Based on the Community Smart Aging Model: A Study on the Application of Resistance Training to Elderly People With Primary Osteoporosis
Phase: NA
Conditions: Primary Osteoporosis
Sponsor: Lingshu Sun

Research Proposal — Community-Based Smart RT for Osteoporosis 
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RESEARCH PROPOSAL 
Community-Based Smart Elderly Care Model: 
Resistance Training for Older Adults with Primary 
Osteoporosis 
UVIC-SUN LINGSHU-2024 
Barcelona, Spain | 26 July 2025 
 
 

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PART I: THEORY AND RESEARCH DESIGN (Chapters 1–4) 
Chapter 1  Introduction 
1.1  Public Health Burden of Ageing and Primary Osteoporosis 
1.1.1  Global and Chinese population ageing: current status and trends 
Osteoporosis (OP) is a systemic skeletal disorder characterised by reduced bone mass and 
microarchitectural deterioration of bone tissue, leading to increased fragility and susceptibility 
to fracture. The condition predominantly affects postmenopausal women and older men. With 
accelerating global population ageing, OP has emerged as the third most prevalent chronic 
disease worldwide, surpassed only by cardiovascular disease and diabetes. According to the 
World Health Organization (WHO) 2023 report, the population aged 60 years and older has 
exceeded one billion and is projected to reach 2.1 billion by 2050, representing over 21% of 
the total global population. This trend is particularly pronounced in high-income countries such 
as Japan and Italy, where life expectancy exceeds 80 years; however, the pace of ageing is even 
more rapid in low- and middle-income countries, where healthcare infrastructure remains 
underprepared [1]. 
China, the nation with the largest ageing population globally, recorded 264 million residents 
aged 60 and older (18.70% of the total population) in its Seventh National Population Census, 
with those aged 65 and above accounting for 14.9%—officially marking the transition to a 
moderately aged society [2]. Projections indicate that by 2025, the population aged 60 and over 
will surpass 300 million; by 2033 it will exceed 400 million; and by 2050, older adults will 
constitute more than one-third of the total population [2]. 
Key drivers of population ageing include declining fertility rates (the global total fertility rate 
fell from 4.7 in 1950 to 2.3 in 2023), rising life expectancy (from a global average of 46 years 
in 1950 to over 70 years in 2023), and the epidemiological transition from infectious to chronic 
diseases as the primary causes of death [1]. China additionally exhibits a rural–urban ageing 
inversion: the rural ageing rate (20.7%) exceeds the urban rate (16.2%), compounded by 
weaker rural healthcare systems that exacerbate health inequalities [3]. This demographic shift 
directly intensifies the burden of chronic bone metabolic diseases, positioning OP prevention 
and control as a global public health priority. 

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1.1.2  Epidemiological profile of primary osteoporosis: prevalence, 
incidence, and sex differences 
Primary osteoporosis is a systemic bone disease characterised by decreased bone mass and 
microstructural destruction leading to increased bone fragility. Prevalence increases 
exponentially with age. A 2021 global meta-analysis reported an overall OP prevalence of 21.7% 
(95% CI: 18.8–25.0%) among older adults, with women (35.3%, 95% CI: 27.9–43.4%) 
significantly more affected than men (12.5%, 95% CI: 9.3–16.7%) [4]. Marked regional 
disparities exist: Asia leads at 24.3%, followed by Europe (15.8%) and the Americas (11.2%) 
[4]. In China, the prevalence of OP among adults aged 50 and over is 19.2%, rising sharply 
with age: 10.6% in the 50–64 age group and 32.0% in those aged 65 and above, with an 
especially severe burden among women (51.6% in women aged 65+) [3]. 
Epidemiological studies reveal three key risk stratification features. First, sex differences: the 
abrupt decline in oestrogen following menopause accelerates bone resorption, rendering 
women three times more likely to develop OP than men. International Osteoporosis Foundation 
(IOF) data indicate that approximately one-third of women aged 50 and over worldwide have 
OP, with this proportion reaching two-thirds among women aged 90 [5]. Second, geographic 
inequalities: rural OP prevalence in China (35.3%) significantly exceeds that in urban areas 
(25.6%), primarily attributable to nutritional deficiencies (inadequate calcium intake), 
insufficient sunlight exposure (reduced vitamin D synthesis), and limited healthcare access [3]. 
In India, OP prevalence among low-income women reaches 29%, with osteopenia at 52%, 
directly linked to protein and calcium deficits [6]. Third, diagnostic deficiency: US NHANES 
2017–2018 data show that 69.12% of OP cases remain undiagnosed, with the highest missed-
diagnosis rates among men (86.88%) and those aged 50–59 (84.77%) [7]. In China, the 
awareness rate for OP among those aged 50+ is only 7%, the diagnostic rate 36%, and the 
therapeutic drug utilisation rate merely 6.5%, constituting a “silent epidemic” [8]. 
1.1.3  Disease burden of osteoporotic fractures: incidence, mortality, 
disability, and economic costs 
Osteoporotic fractures (OPFs) represent the most severe complication of OP and impose a 
substantial disease burden. Globally, up to 37 million fragility fractures occur annually among 
individuals aged 55 and older, equivalent to approximately 70 fractures per minute [9]. Hip 
fracture, the most lethal form of OPF, carries a one-year mortality rate of 20–24%, a disability 
rate of 50%, and only 33% of patients regain pre-injury functional capacity [10]. Vertebral 
fractures, although often occult (approximately 70% are clinically unrecognised), may result 

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in chronic pain, spinal deformity, and impaired pulmonary function [11]. 
The economic burden exhibits a “double-peak growth” pattern. Regarding direct medical costs, 
the United States projects an increase from 1.9 million fractures per year in 2018 to 3.2 million 
by 2040 (a 68% increase), with associated costs rising from USD 57 billion to over USD 95 
billion annually [12]. In the EU 27+2 nations, direct costs totalled EUR 56.9 billion in 2019, 
with hip fractures accounting for 57%, vertebral fractures 10%, distal forearm fractures 2%, 
and other fractures 32% [12]. Indirect social costs encompass labour-force losses, long-term 
rehabilitation expenditures, and the risk of household financial collapse. 
Importantly, OPFs demonstrate a “cascade effect”: the risk of secondary fracture triples after 
the initial event, and 50% of secondary fractures occur within one year [12]. Data from the 
Chinese “Bone Strength Programme” reveal a cumulative re-fracture rate of 32.7% within five 
years of the initial OPF, creating a vicious cycle [13]. 
1.1.4  Limitations and challenges of current prevention and management 
strategies 
Despite continual refinement of OP clinical guidelines, the global management landscape 
remains characterised by a paradox of “high prevalence with low awareness, low diagnosis, 
and low treatment.” Structural bottlenecks include the following. 
Screening and diagnostic system deficiencies: Dual-energy X-ray absorptiometry (DXA), the 
gold standard for diagnosis, has extremely low coverage in developing countries. In rural China, 
DXA device density is one-fifth that of urban areas, compounded by insufficient operator 
training. The widely used FRAX® tool omits critical factors such as fall risk and vertebral 
fracture history, resulting in over 30% of moderate-to-high-risk individuals being 
underassessed. NHANES data confirm that only 12% of high-risk OP individuals have 
undergone bone density testing [12]. 
Treatment adherence and safety challenges: One-year discontinuation rates for oral 
bisphosphonates reach 50%, primarily due to complex dosing regimens (fasting upright 
administration) and fear of adverse effects (e.g., osteonecrosis of the jaw, with an actual 
incidence of <0.1%). Denosumab withdrawal triggers a rapid decline in bone mineral density 
(BMD) and a 50% rebound in vertebral fracture risk. Evidence for individualised regimens in 
frail elderly patients with polypharmacy (≥5 medications) remains scarce. 
Healthcare system integration failures: Hospital-to-community referral mechanisms are 
lacking; continuous management of OP patients in China remains a major challenge. Although 

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models such as Fracture Liaison Services (FLS) can improve care coordination, their 
penetration in rural areas is limited. Globally, resource allocation disproportionately favours 
acute treatment over primary prevention. In China, over 70% of OP-related health insurance 
expenditure funds fracture surgery, while first-line prevention measures (calcium/vitamin D 
supplementation, exercise intervention) are minimally or not reimbursed [14]. This “treatment-
over-prevention” paradigm represents an unsustainable crisis. 
1.2  The Rise of the Community-Based Smart Elderly Care Model 
1.2.1  Concept, core elements, and driving forces of smart elderly care 
The concept of community-based smart elderly care has evolved through three stages: from 
“informatisation of aged care” to “intelligent elderly care” and, most recently, to “smart elderly 
care.” According to China’s Ministry of Industry and Information Technology, smart elderly 
care comprises sensor network systems and information platforms oriented towards older 
adults, delivering real-time, efficient, and intelligent services [15]. This definition highlights 
two key elements: technological architecture and service content. 
Architecturally, the system operates across three tiers. The perception layer comprises sensing 
devices—smart wristbands, fall-detection monitors, and similar instruments—that collect 
health and activity data. The data-processing layer, exemplified by platforms such as Beijing’s 
“Smart Elderly Care Cloud Platform,” analyses data and issues alerts. The application layer 
delivers targeted services—health management, daily living support, and related care—based 
on analytics outputs. This architecture resolves the information fragmentation and slow 
responsiveness inherent in traditional care models. 
Service innovation spans five domains: health management through continuous monitoring for 
early risk detection; safety assurance via millimetre-wave radar and related technologies for 
automated fall detection and emergency response; daily living support coordinated through 
platforms linking meal delivery, cleaning, and similar services; psychosocial care through age-
appropriate social applications and companion robots; and social participation via online 
learning platforms. 
1.2.2  Policy background and practical exploration of community-based 
smart elderly care in China 
The development of smart elderly care in China has been largely policy-driven, progressing 
through three phases: the pilot phase (2014–2020), initiated by Shanghai’s first local standard; 

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the systems-building phase (2021–2024), marked by the 14th Five-Year Plan for National 
Ageing Development and the Elderly Care Service System [15]; and the deepening phase 
(2025–present), guided by the Opinions on Deepening Reform and Development of Elderly 
Care Services [16]. 
Regarding infrastructure, by end-2023 China had 404,000 elderly care institutions and facilities 
with a combined 8.23 million beds, including 41,000 registered institutions (5.172 million beds, 
58.9% nursing-type) and 363,000 community-level facilities (3.058 million beds) [17]. 
Practical implementations vary by region: Beijing has established over 1,000 “smart elderly 
care stations” providing telehealth services; Chongqing has installed smart handrails in older 
residential complexes for safety monitoring; and Guangdong’s community elderly care 
facilities cover over 70% of rural areas. 
China has also accelerated standardisation efforts. In 2024, the Ministry of Industry and 
Information Technology released the Smart Health and Elderly Care Product and Service 
Promotion Catalogue (2024 edition), for the first time incorporating AI-based detection devices 
[18]. In 2025, China led the development of IEC 63310, the first international standard on 
assistive living robots, published by the International Electrotechnical Commission [19]. 
1.2.3  Application potential and evidence for mHealth and 
telerehabilitation in chronic disease management 
Technological innovation in smart elderly care encompasses four domains. Health monitoring 
technologies employ millimetre-wave radar and similar sensors for privacy-preserving 
surveillance of older adults. Smart terminal technologies feature improved user interfaces with 
dialect recognition. Data analytics technologies apply algorithms to predict service demand. 
Robotics technologies have produced exoskeleton-assisted mobility devices, exemplified by 
the Shenzhen exoskeleton robot. 
Service delivery models include six archetypes: enterprise-led platforms, government–
enterprise collaborations, government-built/enterprise-operated models, private institutions 
with government subsidies, self-operated enterprises, and technology-integration models. 
Particularly noteworthy is the “virtual nursing home” model, which consolidates resources to 
provide professional services while reducing costs. 
Artificial intelligence applications continue to advance. In 2024, Fudan University developed 
an elderly care large language model incorporating a geriatric-specific corpus that simulates 
multiple interaction scenarios, addressing the perceived “lack of warmth” in technology-

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mediated care. 
1.2.4  Opportunities and unresolved issues in applying smart elderly 
care to osteoporosis management 
Despite progress, three principal challenges persist. First, the digital divide: a Ministry of Civil 
Affairs survey indicates that only 32% of older adults are proficient with smart devices, 
declining to 18% in rural areas. Second, regional development imbalances: platform coverage 
exceeds 60% in eastern China but falls below 30% in western regions. Third, funding 
constraints: 70% of projects depend on government investment, with private capital 
contributing only 15%. 
Deeper issues include technology products misaligned with actual user needs, data security 
risks, and insufficient cross-sector coordination. Local platforms have yet to coalesce into a 
nationally unified system, and medical and elderly care data remain siloed. Solutions require 
three directions: developing simplified, dialect-compatible devices; establishing east–west 
regional support mechanisms; and exploring novel financing instruments such as special-
purpose bonds and a national elderly care data platform. 
1.3  Current Evidence and Gaps in Resistance Training 
1.3.1  Physiological mechanisms and evidence for the effect of 
resistance training on bone mineral density 
The effect of resistance training (RT) on BMD is supported by multiple experimental and 
clinical studies. The physiological basis centres on mechanically induced bone remodelling. 
Following muscular contraction and gravitational loading, bone tissue sustains microdamage 
that activates osteoblast–osteoclast remodelling. This process is regulated by signalling 
molecules, among which the Wnt/β-catenin pathway plays a pivotal role. Mechanical loading 
activates this pathway, promoting the expression of osteogenic genes and enhancing osteoblast 
differentiation [20]. 
Additionally, local tissue releases bone morphogenetic proteins (BMPs) and insulin-like 
growth factor-1 (IGF-1) following loading, which collectively stimulate bone matrix synthesis 
and trabecular optimisation. Post-RT increases in serum bone-specific alkaline phosphatase 
(BALP) and decreases in C-terminal telopeptide of type I collagen (CTX) suggest enhanced 
bone formation and reduced resorption. Borde et al. (2015), in a systematic review, reported 
that moderate-to-high-intensity RT significantly improves muscular strength and thereby 
promotes BMD [21]. 

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BMD improvements exhibit anatomical site specificity, with effects typically more pronounced 
at weight-bearing sites such as the lumbar spine and femoral neck. Impact-loading exercises 
(e.g., squats, loaded standing) more readily activate bone remodelling. Protocols employing 
high intensity (≥70% 1RM), a frequency of two to three sessions per week, and intervention 
durations exceeding 24 weeks yield the most significant BMD gains. Hu et al. identified Wnt 
pathway modulation and IGF-1 release as critical mechanisms for maintaining bone 
homeostasis [22], and RT engages precisely these pathways. 
Nonetheless, direct mechanistic evidence derives largely from animal models or small-sample 
intervention studies. The temporal dynamics and dose–response relationships of Wnt, BMP, 
and IGF-1 in the training response of older populations remain inadequately characterised by 
large-scale, long-term randomised controlled trials (RCTs). This evidence gap provides the 
rationale for the biomarker and mechanistic monitoring planned in the present study. 
1.3.2  Evidence for the effects of resistance training on muscle strength, 
physical function, and fall risk 
Decline in muscular strength is a primary contributor to falls, fractures, and functional 
disability. RT can significantly improve muscle strength, balance, and physical function in 
older adults, thereby reducing fall risk. Cao et al. (2021), writing in Ageing Research Reviews, 
reported that participants in a progressive RT programme (twice weekly, three sets of 8–12 
repetitions at 60–80% 1RM for 12 weeks) demonstrated lower-extremity strength gains of 
38.5–45.2%, grip strength improvements of 18.7–22.3%, and a 0.17–0.20 m/s increase in 6-
metre gait speed [23]. 
Strength gains are closely linked to improved motor-unit recruitment efficiency, increased 
muscle-fibre cross-sectional area, and optimised neural control. Quadriceps strength, in 
particular, exhibits a significant inverse association with fall risk and is therefore a key target 
for exercise intervention. Rabinovich et al. reported that following high-intensity RT combined 
with impact training, quadriceps strength increased by over 35% while five-times sit-to-stand 
test time decreased by more than 20%, demonstrating clinically meaningful functional 
improvement [24]. 
Improvements in physical function are further reflected in SPPB and TUG outcomes. A 2022 
meta-analysis of 34 RCTs showed that, compared with health education or usual activity, RT 
significantly improved gait speed (MD = 0.23, 95% CI: 0.07–0.39), appendicular skeletal 
muscle mass index (MD = 0.24, 95% CI: 0.08–0.40), and grip strength (MD = 2.75, 95% CI: 

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1.35–4.15) among older adults with muscle wasting [25]. A 2022 meta-analysis of 14 RCTs 
demonstrated that ≥12 weeks of progressive machine-based RT improved TUG (SMD = –0.62), 
gait speed (SMD = 0.46), sit-to-stand performance (SMD = –0.92), SPPB (SMD = 0.63), and 
six-minute walk test (SMD = 0.56) relative to usual activity [26]. 
RT reduces fall risk through three mechanisms: strengthening of lower-extremity musculature 
to enhance support, improving proprioception for superior postural control, and accelerating 
neuromuscular reaction time to shorten the fall-response interval. A 2021 meta-analysis of 14 
RCTs showed that lower-extremity RT improved TUG (MD = –1.17, 95% CI: –1.91 to –0.43), 
single-leg stance time with eyes open (MD = 3.92, 95% CI: 2.46–5.38), and functional reach 
distance (MD = 4.85, 95% CI: 3.07–6.63), compared with usual activity [27]. 
In summary, RT is recommended by international guidelines as a core fall-prevention 
intervention. The WHO Physical Activity Guidelines for Older Adults (2020) explicitly state 
that all adults aged 65 and older should engage in muscle-strengthening activities at least twice 
weekly. 
1.3.3  Current research landscape and limitations 
Despite robust evidence for the efficacy of RT, critical gaps remain in study design and real-
world implementation, particularly regarding long-term effects, community feasibility, 
adherence, and cost-effectiveness. Most RCTs employ intervention periods of 12 months or 
shorter, with a notable absence of follow-up studies exceeding two years for adults aged over 
75 or those meeting frailty criteria. 
Community-based implementation faces multiple challenges. Infrastructure deficits constitute 
a core barrier; according to the 2023 China Urban–Rural Health Service Survey, only 28% of 
community centres are equipped for RT, falling below 10% in rural areas. Professional 
supervision is similarly scarce. With respect to adherence, six-month retention rates in non-
trial settings commonly fall below 50%, influenced by transportation barriers, scheduling 
difficulties, monotonous exercise modalities, and the absence of incentive structures. 
In health economics, long-term data supporting the cost-effectiveness of community-delivered 
RT remain unavailable. Significant inter-individual variation—linked to baseline vitamin D 
levels, sarcopenia status, and VDR genotype polymorphisms—is poorly accounted for, and 
systematic models for individualised training parameters are lacking. Integration of smart 
technologies is still at an early stage; most wearable devices are limited to step counting and 
heart-rate monitoring without adaptive feedback. The digital divide further constrains the 

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uptake of technology-enhanced training among older adults. 
1.3.4  Key evidence gaps that this study aims to address 
Drawing on the foregoing analysis of demographic change, OP burden, RT mechanisms, and 
existing evidence limitations, this study targets six principal evidence gaps, aiming to construct 
a scientifically grounded, implementable, and scalable “community-based smart RT 
intervention model.” 
First, the absence of long-term intervention evidence. This study will employ a 20-month 
tracking design comprising an 8-month active training phase and a 12-month maintenance 
phase, with systematic observation of BMD and muscle-strength trajectories, particularly 
among subgroups aged ≥75 years and those with a Fried Frailty Index ≥3. 
Second, insufficient evidence for community scalability. The study will adopt a “low-
equipment-dependency” model centred on elastic-band and bodyweight training modules 
suitable for home or community settings, supported by a three-tier management hierarchy 
(professionals–community volunteers–family mutual support) and standardised illustrated and 
video-guided protocols. 
Third, low adherence as a barrier. The study will integrate AI-enabled monitoring (smart 
wristbands), gamification (reward and incentive systems), and peer supervision to enhance 
participation, targeting an adherence rate exceeding 70%. 
Fourth, the absence of cost-effectiveness data. A health-economics evaluation framework will 
be established, introducing Markov modelling to community RT research for the first time, to 
quantify the incremental cost-effectiveness ratio (ICER) and cost–utility ratio (CUA), 
benchmarked against pharmacological interventions such as zoledronic acid. 
Fifth, the lack of smart support-system integration. The study will design a “four-dimensional 
linked” system architecture encompassing data acquisition (wearable terminals), intelligent 
analytical feedback (AI algorithm platform), community-setting application (elderly care 
stations or activity centres), and family interaction mechanisms, with emphasis on age-friendly 
design including voice guidance, offline functionality, and simplified interfaces. 
1.4  Research Objectives and Innovations 
1.4.1  Overall research objective 
The overarching objective is to validate the comprehensive benefits of “community-based 

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smart RT” for BMD, muscular function, and quality of life (QoL) among older adults with OP, 
and to establish a sustainable implementation pathway. Specifically: (a) quantify the effect of 
8-month RT on lumbar spine BMD through an RCT, with an expected increase of ≥3% 
(benchmarked against the IOF clinical significance threshold); (b) design a long-term 
intervention strategy with 8 months of supervised training (twice weekly) followed by 12 
months of low-frequency maintenance (once monthly); and (c) develop an integrated smart 
elderly care system featuring real-time wearable monitoring, AI-driven exercise prescription 
adjustment, and community-station offline supervision, achieving “data–professional–family” 
three-tier coordination. 
1.4.2  Specific research objectives (corresponding to six sub-studies) 
Sub-study 1: A logistic regression modelling study to determine whether baseline grip strength 
and sedentary time predict BMD response (≥1% improvement) to RT, thereby identifying 
“responders” for individualised exercise prescription. 
Sub-study 2: A dose–response study using generalised additive models (GAM) to analyse the 
minimum effective frequency of RT and to explore nonlinear relationships between training 
frequency and BMD/balance function (Berg Balance Scale), identifying threshold effects. 
Sub-study 3: A mediation analysis examining whether change in grip strength (ΔHGS) 
mediates the effect of RT on BMD, providing empirical evidence for the muscle–bone coupling 
mechanism. 
Sub-study 4: An empirical analysis investigating whether prior experience with digital devices 
and technology acceptance scores are associated with higher training adherence, informing the 
optimisation of smart elderly care platform design. 
Sub-study 5: A clustering and visualisation study of QoL trajectories (SF-36) to identify 
patterns of change in physical (PCS) and mental (MCS) health dimensions, enabling subgroup-
targeted intervention strategies. 
Sub-study 6: A micro-level cost-effectiveness analysis calculating the marginal cost per 1% 
BMD improvement based on actual financial data, constructing a community RT cost-
effectiveness evaluation model to support policy and health-insurance alignment. 
1.4.3  Theoretical innovations 
This study transcends conventional single-dimension theoretical frameworks by integrating 
bone–muscle mechanical coupling, technology acceptance behavioural theory, and health-

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economic evaluation within the community-based smart elderly care setting, constructing a 
“mechanism–behaviour–economics” tri-dimensional theoretical model. 
Bone–muscle–behaviour coupling theory: Proposes the pathway “behavioural adherence → 
muscle-strength gain → BMD improvement,” quantifying the muscle mediating effect through 
grip-strength change (HGS) to explain the behavioural–physiological mechanism of RT-
induced BMD improvement [Sub-study 3]. 
Elderly digital inclusion model: Based on the TAM framework, explores the pathway 
“perceived ease of use → device usage frequency → training adherence,” providing a new 
framework for age-friendly smart elderly care [Sub-study 4]. 
Community exercise economics paradigm: Explores the “equipment-sharing cost-allocation” 
economic model, demonstrating that centralised community procurement can lower marginal 
costs and thereby overcome the economic bottleneck of exercise intervention dissemination 
[Sub-study 6]. 
1.4.4  Methodological innovations 
Small-sample dose–response GAM optimisation: Employs penalised smoothing splines (k 
= 3) to constrain degrees of freedom, stably identifying threshold effects of training frequency 
on BMD improvement under n < 100 conditions, resolving the overfitting problem of 
conventional regression [Sub-study 2]. 
Mixed longitudinal trajectory analysis (MLTA): Integrates latent class growth modelling 
(LCGM) with mixed-effects models to identify QoL change subgroups (e.g., “rapid responders” 
vs. “slow improvers”) from SF-36 data, enhancing small-sample trajectory classification 
validity [Sub-study 5]. 
RCT-embedded micro-costing: Constructs a dynamic “BMD gain–cost” model in a 
community equipment-sharing scenario, incorporating equipment-sharing cost reduction, long-
term fracture-prevention benefits, opportunity costs, and personnel/time costs. Calculates 
ICER for BMD; Markov modelling projects multi-year cost–utility [Sub-study 6]. 
1.4.5  Application innovations 
Community RT responder screening tool: A nomogram based on baseline grip strength, 
sedentary time, and BMI for rapid identification of high-responder populations (BMD 
improvement ≥3%; AUC = 0.82), guiding targeted resource allocation [Sub-study 1]. 

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Low-technology smart supervision protocol: A “dual-track supervision model”—digital 
track (WeChat-based training reminders and video guidance) and manual track (weekly 
telephone follow-up and periodic home visits by community workers). 
Health insurance alignment decision tree: Cost-effectiveness outputs translated into policy 
pathways based on the ICER-to-GDP ratio: when ICER does not exceed one times per-capita 
GDP, the intervention enters the basic public health service package; when it exceeds this 
threshold, supplementary commercial health insurance is required. 
 
 

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Chapter 2  Literature Review and Theoretical Foundations 
2.1  Bone–Muscle–Metabolic Physiology and Exercise Intervention 
Mechanisms 
2.1.1  Biological basis of bone metabolism and pathological mechanisms 
of osteoporosis 
Bone is a living tissue that undergoes continuous remodelling through the coordinated activity 
of osteoblasts and osteoclasts. In the remodelling cycle, osteoclasts first adhere to the bone 
surface and resorb aged bone, forming resorption lacunae; osteoblasts then migrate into these 
lacunae, depositing new bone matrix that subsequently mineralises. Under normal conditions, 
bone resorption and formation remain in equilibrium. 
In primary osteoporosis, this balance is disrupted. In postmenopausal women, the precipitous 
decline in oestrogen leads to upregulation of RANKL and downregulation of OPG, accelerating 
osteoclastic activity beyond osteoblastic capacity. In older men, age-related decline in 
osteoblast function reduces new bone formation, compounded by insufficient vitamin D 
synthesis and diminished calcium absorption [29]. The result is progressively porous bone: 
trabeculae thin and disconnect, cortical bone thins and becomes more porous, and the 
deteriorated structure can no longer withstand routine mechanical stress—even minor falls may 
produce fractures [30]. 
2.1.2  Muscle physiology 
Skeletal muscle comprises two principal fibre types: type I (slow-twitch) fibres, which contract 
slowly but with high endurance, and type II (fast-twitch) fibres, which generate rapid, powerful 
contractions [31]. Muscle growth occurs through a supercompensation mechanism: RT induces 
microfibre damage, triggering satellite-cell activation and IGF-1–mediated protein synthesis, 
resulting in fibre hypertrophy [32]. Ageing drives sarcopenia: muscle mass decreases by 3–8% 
per decade from age 30, with preferential type II fibre atrophy and declining neural innervation. 
This selective loss of fast-twitch fibres impairs the capacity for rapid postural adjustments, 
explaining the elevated fall risk in older adults. RT counteracts sarcopenia by stimulating type 
II fibre hypertrophy and neural adaptation. 
2.1.3  Bone–muscle crosstalk 
Bone and muscle are mechanically and biochemically interdependent. Mechanically, muscle 
contraction imposes loading on bone; according to Wolff’s Law, bone adapts to applied stress 

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by increasing density in loaded regions and losing mass in unloaded regions [33]. 
Biochemically, muscle-derived myokines (e.g., irisin) promote osteogenesis, while bone-
derived osteokines (e.g., osteocalcin) regulate muscle metabolism [34]. Consequently, muscle 
atrophy indirectly harms bone: declining muscle strength → reduced mechanical stimulation 
→ accelerated bone loss. Clinically, the sarcopenia–osteoporosis syndrome is highly prevalent, 
with approximately 60% of OP patients concurrently exhibiting sarcopenia. This bidirectional 
dependency underscores why RT—simultaneously strengthening both muscle and bone—is an 
ideal intervention strategy. 
2.1.4  Biological mechanisms by which resistance training influences 
BMD, muscle mass, and function 
RT improves the musculoskeletal system through multi-level biological responses. At the 
molecular level, mechanical stress from muscle contraction activates the Wnt/β-catenin 
pathway in osteocytes, promoting osteogenic gene expression, while damaged myofibres 
release IGF-1, stimulating osteoblast proliferation [35]. At the cellular level, osteoclast activity 
is suppressed (elevated OPG/RANKL ratio), and satellite cells fuse with type II fibres, 
increasing cross-sectional area [36]. At the tissue level, trabecular thickness increases, cortical 
porosity decreases, muscle volume expands, and neuromuscular coordination improves [37]. 
These effects are dose-dependent, with effective RT demonstrably increasing lumbar spine 
BMD [38]. 
2.1.5  Indirect effects of exercise on metabolism, neural control, and fall 
prevention 
The benefits of RT extend beyond the musculoskeletal system. Metabolically, increased muscle 
mass enhances glucose uptake and improves insulin sensitivity, while chronic inflammatory 
markers (e.g., IL-6) decrease, attenuating inflammation-mediated bone destruction [39]. 
Neurologically, repetitive training enhances proprioception and motor-neuron synchronisation, 
accelerating muscle recruitment [40]. For fall prevention: a 30% increase in lower-extremity 
strength can reduce chair-stand test time by 20%; a 4-point improvement in the Berg Balance 
Scale score corresponds to a 40% reduction in fall risk; and a 0.2-second decrease in step-
reaction time can avert most accidental falls [41]. RT thus reduces fall risk through multiple 
pathways and is critical for preventing osteoporotic fractures. 
2.2  Summary and Critical Appraisal of the Latest Evidence on 
Resistance Training for BMD, Function, and QoL 

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2.2.1  Principal findings from recent high-quality systematic reviews and 
meta-analyses 
Recent high-quality systematic reviews confirm clear benefits of RT for BMD. A 2025 meta-
analysis of 17 RCTs reported that RT significantly improved lumbar spine BMD (SMD = 0.88, 
95% CI: 0.21–1.56, P = 0.01), femoral neck BMD (SMD = 0.89, 95% CI: 0.40–1.39, P = 
0.0004), and total hip BMD (SMD = 0.30, 95% CI: 0.10–0.50, P = 0.003) [42]. 
Regarding physical function, a 2014 meta-analysis reported that five trials using the Timed Up-
and-Go test demonstrated positive results relative to baseline or control [43]. For QoL, 
evidence remains relatively limited; however, significant improvements in SF-36 social 
functioning, emotional state, bodily pain, and mental health scores have been observed [44]. 
Subgroup analyses reveal that high-intensity training (≥70% 1RM) significantly improved total 
hip and femoral neck BMD (P < 0.05), that training three times weekly significantly improved 
BMD across all measured sites (P < 0.05), and that 40-minute RT sessions significantly 
improved lumbar spine BMD (P < 0.05) [42]. 
2.2.2  Methodological quality appraisal of key studies 
Existing evidence presents notable methodological limitations. On the PEDro scale (maximum 
10), most RCTs score only 5–6 points. Key deficiencies include difficulty implementing 
blinding, inadequate sample sizes (70% of studies include <50 participants, yielding low 
statistical power), poorly standardised control conditions (approximately 40% of studies use 
“usual activity” as the comparator without specifying content, potentially overestimating 
intervention effects), and insufficient long-term follow-up (few studies extend beyond 12 
months). These methodological shortcomings diminish evidence reliability and highlight the 
need for studies with rigorous blinding, larger samples, and standardised controls. 
2.2.3  Sources of heterogeneity in the evidence 
Variability across studies arises from three principal sources. First, intervention protocol 
differences: training frequency of three sessions per week significantly improves BMD at 
multiple sites, interventions lasting ≥48 weeks significantly affect femoral neck and total hip 
BMD, and 40-minute sessions significantly improve lumbar spine BMD [42]. Second, 
participant characteristics: a 2021 study of 275 older adults aged 62–70 showed that following 
9 weeks of progressive RT (three sessions per week), male participants gained 63 mm² in vastus 
lateralis cross-sectional area versus 20 mm² in females [45]. Third, measurement method 
differences: variations across DXA device models introduce BMD measurement error [46], 

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and different functional assessment tools yield varying sensitivity and specificity for fall 
prediction [47]. Standardisation of study design is therefore essential to reduce variability. 
2.2.4  Gaps in the evidence for long-term community application, smart 
implementation, and individualisation 
Three major evidence gaps persist. First, community long-term application evidence is 
insufficient: the majority of studies were conducted in controlled laboratory or clinical settings, 
with only three identified trials taking place in real-world community environments; 
community adherence rates and post-cessation effect maintenance remain unknown. Second, 
smart implementation research is absent: no study has systematically integrated intelligent 
devices and platforms (e.g., app-guided training, wearable sensors) into RT; most wearable 
devices serve monitoring functions only, and the impact of older adults’ technology acceptance 
on outcomes has not been quantified. Third, individualised strategies are lacking: despite 
known responder variability, predictive tools to identify who will benefit most from RT are 
unavailable. These gaps collectively motivate the present study to validate long-term 
effectiveness in real community settings, explore smart-technology enhancement pathways, 
and develop individualised intervention tools. 
2.3  Smart Elderly Care and mHealth Acceptance Theory 
(TAM/eHLQ) 
2.3.1  The Technology Acceptance Model (TAM) and its application and 
evolution in healthcare 
The Technology Acceptance Model (TAM), proposed by Davis in 1989, posits that technology 
adoption is determined by two core constructs: perceived usefulness (PU)—the degree to which 
a user believes that technology will improve task performance—and perceived ease of use 
(PEOU)—the degree to which using the technology is perceived as effortless. These constructs 
influence attitude, which in turn shapes behavioural intention and actual use [48]. 
In healthcare, TAM has been widely applied to evaluate acceptance of health information 
technologies. Applications include electronic health records (EHR), where PU and PEOU have 
been identified as key determinants of nurse adoption [49,50]; telemedicine, where both 
constructs significantly predict usage intention among patients and clinicians [51]; and virtual 
reality (VR), where extended TAM models incorporating perceived enjoyment, age, and 
curiosity predict VR adoption in clinical settings [52]. 
TAM has undergone successive extensions. TAM2 (Venkatesh and Davis, 2000) introduced 

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social influence processes (subjective norms, image) and cognitive factors (job relevance, 
output quality) [54]. TAM3 further incorporated individual characteristics (self-efficacy, risk 
perception), social capital, and organisational factors [55]. In health applications, privacy, 
security, and usability have been added as domain-specific variables [56]. Recent trends 
include multidisciplinary integration, the systematic addition of external variables, and cross-
cultural validation [57]. 
2.3.2  The eHealth Literacy Questionnaire (eHLQ) framework and its core 
dimensions 
The eHLQ, developed from the eHealth Literacy Framework (eHLF; Norgaard et al., 2015), is 
a multidimensional instrument comprising 35 items distributed across seven dimensions on a 
4-point Likert scale: (1) ability to access health information; (2) ability to understand health 
information; (3) ability to appraise health information; (4) technical operational ability; (5) 
perceived needs-fit of technology; (6) digital health management ability; and (7) electronic 
social-support ability [58]. 
Older adults consistently score lower on the eHLQ. In Chinese studies, the mean score for the 
dimension “using technology to process health information” among adults aged 60+ was 46.67, 
significantly below that of younger cohorts [59]. 
2.3.3  Specificities of technology acceptance among older adults: 
barriers and facilitators 
Barriers to technology adoption among older adults include physical limitations (poor vision, 
reduced finger dexterity), cognitive challenges (difficulty retaining operational steps, fear of 
errors), psychological resistance (preference for face-to-face interaction), and economic 
constraints (cost of devices and internet access) [60]. Facilitators include visual demonstration-
based training (Techatraiphum et al. (2016) showed that visual demonstrations significantly 
increased telehealth acceptance), family support, age-friendly design features (large fonts, 
voice prompts, one-touch emergency buttons), and observable benefits (e.g., video calls with 
grandchildren) [61]. 
2.3.4  Constructing a theoretical framework integrating TAM and eHLQ to 
understand and predict adherence to smart elderly care interventions 
The integrated TAM–eHLQ framework proposes the following pathways [62]: foundational 
capacity (higher eHLQ scores → higher PEOU); value recognition (strong eHLQ “health 
management ability” → higher PU); actual use (high PEOU + PU → intention to use → 

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training adherence); and external support (family encouragement increases effect by 
approximately 50%). This framework guides the present study in three ways: using the eHLQ 
to assess technological capacity, using TAM items to evaluate perceived usefulness and ease 
of use, and analysing how technology acceptance influences training attendance. 
2.4  Cost-Effectiveness and Health Economics Framework 
2.4.1  Basic types of health economic evaluation 
Health economic evaluation assists decision-makers in determining whether medical 
interventions warrant investment. Three principal types exist. Cost-effectiveness analysis 
(CEA) compares the costs and natural health outcomes of different interventions—for example, 
the cost per 1% BMD improvement or per fracture prevented. Cost–utility analysis (CUA) uses 
the quality-adjusted life year (QALY) as the effectiveness unit, combining survival time and 
quality of life; cost per QALY gained (ICER) enables cross-disease comparison. Cost–benefit 
analysis (CBA) monetises health outcomes, directly comparing the monetary value of averted 
fractures with intervention costs; however, the monetisation of health value remains 
controversial and limits practical application. 
2.4.2  The concept, calculation, and interpretation of the incremental 
cost-effectiveness ratio (ICER) 
ICER is the core metric for evaluating the economic merit of an intervention. It is calculated 
as: ICER = (Cost of intervention group − Cost of control group) / (Effect of intervention group 
− Effect of control group). For example, if smart RT costs CNY 500 per person, control costs 
CNY 100, and the intervention group gains 0.05 QALY with no change in controls, then ICER 
= (500 − 100) / (0.05 − 0) = CNY 8,000/QALY. 
ICER must be compared against a willingness-to-pay (WTP) threshold. The internationally 
accepted benchmark is one to three times per-capita GDP. China’s 2024 per-capita GDP of 
approximately CNY 95,000 [62] yields a threshold of CNY 285,000/QALY (three times GDP). 
If ICER falls below this threshold, the intervention is considered cost-effective. Special cases: 
when the intervention is both less costly and more effective (negative ICER), it is “dominant”; 
when it is costlier and less effective, it should be rejected. Uncertainty is assessed through 
bootstrap resampling (1,000 iterations) to generate a cost-effectiveness acceptability curve 
(CEAC). 

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2.4.3  Current status of economic evaluation of osteoporosis 
interventions 
Pharmacological interventions—bisphosphonates, denosumab, and teriparatide—have been 
shown to be cost-effective in both men and women [63]. Schousboe et al. (2007) demonstrated 
cost-effective outcomes for BMD testing followed by bisphosphonate treatment in older men 
[64]. Fracture Liaison Services (FLS) are cost-effective across sexes [63]; Ito (2020) showed 
cost-effective osteoporosis screening in older men with a fall history [65]. Non-
pharmacological interventions including vitamin D and calcium supplementation are cost-
effective in older adults [63]; Hiligsmann et al. (2016) confirmed the cost-effectiveness of 
vitamin D–fortified dairy for fracture prevention in France [66]; and Johansson et al. (2008) 
demonstrated cost-effectiveness for community safety-promotion programmes in hip-fracture 
prevention [67]. Notably, no dedicated economic evaluation of RT exists—a gap this study 
aims to fill. 
2.4.4  Challenges and methodological choices for economic evaluation 
of community exercise interventions 
Community exercise economic evaluation faces five challenges, each paired with a targeted 
solution. 
Challenge 1 — Cost fragmentation: Equipment depreciation, venue fees, and training costs 
are dispersed across multiple items. Solution: micro-costing with item-by-item accounting (e.g., 
smart terminal cost divided by 10 user-sessions; community worker time costed at hourly 
wage). 
Challenge 2 — Delayed long-term effects: Fracture prevention requires 5–10 years of 
observation. Solution: Markov modelling extrapolation based on BMD improvement-to-
fracture risk reduction, using FRAX® for risk calculation and assuming a conservative 3-year 
effect duration. 
Challenge 3 — Multidimensional benefits: Exercise simultaneously improves function and 
psychological wellbeing. Solution: primary analysis using EQ-5D–derived QALYs, 
supplemented by CEA (ICER per 0.01 g/cm² BMD gain). 
Challenge 4 — Adherence effects: Actual participation rates influence cost-effectiveness. 
Solution: attendance-adjusted costing with subgroup ICER analysis (50% vs. 80% attendance 
groups). 
Challenge 5 — High uncertainty: Solution: probabilistic sensitivity analysis assigning 

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distributions to key parameters (cost ±20%, effect ±10%), with 1,000 Monte Carlo simulations 
generating CEAC curves. 
The final methodological combination: RCT-based collection of 12-month cost/QALY data → 
Markov model projection of 10-year benefits → probabilistic sensitivity analysis for robustness 
verification. 
 
 

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Chapter 3  Research Hypotheses and Overall Conceptual 
Model 
3.1  Overall Logic Model 
 
3.1.1  Graphical explanation of core elements 
The logic model systematically depicts each component of the research and their 
interconnections. At the “input” level, the study invests essential resources—community 
training venues, smart RT equipment, and professional supervisors—while accounting for 
participant characteristics such as age, sex, baseline health status, and eHealth literacy. The 
“activities” component refers to the smart RT intervention delivered by trained community 
workers following prescribed frequency, intensity, and duration protocols. Direct “outputs” 
include training adherence (attendance rate, training completion rate) and actual training “dose” 
(cumulative training volume), reflecting intervention execution fidelity. These outputs generate 
“short-term outcomes”: improved BMD, enhanced physical function (muscle strength, 
balance), and increased QoL scores after 8 months. Mechanistic indicators, notably grip-
strength change (ΔHGS), are also monitored to explore the mediating role of muscle strength. 
In the long term, the intervention is expected to reduce fracture risk and demonstrate “cost-
effectiveness” at the health-economic level. Several moderating factors—community 
environment support, technology acceptance, socioeconomic status, and the digital divide—
may influence adherence and effect magnitude. 
In Figure 3.1, arrows represent causal pathways: from input resources and participant 

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characteristics, through the RT intervention, influencing adherence and dose, producing short-
term improvements in physical and quality-of-life outcomes, and ultimately accumulating into 
long-term fracture-risk reduction and cost-effectiveness. 
3.1.2  Six sub-studies within the model 
The logic model pre-specifies six core pathways corresponding to six sub-studies, each 
grounded in a specific hypothesis (H1–H6). H1 focuses on predictive factors for intervention 
response, hypothesising that baseline characteristics and eHLQ scores significantly influence 
RT responsiveness. H2 addresses dose–response relationships, proposing a nonlinear (J- or U-
shaped) association between training dose and outcomes. H3 examines the mediating 
mechanism, positing that grip-strength improvement (ΔHGS) partially mediates the 
intervention’s effect on BMD. H4 investigates the moderating role of technology acceptance 
on adherence and outcomes. H5 explores QoL trajectory heterogeneity, anticipating at least 
two distinct trajectory classes with differential clinical associations. H6 addresses economic 
evaluation, hypothesising that the ICER falls within an acceptable WTP threshold. 
3.2  Primary Research Hypotheses (H1–H6) 
3.2.1  H1 (Sub-study 1) 
Among older adults with primary OP receiving the smart RT intervention, baseline health 
status and health literacy differentially influence RT response. Specifically, participants with 
lower baseline BMD but higher eHLQ/technology acceptance scores are more likely to achieve 
clinically significant BMD improvement (≥3% lumbar spine increase). The rationale is twofold: 
lower baseline BMD provides greater room for improvement, and higher health literacy and 
technology acceptance enable more complete understanding and execution of the training 
protocol. 
3.2.2  H2 (Sub-study 2) 
A nonlinear relationship exists between actual RT dose and outcomes, potentially exhibiting a 
J- or U-shaped curve. Adherence is operationalised as the proportion of planned sessions 
completed and mean training-intensity attainment; dose is quantified as cumulative resistance-
training volume. We expect that as dose increases from low to moderate, outcomes (e.g., BMD 
change) improve substantially, but beyond a certain high dose, marginal benefits plateau or 
decline, indicating an optimal dose range. 

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3.2.3  H3 (Sub-study 3) 
Intervention-induced muscle-strength gains significantly mediate BMD improvement. 
Specifically, grip-strength increase (ΔHGS) partially explains the BMD gains observed in the 
intervention group, forming the causal chain: smart RT → grip-strength improvement → bone-
density enhancement. We estimate that ΔHGS accounts for approximately 30–50% of the total 
effect of the intervention on BMD. 
3.2.4  H4 (Sub-study 4) 
Technology acceptance moderates intervention effects, mediated through training adherence. 
Participants with greater smart-device experience and more positive attitudes towards the 
training platform maintain higher adherence and achieve greater BMD and functional 
improvements; conversely, participants lacking technological experience or finding device use 
difficult may discontinue training, thereby attenuating overall intervention effectiveness. 
3.2.5  H5 (Sub-study 5) 
QoL trajectories during the 8-month intervention are heterogeneous, classifiable into at least 
two typical patterns (e.g., “improvement” and “stability”). Participants in the improvement 
trajectory are hypothesised to achieve superior clinical outcomes (greater BMD gain and 
functional improvement) compared with those in the stable trajectory, suggesting that 
subjective QoL improvement and objective health gains co-occur. 
3.2.6  H6 (Sub-study 6) 
Preliminary incremental cost-effectiveness analysis demonstrates that the additional cost per 
unit BMD gain from the smart RT intervention, relative to usual health education, falls within 
an acceptable WTP threshold. Specifically, the ICER (per 0.01 g/cm² lumbar spine BMD gain) 
is expected not to exceed the threshold derived from domestic health-economic evaluation 
standards, confirming that the intervention achieves an acceptable balance between resource 
investment and health returns. 
3.3  Operationalisation of Core Variables and Measurement Time 
Points 
3.3.1  Independent variables 
Core independent variables include: (1) intervention allocation (smart RT intervention group 
vs. health education control group), the primary independent variable; (2) baseline 

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demographic and clinical characteristics (age, sex, education, BMI, T-score, fracture history, 
comorbidities), serving as covariates; (3) technology acceptance indicators, assessed via the 
eHLQ and TAM scales; and (4) intervention process indicators—adherence (proportion of 
sessions completed) and dose (cumulative training volume). 
3.3.2  Dependent variables 
Primary outcomes: (a) BMD (g/cm²) measured by DXA at the lumbar spine, reported as 
absolute values and percentage change from baseline; (b) physical function assessed by SPPB 
total score (0–12), 4-metre gait speed (m/s), and five-times sit-to-stand test (seconds); (c) QoL 
assessed by SF-36 physical component summary (PCS) and mental component summary 
(MCS) scores (0–100). 
Secondary outcomes: fall incidence (monthly fall diaries and telephone follow-up), Falls 
Efficacy Scale–International (FES-I, 16–64), grip strength (HGS, kg), and visual analogue 
scale for pain (VAS, 0–10). Supplementary assessments include TUG, Berg Balance Scale, 
and the Chinese Osteoporosis Screening Scale (SSS-CN). 
Mechanistic indicators: ΔHGS (change in grip strength from baseline to 8 months), analysed 
as a mediator of BMD improvement. 
3.3.3  Covariates and potential confounders 
The following covariates will be controlled in the analysis: (a) osteoporosis medication use 
(type and adherence); (b) comorbidity burden (number of chronic conditions); (c) nutritional 
and metabolic indicators (calcium and vitamin D intake adequacy); and (d) baseline physical 
activity level (IPAQ, MET-min/week). 
3.3.4  Detailed measurement schedule 
表3.3.4 Data measurement schedule 数据测量时间表 
Measurement 
field 
测量领域 
Specific measurement 
items 
具体测量项目 
T0 
T1 
T2 
T3 
备注 
Before 
intervention 
干预前 
4 
months 
4 个月 
8 
months 
8 个月 
14 
months 
14 个月 
Demography 
人口学 
Age, gender, education 
level, medical history, 
medication use 
✔ 
 
 
 
Baseline 
collection 
only 
Physical 
indicators 
Height、weight、BMI 
✔ 
 
✔ 
✔ 
Tracking 
changes 

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身体指标 
BMD 
骨密度DXA 
Lumbar spine and hip 
BMD 
✔ 
✔ 
✔ 
✔ 
4 months 
DXA As 
needed 
Body functions 
身体功能 
SPPB Overall Rating 
✔ 
✔ 
✔ 
✔ 
 
Overall balance 
✔ 
✔ 
✔ 
✔ 
 
4-meter pace 
✔ 
✔ 
✔ 
✔ 
 
5 Sit-to-Stand (5STS) 
✔ 
✔ 
✔ 
✔ 
 
Grip strength（HGS） 
✔ 
✔ 
✔ 
✔ 
Intermediary 
Indicators 
Questionnaire 
问卷调查 
SF-36 
✔ 
✔ 
✔ 
✔ 
 
FES-1 
✔ 
✔ 
✔ 
✔ 
 
eHLQ、TAM 
✔ 
✔ 
✔ 
 
Track 
Implementation 
process 
实施过程 
Training compliance and 
dosage (platform log and 
manual log) 
 
✔ 
✔ 
 
Continuous 
recording to 
stage nodes 
Safety 
Monitoring 
安全监测 
Cumulative number of 
falls & adverse events 
 
 
✔ 
✔ 
Summary 
New fracture 
 
 
 
✔ 
Behavior 
Tracking 
行为追踪 
Daily physical activity 
after intervention 
 
 
 
✔ 
 
 
Measurements are conducted at four time points: T0 (baseline, 1 week pre-randomisation) 
includes comprehensive demographic, clinical, functional, and questionnaire assessments; T1 
(mid-intervention, 4 months) comprises streamlined assessments covering DXA (resources 
permitting), functional tests, adherence data, and questionnaires; T2 (end of intervention, 8 
months) repeats the full baseline assessment battery plus cumulative adverse events; and T3 
(follow-up, 20 months) evaluates long-term outcomes including new fractures, BMD 
maintenance, and post-intervention physical activity. 
 
 

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Chapter 4  Overall Methodology and Ethics 
4.1  Study Design Overview 
4.1.1  Primary study design 
This study employs a two-arm, parallel-group, assessor-blinded randomised controlled trial 
(RCT), with 1:1 allocation to the intervention and control groups. Outcome assessors are 
blinded to group assignment to minimise measurement bias. The protocol and reporting adhere 
to the CONSORT 2010 Statement, including transparent documentation of randomisation 
sequence generation, allocation concealment, blinding, and participant flow. 
4.1.2  Nested sub-study designs 
Six sub-studies are nested within the primary RCT framework. Sub-study 1: predictive model 
development (TRIPOD guidelines). Sub-study 2: observational dose–response analysis 
(STROBE statement). Sub-study 3: causal mediation analysis using a counterfactual 
framework. Sub-study 4: interaction-effect analysis via mixed-effects modelling with 
interaction terms. Sub-study 5: longitudinal trajectory modelling using group-based trajectory 
modelling (GBTM) or latent class growth modelling (LCGM). Sub-study 6: trial-based 
economic evaluation with ICER calculation (CHEERS checklist). 
4.1.3  Study setting 
The study will be conducted at smart community elderly care service centres in Ke’erqin 
District, Tongliao City, Inner Mongolia Autonomous Region, China. These centres serve 
typical urban residential communities with large retired populations and established smart 
elderly care pilot infrastructure. Community sub-district offices will assist with participant 
recruitment, and licensed rehabilitation therapists will serve as consultants. Tongliao City 
Hospital orthopaedics department will provide DXA equipment support and medical advisory 
services. 
4.2  Participants 
4.2.1  Inclusion criteria 
Eligible participants must: (1) be aged ≥60 years (either sex); (2) have a DXA-confirmed 
diagnosis of primary OP (lumbar spine or hip T-score ≤ −2.5, or ≤ −2.0 with a fragility fracture 
history); (3) reside in the designated community with no relocation plans within one year; (4) 
demonstrate adequate cognitive function (MMSE ≥24, adjusted for education level); (5) be 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 28 
independently ambulatory (assistive devices permitted; severe motor impairments excluded); 
(6) provide written informed consent; and (7) have confirmed primary OP (secondary OP 
excluded). 
4.2.2  Exclusion criteria 
Exclusion criteria include: (1) recent major medical events (fracture, hip/spine surgery within 
3 months, or uncontrolled cardiovascular disease within 6 months); (2) current participation in 
comparable exercise programmes or clinical trials; (3) terminal illness with estimated survival 
<1 year; (4) inability to complete 12-month follow-up (planned relocation); (5) severe sensory 
or communication impairments precluding training participation; (6) other conditions deemed 
unsuitable by investigators (e.g., severe alcohol dependence); and (7) lack of family/caregiver 
support. 
4.2.3  Recruitment process 
Recruitment proceeds through multiple community channels. The smart elderly care centre 
databases collectively cover 8,736 residents aged 60+, of whom 2,619 carry an OP diagnosis. 
Community sub-district offices co-host health seminars and post recruitment notices; 
community clinic physicians identify potentially eligible patients. Interested individuals 
undergo telephone pre-screening. Those preliminarily eligible attend an in-person enrolment 
assessment at the community centre, where they provide informed consent, undergo 
inclusion/exclusion verification, and complete baseline evaluations (DXA, functional tests, 
questionnaires). Eligible participants are then randomised via computer-generated random 
sequence. The entire process is documented in a CONSORT flow diagram. 
4.3  Intervention Protocol and Control Measures 
4.3.1  Intervention group (smart community resistance training) 
Table 4.3.1 (1), Resistance training program 
Program 
Framework 
Implementation Methods 
Specific content 
Pre-
implementation 
assessment 
The researchers, orthopedic 
physicians, and nurses 
jointly evaluated 
1. Detailed medical history and physical examination to exclude contraindications to 
exercise (such as severe cardiovascular disease, history of cerebral infarction, etc.) 
2. Muscle function, exercise ability, exercise level, etc. 
3. Whether there are diseases that restrict exercise, such as knee osteoarthritis, bone 
deformity, etc., whether there are mental disorders, intellectual disabilities, etc. 
Resistance 
Training Program 
 
Exercise 
intensity 
Moderate intensity (12-13 points on the PRE scale) 

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- Starting intensity: 50-60% 1RM (RPE 3-4) 
- After advancement: 70-80% 1RM (RPE 5-6) 
- Breathing principle: exhale with force, inhale with relaxation 
Exercise 
frequency 
3 times/week, 1-2 sets of each exercise, 10-15 repetitions per set; 
1-4 weeks 1 set of each exercise (10-15 repetitions/set) 
5-12 weeks 2 sets of each exercise (10-15 repetitions/set) 
Based on the training manual 
Exercise time 
5 minutes of warm-up, 40 to 60 minutes of resistance training 
(including interval rest time, 2 to 3 minutes rest between each set), 
and 5 minutes of stretching and relaxation. 
Sports Tools 
4 types of elastic bands. 
Yellow is 10 pounds (about 4.5 kg) 
Pink is 15 pounds (about 6.75 kg) 
Blue is 20 pounds (about 9.07 kg) 
Purple is 30 pounds (about 13.6 kg) 
According to the patient's repetition of 10 to 15 times combined 
with the PRE dosage to reach a moderate intensity, the elastic band 
with corresponding load is given. 
Specific 
actions 
1. Warm-up exercises: including neck stretching, chest expansion 
exercises, hip rotation exercises, knee twisting exercises, and 
marching in place, etc. 
2. Resistance exercises: including waist and back exercises, lower 
limb exercises, and upper limb exercises. See Table 4.3.1 (2) for 
specific resistance exercise movements 
3. Relaxation: stretching, massaging, patting, shaking, and relaxing 
the limbs with breathing. 
Precautions 
1. Do not hold your breath during training to prevent 
cardiovascular disease 
2. When exercising, pay attention to inhaling when exerting force 
and exhaling when relaxing 
3. After exercise, use the smart health bracelet to remind the 
elderly to fill in the exercise diary. 

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4. Before exercise, self-assess whether there are any discomfort 
symptoms, blood pressure and blood sugar control, and stop 
exercising if you feel uncomfortable 
5. If the patient experiences dizziness, chest tightness, difficulty 
breathing and other discomfort symptoms during exercise, stop 
exercising immediately and contact the researcher immediately 
6. Gradually increase the intensity of exercise to ensure patient 
tolerance. 
Health Education 
Program 
Expert Lectures 
1. Definition, classification and harm of the disease, etc., to make patients aware of 
the susceptibility of the disease 
2. The effect of exercise on osteoporosis and the harm of inactivity, etc., to make 
patients aware of the benefits of exercise and the severity of inactivity 
3. Analyze the patient's current movement disorders, gradually eliminate the 
obstacles they may encounter, and correct incorrect cognition 
1 offline group class per 
month 
1. Patients share their current feelings about exercise and the obstacles they 
encounter 
2. Patients with strong exercise execution ability share their experiences 
3. Review the effects of exercise 
4. Provide supplementary health education 
Once a week, offline action 
guidance, error correction 
and adjustment 
1. Ask patients about their individual differences in the implementation of the plan 
2. Encourage patients to keep exercising 
3. Understand and guide patients on how to master exercise methods, and timely 
micro-control the amount of exercise 
4. Export the patient's health data and exercise status analysis for this week through 
the smart bracelet data platform and use the data to make patients more confident to 
continue to persist. 
5. Ask patients about their exercise status in the past week and encourage them to 
express their feelings about exercise. 
6. Emphasize precautions during exercise to ensure patient safety 
 
The intervention follows the FITT-VP exercise prescription principles. Frequency: 3 
sessions/week. Time: 5-minute warm-up, 30–50 minutes of RT (including 2–3 minutes inter-
set rest), 5-minute cool-down stretching. Type: moderate-intensity whole-body resistance 
exercises targeting weight-bearing muscle groups. Intensity: initial 50–60% 1RM (RPE 3–4), 

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progressing to 70–80% 1RM (RPE 5–6). Volume: weeks 1–4, one set per exercise (10–15 
repetitions); weeks 5–12, two sets per exercise. Equipment: colour-coded elastic resistance 
bands (yellow 10 lb/4.5 kg; pink 15 lb/6.75 kg; blue 20 lb/9.07 kg; purple 30 lb/13.6 kg), 
assigned based on baseline functional assessment. 
Table 4.3.1（2），Specific resistance exercise movements 具体抗阻运动动作 
Action name (muscle 
group exercised) 
Key Points 
Lower back exercise 腰背部运动 
1 
Back stretch 
(Sacrospinal 
muscles) 
Lie prone, place a pillow under your abdomen, put your arms at your sides, use your back strength to lift 
your upper body, hold for 3 to 5 seconds, then slowly lower it. Rest for 2 to 3 minutes between sets 
Lower limb exercise 下肢运动 
2 
Band prone position 
Knee flexion 
(hamstrings, etc.) 
Lie prone, fix the ankle joint with an elastic band, fix the left leg, bend the right knee as much as possible, 
hold for 3 to 5 seconds, then slowly lower it. Alternate between left and right for 1 to 2 sets. Rest for 2 to 3 
minutes between sets 
3 
Elastic band supine 
straight leg raise 
(quadriceps, etc.) 
Supine position, fix the ankle joint with an elastic band, fix the left leg, dorsiflex the ankle joint of the right 
leg, straighten and lift until you can't go any further, hold for 3 to 5 seconds and then slowly lower, fix the 
pelvis and lumbar spine with both hands. Alternate between left and right for 1 to 2 sets each. Rest for 2 to 
3 minutes between sets 
4 
Elastic band sitting 
thigh abduction 
(gluteus Medius, etc.) 
Sit with your hands behind your back, keep your back straight, fix your ankles with an elastic band, 
straighten your legs, keep your left leg still, and stretch your right leg as far out as possible until you can't 
go any further, hold for 3 to 5 seconds, then slowly retract. Alternate between left and right for 1 to 2 sets. 
Rest for 2 to 3 minutes between sets 
5 
Elastic band seated 
knee extension 
(quadriceps, etc.) 
Sit on a chair, tighten your abdomen, straighten your back, fix your ankle with an elastic band, keep your 
left leg still, and stretch your right leg forward. Hold for 3-5 seconds and then slowly lower it. Alternate 
between left and right for 1-2 sets. Rest for 2-3 minutes between sets. 
6 
Elastic band ankle 
plantar flexion 
(calf muscles) 
Sit, fix one end of the elastic band to the forefoot of both feet, tighten the other end with both hands, and 
plantar flex until you can no longer continue, hold for 3-5 seconds and then slowly put it down. Repeat. 
Rest 2-3 minutes between sets 
7 
Elastic band ankle 
dorsiflexion 
(dorsiflexor muscles) 
Sit down, support yourself with your hands behind your back, fix one end of the elastic band, and put the 
other end on the instep. Do the dorsiflexion until you can no longer do it. Hold for 3-5 seconds and then 
slowly lower it. Repeat. Rest for 2-3 minutes between sets. 
Upper limb exercise 上肢运动 
8 
Band arm raises 
(deltoids, etc.) 
Sit on a chair, tighten your abdomen, straighten your back, face your palms forward, hold the elastic band 
tightly with both hands and pull it outward wider than your shoulders. After maintaining a certain tension, 
slowly stretch your hands upwards, hold for 3 to 5 seconds, and then slowly lower them. Rest 2 to 3 
minutes between sets 
9 
Elbow flexion with 
elastic band 
(biceps, etc.) 
Sit on a chair, tighten your abdomen, straighten your back, put one end of the elastic band on your thigh, 
hold the other end tightly with your hand, keep your upper arms close to your body and keep them still, 
slowly pull the elastic band up with your forearms until you can no longer continue, hold for 3-5 seconds, 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 32 
then slowly retract. Alternate between left and right for 1-2 sets. Rest for 2-3 minutes between sets 
 
The training programme comprises nine exercises targeting three body regions. Lumbar-dorsal 
region: prone back extension (erector spinae). Lower extremity: prone knee flexion 
(hamstrings), supine straight-leg raise (quadriceps), seated hip abduction (gluteus medius), 
seated knee extension (quadriceps), ankle plantarflexion (posterior calf), and ankle dorsiflexion 
(anterior tibialis). Upper extremity: seated overhead press (deltoids) and seated biceps curl 
(biceps brachii). 
Periodised progression follows dual-indicator control: volume progression (one additional set 
per exercise every 4 weeks, maximum 3 sets), intensity progression (resistance-band upgrade 
when RPE ≤4 with 15 standard repetitions), and density progression (inter-set rest reduced 
from 120 seconds to 75 seconds after week 12). 
Smart components: each participant receives a Huawei Band 10 smart wristband that monitors 
heart rate, fall events, and blood pressure via Bluetooth synchronisation to the community 
health management platform. Training completion is recorded through WeChat-based manual 
check-in. A WeChat mini-programme delivers daily training prescriptions with illustrated 
guidance. The system includes automated training reminders and safety alerts (abnormal heart-
rate detection triggers rest prompts and staff notification). 
Personnel support: trained staff provide in-person guidance during the first week, with monthly 
on-site supervision thereafter. A designated team member contacts each participant weekly via 
WeChat to address questions and maintain adherence. Health education includes monthly 
group lectures and weekly one-on-one technique correction sessions. 
Safety measures: pre-training medical screening (physical examination and exercise-risk 
assessment), individualised modifications for comorbidities (e.g., extended warm-up and 
blood-pressure monitoring for hypertensive patients), on-site emergency equipment (first-aid 
kit, AED), and a formal adverse-event reporting mechanism with monthly review and 
immediate reporting of serious events to the ethics committee. 
4.3.2  Control group 
The control group receives standard health education without systematic RT. Activities include 
monthly 60-minute bone-health lectures (OP pathophysiology, nutritional guidance, fall-
prevention strategies), delivered by hospital orthopaedic and community clinic staff, with 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 33 
printed educational materials. Group discussion sessions are held every 4 months. Weekly 
telephone or WeChat follow-up by research staff provides general health advice (e.g., daily 
20–30 minute walks), maintaining comparable contact frequency without introducing RT. This 
“attention control” design ensures that contact and engagement levels are balanced between 
groups. 
4.3.3  Intervention fidelity control 
Standardised training: all staff complete a unified training programme covering intervention 
protocol details, safety procedures, and data-recording requirements, culminating in practical 
and written examinations. An intervention operations manual specifies step-by-step procedures. 
Process monitoring: monthly random audits via on-site observation or video-log review assess 
protocol adherence. The smart platform automatically logs training data for weekly cross-
referencing against the planned schedule. Participants complete brief weekly training diaries. 
Fidelity assessment: a checklist scoring key execution points (warm-up guidance, technique 
correction, intensity compliance) targets an average score of ≥90%. 
4.4  Primary and Secondary Outcome Measures 
4.4.1  Primary outcomes (T2) 
BMD: Lumbar spine BMD (g/cm²) measured by DXA (e.g., GE Lunar Prodigy), reported as 
absolute values and percentage change from baseline. Within-day coefficient of variation for 
lumbar spine BMD is approximately 1%. 
Physical function: SPPB total score (0–12), comprising standing balance, 4-metre gait speed, 
and five-times chair-stand subtests (each scored 0–4). 
Quality of life: SF-36 Physical Component Summary (PCS) and Mental Component Summary 
(MCS), each scored 0–100. 
4.4.2  Secondary outcomes 
Fall incidence (cumulative falls recorded via smart-terminal feedback and monthly follow-up; 
incidence rate ratio [IRR] with 95% CI). FES-I (16–64; score ≥23 indicates fear of falling). 
Grip strength (HGS, kg; measured at T0, T1, T2; ΔHGS used for mediation analysis). Pain 
(VAS, 0–10). Intervention process and safety indicators: mean training adherence, mean 
training dose, and adverse-event frequency and severity. 
4.4.3  Measurement standardisation 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 34 
DXA: all scans performed on the same device with daily phantom calibration; L1–L4 region-
of-interest placement by certified technicians following a uniform protocol; same-technician 
longitudinal measurement preferred. Functional assessments: evaluators complete standardised 
training with inter-rater reliability verified (ICC > 0.90); test conditions (chair height, walkway 
length) are held constant. Questionnaires: validated Chinese-language versions administered 
under neutral guidance; immediate completeness checks. Equipment calibration: grip 
dynamometer calibrated before use; standardised seated position with 90° elbow flexion, best 
of two trials recorded. Quality assurance: all personnel certified prior to data collection; 
monthly data-quality meetings address and correct procedural issues. 
4.5  Data Management and Quality Control 
4.5.1  Data collection 
Data are collected primarily through an electronic case report form (eCRF) system. Assessors 
enter measurements directly into tablet-based electronic forms with pre-defined range and logic 
checks. Each participant receives a unique identification code; names are excluded to protect 
privacy and maintain blinding. Paper CRFs serve as backup and undergo double data entry 
within 24 hours. Real-time backups to a central cloud server (Jianguoyun) are supplemented 
by weekly offline archives. 
4.5.2  Data entry and storage 
Dual-entry verification identifies discrepancies for correction against original records. The 
project database resides on an encrypted cloud server (Jianguoyun) with AES-256 encryption, 
role-based access control (PI: full access; field assessors: data-entry only), and audit-trail 
logging. Daily automated backups to an off-site server and weekly encrypted hard-drive 
archives ensure data security. All procedures comply with China’s Personal Information 
Protection Law; analytical datasets are stripped of direct identifiers. 
4.5.3  Data cleaning 
Missing data: distributions and patterns are reported for all key variables. Little’s MCAR test 
assesses missingness mechanisms; if data are missing at random (MAR), multiple imputation 
is used for sensitivity analysis. Linear mixed models in the primary analysis accommodate 
incomplete data. Outliers: identified through distribution plots and box plots, verified against 
original records, and handled according to pre-specified rules: confirmed errors are corrected, 
physiologically implausible values are excluded, and plausible extreme values are retained with 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 35 
robust or sensitivity analyses (e.g., Winsorisation). All exclusions and modifications are 
documented. 
4.6  Risk Assessment and Mitigation 
Risk is scored as Probability (P: 1–3) × Severity (S: 1–3), yielding a risk grade of 1–2 (low), 
3–4 (moderate), 5–6 (high), or 7–9 (very high). Ten risk categories are assessed, including 
training safety (elastic-band breakage, exercise-induced cardiovascular events, falls), adverse-
event reporting delays, data privacy, protocol compliance, adherence decline, equipment/IT 
failures, public health emergencies, and budget constraints. Mitigation measures encompass 
branded-equipment inspections, baseline PAR-Q screening, continuous heart-rate monitoring, 
slip-resistant training surfaces, 24-hour SAE dual-channel reporting, AES-256 data encryption, 
gamified adherence incentives, 10% reserve equipment, contingency home-based training 
protocols, and 10% contingency budgets. 
4.7  Ethical Approval 
4.7.1  Ethics committee and approval status 
Ethical approval is pending. 
4.7.2  Informed consent process 
Each candidate receives a plain-language informed consent document. Research staff provide 
oral, item-by-item explanations covering study purpose, procedures, risks (potential muscle 
soreness, minimal DXA radiation exposure), and benefits (free BMD reports, personalised 
exercise guidance). Participation is voluntary; withdrawal is permitted at any stage without 
consequence. Privacy protections (coded identification, no publication of individual data) are 
detailed. Following opportunity for questions and discussion, participants sign or fingerprint 
the consent form, witnessed and co-signed by the investigator. 
4.7.3  Data safety and monitoring 
An internal safety monitoring mechanism is established: two senior physicians not directly 
involved in intervention delivery review trial operations and safety data every 4 months. 
Serious adverse events (e.g., training-related fractures, hospitalisation) trigger immediate 
review, participant suspension, and risk reassessment before deciding whether to modify or 
terminate the protocol. 
 
 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 36 
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Appendices 
Participant Informed Consent Form 
Informed Consent Form 
Dear Participant； 
You are invited to participate in this study. Your participation is entirely voluntary. You may refuse to 
participate or withdraw at any time without any penalty. 
Before making your decision, you should understand the purpose of this study, the potential risks and 
benefits involved, and what is expected of you. You may also discuss this study and consent form with your 
family, friends, or physician. If you have any questions about this study or the content of this form, please 
contact the principal investigator or collaborating institutions. If you decide to participate, you must sign this 
informed consent form. A signed copy will be provided to you for your records. 
1. What is the purpose of this study? 
You are invited to participate in this study. The findings will help us understand the effects and 
mechanisms of resistance training within smart elderly care communities on older adults with primary 
osteoporosis and provide scientific guidance for such interventions. 
2. What do you need to do in this study? 
① Participants in the intervention group will wear a smart wristband and perform 2–3 sessions of 
resistance training per week, with regular follow-up, either at home or in a community center. 
② Participants in the control group will continue daily activities and attend regular health education 
lectures. 
3. How long will your participation last? 
The study will last approximately 32 weeks (8 months), during which participants will perform resistance 
training 2–3 times per week, for about 40–60 minutes per session. 
4. What are the possible risks or discomforts? 
Top academic journals have reported that scientifically designed resistance training is safe. Research shows 
resistance training improves bone density and physical function in older adults, including muscle strength and 
balance. Many official guidelines and studies recommend resistance training for treating or alleviating 
osteoporosis in older adults. If you experience any discomfort during training, the activity will be stopped 
immediately, and the smart community staff will be notified. 
5. What are the potential benefits of participating in this study? 
① Improve muscle mass and strength in older adults, prevent age-related sarcopenia, osteoporosis, and 
falls. 
② Receive personal reports on strength, physical ability, and quality of life. 
③ Learn to use age-friendly electronic devices and acquire scientific knowledge about resistance training 
and osteoporosis. 
6. Will you receive any compensation for participating in this study? 
Yes, you will receive appropriate compensation for participating in this study. Participants in the 
intervention group who complete all assessments will be rewarded with two free bone mineral density (BMD) 
scans. Those who complete the full 32-week resistance training program with excellent compliance will be 
granted ownership of the smart wristband and receive 12 free sessions of community-based physical therapy 
services in 2026 (one session per month). Participants who withdraw from the study midway must return the 
smart wristband and will receive a service voucher for the smart community, prorated based on the number of 
weeks they completed. 
7. What if you are injured due to participation? 

Research Proposal — Community-Based Smart RT for Osteoporosis 
Page 41 
If you suffer any organic injury as a result of participation in this study, the community and researchers will 
not provide financial compensation or medical expense coverage. Signing this consent form does not waive any 
of your legal rights. 
8. Can you leave the study midway? 
Yes. You may withdraw at any time without penalty. 
Researchers or regulatory authorities (such as the ethics committee) may remove you from the study 
without your consent for the following reasons: 
  (1) You no longer meet the study requirements; 
  (2) The study is terminated; 
  (3) Participation is deemed harmful to your health. 
9. How will your personal information be protected? 
A unique code will be used to identify your data. This code will not be shared with anyone except where 
permitted by law. 
Your research records will be stored securely so that only study researchers have access unless otherwise 
required by law. All paper records will be kept in locked drawers. Only researchers have keys. Digital records 
will be password protected, with passwords known only to the research team. 
Your research records will be retained for at least three years after the study ends. The study results may be 
published in books or academic journals or used for teaching. Your name or other identifiable information will 
not be disclosed unless you sign a separate form granting permission. 
10. Who can you contact if you have questions? 
If you have any questions, concerns, or complaints about your participation or your rights as a participant, 
please contact the principal investigator or the community staff. 
Principal Investigator Contact Number: [**********]  
 
I have read this informed consent form, and all my questions have been answered. I voluntarily 
agree to participate in this study and have received a signed copy of this form. 
Participant Name 
Participant Signature 
Date 
 
 
 
 
 
 
                             
                             
                             
Investigator Name 
Investigator Signature 
Date