AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster Randomized Controlled Trial for Fall Prevention Among Rural Older Adults
Phase: NA
Conditions: Falls, Falls Injury, Fall Prevention
Sponsor: Peking University

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
1 
Study Protocol 
 
AI-assisted, Village Doctors and Express 
Services-Involved, Community-Based 
Multicenter Cluster Randomized Controlled 
Trial for Fall Prevention Among Rural Older 
Adults 
 
 
 
 
Approval Date: 2, August, 2024 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
2 
Content 
Introduction ............................................................................................................................... 3 
Theoretical framework and approach ........................................................................................ 7 
Study objectives ...................................................................................................................... 10 
Methods .................................................................................................................................... 11 
Trial design ....................................................................................................................... 11 
Regulatory approvals and ethical considerations ............................................................ 12 
Study setting and participants .......................................................................................... 13 
Randomization and blinding ............................................................................................ 15 
Intervention strategy ........................................................................................................ 16 
Control Group .................................................................................................................. 25 
Primary Outcome Measures ............................................................................................ 26 
Secondary Outcome Measures ........................................................................................ 27 
Data collection and management ..................................................................................... 29 
Health Economic Evaluation ........................................................................................... 31 
Process Evaluation .......................................................................................................... 33 
Statistical Analysis .......................................................................................................... 34 
Funding .................................................................................................................................... 36 
Reference ................................................................................................................................. 36 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
3 
Introduction 
Falls are the leading cause of injury-related deaths among older adults aged 65 and above 
in China, and the primary cause of injury-related disability and doctor’s visits in this population 
[1–3]. According to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 
2019), the incidence of falls, fall-related deaths, and disability-adjusted life years (DALY) due 
to falls has consistently increased from 1990 to 2019 in China [4]. Alarmingly, rural areas have 
experienced a faster rise in the burden of fall-related diseases, with the incidence of falls in 
rural settings far exceeding that in urban areas[5]. This disparity underscores the need for 
targeted, effective fall prevention strategies tailored to the unique characteristics and challenges 
of rural settings [6].  
Despite the robust evidence supporting the effectiveness of fall prevention interventions—
ranging from multifactorial risk assessments to exercise and home safety modifications[7–10] 
—most research has focused on urban populations, with little attention paid to rural areas, 
particularly in developing countries including China. A systematic review of fall prevention 
strategies highlighted that while interventions have been successful in urban settings, their 
applicability and feasibility in rural communities remain underexplored [6]. The limited 
healthcare infrastructure, shortage of trained healthcare professionals, and geographical barriers 
in rural China contribute to the slow adoption of fall prevention strategies [11]. Furthermore, 
older adults in rural areas often face barriers to participation, including lack of awareness, fear, 
stigma, and inadequate social support. The Chinese government has recognized the growing 
need for fall prevention, with the National Health Commission issuing the "Technical 
Guidelines for Fall Prevention and Control in Community-Dwelling Older Adults"[12]. These 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
4 
guidelines provide evidence-based recommendations but face significant challenges in 
implementation, particularly in resource-limited rural regions[6,13]. The gap between 
evidence-based guidelines and their real-world implementation in rural areas is widening, 
highlighting the pressing need for locally tailored, scalable implementation strategies. 
Implementation science provides a valuable framework for addressing these challenges 
by focusing on the factors that influence the uptake and sustained integration of evidence-
based practices in real-world settings[14]. Previous studies have shown that while fall 
prevention programs can be effective in controlled settings, their real-world implementation is 
often hindered by a range of barriers[15], including a lack of healthcare professional training, 
insufficient community awareness, and limited integration of fall prevention into routine 
care[11]. In particular, health professionals in rural areas may face barriers such as time 
constraints, inadequate support, and insufficient knowledge about fall prevention 
guidelines[16]. Furthermore, older adults may not recognize their own fall risk or may be 
reluctant to participate in fall prevention programs due to fear, stigma, or lack of social 
support[17]. As such, the need for feasible, rural-specific interventions is more critical than 
ever. This highlights the gap between evidence-based fall prevention guidelines and their 
effective implementation in rural areas, where resources are constrained. 
To bridge the gap between evidence-based guidelines and real-world implementation in 
rural China, we initiated the EXpress Prevent Rural oldEr adults’ fallS, fractureS and 
dependency (EXPRESS) project. This initiative aims to evaluate the effectiveness of a 
multifactorial, AI-assisted, community-based fall prevention intervention that integrates the 
efforts of village doctors and express service providers. By embedding the intervention within 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
5 
existing rural health structures and community resources, EXPRESS seeks to improve 
accessibility, feasibility, and scalability in resource-limited settings. A central innovation of 
the EXPRESS intervention lies in its population-wide fall risk screening and tailored response 
strategy. Rather than adopting a one-size-fits-all model, EXPRESS combines subjective risk 
assessments conducted by trained village doctors with objective motion analysis captured via 
smartphones and analyzed using AI algorithms. These assessments are synthesized into an AI-
powered decision support system that generates personalized fall prevention packages for 
each older adult. These packages address a range of modifiable risk domains—including 
physical inactivity, environmental hazards, social isolation, and low health literacy—and are 
delivered directly to participants by trained couriers, enhancing both precision and 
community trust. Emerging evidence supports multifactorial risk reduction strategies, 
particularly those grounded in systematic assessments and tailored to individual profiles, as 
effective in reducing fall incidence among high-risk populations [18].  
Notably, this approach—universal screening coupled with individualized intervention—
resonates with the logic of the Prevention Paradox, a foundational concept in public health. 
First introduced by Geoffrey Rose, the Prevention Paradox describes a key challenge in 
preventive strategies, that is, population-wide interventions can achieve substantial public 
health gains, even though the benefits to any single individual may appear minimal[19]. In 
contrast, targeting only high-risk individuals may yield large individual benefits but has 
limited impact on the overall burden of disease [19]. EXPRESS addresses this paradox 
through a pragmatic, dual-strategy design. By conducting universal risk factor screening 
among all older adults, the intervention ensures broad population coverage and inclusivity. At 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
6 
the same time, the results of these assessments enable the delivery of risk-informed, tailored 
interventions to those with specific vulnerabilities. This continuum of care allows lower-risk 
individuals to benefit from general education and prevention resources, while those with 
higher risk receive intensified support, including AI-guided exercise prescriptions and 
behavioral recommendations. By embedding this model into the routine public health 
workflow of village doctors and leveraging trusted local actors for delivery, EXPRESS 
ensures that individual relevance and population reach are not mutually exclusive. It 
exemplifies a hybrid strategy that operationalizes the key insight of the Prevention Paradox, 
that is, in order to achieve meaningful improvements in population health, interventions must 
combine universal engagement with risk-sensitive personalization.  
To ensure the effective delivery and sustainability of personalized fall prevention 
services in rural settings, the EXPRESS project adopts a task-sharing model that mobilizes 
local resources beyond the traditional health workforce. In particular, village-based express 
mail service (EMS) providers—who routinely reach nearly all households—are engaged as 
key delivery agents for the intervention. These couriers are not only geographically accessible 
but are also well integrated into local social networks, making them ideal partners for 
community-level health promotion efforts. In rural China, local community leaders, village 
doctors, and EMS providers are essential to the provision of health services and the delivery 
of medical supplies [20]. These stakeholders are trusted by older adults and are crucial to 
ensuring the success and sustainability of the intervention[21]. By leveraging these local 
resources, the EXPRESS aims to integrate fall prevention into existing healthcare structures, 
utilizing a community-based delivery model that is both culturally and contextually relevant. 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
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AI-assisted tools are combined with the practical experience and social familiarity of these 
local agents to provide a personalized, scalable intervention that enhances feasibility and 
fosters community trust. 
Theoretical framework and approach 
The EXPRESS project is underpinned by a hybrid theoretical foundation that integrates 
population-level and risk targeted strategies through a dual-track approach. Central to this 
design is the Prevention Paradox, which suggests that while interventions targeting high-risk 
individuals may have a significant impact on individual outcomes, population-level health 
gains often depend on universal interventions that reach a broader audience [19]. EXPRESS 
reconciles this paradox by combining universal fall risk screening with personalized, AI-
assisted interventions, ensuring that all older adults benefit from fall prevention resources 
while those with multiple risk factors for falls receive tailored support. To operationalize this 
strategy in rural, resource-constrained contexts, the project draws upon the COATS 
framework (Concepts and Opportunities to Advance Task Shifting and Task Sharing) [22]. 
COATS guides the redistribution of fall prevention tasks across a broader, community-
embedded workforce. Village doctors are responsible for professional assessments and 
clinical oversight, while local express mail service (EMS) couriers—already embedded in the 
daily lives of residents—are trained to deliver educational content, assist with intervention 
logistics, and provide ongoing participant engagement. This task-sharing model enhances 
coverage, reduces costs, and increases feasibility by tapping into the existing trust and reach 
of non-specialist workers. 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
8 
The integration of artificial intelligence (AI) further strengthens this model. AI-assisted 
tools support village doctors in conducting efficient and standardized risk assessments, while 
simultaneously generating individualized intervention plans based on a combination of 
subjective and objective data inputs. The resulting workflow allows health professionals to 
focus on tasks requiring specialized expertise, while enabling trained couriers to manage 
community-facing responsibilities. To guide implementation, EXPRESS employs the EPIS 
framework (Exploration, Preparation, Implementation, and Sustainment) from 
implementation science [23]. During the Exploration phase, community needs and local 
capacities are assessed. The Preparation phase includes team formation, training on AI tools 
and fall prevention practices, and planning of delivery workflows. The Implementation phase 
focuses on deploying the intervention and promoting its uptake across villages. Finally, the 
Sustainment phase ensures ongoing delivery through periodic refresher training, feedback 
loops, and integration into routine public health services. This phased framework supports 
long-term integration by aligning intervention components with community capacity, cultural 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
9 
relevance, and existing healthcare infrastructure. Figure 1 shows the theoretical framework of 
this study.  
Figure 1. The theoretical framework of this study. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
10 
Study objectives 
The primary objective of the EXPRESS trial is to evaluate the effectiveness of an AI-
assisted, personalized, village doctors and express services-involved, community-based fall 
prevention intervention in reducing fall rates among rural older adults. Secondary objectives 
include assessing the impact of the intervention on quality of life, healthcare utilization, and 
cost-effectiveness compared to standard fall prevention strategies. Our research questions are 
as follows:  
1. Effectiveness and Precision of AI-assisted Fall Prevention: What is the impact of an 
AI-assisted, personalized, village doctors and express services-involved, community-based fall 
prevention intervention on reducing fall-related injuries and improving health outcomes among 
older adults in rural China, compared to standard care? How does the use of AI in risk 
assessment enhance the precision and scalability of fall prevention efforts? 
2. Feasibility, Stakeholder Engagement, and Contextual Adaptation: How feasible is 
the implementation of the AI-assisted fall prevention strategy in rural settings, and what are the 
key barriers and facilitators for its successful integration into routine care? What role do local 
healthcare providers, community leaders, and stakeholders play in adapting and sustaining the 
intervention? 
3. Cost-Effectiveness and Scalability for Public Health: What is the cost-
effectiveness of the AI-assisted fall prevention intervention in a rural healthcare context, and 
how do its economic impacts compare to potential reductions in healthcare utilization and 
disability? How scalable is this model for broader adoption in other low-resource settings, 
both within China and globally? 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
11 
Methods 
Trial design 
The EXPRESS study is a cluster-randomized controlled trial with randomization at the 
village level and a 1:1 allocation to parallel intervention and control groups. The EXPRESS 
trial is conducted in 16 rural villages across four provinces in China: Beijing, Hubei, Shanxi, 
and Anhui. Villages were selected using a stratified cluster sampling method to ensure 
geographical diversity and contextual representativeness. Within each province, villages were 
first stratified by key characteristics such as location, population size, and healthcare 
infrastructure. From each stratum, villages were randomly selected, with four villages per 
province ultimately enrolled—two allocated to the intervention group and two to the control 
group. Recruitment of villages started in June 2024 and was completed in July 2024. Baseline 
survey prior to randomization started on August 6 2024 and was completed on August 27 
2024. The intervention was delivered since September 1 2024, after the completion of the 
baseline survey. The intervention will last for 12 months and the effectiveness evaluation will 
be conducted in August 2025. 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
12 
Regulatory approvals and ethical considerations 
The Biomedical Ethics Review Committee of Peking University approved the EXPRESS 
protocol on August 2, 2024 (IRB00001052-24070). The study employs a tiered monitoring 
system, with the independent Data Safety and Monitoring Board conducting annual reviews 
and implementing triggered additional reviews when pre-specified safety thresholds 
(participants experience serious adverse events such as injury falls, fractures, or deaths) are 
reached. The External Advisory Board maintains annual oversight of overall trial conduct. All 
study process will adhere to ethical guidelines of the Declaration of Helsinki[24], and informed 
consent will be obtained from all participants before their inclusion in the study. Participants 
will be fully informed about the nature of the study, the data being collected, and their right to 
withdraw at any time. Data will be managed according to strict privacy standards, and 
confidentiality will be maintained throughout the study period. To ensure participants’ privacy, 
all personal and health-related data will be anonymized, and secure storage protocols will be 
followed. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
13 
Study setting and participants 
The study will be conducted across 16 rural villages in four provinces of China—
Beijing, Hubei, Shanxi, and Anhui. Four villages from each province will be randomly 
assigned to either the intervention group or the control group. The sample size calculation for 
this study is based on the following formula[25]:   
𝑛=
(𝑍𝛼+ 𝑍𝛽)
2[𝑃1(1 −𝑃1) + 𝑃2(1 −𝑃2)]
(𝑃1 −𝑃2)2
 
Here, n represents the required sample size per group, P1 denotes the pre-intervention fall 
incidence, P2 refers to the post-intervention fall incidence, 𝑍𝛼 is the standard normal value 
corresponding to the significance level of α, and 𝑍𝛽 is the standard normal value 
corresponding to the power level (1-β). Based on a prior meta-analyses, the fall incidence 
among older adults in rural China is estimated to be 23.1% [26], which is adopted as the 
likely fall incidence without intervention P1. Assuming the intervention could reduce the fall 
incidence in the intervention group by 10% (P2 = 13.1%)), and setting α= 0.05 and β= 0.20 
(80% power), the calculated minimum sample size per group is 233 participants. For cluster 
randomized controlled trials, the sample size calculation should account for the cluster design 
effect (CDE), i.e., the reduction in statistical power caused by clustering[27]. The CDE is 
calculated as follows:  
𝐶𝐷𝐸= 1 + (𝑚−1) × 𝜌 
Here, m is the average cluster size, and ρ is the intra-cluster correlation coefficient 
(ICC). Assuming ρ = 0.01 and that the average number of older adults per village is 120, the 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
14 
calculated CDE is 2.19. After adjusting for the CDE, the minimum required sample size per 
group is 510 participants, leading to a total sample size of 1,020 participants.  
Villages eligible for inclusion in this study are as follows: Each village should have a 
health clinic with at least one full-time village doctor, ensuring access to primary healthcare 
services that are essential for the implementation of the intervention. Additionally, both 
village officials and village doctors agree to participate in the study and agreed to establish a 
cooperative relationship between the two to ensure community engagement and successful 
delivery of fall prevention intervention. Villages must be situated more than 2 kilometers from 
other villages to reduce potential contamination between them. Exclusion criteria include 
villages that serve as administrative centers for township governments, as these may 
experience confounding due to the centralized use of medical resources, which could bias the 
study results. Villages that have previously participated in fall-related health promotion or 
intervention trials are also excluded. 
As for participants, the inclusion criteria are aged 65 or older and have lived in the 
village for at least 12 months before enrollment. Exclusion criteria included: (1) individuals 
with severe diseases, such as cancer, stroke; (2) Individuals with disabilities, cognitive 
impairments, or significant hearing or language impairments. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
15 
Randomization and blinding 
A cluster randomization approach will be employed to allocate the 16 villages across 
four provinces (Beijing, Hubei, Shanxi, and Anhui) into either the intervention group or the 
control group. Randomization will be conducted after the baseline (T0) to ensure that any pre-
existing differences between villages do not bias the allocation process. The randomization 
will be carried out by a third-party statistician using a computer-generated randomization 
schedule to ensure impartiality. In each province, two villages will be randomly assigned to 
the intervention group, which will conduct the AI-assisted, village doctors and express 
services-involved, community-based intervention strategy, and two villages will be assigned 
to the control group, where standard health management practices will be followed. This 
randomization will help control for potential confounding factors at the village level, such as 
regional healthcare availability or socioeconomic status, ensuring that the effects observed 
can be attributed to the intervention rather than other extraneous variables[28]. Allocation 
concealment will be ensured through the use of centralized randomization managed by an 
independent data management team. The allocation sequence will be stored securely, and 
intervention assignments will only be revealed after baseline assessments. This study is a 
single blind design, where the researchers know the grouping after randomization, but the 
village doctors and older people after grouping do not know the grouping situation. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
16 
Intervention strategy 
The intervention strategy for the EXPRESS project is grounded in high-level evidence 
recommended by the guidelines, including the Technical Guidelines for Fall Prevention and 
Control in Community-Dwelling Older Adults published by China’s National Health 
Commission [12]. All components of the intervention have been carefully adapted to address 
the unique characteristics and constraints of rural areas, where healthcare resources are often 
limited, and older adults face significant barriers to accessing preventive services. By 
incorporating local knowledge, cultural practices, and existing infrastructure, the intervention 
was designed to be contextually relevant, operationally feasible, and sustainable in rural 
settings. 
The intervention design of the EXPRESS project draws upon core principles of 
implementation science, aiming to bridge the gap between evidence-based fall prevention 
guidelines and real-world application in under-resourced rural settings. Grounded in the EPIS 
framework (Exploration, Preparation, Implementation, and Sustainment), the strategy 
emphasizes local feasibility, adaptability, sustainability, and stakeholder engagement. It also 
integrates the COATS framework (Concepts and Opportunities to Advance Task Shifting and 
Sharing), redistributing fall prevention tasks among village doctors, community workers, and 
couriers to address the critical shortage of healthcare professionals in rural China. The 
EXPRESS intervention is structured around five key components—SAFER (Table 1), each 
designed to address rural health resource limitations, enhance accessibility, and provide 
personalized fall prevention services through technology and community integration. 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
17 
S – Screening: Fall risk screening and assessment tailored to rural older adults. All 
participants undergo comprehensive fall risk assessments through a dual approach. First, 
village doctors administer a 10-item rural-adapted fall risk scale, developed by the research 
team for ease of use and specificity to the rural aging population. The scale captures key 
indicators such as mobility, balance, and prior fall history, offering a structured yet practical 
tool for frontline healthcare workers in resource-limited settings. Second, participants are 
instructed to complete sit-to-stand and 6-meter walking task (3 meters out and back) at their 
natural pace on a marked 3-meter mat. The smartphone is mounted on a specialized tripod on 
the opposite side, 2 meters from the endpoint of the mat, with a 30° downward angle to 
optimize the motion capture field of view. These recordings are analyzed using deep learning 
model and AI-powered motion analysis to extract five objective dimensions associated with 
fall risk: (1) balance (via sway and bilateral symmetry), (2) gait speed, (3) stride length, (4) 
trunk rotation (via turning motion), and (5) lower limb strength (via sit-to-stand movement). 
This combined subjective-objective assessment ensures accurate risk assessment and supports 
tailored interventions. Importantly, this risk screening and tailored health counseling process 
has been designed to be embedded within China’s National Essential Public Health Service 
(EPHS) framework, particularly under the older adult health management program, which 
requires village doctors to conduct an annual physical examination for all residents aged 65 
and above. As part of this initiative, the EXPRESS project incorporates the fall risk 
assessment and individualized consultation into the existing annual check-up workflow, 
allowing the service to be delivered efficiently during the routine health visit. To further 
support integration and incentivize adoption, the EXPRESS project offers a performance-

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
18 
based subsidy of RMB 6 per older adult to village doctors who complete both the fall risk 
screening and individualized counseling during the EPHS check-up. This approach not only 
promotes the institutionalization of fall prevention services, but also enhances sustainability 
and alignment with national health priorities in rural China. 
A – AI-assisted recommendations: Personalized, AI-assisted falls prevention 
suggestions and health education. Collected assessment data are integrated into a decision-
support system powered by artificial intelligence, which synthesizes evidence-based 
guidelines, expert consensus, and real-world data. The AI system generates personalized fall 
prevention plans tailored to each participant’s risk profile and rural lifestyle. These plans 
assist village doctors in making informed, evidence-based decisions and provide actionable 
recommendations suitable for local implementation. Through the "1+1" transmission 
mechanism, village doctors conduct one-on-one risk assessments and explain the plan's key 
content, improving older people's health literacy and awareness of fall prevention. 
Meanwhile, a risk assessment and health recommendation report combining text and images 
will be issued to participants, using large fonts and including risk warning icons to ensure 
they are age and cultural appropriate. 
F – Fitness: Tailored balance and strength training via Health Intervention Cartoon 
Brochure and Action Demonstration Video. Based on the risk assessment results, each older 
person will receive an individualized training recommendation customized by rehabilitation 
experts, which includes targeted training movements (such as balance training, lower limb 
strength training, etc.), supporting graphic and textual list (large font, easy to understand), and 
operation demonstration videos. Additionally, exercise rehabilitation experts develop a 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
19 
manual with 10 low-barrier exercises suitable for rural life scenarios, such as "bench balance 
training" and "big arm swings." Instructional videos are distributed to village doctors and 
participants. Village doctors are encouraged to lead group exercise sessions at village clinics, 
creating a community-driven atmosphere for physical activity. 
E – Events-based engagement: Seasonal prevention activities and prevention materials 
integrated with traditional festivals and solar terms of the traditional Chinese calendar. To 
improve community engagement and intervention sustainability, the EXPRESS trial designed 
three round of intervention leveraging seasonal intervention packages (Figure 2) that align 
with key traditional cultural events: the Chongyang Festival (October), the Spring Festival 
(January/February), and the Qingming Festival (April). These packages include culturally 
relevant and visually engaging materials, such as fall prevention calendars, fans printed with 
safety messages, and weather-specific tips. In addition, each package includes seasonal fall 
prevention tips, such as staying indoors during snowy weather, wearing non-slip shoes in 
winter, and being cautious during spring farming activities, thereby strengthening fall 
prevention awareness among older adults. The distribution of these packages aims to 
strengthen and consolidate the knowledge, beliefs, and behaviors of fall prevention among 
participants at different time points, leveraging local traditions to promote adherence, 
enhancing their sense of participation and sustainability in the intervention. 
R – Reach-home delivery: The final and most important intervention strategy is home 
delivery of fall prevention services by express service. To alleviate the burden on village doctors 
and address the shortage of healthcare personnel in rural areas, the EXPRESS trial inspired by 
the Task Shifting/Sharing Theory and incorporates local couriers as community health support 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
20 
agents. Fall prevention intervention materials are remotely prepared by the technical team and 
distributed through a task-sharing platform. These couriers did their routine job and delivered 
intervention materials—including personalized health reports, educational calendars, and 
seasonal care packages—directly to participants’ homes. This delivery model ensures the 
timeliness and coverage of fall prevention services to the doorsteps of older adults in rural and 
other regions with limited health care personnel. Figure 3 shows the framework of intervention 
strategies. 
 
Figure 2. The contents of three festival packages. 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
21 
 
Figure 3 Framework of intervention strategies 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
22 
Table 1 EXPRESS five core intervention matrix 
Intervention project 
Implementation 
form 
Technical support 
Execution subject| 
S – Screening: Fall risk 
screening and assessment 
10 subjective risk 
factors 
Five objective 
dimensions 
10-item 
assessment scale 
and smartphone 
Village doctors 
A – AI-assisted falls 
prevention 
recommendations 
Risk assessment 
reports/manuals 
AI decision-
making system 
Research team 
F – Fitness: Tailored 
balance and strength 
training.  
Customized 
Exercise Plan 
Exercise 
Prescription 
Rehabilitation 
experts 
E – Events-based 
engagement: Seasonal 
prevention activities and 
materials 
Three festival 
packages 
AI decision-
making system 
Research team 
R – Reach-home delivery: 
Home delivery of fall 
prevention services 
Express delivery 
system 
Task sharing 
platform 
Courier 
 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
23 
The implementation framework of intervention strategy 
Figure 4 outlines the research activities and timeline across each phase of the EPIS 
(Exploration, Preparation, Implementation, and Sustainment) framework, which guides the 
structured deployment of the EXPRESS intervention. 
During the Exploration phase, a comprehensive needs assessment was conducted in each 
participating village to identify local barriers and facilitators to fall prevention. This included 
evaluating community-specific risk factors, healthcare infrastructure, and stakeholder 
engagement. Input was gathered from older adults, village doctors, local officials, and 
couriers to ensure contextual relevance and to inform subsequent program design. In the 
Preparation phase, village-level local change teams were formed, comprising village doctors, 
village officials, and trained couriers. These teams received standardized training sessions 
focused on fall risk assessment using both subjective and objective tools, operation of the AI-
assisted decision support system, and strategies for effective fall prevention education. This 
phase emphasized building local capacity and fostering cross-sector collaboration.  
The Implementation phase involved the staged rollout of the five-component 
intervention strategy. Activities included the delivery of personalized fall prevention materials 
to older adults’ homes, risk-based exercise recommendations, culturally adapted seasonal 
packages, and coordinated outreach efforts led by the local change teams. Intervention 
delivery was aligned with major cultural festivals to maximize community participation and 
behavioral reinforcement. Finally, the Sustainment phase focuses on maintaining the 
intervention’s long-term effectiveness and integration into routine village health services. This 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
24 
includes ongoing technical support, refresher trainings for local team members, and periodic 
community engagement activities. 
 
Figure 4. The research activities and timeline for each EPIS phase 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
25 
Control Group 
The control group will receive standard health management through Primary Public 
Health Services and will be provided with a basic calendar to record fall events.  
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
26 
Primary Outcome Measures 
The primary measure will be the fall incidence (rate of falls per person-year), the 
proportion of participants with ≥1 fall and that of fall-related injuries (including fractures) 
over the 12-month intervention period.  
Fall incidents will be recorded using multiple methods to reduce recall bias: self-
reporting by older adults and their families on the fall calendar, quarterly follow-ups by 
couriers, and a final interview-based endpoint assessment.  
Fall-related injuries, such as fractures, head injuries, hospitalizations, other events 
requiring medical attention, will also be collected via self-reporting, quarterly check-ups, and 
endpoint assessments. To ensure consistency and reliability in the reporting of falls, we will 
ask participants to refer to the three fall types as defined by the ICD-11 classification 
system[29]. Participants will be asked about their fall incidents based on the ICD-11 
classification for falls. This will include three fall type: (1) PA60 (Unintentional fall on the 
same level or from less than 1 meter): this category encompasses falls from slipping, tripping, 
or stumbling, as well as falls involving stairs, steps, ramps, and escalators. PA60 also covers 
falls from furniture, such as from chairs, beds, and playground equipment. (2) PA61 
(Unintentional fall from a height of 1 meter or more): this category includes falls from greater 
heights, such as ladders, scaffolding, trees, or buildings. (3) PA6Z (Unintentional fall from 
unspecified height): the category includes falls where the height is unknown, such as falls 
from standing positions or accidental falls at home. The use of ICD-11 will enhance the 
accuracy of the fall data and valid comparisons across the two groups. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
27 
Secondary Outcome Measures 
Secondary outcomes include fall health literacy, activities of daily living (ADL), quality 
of life, depressive symptoms, cognitive function, dementia, physical function and 
performance, incidence of chronic non-communicable diseases, frailty, and sleep quality.  
Fall health literacy will be measured using a scale developed by the researchers, which 
will assess changes in participants’ knowledge of fall prevention strategies. ADL will be 
assessed using the Barthel Index[30] to determine if the intervention has an effect on the 
participants’ independence and daily functioning. Quality of life will be measured using the 
EQ-5D-5L questionnaire [31]. Depressive symptoms will be assessed using the short version 
of Geriatric Depression Scale [32]. Cognitive function will be evaluated using the mini-
mental state examination [33], and dementia will be assessed using the Clinical Dementia 
Rating [34] and evaluated by a professional physician. 
Physical function and performance will be assessed using an AI-assisted analysis of 
standardized physical tasks, including a sit-to-stand test and a 6-meter walk test on a 3-meter 
mat. A smartphone records participants’ movements, which are analyzed by a deep learning 
model to extract indicators such as gait speed, stride length, center of pressure sway area 
(reflect the balance situation), trunk rotation, and lower limb strength. The chronic non-
communicable diseases include hypertension, diabetes, arthritis, sarcopenia, cervical 
spondylosis, heart problem, osteoporosis, kidney disease, hyperlipidemia, liver disease, lung 
disease, stomach disease, intestinal diseases. The incidence of chronic non-communicable 
diseases will be determined based on physician-diagnosed medical records, collected during 
routine health check-ups and follow-up visits by trained medical staff. Frailty will be assessed 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
28 
using the FRAIL scale, a validated five-item questionnaire evaluating fatigue, resistance, 
ambulation, illness, and weight loss[35]. Sleep quality will be measured using the Pittsburgh 
Sleep Quality Index (PSQI), a standardized self-reported questionnaire that evaluates sleep 
duration, latency, disturbances, and overall quality over the past month[36]. 
 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
29 
Data collection and management 
Data will be collected at three time points: baseline (T0: August 2024), immediately after 
the intervention period (T1: August 2025), and 12 months post-intervention (T2: August 
2026). At baseline, demographic information, health status, and fall risk data will be collected 
from participants. We will also collect data from participating villages, including healthcare 
infrastructure and community engagement metrics, at baseline to ensure consistency and 
comparability between intervention and control villages. At T1, falls, falls injuries, and health 
status in the past year data will be collected. At T2, fall-related information and health status 
will be gathered again to evaluate the sustainability of the intervention’s effects over times. 
Data collection will be conducted using bespoke electronic questionnaires via 
smartphone. Investigators will be trained to access the questionnaire on smartphones and 
conduct one-on-one interviews with study participants. These investigators will be blinded to 
the study's grouping (intervention vs. control) to prevent any bias in the data collection 
process. The training will include standardized procedures for administering questionnaires, 
conducting interviews, and recording fall-related incidents. The electronic questionnaires will 
include sections on demographics, health status, mobility, prior fall history, and outcome 
measures such as fall rates, quality of life, and health service utilization. Once the survey is 
completed, investigators will click "submit" to transfer the responses to a secure database. 
Upon submission, data will be immediately stored on a centralized server and cannot be 
modified, ensuring data integrity. Each submission will receive a timestamp, confirming the 
exact time of data entry. The process is designed to minimize data entry errors by eliminating 
manual data handling. To promote data accuracy and minimize error rates, investigators will 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
30 
undergo standardized training on questionnaire administration, including guidelines for 
ensuring participant comprehension and handling any issues during the interview. In addition, 
random audits will be conducted where 10% of the completed questionnaires will be cross-
checked for accuracy by a second investigator. These audits will help ensure that the data 
collection process is being carried out consistently and with high quality. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
31 
Health Economic Evaluation 
Health economic evaluations will be integrated into the design of the EXPRESS trial and 
be conducted primarily from a societal perspective over 12 months. The primary economic 
evaluation will take the form of cost-effectiveness analysis, based on the reduction in fall and 
fall-related injuries. Incremental cost-effectiveness ratios (ICER) [37], expressed as the 
incremental cost per additional reduction in fall and fall-related injuries, will be calculated. A 
cost-utility analysis will also be performed. Responses to the EQ-5D-5L questionnaire will be 
converted to health utility scores using a value set for the Chinese population [38]. Based on 
these utility scores, Quality-Adjusted Life Year (QALYs) [38] – a widely accepted measure that 
combines both the quantity and quality of life years gained through the intervention[39,40] – 
will be estimated. Cost-effectiveness will be assessed in terms of the incremental cost per 
QALY gained, calculated by dividing the difference in mean costs between the two treatment 
groups by the differences in their mean QALYs [32].  
Cost-effectiveness analysis requires a comprehensive assessment of the costs associated 
with the intervention and its comparator. These will include the cost of the intervention, 
healthcare resource use and the wider societal costs (e.g. out-of-pocket costs and productivity 
loss). Cost data will be collected via a bespoke resource use questionnaire. Unit costs will be 
mainly derived from local and national sources. 
To fully consider the uncertainty in important components of economic evaluation, several 
sensitivity analyses will be conducted[41]. These include a sensitivity analysis from a narrower 
healthcare perspective and another under alternative missing data assumptions [42]. A subgroup 
analysis will also be conducted to assess the cost-effectiveness of the intervention among 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
32 
specific demographic or health status groups, such as older adults with mobility impairments 
or those at higher risk of falls, informed by the data collected from the main trial. Cost-
effectiveness planes and cost-effectiveness acceptability curves [43] will be reported. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
33 
Process Evaluation 
A qualitative process evaluation of the EXPRESS will be conducted to assess key aspects 
of the intervention's feasibility, implementation, and impact [44,45]. This evaluation will 
provide a deeper understanding of how the intervention is received by participants, healthcare 
providers, and the broader community, as well as the factors that influence its success or 
challenges during the implementation phase. The evaluation will be structured around three 
core modules: feasibility, impact, and implementation. 
 
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
34 
Statistical Analysis 
The statistical analysis for this study will follow an intention-to-treat principle[46], 
ensuring that all participants, regardless of their adherence to the intervention, are included in 
the analysis. Descriptive statistics will summarize baseline characteristics, with continuous 
variables reported as means ± standard deviations (SD) and categorical variables as 
frequencies and proportions. We will assess the baseline characteristics between the 
intervention and control groups to ensure comparability, and account for potential 
confounders that could affect the outcomes, including age, gender, socioeconomic status, and 
baseline fall risk. 
For primary analyses, mixed-effects regression models will be used to compare fall 
incidence and fall-related injury rates between intervention and control groups, incorporating 
fixed effects for treatment allocation, baseline covariates (age, gender, socioeconomic status), 
and random intercepts to account for clustering at the village level. Secondary outcomes 
(health literacy, quality of life, activities of daily living [ADLs]) will be analyzed via 
generalized linear mixed models (GLMMs) with appropriate link functions (e.g., logit for 
binary outcomes, identity for continuous). Prior to the main analysis, a preliminary data 
cleaning process will be carried out to assess the completeness and integrity of the dataset. If 
necessary, appropriate estimation techniques such as multiple imputation will be applied to 
impute missing data in the main analysis. Sensitivity analyses will also be conducted to 
evaluate the robustness of findings to alternative missing data assumptions and model 
specifications (e.g., inclusion/exclusion of covariates).  

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
 
35 
In addition, subgroup analyses will be also used to examine how the intervention affects 
outcomes like fall risk and mobility among different groups based on baseline characteristics 
(e.g., age, sex). Interaction terms will be included in the regression models to assess the 
differential effects of the intervention by subgroup, allowing for a deeper understanding of the 
potential heterogeneity of treatment effects across different demographic groups. To account 
for the clustering of participants within villages, we will adjust for intra-cluster correlation by 
clustering the standard errors at the village level. All analyses will be conducted in STATA 
17.0 (Stata Corp, College Station, TX, USA), with statistical significance defined as a two-
tailed p < 0.05.  
 

Title: AI-assisted, Village Doctors and Express Services-Involved, Community-Based Multicenter Cluster 
Randomized Controlled Trial for Fall Prevention Among Rural Older Adults 
Study Protocol (Approval Data: 2 August, 2024) 
36 
Funding 
The EXPRESS trial is funded by National Natural Science Foundation of China (grant numbers: 
72374013). 
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