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NCT Number: NCT07761663

Smart Nursing AI Agent for Health Self-Management

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.

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Key information

Age range

50 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

National Yang Ming Chiao Tung University Nursing Building Room 504A Taipei, Nursing Building, No.155, Sec.2, Li-Nong Street,, Taiwan, 112

Taipei, 112, Taiwan

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

  • 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

Treatment and study plan

Smart Nursing AI Agent

Behavioral

Smart Nursing AI Agent which provides daily interaction, reminder systems, personalized recommendations, activity monitoring, goal setting, motivational support, and health self-management education.

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

Secondary outcomes

  1. Change from baseline in digital platform stickiness (average daily app usage time) at 1 and 2 months

    Time frame: Baseline, 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.

  2. User satisfaction was assessed two months after the experimental group was administered

    Time frame: 2 months

    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).

  3. Change from baseline in health education completion rate (percentage) at 1 and 2 months

    Time frame: Baseline, 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.

  4. Change from baseline in task adherence rate (percentage) at 1 and 2 months

    Time frame: Baseline, 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Cheng Ting CHIH, PhD

CONTACT

[email protected]

+886-972701990

Heng-Hsin Tung, PhD, RN

CONTACT

[email protected]

+886-2-28267000 ext. 67991

Sponsors and collaborators

Lead sponsor

National Yang Ming Chiao Tung University

Other

Collaborators

  • Taichung Veterans General Hospital

Registry information

Official study title

Smart Nursing AI Agent for Health Self-Management Among Middle-aged and Older Adults

Important dates

Study start
2026
Primary completion
2027
Study completion
2028
First posted
Aug 12, 2026
Registry last updated
Aug 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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