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Exergames have demonstrated potential as effective interventions for promoting physical activity and preventing type 2 diabetes (T2D), particularly among older adults. Kinect-based exergames, in particular, have been associated with improved adherence to exercise regimens and positive health outcomes. However, widespread implementation is limited by the high cost and reduced accessibility of the required hardware, restricting their use in home-based settings. Recent advances in computer vision have enabled the development of exergames using monocular camera systems, which may represent a cost-effective and scalable alternative. This study investigates the feasibility of monocular-camera-based exergames as a cost-effective and convenient alternative for home-dwelling individuals.
A total of 45 community-dwelling older adults aged 60-74 years, identified as high risk for T2D were recruited through local community health centers. Participants were randomly assigned to one of three groups (n = 15 per group): (1) Control group (traditional offline exercise with printed instructions), (2) Kinect group (Kinect-based exergames targeting aerobic capacity, balance, and strength), and (3) Monocular group (monocular-camera-based exergames using real-time 2D pose estimation). The intervention lasted six weeks, with participants completing three 30-minute sessions per week at home. Primary outcomes included exercise performance (completion rate and movement accuracy) and intrinsic motivation. Secondary outcomes included perceived enjoyment, challenge, and usability. Data were analyzed using one-way ANOVA.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control group | Active Comparator | Receive traditional offline exercise with printed instructions |
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| Kinect group | Active Comparator | Use Kinect based mobile exergame |
|
| Monocular group | Experimental | Used monocular-camera-based exergame |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Control group | Behavioral | Control Group consists of six motion videos, including: crossover steps, high knees, lateral raises, punching movements, downward leg punches from a standing position, and elbow-to-chest expansions |
| Measure | Description | Time Frame |
|---|---|---|
| Heart rate | The post-intervention heart rate was collected during the first 10 seconds post-exercise to estimate physiological responses. Considering the general physical condition of individuals at high risk of diabetes, the study used 50%-80% of the maximum heart rate as the target exercise intensity. The maximum heart rate was calculated using the formula: 208 - (age × 0.7). | Baseline, Immediately after the intervention |
| Perceived fatigue | perceived fatigue, a widely accepted parameter in exercise assessments for diabetes-related fields, was employed to supplement the evaluation of physical activity. The Borg RPE (Rating of Perceived Exertion) scale[18], ranging from 6 to 20, was utilized to assess subjective fatigue and compare perceived exercise intensity across groups. | Baseline, Immediately after the intervention |
| Measure | Description | Time Frame |
|---|---|---|
| Game experience | At the first session of intervention, participants in the Kinect and monocular groups completed the Game Experience Questionnaire (GEQ) to assess their user experience[19]. The GEQ was used to evaluate and compare the impact of different technologies on immersion and the overall experience of exergames. | Immediately after the intervention |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hongqiao Community | Shanghai | Shanghai Municipality | 200240 | China |
The information might be shared after the research group finished the data analysis process.
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| ID | Term |
|---|---|
| D035061 | Control Groups |
| ID | Term |
|---|---|
| D015340 | Epidemiologic Research Design |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D012107 | Research Design |
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| Kinect group | Behavioral | Kinect Group features the same software functionality and design as the application based on bullet-screen cameras, but the hardware is built using a Kinect sensor. The gamified platform is primarily developed and presented using Unity software. The Kinect 3D motion-sensing camera incorporates real-time dynamic capture and image recognition capabilities, offering new possibilities for interactive approaches to motion therapy |
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| Monocular group | Behavioral | The game employs a virtual avatar to replicate users' movements, thereby fostering an immersive and engaging experience. A monocular camera captures users' movements in real time, which are analyzed through pose estimation algorithms and subsequently mapped onto the virtual avatar. Users interact with the game by following on-screen visual demonstrations, presented as either static images or animations, to perform the prescribed exercises. Movement accuracy is evaluated by the system, with scores awarded based on performance. To enhance user motivation and adherence, the game incorporates a reward system, where points earned through accurate execution can be redeemed for in-game rewards, such as unlocking background music, avatar customization options, and new virtual environments. |
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| Intrinsic motivation | Furthermore, the Interest/Enjoyment Subscale of the Intrinsic Motivation Inventory (IMI) was administered to participants in the control group, Kinect group, and monocular group[20]. This subscale evaluated and compared intrinsic motivation and enjoyment associated with physical activity across the three groups. | Immediately after the intervention |
| User engagement | At the end of one-week experiment period, user engagement was further quantified by tracking the frequency of intervention use over the one-week period, based on participants' video-recorded usage logs. | Follow-up (one week after the intervention) |
| D008722 | Methods |