Mobility LimitationsNCT07761663
Smart Nursing AI Agent for Health Self-Management
A study testing Smart Nursing AI Agent in people with Mobility Limitations and Community-dwelling Older Adults, run by National Yang Ming Chiao Tung University at 1 site in Taiwan.
Trial summary
This study aims to develop and evaluate a smart nursing AI agent to support health self-management among middle-aged and older patients, and further to examine its effectiveness in home care and health self-management. Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Mobility Limitations, Community-dwelling Older Adults, Middle-aged and Older Adults
- What is being tested?
- Smart Nursing AI Agent
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 102 participants
- Where
- 1 siteTaiwan
- Study dates
- 5 Aug 2026to 5 Aug 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged 50 years or older who present with mobility limitations such as gait instability, sarcopenia, joint degeneration, chronic pain, or functional decline
- Able to ambulate
- Alert and oriented
Who cannot join
- Terminal cancer or major illness with a prognosis of left 6 months
- Moderate to severe dementia or language expression disorders
- Individuals with significant audiovisual impairments that prevent AI conversation
- Unable to complete walking tests on their own
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
National Yang Ming Chiao Tung University Nursing Building Room 504A Taipei, Nursing Building, No.155, Sec.2, Li-Nong Street,, Taiwan, 112
Taipei, 112
About this study
Official title: Smart Nursing AI Agent for Health Self-Management Among Middle-aged and Older Adults
This study aims to develop and evaluate a smart nursing AI agent to support health self-management among middle-aged and older patients, and further to examine its effectiveness in home care and health self-management. The goal is to propose innovative strategies for middle-aged and older care that enhance the quality of home care and strengthen patients' health self-management abilities. A randomized controlled trial design will be adopted, with a planned enrollment of 102 participants. Participants will be randomly assigned to either the experimental group or the control group. Regardless of group assignment, their routine medical care will remain unchanged. Both groups will participate in the study for 12 weeks (8 weeks of intervention and 4 weeks of follow-up), with identical frequency and number of follow-ups, interviews, and evaluations. The experimental group will receive an 8 week intervention using the smart nursing AI agent care model, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education. The control group will continue with the existing standard health education approach. Data collection will include demographic information, medical history, physiological indicators, functional activity measures (e.g., steps, distance, exercise duration), sarcopenia screening, nutritional assessment, physical activity levels, health self-management ability, health literacy, quality of life, and digital usage behaviors. Statistical analyses will be conducted to evaluate the effectiveness, feasibility, and acceptability of the intervention.
Read the detailed description
Taiwan has entered a super-aged society, where many middle-aged and older adults experience activity limitations due to muscle loss, joint degeneration, or declining physical strength. These conditions often lead to gait instability, fatigue, and an increased risk of falls, which affect daily life and raise safety concerns. Traditionally, hospital discharge or outpatient education is delivered through one-time paper handouts, which patients often forget or find difficult to follow at home. Digital nursing technologies, such as mobile applications, telehealth, and wearable devices, can overcome geographical barriers, provide continuous support, and enhance health self-management abilities. Therefore, this study aims to develop and evaluate a smart nursing AI agent to support health self-management among middle-aged and older patients, addressing challenges related to patients' declining functional capacity and nurse workforce shortages. The goal is to propose innovative strategies to improve the quality of home care and strengthen patients' self-management of health. The inclusion criteria for this study are patients aged 50 years or older who present with mobility limitations such as gait instability, sarcopenia, joint degeneration, chronic pain, or functional decline, and who can ambulate and are alert and oriented. Exclusion criteria include patients with terminal cancer or major illness with a life expectancy of less than six months, those with moderate to severe dementia or language impairment, those with severe visual or hearing impairment preventing AI interaction, and those unable to complete walking tests independently.
This study adopts a randomized controlled trial design with a planned enrollment of 102 participants. Participants will be randomly assigned to either the experimental group or the control group, while their routine medical care will remain unchanged. Both groups will participate in the study for 12 weeks, including 8 weeks of intervention and 4 weeks of follow-up, with an identical frequency of follow-ups, interviews, and evaluations. The experimental group will receive an 8-week intervention using a smart nursing AI agent model, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education. In contrast, the control group will continue with standard health education practices. Data collection will include demographic information, medical history, physiological indicators, functional activity measures (such as steps, distance, and exercise duration), sarcopenia screening, nutritional assessment, physical activity levels, health self-management ability, health literacy, quality of life, and digital usage behaviors.
Participants will undergo routine assessments, including physiological indicators, sarcopenia screening, nutritional assessment, and physical activity levels, twice (baseline and three months post-intervention), and complete additional questionnaires, including health self-management ability, health literacy, and quality of life, three times (baseline, one month post-intervention, and two months post-intervention). Statistical analyses will be conducted to evaluate the effectiveness, feasibility, and acceptability of the intervention.
This study expects that the introduction of a smart nursing AI agent model will provide middle-aged and older patients with more continuous and effective support, thereby promoting health self-management and improving quality of life.
- Being tested
- Smart Nursing AI Agent
- Keywords
- Health Self-Management, Smart Nursing AI Agent, eHealth Literacy, community-dwelling older adults, Middle-aged and older adults, mobility limitations
- Medical subject headings
- Mobility Limitation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Smart Nursing AI AgentBehavioural
Smart Nursing AI Agent which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education.
| Study group | Role | What they receive |
|---|---|---|
| Smart nursing AI agent group | Experimental | The experimental group will receive an 8-week intervention using the smart nursing AI agent, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education Receives: Behavioral: Smart Nursing AI Agent |
| Existing standard group | No intervention | The control group will continue with the existing standard health education approach. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change from baseline in Sarcopenia (SARC-F) questionnaire score at 3 months
The SARC-F questionnaire is a rapid, 5-question clinical screening tool. The tool evaluates five key components of functional decline (Strength, Assistance with walking, Rise from a chair, Climb stairs, Falls). For each question, patients score between 0 (None) and 2 (A lot or unable or ≧ 4 falls) depending on the severity of their limitation.
Measured: Baseline and 3 months
Change from baseline in Mini Nutritional Assessment (MNA) score at 3 months
The Mini Nutritional Assessment (MNA) is a validated, 6-question screening tool used to quickly identify adults aged 65 and older who are malnourished or at risk of malnutrition.The tool uses a scoring system with a maximum of 14 points. The score determines the individual's nutritional category:12 to 14 points is normal nutritional status, 8 to 11 points is risk of malnutrition, and 0 to 7 points is malnourished.
Measured: Baseline and 3 months
Change from baseline in International Physical Activity Questionnaire-Short Form (IPAQ-SF) score (MET-minutes/week) at 3 months
The International Physical Activity Questionnaire-Short Form (IPAQ-SF) is a widely used, 7-item survey designed to measure health-related physical activity and sedentary behavior. It evaluates time spent walking, as well as moderate and vigorous-intensity activities over the last 7 days. The score is expressed as metabolic equivalent tasks (MET)-minutes per week, calculated by multiplying the MET level of an activity (walking, moderate, or vigorous) by its duration and frequency. Higher MET-minutes/week values indicate higher levels of physical activity.
Measured: Baseline and 3 months
Change from baseline in daily step count at 1 and 2 months
This measure assesses the physical activity level of participants based on the average number of daily steps. Data are automatically tracked and collected via the platform's backend system using the designated JoiiCare mobile application.
Measured: Baseline, 1 and 2 Months
Change from baseline in Partners in Health (PIH) scale score at 1 and 2 months
The PIH scale has demonstrated potential to be a reliable and valid measure of chronic condition self-management. It comprises 12 items, with response options ranging from 0 (very little or never) to 8 (always or very well), and a total score ranging from 0 to 96 points. Higher scores indicate better self-management ability / a higher level of self-management.
Measured: Baseline, 1 and 2 Months
Change from baseline in eHealth Literacy Scale (eHEALS) score at 1 and 2 months
The eHEALS is an 8-item measure of eHealth literacy designed to assess consumers' combined knowledge, comfort, and perceived skills in finding, evaluating, and applying electronic health information to health problems.
It comprises 8 items, with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The total score ranges from 8 to 40 points. Higher scores indicate a higher level of perceived eHealth literacy.
Measured: Baseline, 1 and 2 Months
Change from baseline in EQ-5D index score at 1 and 2 months
The EuroQol 5-Dimension (EQ-5D) index score assesses health-related quality of life across 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The health states are converted into a single index score using a standard country-specific value set. The score typically ranges from less than 0 (where 0 represents death and 1 represents perfect health), with higher scores indicating a better health state.
Measured: Baseline, 1 and 2 Months
Change from baseline in daily walking distance (meters) at 1 and 2 months
This measure evaluates the average physical distance covered by participants per day. Data are automatically tracked and retrieved from the platform's backend database via the JoiiCare mobile application.
Measured: Baseline, 1 and 2 Months
Change from baseline in daily exercise duration (minutes) at 1 and 2 months
This measure records the average amount of time (in minutes) participants spend engaging in exercise daily. Data are captured and compiled through the platform's backend system based on the JoiiCare mobile application activity logs.
Measured: Baseline, 1 and 2 Months
Change from baseline in EQ Visual Analog Scale (EQ-VAS) score at 1 and 2 months
The EQ Visual Analog Scale (EQ-VAS) records the participant's self-rated health on a vertical visual analog scale. The score ranges from 0 to 100, where 0 represents the worst health you can imagine, and 100 represents the best health you can imagine.
Measured: Baseline, 1 and 2 Months
Secondary outcomes (4)
Change from baseline in digital platform stickiness (average daily app usage time) at 1 and 2 months
This measure evaluates participant engagement and stickiness with the digital platform, quantified as the average frequency or duration of app usage. Data are automatically tracked and retrieved from the JoiiCare mobile application backend system.
Measured: Baseline, 1 and 2 Months
User satisfaction was assessed two months after the experimental group was administered
This survey is designed to assess participants' perceptions and evaluations of software usability. It comprises 10 items, with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
Measured: 2 months
Change from baseline in health education completion rate (percentage) at 1 and 2 months
This measure assesses the percentage of assigned digital health education materials or modules successfully completed by the participant. The rate is expressed as a percentage (0% to 100%), with higher percentages indicating better compliance. Data are compiled via the JoiiCare backend database.
Measured: Baseline, 1 and 2 Months
Change from baseline in task adherence rate (percentage) at 1 and 2 months
This measure tracks the participant's adherence to scheduled nursing or health tasks on the digital platform. It is calculated as the ratio of completed tasks to total assigned tasks, expressed as a percentage (0% to 100%). A higher percentage indicates greater adherence. Data are automatically logged by the JoiiCare backend system.
Measured: Baseline, 1 and 2 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2026
- Study start5 Aug 2026 (estimated)
- Primary completion5 Aug 2027 (estimated)
- Study completion5 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mobility LimitationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- control group and experimental group
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 102 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Yang Ming Chiao Tung University
- Sponsor type
- University, hospital or other
- Collaborators
- Taichung Veterans General Hospital
- Responsible party
- Sponsor
- Investigators
- Heng-Hsin Tung, PhD, RN, Principal investigator, National Yang Ming Chiao Tung University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07761663
- Sponsor's protocol ID
- TVGH_CG26018B
- Status last verified
- Jul 2026
- First submitted
- 29 Jul 2026
- Last updated
- 12 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Smart Nursing AI Agent for Health Self-Management Among Middle-aged and Older Adults
- ClinicalTrials.gov ID
- NCT07761663
- Acronym / sponsor ID
- TVGH_CG26018B
- Keywords
- Health Self-Management, Smart Nursing AI Agent, eHealth Literacy, community-dwelling older adults, Middle-aged and older adults, mobility limitations
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 102 participants (estimated)
- Lead sponsor
- National Yang Ming Chiao Tung UniversityUniversity, hospital or other
- Collaborators
- Taichung Veterans General Hospital
- Study start
- 5 Aug 2026 (estimated)
- Primary completion
- 5 Aug 2027 (estimated)
- Study completion
- 5 Aug 2028 (estimated)
- First posted
- 12 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to develop and evaluate a smart nursing AI agent to support health self-management among middle-aged and older patients, and further to examine its effectiveness in home care and health self-management. The goal is to propose innovative strategies for middle-aged and older care that enhance the quality of home care and strengthen patients' health self-management abilities. A randomized controlled trial design will be adopted, with a planned enrollment of 102 participants. Participants will be randomly assigned to either the experimental group or the control group. Regardless of group assignment, their routine medical care will remain unchanged. Both groups will participate in the study for 12 weeks (8 weeks of intervention and 4 weeks of follow-up), with identical frequency and number of follow-ups, interviews, and evaluations. The experimental group will receive an 8 week intervention using the smart nursing AI agent care model, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education. The control group will continue with the existing standard health education approach. Data collection will include demographic information, medical history, physiological indicators, functional activity measures (e.g., steps, distance, exercise duration), sarcopenia screening, nutritional assessment, physical activity levels, health self-management ability, health literacy, quality of life, and digital usage behaviors. Statistical analyses will be conducted to evaluate the effectiveness, feasibility, and acceptability of the intervention.
Detailed description
Taiwan has entered a super-aged society, where many middle-aged and older adults experience activity limitations due to muscle loss, joint degeneration, or declining physical strength. These conditions often lead to gait instability, fatigue, and an increased risk of falls, which affect daily life and raise safety concerns. Traditionally, hospital discharge or outpatient education is delivered through one-time paper handouts, which patients often forget or find difficult to follow at home. Digital nursing technologies, such as mobile applications, telehealth, and wearable devices, can overcome geographical barriers, provide continuous support, and enhance health self-management abilities. Therefore, this study aims to develop and evaluate a smart nursing AI agent to support health self-management among middle-aged and older patients, addressing challenges related to patients' declining functional capacity and nurse workforce shortages. The goal is to propose innovative strategies to improve the quality of home care and strengthen patients' self-management of health. The inclusion criteria for this study are patients aged 50 years or older who present with mobility limitations such as gait instability, sarcopenia, joint degeneration, chronic pain, or functional decline, and who can ambulate and are alert and oriented. Exclusion criteria include patients with terminal cancer or major illness with a life expectancy of less than six months, those with moderate to severe dementia or language impairment, those with severe visual or hearing impairment preventing AI interaction, and those unable to complete walking tests independently.
This study adopts a randomized controlled trial design with a planned enrollment of 102 participants. Participants will be randomly assigned to either the experimental group or the control group, while their routine medical care will remain unchanged. Both groups will participate in the study for 12 weeks, including 8 weeks of intervention and 4 weeks of follow-up, with an identical frequency of follow-ups, interviews, and evaluations. The experimental group will receive an 8-week intervention using a smart nursing AI agent model, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education. In contrast, the control group will continue with standard health education practices. Data collection will include demographic information, medical history, physiological indicators, functional activity measures (such as steps, distance, and exercise duration), sarcopenia screening, nutritional assessment, physical activity levels, health self-management ability, health literacy, quality of life, and digital usage behaviors.
Participants will undergo routine assessments, including physiological indicators, sarcopenia screening, nutritional assessment, and physical activity levels, twice (baseline and three months post-intervention), and complete additional questionnaires, including health self-management ability, health literacy, and quality of life, three times (baseline, one month post-intervention, and two months post-intervention). Statistical analyses will be conducted to evaluate the effectiveness, feasibility, and acceptability of the intervention.
This study expects that the introduction of a smart nursing AI agent model will provide middle-aged and older patients with more continuous and effective support, thereby promoting health self-management and improving quality of life.
Study design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (care provider)
- Number of arms
- 2
- Model description
- control group and experimental group
| Arm | Type | Receives |
|---|---|---|
| Smart nursing AI agent group | Experimental | Behavioral: Smart Nursing AI AgentThe experimental group will receive an 8-week intervention using the smart nursing AI agent, which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education |
| Existing standard group | No intervention | –The control group will continue with the existing standard health education approach. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients aged 50 years or older who present with mobility limitations such as gait instability, sarcopenia, joint degeneration, chronic pain, or functional decline
- Able to ambulate
- Alert and oriented
Key exclusion criteria (4 listed)
- Terminal cancer or major illness with a prognosis of left 6 months
- Moderate to severe dementia or language expression disorders
- Individuals with significant audiovisual impairments that prevent AI conversation
- Unable to complete walking tests on their own
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Smart Nursing AI AgentSmart Nursing AI Agent which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education. | Behavioural | Smart nursing AI agent group |
Primary outcome
Change from baseline in Sarcopenia (SARC-F) questionnaire score at 3 months
Time frame: Baseline and 3 months
The SARC-F questionnaire is a rapid, 5-question clinical screening tool. The tool evaluates five key components of functional decline (Strength, Assistance with walking, Rise from a chair, Climb stairs, Falls). For each question, patients score between 0 (None) and 2 (A lot or unable or ≧ 4 falls) depending on the severity of their limitation.
Change from baseline in Mini Nutritional Assessment (MNA) score at 3 months
Time frame: Baseline and 3 months
The Mini Nutritional Assessment (MNA) is a validated, 6-question screening tool used to quickly identify adults aged 65 and older who are malnourished or at risk of malnutrition.The tool uses a scoring system with a maximum of 14 points. The score determines the individual's nutritional category:12 to 14 points is normal nutritional status, 8 to 11 points is risk of malnutrition, and 0 to 7 points is malnourished.
Change from baseline in International Physical Activity Questionnaire-Short Form (IPAQ-SF) score (MET-minutes/week) at 3 months
Time frame: Baseline and 3 months
The International Physical Activity Questionnaire-Short Form (IPAQ-SF) is a widely used, 7-item survey designed to measure health-related physical activity and sedentary behavior. It evaluates time spent walking, as well as moderate and vigorous-intensity activities over the last 7 days. The score is expressed as metabolic equivalent tasks (MET)-minutes per week, calculated by multiplying the MET level of an activity (walking, moderate, or vigorous) by its duration and frequency. Higher MET-minutes/week values indicate higher levels of physical activity.
Change from baseline in daily step count at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
This measure assesses the physical activity level of participants based on the average number of daily steps. Data are automatically tracked and collected via the platform's backend system using the designated JoiiCare mobile application.
Change from baseline in Partners in Health (PIH) scale score at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
The PIH scale has demonstrated potential to be a reliable and valid measure of chronic condition self-management. It comprises 12 items, with response options ranging from 0 (very little or never) to 8 (always or very well), and a total score ranging from 0 to 96 points. Higher scores indicate better self-management ability / a higher level of self-management.
Change from baseline in eHealth Literacy Scale (eHEALS) score at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
The eHEALS is an 8-item measure of eHealth literacy designed to assess consumers' combined knowledge, comfort, and perceived skills in finding, evaluating, and applying electronic health information to health problems. It comprises 8 items, with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The total score ranges from 8 to 40 points. Higher scores indicate a higher level of perceived eHealth literacy.
Change from baseline in EQ-5D index score at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
The EuroQol 5-Dimension (EQ-5D) index score assesses health-related quality of life across 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The health states are converted into a single index score using a standard country-specific value set. The score typically ranges from less than 0 (where 0 represents death and 1 represents perfect health), with higher scores indicating a better health state.
Change from baseline in daily walking distance (meters) at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
This measure evaluates the average physical distance covered by participants per day. Data are automatically tracked and retrieved from the platform's backend database via the JoiiCare mobile application.
Change from baseline in daily exercise duration (minutes) at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
This measure records the average amount of time (in minutes) participants spend engaging in exercise daily. Data are captured and compiled through the platform's backend system based on the JoiiCare mobile application activity logs.
Change from baseline in EQ Visual Analog Scale (EQ-VAS) score at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
The EQ Visual Analog Scale (EQ-VAS) records the participant's self-rated health on a vertical visual analog scale. The score ranges from 0 to 100, where 0 represents the worst health you can imagine, and 100 represents the best health you can imagine.
4 secondary outcomes
Change from baseline in digital platform stickiness (average daily app usage time) at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
User satisfaction was assessed two months after the experimental group was administered
Time frame: 2 months
Change from baseline in health education completion rate (percentage) at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
Change from baseline in task adherence rate (percentage) at 1 and 2 months
Time frame: Baseline, 1 and 2 Months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Aug 2026.
- Individual participant dataSharing planned: No
Related trials for Mobility Limitations
Active, not recruiting
Randomized Clinical Trial to Improve Mobility After Hospitalization
Hypertension, Diabetes
Active, not recruitingObservational study
Effects of External Upper Limb Loading on Gait and Trunk Muscle Activity in Cardiac Rehabilitation Patients
Cardiovascular Diseases, Cardiac Rehabilitation
Active, not recruiting
Testing Interventions for Mobility Through Exercise (TIME)
Walking, Difficulty, Mobility Limitation
RecruitingPhase 3
Improving Mobility After Revascularization in Peripheral Artery Disease
Peripheral Artery Disease, Intermittent Claudication
Recruiting
Balance4Mobility: Effects of Walkasins Use in Individuals With Peripheral Neuropathy and Balance Problems
Peripheral Neuropathies, Peripheral Neuropathy Due to Chemotherapy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Heng-Hsin Tung, PhD, RN principal investigator
National Yang Ming Chiao Tung University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Smart Nursing AI Agent in people with Mobility Limitations and Community-dwelling Older Adults, run by National Yang Ming Chiao Tung University at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Cheng Ting CHIH, PhD (+886-972701990, chengting.ns14@nycu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change from baseline in Sarcopenia (SARC-F) questionnaire score at 3 months (time frame: Baseline and 3 months). The study lists 10 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Aug 2026; AzTrials last refreshed its copy on 26 Oct 2026.