Behavior Change Interventions
Conditions
Keywords
Exergame, Individuals at High Risk of Type 2 Diabetes
Brief summary
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.
Interventions
BEHAVIORALControl group
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
BEHAVIORALKinect group
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
BEHAVIORALMonocular group
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.
Sponsors
Shanghai Jiao Tong University School of Medicine
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
DOUBLE (Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
60 Years to 74 Years
Healthy volunteers
Yes
Inclusion criteria
1. age between 60-74; 2. a score of 25 or higher on the Chinese Diabetes Risk Score; 3. community-dwelling individuals, not reside in assisted living or long-term care facilities; 4. physically capable of engaging in light to moderate exercise, as determined by self-report and/or physician clearance; 5. normal cognitive function that enable the participant to complete the experiment independently or with minimal assistance; 6. Written informed consent provided by participants or their families.
Exclusion criteria
1. diagnosis of type 1 or type 2 diabetes; 2. current participation in another exercise intervention study; 3. severe cognitive impairment with MMSE \< 24; 4. major mobility limitations such as severe osteoarthritis and recent orthopedic surgery.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Heart rate | Baseline, Immediately after the intervention | 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). |
| Perceived fatigue | Baseline, Immediately after the intervention | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Game experience | Immediately after the intervention | 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. |
| Intrinsic motivation | Immediately after the intervention | 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. |
| User engagement | Follow-up (one week after the intervention) | 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. |
Countries
China
Outcome results
None listed