Community Smart Aging Model - Resistance Training

Based on the Community Smart Aging Model: A Study on the Application of Resistance Training to Elderly People With Primary Osteoporosis

This clinical trial aims to evaluate the effectiveness of a smart community-based resistance training program in older adults aged 60 and older with primary osteoporosis. The main questions it aims to answer are:

Does the 32-week smart resistance training improve lumbar spine bone mineral density (BMD)? Does the training improve lower extremity physical function? Researchers will compare the smart resistance training group with a routine health education control group to see if the resistance training effectively improves bone health and physical capabilities.

Participants will:

Be randomly assigned to either the resistance training group or the health education group.

If in the training group: Wear a smart health bracelet and complete 40 to 60 minutes of elastic band resistance training 3 times a week for 32 weeks, with guidance from smart devices and community staff.

If in the health education group: Maintain usual daily activities and attend a monthly group health education lecture on osteoporosis.

Complete clinical assessments, including bone density scans (DXA), physical performance tests, and questionnaires at the start, at 16 weeks, and at 32 weeks.

Enter a 12-month observational follow-up phase after the 32-week intervention to evaluate the long-term sustainability of the outcomes.

Study Overview

• Status: Active, not recruiting
• Conditions: Primary Osteoporosis
• Intervention / Treatment: Behavioral: Smart Community-Based Resistance Training; Behavioral: Routine Health Education

Detailed Description

Primary osteoporosis presents a significant global public health burden. Resistance training effectively improves bone mineral density (BMD) and physical function in older adults. Traditional supervised training programs face practical barriers regarding spatial accessibility, temporal constraints, and long-term adherence. Mobile health (mHealth) and smart community facilities offer scalable solutions for home-based interventions. The clinical efficacy of high-intensity digital exercise prescriptions and the specific mechanistic role of technology acceptance require rigorous validation through randomized controlled trials.

This study is a single-center, single-blind, parallel-group randomized controlled trial. 98 older adults diagnosed with primary osteoporosis will be recruited from a smart elderly care community. Participants will be randomly assigned to either an intervention group or a control group. The study consists of a 32-week core intervention period followed by a 12-month observational follow-up phase to evaluate long-term effectiveness.

The intervention group will undergo a smart community-based resistance training program. Participants will complete 40 to 60 minutes of structured elastic band training 3 times a week. The protocol applies progressive overload, starting at 50-60% of 1-repetition maximum (1RM) and advancing to 70-80% 1RM. Smart health bracelets and mobile applications will deliver standardized video demonstrations, monitor real-time physiological metrics, and track attendance. Community staff holding fitness certifications will provide periodic offline coaching, error correction, and safety supervision.

The control group will receive routine care. Participants will maintain their usual daily activities and attend a monthly offline group seminar covering osteoporosis prevention, nutrition, and fall prevention strategies.

The primary objective is to evaluate longitudinal changes in lumbar spine BMD and physical performance, measured by the Short Physical Performance Battery (SPPB), at 16 and 32 weeks. Secondary objectives include assessing upper limb handgrip strength, health-related quality of life (SF-36), and dimensions of technology acceptance during the intervention and the subsequent 12-month follow-up period. The study will utilize mediation models and machine learning frameworks to explore whether baseline technology acceptance directly influences clinical outcomes or serves as an antecedent driving initial adherence.

• Study Type: Interventional
• Enrollment (Actual): 98
• Phase: Not Applicable

Contacts and Locations

Study Locations

• China
  • Inner Mongolia
    • Tongliao, Inner Mongolia, China, 028000
      • Inner Mongolia Changxiao Smart Elderly Care Service Co., Ltd.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Aged 60 years or older.
• Diagnosed with primary osteoporosis via DXA scan (lumbar spine or hip T-score ≤ -2.5, or ≤ -2.0 with a fragility fracture history).
• Possess basic smartphone operation skills. Normal basic cognitive ability (MMSE score ≥ 24) and capable of independent walking.
• Reside in the designated community without relocation plans within the next year.
• Voluntarily sign the informed consent form.

Exclusion Criteria:

• Diagnosed with secondary osteoporosis.
• Recent major medical events (e.g., fractures or hip/spine surgery within the past 3 months) or uncontrolled severe cardiovascular diseases.
• Completely unable to operate smart devices due to severe sensory or communication impairments.
• Currently participating in similar exercise interventions or clinical trials.
• Terminal illness or life expectancy of less than 1 year.
• Lack of support or consent from children or guardians.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Smart Community-based Resistance Training Group; Participants in this arm will receive a 32-week high-intensity resistance training program. The training consists of 9 specific exercises using elastic bands, covering major muscle groups of the spine and limbs. Frequency is 3 sessions per week, with each session lasting 40-60 minutes. Intensity starts at 50-60% 1RM and progresses to 70-80% 1RM. Smart health bracelets and mobile apps are used for real-time monitoring and adherence tracking. After the 32-week intervention, participants will enter a 12-month observational follow-up period	Behavioral: Smart Community-Based Resistance Training; Participants complete a 32-week resistance training program using elastic bands. Sessions occur 3 times per week, lasting 40-60 minutes each. The training intensity progressively increases from 50-60% of 1RM to 70-80% of 1RM. A smart health bracelet and mHealth application monitor heart rate and adherence in real-time.
Active Comparator: Routine Health Education Control Group; Participants in this arm will maintain their usual daily activities without structured resistance training. They will attend one offline group health education seminar per month. Topics include osteoporosis prevention, nutrition (calcium/Vitamin D intake), and fall prevention strategies. After the 32-week study period, they will also participate in the 12-month observational follow-up phase.	Behavioral: Routine Health Education; Participants maintain their usual daily activities without structured resistance training. Participants attend monthly 60-minute group health education lectures. The lectures cover osteoporosis knowledge, physical activity benefits, and fall prevention strategies.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Lumbar Spine Bone Mineral Density (BMD)	Measured by Dual-energy X-ray Absorptiometry (DXA). The absolute BMD value is reported in g/cm².	Baseline ，16 weeks ，32 weeks and Follow-up ended (12 months after the 32-week intervention).
Short Physical Performance Battery (SPPB) Score	The Short Physical Performance Battery (SPPB) assesses lower extremity function through three tests: static balance, 4-meter gait speed, and 5-time chair stand. The total score ranges from a minimum of 0 to a maximum of 12. Higher scores indicate better physical function and lower fall risk.	Baseline, 16 weeks, 32 weeks and Follow-up ended (12 months after the 32-week intervention).

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Handgrip Strength (HGS)	Maximum isometric muscle strength of the dominant hand measured by an electronic hand dynamometer. The value is reported in kilograms (kg).	Baseline, 16 weeks, 32 weeks and Follow-up ended (12 months after the 32-week intervention).
Health-Related Quality of Life (SF-36)	The 36-Item Short Form Health Survey (SF-36) assesses eight dimensions of health. The individual dimension scores are aggregated and converted to a scale ranging from a minimum of 0 to a maximum of 100. Higher scores indicate better health-related quality of life.	Baseline, 16 weeks, 32 weeks and Follow-up ended (12 months after the 32-week intervention).
Technology Acceptance Model (TAM) Score	The Technology Acceptance Model (TAM) questionnaire assesses perceived usefulness and perceived ease of use using 10 items. Each item is scored on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The total score ranges from a minimum of 10 to a maximum of 70. Higher scores indicate stronger technology acceptance.	Baseline, 16 weeks, and 32 weeks
eHealth Literacy Scale (eHEALS) Score	The eHealth Literacy Scale (eHEALS) assesses the ability to seek, understand, and apply digital health information using 8 items. Each item is scored on a 5-point Likert scale. The total score ranges from a minimum of 8 to a maximum of 40. Higher scores indicate higher levels of perceived eHealth literacy.	Baseline and 32 weeks

Collaborators and Investigators

• Sponsor: Lingshu Sun

Publications and helpful links

General Publications

• Outcomes & Methodology 22.Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85-M94. 23.Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473-483. 24.Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31. 25.CONSORT Group. CONSORT 2025 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials. London: BMJ Publishing Group; 2025. 26.Camacho PM, Petak SM, Binkley N, et al. American Association of Clinical Endocrinologists/American College of Endocrinology clinical practice guidelines for the diagnosis and treatment of postmenopausal osteoporosis-2020 update. Endocr Pract. 2020;26(Suppl 1):1-46. 27.Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-191. 28.Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. 29.Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58(1):267-288. 30.Li N, Beaudart C, Cauley JA, et al. Cost-effectiveness analyses of interventions for osteoporosis in men: a systematic literature review. PharmacoEconomics. 2023;41(4):363-391.
• Technology Acceptance & mHealth 16.Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-340. 17.Kayser L, Karnoe A, Furstrand D, et al. A multidimensional tool based on the eHealth literacy framework: development and initial validity testing of the eHealth Literacy Questionnaire (eHLQ). J Med Internet Res. 2018;20(2):e36. 18.Norman CD, Skinner HA. eHEALS: the eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. 19.Ferrari L, Bochicchio G, Bottari A, et al. Feasibility and effectiveness of a 6-month, home-based, resistance exercise delivered by a remote technological solution in healthy older adults. Arch Gerontol Geriatr. 2024;127:105559. 20.Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci. 2000;46(2):186-204. 21.Deka P, Salahshurian E, Ng T, et al. Use of mHealth technology for improving exercise adherence in patients with heart failure: systematic review. J Med Internet Res. 2025;27:e54524.
• RT Mechanisms & Evidence 8.Hong AR, Kim SW. Effects of resistance exercise on bone health. Endocrinol Metab (Seoul). 2018;33(4):435-444. 9.Borde R, Hortobágyi T, Granacher U. Dose-response relationships of resistance training in healthy old adults: a systematic review and meta-analysis. Sports Med. 2015;45(12):1693-1720. 10.Zhao F, Su W, Sun Y, et al. Optimal resistance training parameters for improving bone mineral density in postmenopausal women: a systematic review and meta-analysis. J Orthop Surg Res. 2025;20(1):523. 11.Hu L, Chen W, Qian A, Li Y. Wnt/β-catenin signaling components and mechanisms in bone formation, homeostasis, and disease. Bone Res. 2024;12(1):1-33. 12.Mende E, Moennig N, Schaller N, et al. Progressive machine-based resistance training for prevention and treatment of sarcopenia in the oldest old: a systematic review and meta-analysis. Exp Gerontol. 2022;163:111767. 13.Liu CJ, Shiroy DM, Jones LY, Doerfler ET. Systematic review of functional training on muscle strength, physical functioning, and activities of daily living in older adults. Eur Rev Aging Phys Act. 2014;11(2):95-106. 14.Sadaqa M, Németh Z, Makai A, et al. Effectiveness of exercise interventions on fall prevention in ambulatory community-dwelling older adults: a systematic review with narrative synthesis. Front Public Health. 2023;11:1209319. 15.Forwood MR, Turner CH. Skeletal adaptations to mechanical usage: results from tibial loading studies in rats. Bone. 1995;17(4 Suppl):197S-205S.
• Epidemiology & Burden 1.Salari N, Darvishi N, Bartina Y, et al. Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16(1):669. 2.Sing CW, et al. Global epidemiology of hip fractures: secular trends in incidence rate, post-fracture treatment, and all-cause mortality. J Bone Miner Res. 2023;38(8):1064-1075. 3.Yin Y, Wang Y, Wang X, et al. Prevalence rate of primary osteoporosis in China: a meta-analysis. BMC Public Health. 2024;24(1):1518. 4.Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006;17(12):1726-1733. 5.National Bureau of Statistics of China. Communiqué of the Seventh National Population Census (No. 5). Published May 11, 2021. 6.World Health Organization. WHO Integrated Care for Older People (ICOPE): Guidance for Person-Centred Assessment and Pathways in Primary Care. Geneva: World Health Organization; 2023. 7.Shen Y, Huang X, Wu J, et al. The global burden of osteoporosis, low bone mass, and its related fracture in 204 countries and territories, 1990-2019. Front Endocrinol (Lausanne). 2022;13:882241.

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2025
• Primary Completion (Actual): March 23, 2026
• Study Completion (Estimated): April 1, 2027

Study Registration Dates

• First Submitted: March 26, 2026
• First Submitted That Met QC Criteria: March 26, 2026
• First Posted (Actual): April 1, 2026

Study Record Updates

• Last Update Posted (Actual): April 7, 2026
• Last Update Submitted That Met QC Criteria: April 1, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Older Adults; Physical Function; mHealth; Resistance Training; SF-36; Technology Acceptance; Primary Osteoporosis; Smart Community; Bone Mineral Density / BMD
• Other Study ID Numbers: UVIC-SLS-2024

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: Data sharing is subject to approval by the local Civil Affairs Bureau and the project's cooperating community center. Since the study is integrated into a government-supported community elderly care pilot, individual participant data (IPD) can only be shared after ensuring full compliance with local administrative regulations and obtaining explicit authorization from the relevant authorities. A final decision will be made upon the completion of the project and internal review.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No