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ArticleJMIR research protocols2025

Chatbot-Delivered Stage of Change-Tailored Web-Based Intervention to Promote Physical Activity Among Inactive Community-Dwelling People Aged 65 years or More: Protocol for a Randomized Controlled Trial.

Xue Liang, Fenghua Sun, Qingpeng Zhang, Yuan Fang, Fuk-Yuen Yu, Danhua Ye, Borui Zhang, Qianwen Liao, Phoenix Kh Mo, Zixin Wang

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06641492 (A Randomized Controlled Trial Evaluating a Trans-Theoretical-Model-based Online Intervention Delivered by Fully-automated Chatbot in Increasing Physical Activity Among Inactive Community-dwelling People Aged ≥65 Years), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06641492 nanot yet recruitingnot on this map

A Randomized Controlled Trial Evaluating a Trans-Theoretical-Model-based Online Intervention Delivered by Fully-automated Chatbot in Increasing Physical Activity Among Inactive Community-dwelling People Aged ≥65 Years

TypeinterventionalSponsorChinese University of Hong KongRan2024 to 2025Enrolled278ConditionsPhysical InactivityArmsChatbot-delivered interventions tailored to one's stage of changes, Chatbot-delivered standard interventions
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xue LiangCentre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0009-0008-2589-1221
Fenghua SunDepartment of Health and Physical Education, Education University of Hong Kong, Tai Po, China (Hong Kong).ORCID 0000-0001-5251-4087
Qingpeng ZhangMusketeers Foundation Institute of Data Science, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-6819-0686
Yuan FangDepartment of Health and Physical Education, Education University of Hong Kong, Tai Po, China (Hong Kong).ORCID 0000-0003-1154-5076
Fuk-Yuen YuCentre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0001-7869-0281
Danhua YeCentre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0009-0008-2031-3799
Borui ZhangDepartment of Health and Physical Education, Education University of Hong Kong, Tai Po, China (Hong Kong).ORCID 0009-0009-2787-4983
Qianwen LiaoDepartment of Health and Physical Education, Education University of Hong Kong, Tai Po, China (Hong Kong).ORCID 0009-0009-2800-5093
Phoenix Kh MoCentre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0001-9822-5424
Zixin WangCentre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-1158-2304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysical activity (PA) has significant health benefits for older adults. However, many older adults in Hong Kong remain physically inactive. Interventions tailored to one's current stage of change (SOC) are more effective than non-SOC-tailored ones in facilitating behavioral changes. Chatbots are potentially useful to deliver SOC-tailored interventions to promote PA among older adults.

objectiveThis randomized controlled trial (RCT) will compare the efficacy of an SOC- versus a non-SOC-tailored intervention in increasing the prevalence of meeting World Health Organization (WHO)-recommended PA levels 6 months after completion of the intervention among inactive community-dwelling individuals aged ≥65 years.

methodsThis is a partially blinded (outcome assessors and data analysts) and parallel-group RCT. A total of 278 inactive community-dwelling people aged 65 years or more will be randomized evenly into either an intervention group or a control group. In the intervention group, a fully automated chatbot with natural language processing (NLP) functions will measure participants' SOC related to PA and deliver web-based interventions tailored to their current SOC every week for 12 weeks. In the control group, the chatbot will not measure participants' SOC but will deliver a non-SOC-tailored web-based intervention every week for 12 weeks. Participants will be interviewed at baseline (T0), after completion of the intervention (T1), and 6 months after T1 (T2). The primary outcome is the prevalence of meeting WHO-recommended PA levels (ie, at least 150 minutes of moderate-intensity aerobic PA, at least 75 minutes of vigorous-intensity aerobic PA, or an equivalent combination of moderate-to-vigorous physical activity [MVPA] every week). PA will be measured using the Chinese version of the International Physical Activity Questionnaire Short Form (IPAQ-SF) and accelerometers at T0, T1, and T2. Secondary outcomes include (1) minutes of MPVA, low-intensity PA, and sedentary time in the past week; (2) step counts in the past week; (3) SOC levels, perceived pros, perceived cons, and perceived self-efficacy related to PA; (4) compliance to the web-based interventions; and (5) cognitive status measured at T0, T1, and T2. Intention-to-treat analysis will be used for data analysis.

resultsRecruitment started in November 2024. By February 2025, a total of 185 participants completed the baseline assessment and were randomly assigned to either the intervention group (n=93, 50.3%) or the control group (n=92, 49.7%). Recruitment will be completed by the end of June 2025. The follow-up assessment at T1 started in March 2025. Data collection is expected to be concluded in February 2026.

conclusionsThe findings will extend the application of SOC and contribute to the evidence of the effectiveness of SOC-tailored and chatbot-delivered interventions. If the chatbot-delivered SOC-tailored intervention is proven effective to increase PA levels, it will require relative less resources to implement and maintain. It can be integrated into the existing WhatsApp groups operated by organizations providing services to older adults in Hong Kong and create public health impacts.

trial registrationClinicalTrial.gov: NCT06641492; https://clinicaltrials.gov/study/NCT06641492. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68796.

Indexed as

ExerciseHealth PromotionInternet-Based InterventionAgedAged, 80 and overFemaleGenerative Artificial IntelligenceHong KongHumansIndependent LivingMaleRandomized Controlled Trials as TopicSedentary Behaviorchatbotinterventionmotivationolder adultphysical activityrandomized controlled trialstage of change

Identifiers

PMID40540737
PMCPMC12228010

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.