaction device in the rehabilitation of CHF patients offers significant advantages over conventional methods. These benefits span improvements in cardiac functional parameters, exercise capacity, quality of life, and patient satisfaction, showcasing the multifaceted potential of integrating advanced technologies into healthcare of CHF patients. By leveraging technology to create engaging, tailored, and autonomous rehabilitation experiences, AR systems could play a transformative role in managing CHF, ultimately improving patient outcomes and quality of life. These findings, while encouraging, should be interpreted with caution given the retrospective, single-centre design and four-week follow-up. Finally, a large-scale, multicenter, randomized trial, with long-term follow-up is expected in the future, which will be critical to fully understanding the efficacy and applicability of AR-based rehabilitation for patients with CHF.
Supplementary Information
Acknowledgments
Clinical trial number
Not applicable.
Abbreviations
- CHF
Chronic heart failure
- AR
Augmented reality
- 6MWD
6-minute walking distance
- Tmax
Maximum activity time
- GSES
General self-efficacy scale
- MLHFQ
Minnesota living with heart failure questionnaire
- LVEF
Left ventricular ejection fraction
- CI
Cardiac index
- ICU
Intensive care unit
- GPU
Graphics processing unit
- 3D
3-dimensional
- RGB
Red, green, blue
- EDTA
Ethylenediaminetetraacetic acid
- ESR
Erythrocyte sedimentation rate
- H-FABP
Heart fatty acid binding protein
- NT-proBNP
N-terminal pro-brain natriuretic peptide
- BMI
Body mass index
- NYHA
New York Heart Association
- LVEDD
Left ventricular end diastolic diameter
- CRP
C-reactive protein
- CPET
Cardiopulmonary exercise testing
- ATS
American Thoracic Society
- HFrEF
Heart failure with reduced ejection fraction
- HFpEF
Heart failure with preserved ejection fraction
- MMSE
Mini-mental state examination
- EEG
Electroencephalogram
Authors’ contributions
QQZ and JHZ were involved in the conception and design, or analysis and interpretation of the data; LM and HZJ the drafting of the paper, revising it critically for intellectual content; JZ the final approval of the version to be published; and that all authors agree to be accountable for all aspects of the work.
Funding
No funding was received.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki and approved by the Human Investigation Review Board of Tianyou Hospital Affiliated to Wuhan University of Science and Technology (approval number:WHC-TH-002).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.