Exergaming System for Exercise-Based Cardiac Rehabilitation in Patients With Heart Failure: Development and Usability Assessment Study of a Device Prototype
Bibliographic record
Abstract
Background: Heart failure (HF) is a growing global health concern, and adherence to early cardiac rehabilitation (CR) remains suboptimal. Exergaming is a promising alternative to conventional exercise programs for patients with HF. However, existing research has limitations, and the integration of exergaming into clinical practice remains challenging. Most notably, current studies often rely on commercially available systems that are not tailored to needs specific to patients with HF, lack long-term adherence strategies, and have limited evaluation in the initial phases of cardiac rehabilitation. Objective: This study aimed to design, develop, and assess the usability of a novel exergaming prototype (ie, HEFMOB), integrating immersive virtual reality (VR), real-time biometric monitoring, and autonomous session management to support early-phase, exercise-based CR in patients with HF. Methods: A multidisciplinary team developed HEFMOB through iterative prototyping. The final system included a pedal-based VR cycling game and an upper-limb mobilization minigame, with real-time monitoring of heart rate, blood pressure, and peripheral capillary oxygen saturation. Usability was assessed in two phases: (1) an expert evaluation and refinement phase and (2) a single-session usability phase involving 10 patients with HF (4 female). The sessions were recorded and individually evaluated by 2 researchers using the Serious Game Usability Evaluator tool. After each session, the participants completed the System Usability Scale (SUS) and a subscale of Intrinsic Motivation Inventory (IMI) to rate the usability of the exergaming prototype and enjoyment, respectively. Descriptive statistics were reported. Results: The participants had a mean age of 64.8 (SD 8.4) years, BMI of 26.7 (SD 4.6) kg/m2, and left ventricular ejection fraction of 40.5% (SD 7.4%). All participants completed the session without adverse events. The SUS score averaged 71.5, SD 17.8 (indicating good usability) and IMI scores indicated very high enjoyment (mean 25.1, SD 3.5). A total of 136 gameplay-related events were recorded: negative (n=76, mostly confusion), neutral (n=49), and positive (n=11). Interface-related issues (n=61) were most common, followed by design (n=52) and hardware (n=23). Conclusions: HEFMOB appears to be a promising, engaging, and well-tolerated tool for delivering tailored exergaming interventions in patients with HF. High usability and enjoyment ratings support its acceptability, while structured user experience analysis provided valuable insights for system refinement. This study marks a critical step toward integrating personalized, gamified exercise in inpatient settings, especially where early mobilization is lacking. Building on these findings, future research will assess long-term usability and clinical impact through a multicenter randomized controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".