Balance Disorders, Chronic Diseases
Conditions
Keywords
Tai Chi, Baduanjin, Artificial Intelligence
Brief summary
This study aims to examine the effects of an AI-based mobile application delivering Tai Chi and Baduanjin exercises on balance and fall risk in adults with chronic diseases. Participants will be randomly assigned to Tai Chi, Baduanjin, or a control group over a 12-week period. The AI application will provide exercise guidance and basic feedback. Outcomes will focus on balance and, fall risk, along with user interaction with the technology. The results are expected to support the use of AI-assisted exercise programs in improving balance and reducing fall risk in individuals with chronic conditions.
Interventions
BEHAVIORALTai Chi Exercise Program
Participants will perform Tai Chi exercises guided by an AI-based mobile application. The program includes structured exercise sessions aimed at improving balance and mobility, with basic feedback provided by the application.
BEHAVIORALBaduanjin Exercise Program
Participants will perform Baduanjin exercises guided by an AI-based mobile application. The program consists of structured movements designed to support balance and physical function, with feedback delivered through the application.
Sponsors
Alexandria University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)
Masking description
Outcome assessors will be blinded to group allocation to reduce assessment bias.
Intervention model description
Participants will be randomly assigned to one of three parallel groups: Tai Chi, Baduanjin, or control.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults * Individuals with one or more health conditions relevant to the study * Able to participate in physical activity * Able to understand study instructions * Willing to provide informed consent
Exclusion criteria
* \- Any condition that may limit safe participation in the study * Any medical or other issue that may interfere with study procedures, as determined by the research team
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Balance Performance (Mini-BESTest Score) | Baseline and Week 12 | Balance will be assessed using the Mini-Balance Evaluation Systems Test (Mini-BESTest), a performance-based clinical scale measuring dynamic balance and postural control. Scores range from 0 to 28, with higher scores indicating better balance. |
| Fall Risk (Timed Up and Go Test Time) | Baseline and Week 12 | Fall risk will be assessed using the Timed Up and Go (TUG) test, measured in seconds. Lower time indicates better functional mobility and lower risk of falls. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Technology Acceptance | Week 12 | User acceptance of the AI-based intervention will be evaluated using a structured questionnaire. |
| Participant Experience (Qualitative Feedback) | Post-intervention after 12 weeks | Assessed using open-ended questions to explore participant experiences with the intervention. |
Countries
Egypt
Contacts
CONTACTMohamed Fakhry Ahmed Salem, PhD
PRINCIPAL_INVESTIGATORMohamed Fakhry Ahmed Salem, PhD in Medical-Surgical Nursin
Alexandria University
Outcome results
None listed