Comparative efficacy of Nine Non-Pharmacological Interventions for Fall Prevention in Older Adults: A Systematic Review and Network Meta-Analysis

Comparative efficacy of Nine Non-Pharmacological Interventions for Fall Prevention in Older Adults: A Systematic Review and Network Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative efficacy of Nine Non-Pharmacological Interventions for Fall Prevention in Older Adults: A Systematic Review and Network Meta-Analysis Zhiyuan Sun, Ming Gao, Deiwei Mao, Xuewen Tian, Qinghui Shang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6880677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Non-pharmacological interventions (NPIs), due to their high safety profile, cost-effectiveness, and ease of implementation, have become a research focus for preventing falls among older persons. This study aims to systematically evaluate the differential preventive effects of various NPI strategies, providing evidence-based guidance to optimize clinical practice and public health policy. Methods: A systematic search was conducted in PubMed, Web of Science, Embase, and the Cochrane Library for English-language randomized controlled trials (RCTs) published up to March 10, 2025. Bayesian network meta-analysis was performed using a random-effects model with R version 4.4.3 and Stata 16.0 software. Heterogeneity was assessed using the I² statistic. Publication bias was evaluated through funnel plots combined with Begg’s and Egger’s tests. The effect sizes were reported as odds ratios (OR) with 95% confidence intervals (CIs). Model consistency was verified using node-splitting analysis. Results: A total of 45 RCTs (n = 17,671) were included. Between-study heterogeneity was low (I² = 17%). Network meta-analysis showed that compared to Usual, Exer+Cog (OR = 0.64, 95% CI: 0.34–0.84), MBE (OR = 0.64, 95% CI: 0.47–0.87), and Education (OR = 0.65, 95% CI: 0.45–0.90) demonstrated superior fall prevention effects. Subgroup analyses revealed: 1) Temporal effects: within intervention periods ≤4 months, Exer+Cog showed the best effect (OR = 0.34, 95% CI: 0.09–0.81), while MBE was significantly effective during the 4–8 month period (OR = 0.41, 95% CI: 0.23–0.73); 2) Gender specificity: in populations with 50–80% female participants, Exer+Cog (OR = 0.50, 95% CI: 0.27–0.81) and Education (OR = 0.62, 95% CI: 0.38–0.97) showed more pronounced effects. Conclusion: Exer+Cog may represent the optimal strategy for preventing falls in older adults, while MBE and Education can serve as effective alternative interventions. It is recommended that individualized fall prevention programs be developed based on the availability of resources and the characteristics of the population. Future research should focus on optimizing intervention dosage and long-term benefits. Falls Non-Pharmacological Interventions Network Meta-Analysis Older Adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Globally, falls have become a significant public health concern. According to the 2023 report by the World Health Organization (WHO), falls are the second leading cause of unintentional injury-related deaths, accounting for approximately 684,000 fatalities annually. Notably, over 80% of these deaths occur in low- and middle-income countries [ 1 ]. Older adults represent the population most significantly affected by this risk. Epidemiological studies indicate that approximately 25% of community-dwelling older adults experience at least one fall annually, with 20–30% of these individuals sustaining moderate to severe injuries, such as hip fractures or traumatic brain injuries. Each year, falls directly result in an estimated 3 million emergency department visits and 800,000 hospitalizations [ 2 ]. Notably, both the incidence of falls and the severity of their associated complications show a significant positive correlation with advancing age. The risk is markedly elevated among older adults with functional impairments, where the likelihood of falling and experiencing adverse outcomes increases substantially [ 3 ]. According to data from the U.S. Centers for Disease Control and Prevention (CDC) in 2020, annual healthcare expenditures related to falls exceeded $ 50 billion. Of this total, direct medical costs—including hospitalizations and outpatient visits—accounted for 4.4%, while expenditures on long-term care comprised as much as 11.8%[ 4 ]. This health threat not only impairs the physical functioning and psychological well-being of older adults but also exacerbates the burden on families and society through a vicious cycle of disability and caregiving dependency. This underscores the urgent need for the implementation of effective interventions. Conventional fall prevention strategies have primarily relied on pharmacological treatments, such as vitamin D supplementation, and surgical interventions, including joint replacement. However, their clinical application is facing significant challenges due to limitations such as adverse drug effects (e.g., an increased risk of hypercalcemia) and the relatively low cost-effectiveness of surgical procedures [ 5 , 6 ]. Against this backdrop, non-pharmacological interventions (NPIs) have increasingly become a focus of research due to their advantages in safety, cost-effectiveness, and ease of implementation. Current NPIs primarily exert their effects through three mechanisms: (1) exercise training to enhance muscle strength and balance function; (2) cognitive interventions to improve risk perception and decision-making abilities; and (3) health education to strengthen adherence to preventive behaviors [ 7 ]. Although numerous randomized controlled trials (RCTs) have confirmed the efficacy of NPIs in fall prevention, the optimal choice of interventions remains contentious. A network meta-analysis by Cheng et al. (2023) demonstrated that traditional Chinese exercises, such as Tai Chi, effectively enhance lower limb muscle strength in older adults, improve their balance, and reduce the risk of falls [ 8 ]; An updated systematic review by Devasahayam et al. (2023) indicated that balance training yields a significant intervention effect, with a fall risk ratio of 0.76 (95% CI = 0.63–0.92) [ 9 ]. It is important to note that the existing body of evidence has significant limitations: first, traditional meta-analyses are often restricted to pairwise comparisons (e.g., exercise versus cognitive training), which cannot address the fragmentation of evidence across multiple interventions; second, key reviews—such as the study by Dautzenberg et al. (2021)—still include mixed interventions involving pharmacological and surgical treatments, resulting in the dilution of evidence specific to NPIs [ 10 ] . Network meta-analysis (NMA) integrates both direct and indirect comparison evidence, enabling the ranking and selection of multiple interventions within a Bayesian framework. Compared to frequentist methods, Bayesian NMA offers three major advantages: (1) it allows inclusion of studies with different control designs; (2) it provides probabilistic conclusions regarding the relative efficacy of interventions; and (3) it incorporates historical evidence through the use of prior distributions [ 11 ]. This study employs a Bayesian random-effects model to systematically evaluate the differential fall prevention effects of nine categories of NPIs, aiming to address three key questions: (1) Are there statistically significant differences in efficacy among various NPIs? (2) Are intervention effects moderated by factors such as duration and population characteristics? (3) How can an evidence-based prioritization framework for interventions be constructed? The findings are expected to provide a decision-making basis for clinical guideline development and the optimization of public health resource allocation. Methods This study was conducted by the PRISMA guidelines for network meta-analyses [ 12 ]. It has been registered on the PROSPERO platform (Registration Number: CRD42023486881), with ongoing real-time updates provided on the progress of the research. Search strategy A systematic search was conducted across PubMed, Web of Science, Embase, and Cochrane Library databases, covering the period from database inception to March 10, 2025, to identify studies evaluating the effectiveness of NPIs for fall prevention in older adults. The search strategy combined both Medical Subject Headings (MeSH) and free-text terms, primarily including keywords related to the study population (e.g., “older adults,” “elderly,” “aging”), intervention measures (e.g., “education,” “exercise,” “cognitive”), and outcomes (e.g., “falls,” “fall prevention”). A combination of MeSH and text words was used for the search (Table S1 ). Additionally, reference lists of included studies and previous relevant research were manually searched to maximize the comprehensiveness of study inclusion. To further ensure completeness, reference lists from related systematic reviews and meta-analyses were also examined and supplemented by manual searching to capture potentially overlooked key studies. All retrieved literature was independently screened by two researchers, and studies meeting the inclusion criteria were selected for meta-analysis. Inclusion and Exclusion Criteria Inclusion criteria: This study includes populations with impaired balance control and an increased risk of falls: (1) generally healthy older adults aged 60 years or above, as well as individuals with neurological diseases, chronic obstructive pulmonary disease (COPD), lower limb amputation or post-joint replacement status, or other individuals with clearly identified fall risk factors. Regarding interventions: (2) NPIs for fall prevention applicable in all settings (e.g., community, acute care) were included, which could be single-factor or multifactorial measures, but pharmacological treatments were excluded. Regarding control groups: (3) eligible control measures included usual care, placebo, no treatment, or other NPIs. Primary outcomes: (4) the main outcome was the number of falls. (5) In terms of study design, only RCTs published in English were included, including cluster RCTs and crossover RCTs. Exclusion criteria: The exclusion criteria of this study included the following: (1) animal or cellular experiments, case reports, study protocols, reviews, and editorials; (2) studies with unavailable full texts; (3) studies with unextractable outcome data; (4) studies combining other therapies such as nutritional support, hormones, or other medications; (5) when multiple studies used the same population, those with smaller sample sizes were excluded; (6) duplicate publications. Data extraction Two researchers will independently extract data from studies meeting the inclusion criteria and perform cross-checking during the extraction process. Any disagreements will be resolved through discussion or consultation with a third-party expert. The extracted data will include the following: (1) basic study information: first author’s name, year of publication, and country of study; (2) study population: sample size, mean age, and gender distribution; (3) intervention details: type of intervention (single-factor or multifactorial), mode of intervention, duration, and setting (e.g., community, hospital); (4) control measures: type of control intervention (usual care, placebo, no treatment, or other NPIs); (5) study outcomes: number of fallers and related data; (6) other key variables that may affect study results, such as adherence and loss to follow-up. All extracted data will be entered into Excel spreadsheets for subsequent analysis. Risk of bias assessment Two researchers independently assessed the quality of the included studies using the Cochrane Risk of Bias tool for randomized trials (RoB 2)[ 13 ]. Any disagreements were resolved through discussion with a third researcher. The RoB 2 tool assessment includes five domains, which are: (D1a) Randomisation process、(D1b) Timing of identification or recruitment of participants、(D2) Deviations from the intended interventions、(D3) Missing outcome data、(D4) Measurement of the outcome、(D5) Selection of the reported result. Each domain is rated as “low risk,” “high risk,” or “some concerns.” Statistical analysis This study was conducted using R version 4.4.3 and Stata 16.0. A Bayesian framework was adopted to construct a random-effects network meta-analysis model. Within the R 4.4.3 environment, the GeMTC 0.8.2 package was combined with JAGS 4.3.0 to build the random-effects Bayesian network model. Parameter estimation was performed using the Markov Chain Monte Carlo (MCMC) algorithm [ 14 , 15 ], with four independent Markov chains run. n initial 10,000 adaptive iterations were conducted to reduce sensitivity to initial values, followed by 50,000 iterations for effective sampling. Convergence was assessed using the Gelman-Rubin statistic, with convergence confirmed when the statistic was below 1.01. Local inconsistency was assessed using the node-splitting method [ 16 ]. A p-value less than 0.05 from the node-splitting test indicates the presence of local inconsistency. Simultaneously, heterogeneity was quantified using a generalized linear mixed model, with the I² statistic used to classify heterogeneity levels as follows: 75% considerable heterogeneity [ 17 ]. Effect sizes are presented as odds ratios (OR) with corresponding 95% Bayesian credible interval (CI). A difference is considered statistically significant if the 95% CI does not include “1.” To facilitate comparison of interventions, league tables are plotted for intuitive visualization. The Surface Under the Cumulative Ranking curve (SUCRA) method was used to quantitatively estimate the effects of different interventions, rank their effectiveness, and classify them into categories. Interventions were ranked based on their SUCRA values, which represent the percentage efficacy or safety of an intervention relative to an “ideal best intervention.” SUCRA values range from 0 to 100%, with values closer to 100% indicating superior intervention effects [ 18 ] . Additionally, this study conducted sensitivity analyses by excluding studies with a high risk of bias and performed subgroup analyses stratified by gender and intervention duration. Publication bias was assessed using funnel plots, where asymmetrical distribution of points may indicate potential bias. Quantitative evaluation of publication bias was further conducted using Begg’s and Egger’s tests. The results were presented through a triad of visualization tools: (1) network evidence plots illustrating the evidence structure; (2) forest plot matrices displaying effect differences; and (3) cumulative ranking probability plots interpreting the SUCRA rankings. Results Study search and characteristics An initial total of 5,943 records were identified through systematic database searches. After removing duplicates, 3,150 records remained. Based on title and abstract screening, 2,712 irrelevant studies were excluded, resulting in 438 articles undergoing full-text assessment. Following rigorous application of the inclusion criteria, 45 randomized controlled trials were ultimately included. The detailed screening process is illustrated in Fig. 1 . The 45 included studies[ 19 – 63 ], published between 2001 and 2024, encompassed a total of 17,671 participants. The geographical distribution covered Europe (n = 12), North America (n = 6), Asia (n = 9), Australia (n = 17), and South America (n = 1). The intervention categories comprised: combined exercise and cognitive training (Exer + Cog, n = 6), cognitive training alone (Cog, n = 4), multimodal exercise (ME, n = 22), mind-body exercise (MBE, n = 13), aerobic exercise (AE, n = 3), resistance exercise (RE, n = 2), health education (Education, n = 9), and relaxation training (Relax, n = 5). Detailed characteristics of each study are presented in Table 1 . Table 1 The basic characteristics of the included studies Author Country Numbers Duration Women (%) Age Measures Dwelling Fallers/ total Katri M Turunen 2022 Finland 314 12 months 59.9 74.5 Exer + cog ME Community 75/155 79/159 Tzu-Ting Huang 2011 Taiwan 186 8 weeks 58.6 68.5 Exer + cog MBE Usual Community 5/62 8/62 8/62 Donald S Lipardo 2020 Philippines 92 12 weeks 79 69.5 Exer + cog ME Cog Usual Community 0/23 5/23 3/23 5/23 G A Rixt Zijlstra 2009 Netherlands 540 2 months 71.9 77.9 Exer + cog Usual Community 46/260 50/280 Hei-Fen Hwang 2016 Taiwan 456 6 months 66.7 72.4 MBE ME Hospital 41/228 75/228 Alexander Voukelatos 2015 Australia 386 48 weeks 74 73.2 AE Usual Community 79/192 96/194 Ibolya Miko 2018 Hungary 100 12 months 100 69.2 MBE Usual Community 6/50 11/50 Anna L Barker 2016 Australia 49 12 weeks 87.8 69.3 MBE Usual Community 6/20 9/29 Lesley Day 2015 Australia 303 48 weeks 69.7 77.7 MBE Relax Community 38/250 42/253 Ruth E Taylor-Piliae 2014 USA 145 12 weeks 47 70 MBE ME Usual Community 13/53 19/44 21/48 Inge H J Logghe 2009 Netherlands 269 13 weeks 71 77 MBE Usual Community 58/138 59/131 Table 1 (continued) Author Country Numbers Duration Women (%) Age Measures Dwelling Fallers/ total Fuzhong Li 2005 USA 256 26 weeks 69.9 77.4 MBE Relax Community 27/125 43/131 Alexander Voukelatos 2007 Australia 702 16 weeks 84 69 MBE Usual Community 61/353 70/349 Fuzhong Li 2021 USA 30 24 weeks 72 76.2 MBE Relax Community 9/15 12/15 Steven L Wolf 2003 USA 311 48 weeks 93.6 80.9 MBE Education Community 59/158 85/153 Toni Rikkonen 2023 Finland 914 12 months 100 76.5 MBE Usual Community 268/457 278/457 Anne Barnett 2003 Australia 163 12 months 55.9 74.9 ME Usual Community 27/83 37/80 Parinaz Jahanpeyma 2021 Turkey 71 12 weeks 74.6 75.2 ME AE Care home 16/35 34/36 Anne-Gabrielle Mittaz Hager 2024 Switzerland 239 12 months 75.3 79.3 ME Usual Hospital 64/158 27/81 Annika Toots 2019 Sweden 186 4 months 75.8 85.1 ME Usual Care home 45/93 42/93 Colleen G Canning 2015 Australia 231 6 months 41.6 71 ME Usual Community 75/115 81/116 Monika Siegrist 2016 Germany 378 16 weeks 75.2 78.1 ME Usual Community 73/222 70/156 David B Matchar 2017 Singapore 354 24 weeks 75.1 75.1 ME Usual Community 54/177 67/177 Jennifer Hewitt 2018 Australia 221 12 months 65.2 86 ME Usual Care home 50/113 78/108 Table 1 (continued) Author Country Numbers Duration Women (%) Age Measures Dwelling Fallers/ total Teresa Liu-Ambrose 2019 Canada 344 12 months 66.5 81.6 ME Usual Clinic 105/172 104/172 Juliana Hotta Ansai 2016 Brazil 69 16 weeks 68.1 82.4 ME RE Usual Community 4/23 8/23 8/23 Fuzhong Li 2018 USA 670 24 weeks 65 77.7 MBE ME Relax Community 85/224 112/223 127/223 Lynne M Taylor 2024 Australia 520 12 months NA 84 ME Usual Care home 194/262 190/258 K Hauer 2001 Germany 57 3 months 100 82 ME Relax Hospital 14/31 16/26 Catherine Sherrington 2020 Australia 336 12 months 76 78 ME Usual Community 72/168 70/168 Eeva Tuunainen 2013 Finland 30 13 weeks 74.6 85 ME RE Community 6/14 7/16 Freddy Mh Lam 2018 Hong Kong 48 8 weeks 60.7 81.4 ME Usual Care home 5/24 4/24 Jens Eg Nørgaard 2023 Denmark 140 12 months 56 72 ME AE Community 34/70 39/70 Thanwarat Chantanachai 2024 Australia 61 6 months 39 80 Cog Usual Community 13/31 13/30 Tzu-Ting Huang 2016 Taiwan 80 8 weeks 50 79.4 Exer + cog Cog Usual Care home 0/27 0/27 10/26 Table 1 (continued) Author Country Numbers Duration Women (%) Age Measures Dwelling Fallers/ total Anna-Liisa Juola 2015 Finland 227 12 months 70.9 83 Education Usual Hospital 42/118 60/109 Anne-Marie Hill 2015 Australia 3606 50 weeks 61.2 81.8 Education Usual Hospital 136/1623 248/1983 Tetsuya Ueda 2017 Japan 60 1 months 68.3 75.8 Education Usual Community 0/30 2/30 Kristie J Harper 2017 Australia 412 6 months 65 79.2 Education Usual Hospital 63/211 67/201 Meg E Morris 2024 Australia 541 20 weeks 62 81 Education Usual Hospital 15/271 23/270 Anne-Marie Hill 2013 Australia 50 1 months 66 78.2 Education Usual Hospital 4/25 9/25 Emily Ang 2011 Singapore 1822 8 months 49.6 70 Education Usual Hospital 4/910 14/912 Anna Barker 2019 Australia 523 12 months 55 73 Education Usual Community 100/263 106/260 Daina L Sturnieks 2024 Australia 769 12 months 71.4 72.5 Exer + cog Cog Usual Community 91/252 110/262 123/255 Lindy Clemson 2012 Australia 210 6 months 54.8 83.7 ME Usual Community 65/105 71/105 Risk of Bias Assessment The assessment of 45 studies using the Cochrane RoB 2.0 tool is presented in Fig. 2 . Among them, 11 studies (24.4%) were rated as high risk of bias, 24 studies (53.3%) as having some concerns (moderate risk), and 10 studies (22.2%) as low risk. Key domains contributing to bias included: implementation of interventions (performance bias in 68.9% of studies), completeness of outcome data (attrition bias in 53.3%), and outcome measurement (detection bias due to lack of blinding in 24.4%). Overall, 34 studies (75.5%) demonstrated an acceptable methodological quality (low to moderate risk of bias). Bayesian NMA Network Plot Figure 3 illustrates the network structure of evidence for the comparative effectiveness of nine NPIs in fall prevention. Each node represents an intervention, with node size proportional to the total sample size involved (range: n = 39 to 7,285). Edges between nodes indicate direct comparisons, with line thickness corresponding to the number of head-to-head studies available for each comparison (ranging from 1 to 8 trials per comparison). Network density analysis identified Usual and ME as central hub nodes (degree = 7), followed by MBE with a degree of 6 and Exer + cog with a degree of 4. Key direct comparisons included ME vs. Usual (15 trials) and MBE vs. Usual (7 trials). In contrast, the effects of Cog and Education were primarily evaluated through indirect evidence. Outcome The Bayesian NMA incorporating data from 45 RCTs demonstrated significant hierarchical differences in the effectiveness of NPIs for preventing falls among older adults (Fig. 4 , Fig. 5 ). Compared with Usual, Exer + Cog(OR = 0.55, 95% CI: 0.34–0.84) showed the most favorable risk reduction effect, followed by MBE (OR = 0.64, 95% CI: 0.47–0.87) and Education (OR = 0.65, 95% CI: 0.45–0.90). Direct comparisons revealed that Exer + Cog was significantly more effective than AE (OR = 2.28, 95% CI: 1.12–5.38) and Relax (OR = 2.01, 95% CI: 1.08–4.09). Similarly, MBE also demonstrated a significant advantage over AE (OR = 1.96, 95% CI: 1.08–3.87) and Relax (OR = 1.73, 95% CI: 1.10–2.73). Although Education had a lower effect magnitude compared to Exer + Cog (SUCRA = 73.6% vs. 86.8%), Education (OR = 0.52, 95% CI: 0.24–0.99) still exhibited a statistically significant benefit over AE. SUCRA ranking The efficacy ranking based on the SUCRA analysis, presented in Fig. 6 , indicates that Exer + Cog has an 86.8% probability of being the optimal fall prevention strategy, significantly outperforming other interventions. The high-efficacy cluster (SUCRA > 70%) includes MBE (75.4%), Education (73.6%), and Cog (73.3%). The moderate-to-low efficacy group consists of ME (49.9%), RE (28.0%), Usual care (26.8%), Relax (21.4%), and AE (14.8%), with AE having the highest probability (39.8%) of being the least effective option. Subgroup analysis reveals that the SUCRA value for MBE further increases to 93.1% (Δ + 17.7%) during the 4–8 month intervention period, supporting its recommendation as a preferred option for primary healthcare services. Subgroup analysis To further explore the differences in study outcomes under various conditions, we conducted a subgroup NMA based on several key factors. The specific factors considered included: gender and intervention duration. Subgroup analysis results based on intervention duration To investigate whether the effectiveness of different interventions is influenced by intervention duration, this study conducted a subgroup analysis based on intervention time. Data were divided into three groups according to intervention duration: short-term (≤ 4 months), medium-term (4–8 months), and long-term (> 8 months) (Fig. 7 ). In the short-term intervention group (≤ 4 months), compared with Usual, Exer + Cog(OR = 0.34, 95% CI: 0.09–0.81) showed a significant preventive effect. However, other interventions did not demonstrate significant effects within this period (Fig. 7 A, Fig. S1 , Fig. S2). For the medium-term intervention group (4–8 months), MBE(OR = 0.41, 95% CI: 0.23–0.73) significantly reduced fall risk compared to Usual. Moreover, MBE(OR = 0.54, 95% CI: 0.35–0.82) showed superior effects compared to ME. Notably, Relax(OR = 2.51, 95% CI: 1.40–3.30) exhibited a significantly lower effect than MBE. Other interventions did not show significant differences during this period (Fig. 7 B, Fig. S3, Fig. S4). In the long-term intervention group (> 8 months), no intervention demonstrated significant differences compared to usual care (Fig. 7 C, Fig. S5, Fig. S6). Subgroup analysis results based on gender The included studies in this research exhibited a notable gender distribution bias among participants. Specifically, three studies (6.67%) exclusively enrolled female participants, while the majority of studies (n = 34) had a female proportion ranging between 50% and 80%. Under this gender distribution characteristic, subgroup analysis revealed that compared to Usual, the combined Exer + Cog (OR = 0.50, 95% CI: 0.27–0.81) and Education(OR = 0.62, 95% CI: 0.38–0.97) demonstrated significant fall prevention effects in female-dominant studies. Notably, the effect of AE(OR = 2.70, 95% CI: 1.17–7.94) was significantly inferior to that of Exer + Cog. No significant intergroup differences were observed for other intervention modalities (Fig. 7 D, Fig. S7, Fig. S8). Heterogeneity and Model Validation This study employed a multidimensional approach to assess evidence heterogeneity and model consistency. The random-effects model indicated low heterogeneity among studies (I² = 17%). Publication bias analysis showed that Begg's test suggested a potential small-study effect (z = -3.13, p = 0.0017), whereas Egger's test did not detect significant bias (t = -0.13, p = 0.9003), which may be attributed to the relatively high publication rate of negative results concerning NPIs. Node-splitting analysis identified local inconsistencies between MBE and ME (direct comparison OR = 0.50 vs. indirect comparison OR = 1.00, p = 0.0265) as well as between ME and AE (direct OR = 24.00 vs. indirect OR = 0.89, p = 0.000075). The adjusted funnel plot demonstrated a symmetrical distribution (Fig. 8 ), supporting the overall reliability of the evidence network. Sensitivity Analysis After excluding 11 studies with a high risk of bias, reanalysis of the remaining 34 studies demonstrated that the core effect sizes remained robust. Consistent with the primary analysis, MBE (OR = 0.72, 95% CI: 0.55–0.95) and Education (OR = 0.73, 95% CI: 0.54–0.96) continued to show significant advantages compared to Usual (Fig S9, Fig S10). However, the effect estimates for Exer + Cog (OR = 0.75, 95% CI: 0.48–1.06) and ME (OR = 0.77, 95% CI: 0.60–0.96) included the null value at the boundary, suggesting that their efficacy estimates may be influenced by lack of blinding and attrition bias in the original studies. Discussion This study conducted an NMA comparing the effectiveness of nine NPIs for preventing falls in older adults, including a total of 45 studies (n = 17,671). The analysis indicated that the Exer+Cog was the most effective (SUCRA = 86.8%), followed by MBE(SUCRA = 75.4%) and Education (SUCRA = 73.6%), all of which were significantly superior to Usual. Other interventions did not show significant differences compared to routine treatment. These findings suggest that the effectiveness of different interventions varies, with Exer+Cog potentially representing the optimal strategy for fall prevention in the elderly. This study systematically evaluated the impact of different intervention durations on fall prevention in older adults through an NMA. The results demonstrated significant temporal differences in intervention effectiveness. In short-term interventions (≤4 months), Exer+Cog(OR = 0.34, 95% CI: 0.09–0.81) demonstrated a significant advantage compared to Usual. This effect is likely attributable to the synergistic interaction between cognitive and physical training, which can rapidly improve balance and gait control [64] . During the mid-term intervention period (4–8 months), MBE demonstrated a more comprehensive preventive effect, significantly reducing the risk of falls compared to the placebo group (OR = 0.41, 95% CI: 0.23–0.73) and outperforming ME (OR = 0.54, 95% CI: 0.35–0.82). In contrast, Relax showed significantly poorer outcomes (OR = 0.46, 95% CI: 0.30–0.71), which may be related to its dual mechanism integrating physical activity and psychological regulation [65] . However, in long-term interventions (>8 months), none of the measures demonstrated significant advantages, suggesting that factors such as decreased adherence and the plateauing of effects need to be considered [66, 67] . These findings provide important evidence for developing phased, personalized fall prevention programs[68] . Future research should further explore the specific effects of different forms of MBE and strategies for sustaining long-term benefits. This study further investigated the impact of gender on intervention effectiveness. Given the high proportion of female participants in the included studies (50–80%), subgroup analysis showed that compared to Usual, Exer+Cog (OR = 0.50, 95% CI: 0.27–0.81) and Education (OR = 0.62, 95% CI: 0.38–0.97) demonstrated significant advantages in female populations, whereas AE( OR = 2.70, 95% CI: 1.17–7.94) was significantly less effective than Exer+Cog, This gender difference may be attributed to: 1) women being more susceptible to bone density loss and decline in balance ability during the aging process [69]；2）and 2) Exer+Cog specifically improving attention, executive function, and lower limb strength in women [70, 71]. Notably, the poor effectiveness of AE interventions may be related to the limited sample size of existing studies [36], highlighting the need for more high-quality research to validate these findings in the future. These findings suggest that fall prevention for older women should prioritize combined cognitive and physical exercise programs. At the same time, they underscore the need to strengthen research on intervention strategies targeting the male population in order to develop a more comprehensive gender-specific prevention framework. The results of this study are highly consistent with previous research evidence, further validating the effectiveness of multiple interventions in preventing falls among older adults. Consistent with the findings of Sherrington et al. [72] , we confirmed that exercise interventions serve as a fundamental approach with clear effectiveness in preventing falls among community-dwelling older adults. Notably, this study expands the existing understanding by:1）Exer+Cog demonstrated a synergistic effect, with the exercise component improving lower limb strength, balance, and gait stability [71, 73] , while the cognitive component enhances executive function and spatial awareness [74], thereby reducing fall risk through both physiological and cognitive pathways;2）The effectiveness of Education aligns with the findings of Dautzenberg et al. [10], confirming that it works by enhancing fall-related awareness and self-management abilities [75]；3）The effect of MBE is consistent with the report by Sherrington et al.[76] , as its unique “body-mind” integrative approach yields benefits by improving neuromuscular coordination and core stability [77, 78] . These findings systematically construct a multidimensional theoretical framework for interventions, providing a more comprehensive evidence base for developing integrated fall prevention strategies. This study has several limitations that should be noted. First, the quality of the included studies was heterogeneous; some studies exhibited deficiencies in randomization methods (e.g., failure to describe specific random sequence generation) and blinding procedures (particularly regarding participants and intervention administrators), which may affect the credibility of the results. Second, although Egger’s test did not detect significant publication bias (p = 0.9003), Begg’s test suggested the possibility of potential bias (p = 0.0017). This conflicting result warrants cautious interpretation. Third, there were variations in the specific implementation of the intervention protocols, such as: 1) within the MBE category, Tai Chi has been confirmed as effective [76] , whereas evidence for other forms (e.g., yoga, dance) remains insufficient; 2) in Exer+Cog interventions, there is considerable variation in the types and difficulty levels of cognitive tasks; and 3) the effects of AE and RE may be underestimated due to limited sample sizes. Fourth, the study did not conduct subgroup analyses based on different population characteristics, such as age stratification, comorbidities, or cognitive status. Fifth, the inclusion of only English-language publications may have introduced selection bias. Finally, due to the limited number of included studies, the effect estimates for certain interventions may be subject to bias. These limitations suggest that future research should: 1) conduct large-scale studies with more rigorous methodologies; 2) refine reporting standards for intervention protocols; 3) enhance the retrieval of non-English literature; and 4) further investigate the interaction between population characteristics and intervention effects to provide a basis for precision fall prevention. Conclusion This research employed a Bayesian network meta-analysis to comprehensively assess the preventative effects of nine non-pharmaceutical interventions on falls in older individuals.The results indicated that Exer+Cog demonstrated the best overall preventive efficacy, with the highest level of evidence supporting its effectiveness in fall prevention. MBE and Education, as secondary effective interventions, hold significant substitutive value in specific populations and under resource-limited conditions. The study recommends that clinical practice should develop individualized prevention intervention systems based on regional resource accessibility, the physiological and psychological characteristics of target populations, and fall risk stratification. Future research should focus on precise exploration of the dose–response relationship of interventions, as well as cost–benefit evaluations supported by long-term follow-up data, to provide more robust evidence for the standardized practice of fall prevention in older persons. Abbreviations NPIs Non-pharmacological interventions NMA Network Meta-Analysis Exer+cog Exercise+cognitive training Cog Cognitive training ME Multimodal exercise MBE Mind-body exercise AE Aerobic exercise RE Resistance exercise Usual Usual care Education Health education Relax Relax training SUCRA Surface Under the Cumulative Ranking curve Declarations Acknowledgements This study was supported by the National Science and Technology Major Project (Project Title: National Multicenter Randomized Controlled Trial of Lifestyle Intervention for High-Risk Populations with Type 2 Diabetes; Project No.: 2024ZD0531803). Author contributions Zhiyuan Sun: Responsible for proposing research ideas, data analysis, and manuscript revision. Ming Gao: Responsible for data collection, data analysis, manuscript writing, and revision. Dewei Mao: Responsible for data collection. Xuewen Tian: Responsible for data collection. Qinghui Shang: Responsible for data collection and manuscript revision. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data availability All data generated or analyzed during this study are included in this published article and supplementary materials. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author details 1 Shandong Sport University, 10600 Century Avenue, Jinan, Shandong 250102, China 2 Division of Physical Education, The Chinese University of Hong Kong, 2001 Longxiang Avenue, Shenzhen 518172, China References Montero-Odasso M, van der Velde N, Alexander NB, Becker C, Blain H, Camicioli R, Close J, Duan L, Duque G, Ganz DA et al : New horizons in falls prevention and management for older adults: a global initiative . Age Ageing 2021, 50 (5):1499-1507. 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Loewenthal J, Innes KE, Mitzner M, Mita C, Orkaby AR: Effect of Yoga on Frailty in Older Adults : A Systematic Review . Ann Intern Med 2023, 176 (4):524-535. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6880677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486499244,"identity":"d8d03b48-02a0-47c3-837c-92bffb89fbc5","order_by":0,"name":"Zhiyuan Sun","email":"","orcid":"","institution":"Shandong Sport university","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Sun","suffix":""},{"id":486499246,"identity":"665c1718-9fd1-40c7-a186-a47d02ba17fd","order_by":1,"name":"Ming Gao","email":"","orcid":"","institution":"Shandong Sport university","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Gao","suffix":""},{"id":486499248,"identity":"f2195b58-5043-46c3-ab82-0566d790e50e","order_by":2,"name":"Deiwei Mao","email":"","orcid":"","institution":"The Chinese University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Deiwei","middleName":"","lastName":"Mao","suffix":""},{"id":486499250,"identity":"3d55b3e8-6d6a-487b-9ffe-5e6ec38d5f8b","order_by":3,"name":"Xuewen Tian","email":"","orcid":"","institution":"Shandong Sport university","correspondingAuthor":false,"prefix":"","firstName":"Xuewen","middleName":"","lastName":"Tian","suffix":""},{"id":486499252,"identity":"21df6260-b9c0-472b-9414-c6af0f13f98a","order_by":4,"name":"Qinghui Shang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PsYrCQBDG8ZGFtRlMO8FguqsXBEUI+CpZhFQnWKawWFF2C7H3MSwtXQKp1t4ywSdId9Vx9nckuc5if/X84RsAz3tDPLamab4Jg/hpqzTfdicjYpoRT6JQZStRubI7mdBQA/IsEerzI6wPrMew8U5XhAUKcDyXikNgjml7ElkjNlTgfHAqH/IaAbn7pT0BqYhEgYv9KHtIx0HQukeC6WtYibON1KxHQlIT3jIUDmfQL0G7n4YqwfDMV5S6Ejt/iY2p60bRMiBmm698OwnMqT35Bf937nme5/3pB4TLRutTU6nvAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Sport university","correspondingAuthor":true,"prefix":"","firstName":"Qinghui","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2025-06-12 13:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6880677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6880677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87043761,"identity":"4a4663d5-6a9e-4be5-9a6c-ceff46689ffe","added_by":"auto","created_at":"2025-07-18 14:20:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87603,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of literature screening\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/4b96087f27e63551db1d617a.png"},{"id":87043762,"identity":"7a9b98a9-dadb-4db1-9512-9ad7ffa6927a","added_by":"auto","created_at":"2025-07-18 14:20:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410110,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of bias assessment for the included studies. (A) Summary of risk of bias across different domains. (B) Risk of bias judgments for each of the 45 included randomized controlled trials.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/9c0f70ef13dcf46c500394b8.png"},{"id":87043765,"identity":"20a470a4-b6cf-42b9-adaa-4ffb5894f65e","added_by":"auto","created_at":"2025-07-18 14:20:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131218,"visible":true,"origin":"","legend":"\u003cp\u003eNMA of the effectiveness of nine different NPIs for fall prevention.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/251b87168b14259322f996b4.png"},{"id":87046247,"identity":"0663e5f6-50a9-4042-bec5-e3cfa16ee84f","added_by":"auto","created_at":"2025-07-18 14:36:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130361,"visible":true,"origin":"","legend":"\u003cp\u003eLeague table of the comparative effectiveness of nine NPIs for fall prevention.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/c530569fcbc0a9337b0945d9.png"},{"id":87046248,"identity":"9d634ce0-7867-4caa-bdbc-bf38eb9b0987","added_by":"auto","created_at":"2025-07-18 14:36:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103803,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of fall risk.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/93048dc52ff767b1049f7340.png"},{"id":87045041,"identity":"eb245d57-0571-44dc-94ab-767f7409b3ca","added_by":"auto","created_at":"2025-07-18 14:28:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54164,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the effectiveness of different interventions based on the SUCRA.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/c87ec53cdf37b625da237915.png"},{"id":87043781,"identity":"e56044a1-b2b0-4da4-844e-4dba1649bb0b","added_by":"auto","created_at":"2025-07-18 14:20:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":55804,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork evidence diagrams of subgroup analyses. (A) Intervention duration ≤ 4 months;(B) Intervention duration 4–8 months;(C) Intervention duration \u0026gt; 8 months;(D) Gender.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/3ab4e7247a645b5902bec3fb.png"},{"id":87043779,"identity":"0e608405-bce6-41b3-ab9a-e45011654567","added_by":"auto","created_at":"2025-07-18 14:20:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":101177,"visible":true,"origin":"","legend":"\u003cp\u003epresents the comparison-adjusted funnel plots. A: Exer+Cog; B: Cog; C: ME; D: MBE; E: AE; F: RE; G: Education; I: Relax.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/a70c4a3ecfa16a71dbd0daad.png"},{"id":90909834,"identity":"4f8eceda-c32a-4ed8-aa13-60216b46fb97","added_by":"auto","created_at":"2025-09-09 13:32:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5843233,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/baea24db-cd9c-4355-8115-940a18f86466.pdf"},{"id":87045039,"identity":"a92c446b-920f-4c76-bc67-834c52073ab8","added_by":"auto","created_at":"2025-07-18 14:28:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":926456,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6880677/v1/7c9f727c5050ba9339ee3c33.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative efficacy of Nine Non-Pharmacological Interventions for Fall Prevention in Older Adults: A Systematic Review and Network Meta-Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, falls have become a significant public health concern. According to the 2023 report by the World Health Organization (WHO), falls are the second leading cause of unintentional injury-related deaths, accounting for approximately 684,000 fatalities annually. Notably, over 80% of these deaths occur in low- and middle-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Older adults represent the population most significantly affected by this risk. Epidemiological studies indicate that approximately 25% of community-dwelling older adults experience at least one fall annually, with 20\u0026ndash;30% of these individuals sustaining moderate to severe injuries, such as hip fractures or traumatic brain injuries. Each year, falls directly result in an estimated 3\u0026nbsp;million emergency department visits and 800,000 hospitalizations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, both the incidence of falls and the severity of their associated complications show a significant positive correlation with advancing age. The risk is markedly elevated among older adults with functional impairments, where the likelihood of falling and experiencing adverse outcomes increases substantially [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to data from the U.S. Centers for Disease Control and Prevention (CDC) in 2020, annual healthcare expenditures related to falls exceeded \u003cspan\u003e$\u003c/span\u003e50\u0026nbsp;billion. Of this total, direct medical costs\u0026mdash;including hospitalizations and outpatient visits\u0026mdash;accounted for 4.4%, while expenditures on long-term care comprised as much as 11.8%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This health threat not only impairs the physical functioning and psychological well-being of older adults but also exacerbates the burden on families and society through a vicious cycle of disability and caregiving dependency. This underscores the urgent need for the implementation of effective interventions.\u003c/p\u003e\u003cp\u003eConventional fall prevention strategies have primarily relied on pharmacological treatments, such as vitamin D supplementation, and surgical interventions, including joint replacement. However, their clinical application is facing significant challenges due to limitations such as adverse drug effects (e.g., an increased risk of hypercalcemia) and the relatively low cost-effectiveness of surgical procedures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Against this backdrop, non-pharmacological interventions (NPIs) have increasingly become a focus of research due to their advantages in safety, cost-effectiveness, and ease of implementation. Current NPIs primarily exert their effects through three mechanisms: (1) exercise training to enhance muscle strength and balance function; (2) cognitive interventions to improve risk perception and decision-making abilities; and (3) health education to strengthen adherence to preventive behaviors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although numerous randomized controlled trials (RCTs) have confirmed the efficacy of NPIs in fall prevention, the optimal choice of interventions remains contentious. A network meta-analysis by Cheng et al. (2023) demonstrated that traditional Chinese exercises, such as Tai Chi, effectively enhance lower limb muscle strength in older adults, improve their balance, and reduce the risk of falls [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; An updated systematic review by Devasahayam et al. (2023) indicated that balance training yields a significant intervention effect, with a fall risk ratio of 0.76 (95% CI\u0026thinsp;=\u0026thinsp;0.63\u0026ndash;0.92) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is important to note that the existing body of evidence has significant limitations: first, traditional meta-analyses are often restricted to pairwise comparisons (e.g., exercise versus cognitive training), which cannot address the fragmentation of evidence across multiple interventions; second, key reviews\u0026mdash;such as the study by Dautzenberg et al. (2021)\u0026mdash;still include mixed interventions involving pharmacological and surgical treatments, resulting in the dilution of evidence specific to NPIs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eNetwork meta-analysis (NMA) integrates both direct and indirect comparison evidence, enabling the ranking and selection of multiple interventions within a Bayesian framework. Compared to frequentist methods, Bayesian NMA offers three major advantages: (1) it allows inclusion of studies with different control designs; (2) it provides probabilistic conclusions regarding the relative efficacy of interventions; and (3) it incorporates historical evidence through the use of prior distributions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study employs a Bayesian random-effects model to systematically evaluate the differential fall prevention effects of nine categories of NPIs, aiming to address three key questions: (1) Are there statistically significant differences in efficacy among various NPIs? (2) Are intervention effects moderated by factors such as duration and population characteristics? (3) How can an evidence-based prioritization framework for interventions be constructed? The findings are expected to provide a decision-making basis for clinical guideline development and the optimization of public health resource allocation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was conducted by the PRISMA guidelines for network meta-analyses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has been registered on the PROSPERO platform (Registration Number: CRD42023486881), with ongoing real-time updates provided on the progress of the research.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSearch strategy\u003c/h2\u003e\u003cp\u003eA systematic search was conducted across PubMed, Web of Science, Embase, and Cochrane Library databases, covering the period from database inception to March 10, 2025, to identify studies evaluating the effectiveness of NPIs for fall prevention in older adults. The search strategy combined both Medical Subject Headings (MeSH) and free-text terms, primarily including keywords related to the study population (e.g., \u0026ldquo;older adults,\u0026rdquo; \u0026ldquo;elderly,\u0026rdquo; \u0026ldquo;aging\u0026rdquo;), intervention measures (e.g., \u0026ldquo;education,\u0026rdquo; \u0026ldquo;exercise,\u0026rdquo; \u0026ldquo;cognitive\u0026rdquo;), and outcomes (e.g., \u0026ldquo;falls,\u0026rdquo; \u0026ldquo;fall prevention\u0026rdquo;). A combination of MeSH and text words was used for the search (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, reference lists of included studies and previous relevant research were manually searched to maximize the comprehensiveness of study inclusion. To further ensure completeness, reference lists from related systematic reviews and meta-analyses were also examined and supplemented by manual searching to capture potentially overlooked key studies. All retrieved literature was independently screened by two researchers, and studies meeting the inclusion criteria were selected for meta-analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria: This study includes populations with impaired balance control and an increased risk of falls: (1) generally healthy older adults aged 60 years or above, as well as individuals with neurological diseases, chronic obstructive pulmonary disease (COPD), lower limb amputation or post-joint replacement status, or other individuals with clearly identified fall risk factors. Regarding interventions: (2) NPIs for fall prevention applicable in all settings (e.g., community, acute care) were included, which could be single-factor or multifactorial measures, but pharmacological treatments were excluded. Regarding control groups: (3) eligible control measures included usual care, placebo, no treatment, or other NPIs. Primary outcomes: (4) the main outcome was the number of falls. (5) In terms of study design, only RCTs published in English were included, including cluster RCTs and crossover RCTs.\u003c/p\u003e\u003cp\u003eExclusion criteria: The exclusion criteria of this study included the following: (1) animal or cellular experiments, case reports, study protocols, reviews, and editorials; (2) studies with unavailable full texts; (3) studies with unextractable outcome data; (4) studies combining other therapies such as nutritional support, hormones, or other medications; (5) when multiple studies used the same population, those with smaller sample sizes were excluded; (6) duplicate publications.\u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eTwo researchers will independently extract data from studies meeting the inclusion criteria and perform cross-checking during the extraction process. Any disagreements will be resolved through discussion or consultation with a third-party expert. The extracted data will include the following: (1) basic study information: first author\u0026rsquo;s name, year of publication, and country of study; (2) study population: sample size, mean age, and gender distribution; (3) intervention details: type of intervention (single-factor or multifactorial), mode of intervention, duration, and setting (e.g., community, hospital); (4) control measures: type of control intervention (usual care, placebo, no treatment, or other NPIs); (5) study outcomes: number of fallers and related data; (6) other key variables that may affect study results, such as adherence and loss to follow-up. All extracted data will be entered into Excel spreadsheets for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eRisk of bias assessment\u003c/h3\u003e\n\u003cp\u003eTwo researchers independently assessed the quality of the included studies using the Cochrane Risk of Bias tool for randomized trials (RoB 2)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Any disagreements were resolved through discussion with a third researcher. The RoB 2 tool assessment includes five domains, which are: (D1a) Randomisation process、(D1b) Timing of identification or recruitment of participants、(D2) Deviations from the intended interventions、(D3) Missing outcome data、(D4) Measurement of the outcome、(D5) Selection of the reported result. Each domain is rated as \u0026ldquo;low risk,\u0026rdquo; \u0026ldquo;high risk,\u0026rdquo; or \u0026ldquo;some concerns.\u0026rdquo;\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThis study was conducted using R version 4.4.3 and Stata 16.0. A Bayesian framework was adopted to construct a random-effects network meta-analysis model. Within the R 4.4.3 environment, the GeMTC 0.8.2 package was combined with JAGS 4.3.0 to build the random-effects Bayesian network model. Parameter estimation was performed using the Markov Chain Monte Carlo (MCMC) algorithm [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with four independent Markov chains run. n initial 10,000 adaptive iterations were conducted to reduce sensitivity to initial values, followed by 50,000 iterations for effective sampling. Convergence was assessed using the Gelman-Rubin statistic, with convergence confirmed when the statistic was below 1.01. Local inconsistency was assessed using the node-splitting method [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A p-value less than 0.05 from the node-splitting test indicates the presence of local inconsistency. Simultaneously, heterogeneity was quantified using a generalized linear mixed model, with the I\u0026sup2; statistic used to classify heterogeneity levels as follows: \u0026lt;25% indicating low heterogeneity, 25\u0026ndash;50% moderate heterogeneity, 50\u0026ndash;75% substantial heterogeneity, and \u0026gt;\u0026thinsp;75% considerable heterogeneity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Effect sizes are presented as odds ratios (OR) with corresponding 95% Bayesian credible interval (CI). A difference is considered statistically significant if the 95% CI does not include \u0026ldquo;1.\u0026rdquo; To facilitate comparison of interventions, league tables are plotted for intuitive visualization. The Surface Under the Cumulative Ranking curve (SUCRA) method was used to quantitatively estimate the effects of different interventions, rank their effectiveness, and classify them into categories. Interventions were ranked based on their SUCRA values, which represent the percentage efficacy or safety of an intervention relative to an \u0026ldquo;ideal best intervention.\u0026rdquo; SUCRA values range from 0 to 100%, with values closer to 100% indicating superior intervention effects [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eAdditionally, this study conducted sensitivity analyses by excluding studies with a high risk of bias and performed subgroup analyses stratified by gender and intervention duration. Publication bias was assessed using funnel plots, where asymmetrical distribution of points may indicate potential bias. Quantitative evaluation of publication bias was further conducted using Begg\u0026rsquo;s and Egger\u0026rsquo;s tests. The results were presented through a triad of visualization tools: (1) network evidence plots illustrating the evidence structure; (2) forest plot matrices displaying effect differences; and (3) cumulative ranking probability plots interpreting the SUCRA rankings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStudy search and characteristics\u003c/h2\u003e\u003cp\u003eAn initial total of 5,943 records were identified through systematic database searches. After removing duplicates, 3,150 records remained. Based on title and abstract screening, 2,712 irrelevant studies were excluded, resulting in 438 articles undergoing full-text assessment. Following rigorous application of the inclusion criteria, 45 randomized controlled trials were ultimately included. The detailed screening process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The 45 included studies[\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], published between 2001 and 2024, encompassed a total of 17,671 participants. The geographical distribution covered Europe (n\u0026thinsp;=\u0026thinsp;12), North America (n\u0026thinsp;=\u0026thinsp;6), Asia (n\u0026thinsp;=\u0026thinsp;9), Australia (n\u0026thinsp;=\u0026thinsp;17), and South America (n\u0026thinsp;=\u0026thinsp;1). The intervention categories comprised: combined exercise and cognitive training (Exer\u0026thinsp;+\u0026thinsp;Cog, n\u0026thinsp;=\u0026thinsp;6), cognitive training alone (Cog, n\u0026thinsp;=\u0026thinsp;4), multimodal exercise (ME, n\u0026thinsp;=\u0026thinsp;22), mind-body exercise (MBE, n\u0026thinsp;=\u0026thinsp;13), aerobic exercise (AE, n\u0026thinsp;=\u0026thinsp;3), resistance exercise (RE, n\u0026thinsp;=\u0026thinsp;2), health education (Education, n\u0026thinsp;=\u0026thinsp;9), and relaxation training (Relax, n\u0026thinsp;=\u0026thinsp;5). Detailed characteristics of each study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe basic characteristics of the included studies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumbers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDuration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDwelling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFallers/ total\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKatri M Turunen 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75/155\u003c/p\u003e\u003cp\u003e79/159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTzu-Ting Huang 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5/62\u003c/p\u003e\u003cp\u003e8/62\u003c/p\u003e\u003cp\u003e8/62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDonald S Lipardo 2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhilippines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eCog\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0/23\u003c/p\u003e\u003cp\u003e5/23\u003c/p\u003e\u003cp\u003e3/23\u003c/p\u003e\u003cp\u003e5/23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG A Rixt Zijlstra 2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNetherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e46/260\u003c/p\u003e\u003cp\u003e50/280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHei-Fen Hwang 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41/228\u003c/p\u003e\u003cp\u003e75/228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlexander Voukelatos 2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e79/192\u003c/p\u003e\u003cp\u003e96/194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIbolya Miko 2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHungary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6/50\u003c/p\u003e\u003cp\u003e11/50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnna L Barker 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6/20\u003c/p\u003e\u003cp\u003e9/29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesley Day 2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eRelax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38/250\u003c/p\u003e\u003cp\u003e42/253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRuth E Taylor-Piliae 2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13/53\u003c/p\u003e\u003cp\u003e19/44\u003c/p\u003e\u003cp\u003e21/48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInge H J Logghe 2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNetherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e58/138\u003c/p\u003e\u003cp\u003e59/131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(continued)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumbers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDuration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDwelling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFallers/ total\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuzhong Li 2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eRelax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27/125\u003c/p\u003e\u003cp\u003e43/131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlexander Voukelatos 2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e61/353\u003c/p\u003e\u003cp\u003e70/349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuzhong Li 2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eRelax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9/15\u003c/p\u003e\u003cp\u003e12/15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSteven L Wolf 2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e59/158\u003c/p\u003e\u003cp\u003e85/153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToni Rikkonen 2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e268/457\u003c/p\u003e\u003cp\u003e278/457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnne Barnett 2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27/83\u003c/p\u003e\u003cp\u003e37/80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParinaz Jahanpeyma 2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTurkey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16/35\u003c/p\u003e\u003cp\u003e34/36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnne-Gabrielle Mittaz Hager 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64/158\u003c/p\u003e\u003cp\u003e27/81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnika Toots 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSweden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e45/93\u003c/p\u003e\u003cp\u003e42/93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColleen G Canning 2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75/115\u003c/p\u003e\u003cp\u003e81/116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonika Siegrist 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGermany\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e73/222\u003c/p\u003e\u003cp\u003e70/156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDavid B Matchar 2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingapore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e54/177\u003c/p\u003e\u003cp\u003e67/177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJennifer Hewitt 2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e50/113\u003c/p\u003e\u003cp\u003e78/108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e(continued)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumbers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDuration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDwelling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFallers/ total\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeresa Liu-Ambrose 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCanada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eClinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e105/172\u003c/p\u003e\u003cp\u003e104/172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuliana Hotta Ansai 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrazil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eRE\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4/23\u003c/p\u003e\u003cp\u003e8/23\u003c/p\u003e\u003cp\u003e8/23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuzhong Li 2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMBE\u003c/p\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eRelax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85/224\u003c/p\u003e\u003cp\u003e112/223\u003c/p\u003e\u003cp\u003e127/223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLynne M Taylor 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e194/262\u003c/p\u003e\u003cp\u003e190/258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK Hauer 2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGermany\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eRelax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14/31\u003c/p\u003e\u003cp\u003e16/26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatherine Sherrington 2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e72/168\u003c/p\u003e\u003cp\u003e70/168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEeva Tuunainen 2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eRE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6/14\u003c/p\u003e\u003cp\u003e7/16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFreddy Mh Lam 2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHong Kong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5/24\u003c/p\u003e\u003cp\u003e4/24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJens Eg N\u0026oslash;rgaard 2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDenmark\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34/70\u003c/p\u003e\u003cp\u003e39/70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThanwarat Chantanachai 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCog\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13/31\u003c/p\u003e\u003cp\u003e13/30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTzu-Ting Huang 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eCog\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCare home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0/27\u003c/p\u003e\u003cp\u003e0/27\u003c/p\u003e\u003cp\u003e10/26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e(continued)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumbers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDuration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDwelling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFallers/ total\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnna-Liisa Juola 2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42/118\u003c/p\u003e\u003cp\u003e60/109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnne-Marie Hill 2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e136/1623\u003c/p\u003e\u003cp\u003e248/1983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTetsuya Ueda 2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0/30\u003c/p\u003e\u003cp\u003e2/30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKristie J Harper 2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63/211\u003c/p\u003e\u003cp\u003e67/201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeg E Morris 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15/271\u003c/p\u003e\u003cp\u003e23/270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnne-Marie Hill 2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4/25\u003c/p\u003e\u003cp\u003e9/25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmily Ang 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingapore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4/910\u003c/p\u003e\u003cp\u003e14/912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnna Barker 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100/263\u003c/p\u003e\u003cp\u003e106/260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaina L Sturnieks 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExer\u0026thinsp;+\u0026thinsp;cog\u003c/p\u003e\u003cp\u003eCog\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91/252\u003c/p\u003e\u003cp\u003e110/262\u003c/p\u003e\u003cp\u003e123/255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLindy Clemson 2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eME\u003c/p\u003e\u003cp\u003eUsual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e65/105\u003c/p\u003e\u003cp\u003e71/105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRisk of Bias Assessment\u003c/h3\u003e\n\u003cp\u003eThe assessment of 45 studies using the Cochrane RoB 2.0 tool is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among them, 11 studies (24.4%) were rated as high risk of bias, 24 studies (53.3%) as having some concerns (moderate risk), and 10 studies (22.2%) as low risk. Key domains contributing to bias included: implementation of interventions (performance bias in 68.9% of studies), completeness of outcome data (attrition bias in 53.3%), and outcome measurement (detection bias due to lack of blinding in 24.4%). Overall, 34 studies (75.5%) demonstrated an acceptable methodological quality (low to moderate risk of bias).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBayesian NMA\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eNetwork Plot\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the network structure of evidence for the comparative effectiveness of nine NPIs in fall prevention. Each node represents an intervention, with node size proportional to the total sample size involved (range: n\u0026thinsp;=\u0026thinsp;39 to 7,285). Edges between nodes indicate direct comparisons, with line thickness corresponding to the number of head-to-head studies available for each comparison (ranging from 1 to 8 trials per comparison). Network density analysis identified Usual and ME as central hub nodes (degree\u0026thinsp;=\u0026thinsp;7), followed by MBE with a degree of 6 and Exer\u0026thinsp;+\u0026thinsp;cog with a degree of 4. Key direct comparisons included ME vs. Usual (15 trials) and MBE vs. Usual (7 trials). In contrast, the effects of Cog and Education were primarily evaluated through indirect evidence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOutcome\u003c/h2\u003e\u003cp\u003eThe Bayesian NMA incorporating data from 45 RCTs demonstrated significant hierarchical differences in the effectiveness of NPIs for preventing falls among older adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared with Usual, Exer\u0026thinsp;+\u0026thinsp;Cog(OR\u0026thinsp;=\u0026thinsp;0.55, 95% CI: 0.34\u0026ndash;0.84) showed the most favorable risk reduction effect, followed by MBE (OR\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.47\u0026ndash;0.87) and Education (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.45\u0026ndash;0.90). Direct comparisons revealed that Exer\u0026thinsp;+\u0026thinsp;Cog was significantly more effective than AE (OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 1.12\u0026ndash;5.38) and Relax (OR\u0026thinsp;=\u0026thinsp;2.01, 95% CI: 1.08\u0026ndash;4.09). Similarly, MBE also demonstrated a significant advantage over AE (OR\u0026thinsp;=\u0026thinsp;1.96, 95% CI: 1.08\u0026ndash;3.87) and Relax (OR\u0026thinsp;=\u0026thinsp;1.73, 95% CI: 1.10\u0026ndash;2.73). Although Education had a lower effect magnitude compared to Exer\u0026thinsp;+\u0026thinsp;Cog (SUCRA\u0026thinsp;=\u0026thinsp;73.6% vs. 86.8%), Education (OR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: 0.24\u0026ndash;0.99) still exhibited a statistically significant benefit over AE.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSUCRA ranking\u003c/h2\u003e\u003cp\u003eThe efficacy ranking based on the SUCRA analysis, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, indicates that Exer\u0026thinsp;+\u0026thinsp;Cog has an 86.8% probability of being the optimal fall prevention strategy, significantly outperforming other interventions. The high-efficacy cluster (SUCRA\u0026thinsp;\u0026gt;\u0026thinsp;70%) includes MBE (75.4%), Education (73.6%), and Cog (73.3%). The moderate-to-low efficacy group consists of ME (49.9%), RE (28.0%), Usual care (26.8%), Relax (21.4%), and AE (14.8%), with AE having the highest probability (39.8%) of being the least effective option. Subgroup analysis reveals that the SUCRA value for MBE further increases to 93.1% (Δ\u0026thinsp;+\u0026thinsp;17.7%) during the 4\u0026ndash;8 month intervention period, supporting its recommendation as a preferred option for primary healthcare services.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eTo further explore the differences in study outcomes under various conditions, we conducted a subgroup NMA based on several key factors. The specific factors considered included: gender and intervention duration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis results based on intervention duration\u003c/h2\u003e\u003cp\u003eTo investigate whether the effectiveness of different interventions is influenced by intervention duration, this study conducted a subgroup analysis based on intervention time. Data were divided into three groups according to intervention duration: short-term (\u0026le;\u0026thinsp;4 months), medium-term (4\u0026ndash;8 months), and long-term (\u0026gt;\u0026thinsp;8 months) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the short-term intervention group (\u0026le;\u0026thinsp;4 months), compared with Usual, Exer\u0026thinsp;+\u0026thinsp;Cog(OR\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.09\u0026ndash;0.81) showed a significant preventive effect. However, other interventions did not demonstrate significant effects within this period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig. S2). For the medium-term intervention group (4\u0026ndash;8 months), MBE(OR\u0026thinsp;=\u0026thinsp;0.41, 95% CI: 0.23\u0026ndash;0.73) significantly reduced fall risk compared to Usual. Moreover, MBE(OR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.35\u0026ndash;0.82) showed superior effects compared to ME. Notably, Relax(OR\u0026thinsp;=\u0026thinsp;2.51, 95% CI: 1.40\u0026ndash;3.30) exhibited a significantly lower effect than MBE. Other interventions did not show significant differences during this period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, Fig. S3, Fig. S4). In the long-term intervention group (\u0026gt;\u0026thinsp;8 months), no intervention demonstrated significant differences compared to usual care (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, Fig. S5, Fig. S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis results based on gender\u003c/h2\u003e\u003cp\u003eThe included studies in this research exhibited a notable gender distribution bias among participants. Specifically, three studies (6.67%) exclusively enrolled female participants, while the majority of studies (n\u0026thinsp;=\u0026thinsp;34) had a female proportion ranging between 50% and 80%. Under this gender distribution characteristic, subgroup analysis revealed that compared to Usual, the combined Exer\u0026thinsp;+\u0026thinsp;Cog (OR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.27\u0026ndash;0.81) and Education(OR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: 0.38\u0026ndash;0.97) demonstrated significant fall prevention effects in female-dominant studies. Notably, the effect of AE(OR\u0026thinsp;=\u0026thinsp;2.70, 95% CI: 1.17\u0026ndash;7.94) was significantly inferior to that of Exer\u0026thinsp;+\u0026thinsp;Cog. No significant intergroup differences were observed for other intervention modalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, Fig. S7, Fig. S8).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity and Model Validation\u003c/h2\u003e\u003cp\u003eThis study employed a multidimensional approach to assess evidence heterogeneity and model consistency. The random-effects model indicated low heterogeneity among studies (I\u0026sup2; = 17%). Publication bias analysis showed that Begg's test suggested a potential small-study effect (z = -3.13, p\u0026thinsp;=\u0026thinsp;0.0017), whereas Egger's test did not detect significant bias (t = -0.13, p\u0026thinsp;=\u0026thinsp;0.9003), which may be attributed to the relatively high publication rate of negative results concerning NPIs. Node-splitting analysis identified local inconsistencies between MBE and ME (direct comparison OR\u0026thinsp;=\u0026thinsp;0.50 vs. indirect comparison OR\u0026thinsp;=\u0026thinsp;1.00, p\u0026thinsp;=\u0026thinsp;0.0265) as well as between ME and AE (direct OR\u0026thinsp;=\u0026thinsp;24.00 vs. indirect OR\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;=\u0026thinsp;0.000075). The adjusted funnel plot demonstrated a symmetrical distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), supporting the overall reliability of the evidence network.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eAfter excluding 11 studies with a high risk of bias, reanalysis of the remaining 34 studies demonstrated that the core effect sizes remained robust. Consistent with the primary analysis, MBE (OR\u0026thinsp;=\u0026thinsp;0.72, 95% CI: 0.55\u0026ndash;0.95) and Education (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: 0.54\u0026ndash;0.96) continued to show significant advantages compared to Usual (Fig S9, Fig S10). However, the effect estimates for Exer\u0026thinsp;+\u0026thinsp;Cog (OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.48\u0026ndash;1.06) and ME (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.60\u0026ndash;0.96) included the null value at the boundary, suggesting that their efficacy estimates may be influenced by lack of blinding and attrition bias in the original studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducted an NMA comparing the effectiveness of nine NPIs for preventing falls in older adults, including a total of 45 studies (n = 17,671). The analysis indicated that the Exer+Cog was the most effective (SUCRA = 86.8%), followed by MBE(SUCRA = 75.4%) and Education (SUCRA = 73.6%), all of which were significantly superior to Usual. Other interventions did not show significant differences compared to routine treatment. These findings suggest that the effectiveness of different interventions varies, with Exer+Cog potentially representing the optimal strategy for fall prevention in the elderly.\u003c/p\u003e\n\u003cp\u003eThis study systematically evaluated the impact of different intervention durations on fall prevention in older adults through an NMA. The results demonstrated significant temporal differences in intervention effectiveness. In short-term interventions (\u0026le;4 months), Exer+Cog(OR = 0.34, 95% CI: 0.09\u0026ndash;0.81) demonstrated a significant advantage compared to Usual. This effect is likely attributable to the synergistic interaction between cognitive and physical training, which can rapidly improve balance and gait control [64] . During the mid-term intervention period (4\u0026ndash;8 months), MBE demonstrated a more comprehensive preventive effect, significantly reducing the risk of falls compared to the placebo group (OR = 0.41, 95% CI: 0.23\u0026ndash;0.73) and outperforming ME (OR = 0.54, 95% CI: 0.35\u0026ndash;0.82). In contrast, Relax showed significantly poorer outcomes (OR = 0.46, 95% CI: 0.30\u0026ndash;0.71), which may be related to its dual mechanism integrating physical activity and psychological regulation\u0026nbsp;[65]\u0026nbsp;. However, in long-term interventions (\u0026gt;8 months), none of the measures demonstrated significant advantages, suggesting that factors such as decreased adherence and the plateauing of effects need to be considered\u0026nbsp;[66, 67]\u0026nbsp;. These findings provide important evidence for developing phased, personalized fall prevention programs[68] . Future research should further explore the specific effects of different forms of MBE and strategies for sustaining long-term benefits.\u003c/p\u003e\n\u003cp\u003eThis study further investigated the impact of gender on intervention effectiveness. Given the high proportion of female participants in the included studies (50\u0026ndash;80%), subgroup analysis showed that compared to Usual, Exer+Cog (OR = 0.50, 95% CI: 0.27\u0026ndash;0.81) and Education (OR = 0.62, 95% CI: 0.38\u0026ndash;0.97) demonstrated significant advantages in female populations, whereas AE( OR = 2.70, 95% CI: 1.17\u0026ndash;7.94) was significantly less effective than Exer+Cog, This gender difference may be attributed to: 1) women being more susceptible to bone density loss and decline in balance ability during the aging process [69]；2）and 2) Exer+Cog specifically improving attention, executive function, and lower limb strength in women\u0026nbsp;[70, 71]. Notably, the poor effectiveness of AE interventions may be related to the limited sample size of existing studies\u0026nbsp;[36], highlighting the need for more high-quality research to validate these findings in the future. These findings suggest that fall prevention for older women should prioritize combined cognitive and physical exercise programs. At the same time, they underscore the need to strengthen research on intervention strategies targeting the male population in order to develop a more comprehensive gender-specific prevention framework.\u003c/p\u003e\n\u003cp\u003eThe results of this study are highly consistent with previous research evidence, further validating the effectiveness of multiple interventions in preventing falls among older adults. Consistent with the findings of Sherrington et al. [72] , we confirmed that exercise interventions serve as a fundamental approach with clear effectiveness in preventing falls among community-dwelling older adults. Notably, this study expands the existing understanding by:1）Exer+Cog demonstrated a synergistic effect, with the exercise component improving lower limb strength, balance, and gait stability\u0026nbsp;[71, 73] , while the cognitive component enhances executive function and spatial awareness\u0026nbsp;[74], thereby reducing fall risk through both physiological and cognitive pathways;2）The effectiveness of Education aligns with the findings of Dautzenberg et al. [10], confirming that it works by enhancing fall-related awareness and self-management abilities\u0026nbsp;[75]；3）The effect of MBE is consistent with the report by Sherrington et al.[76] , as its unique \u0026ldquo;body-mind\u0026rdquo; integrative approach yields benefits by improving neuromuscular coordination and core stability\u0026nbsp;[77, 78] . These findings systematically construct a multidimensional theoretical framework for interventions, providing a more comprehensive evidence base for developing integrated fall prevention strategies.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be noted. First, the quality of the included studies was heterogeneous; some studies exhibited deficiencies in randomization methods (e.g., failure to describe specific random sequence generation) and blinding procedures (particularly regarding participants and intervention administrators), which may affect the credibility of the results. Second, although Egger\u0026rsquo;s test did not detect significant publication bias (p = 0.9003), Begg\u0026rsquo;s test suggested the possibility of potential bias (p = 0.0017). This conflicting result warrants cautious interpretation. Third, there were variations in the specific implementation of the intervention protocols, such as: 1) within the MBE category, Tai Chi has been confirmed as effective [76] , whereas evidence for other forms (e.g., yoga, dance) remains insufficient; 2) in Exer+Cog interventions, there is considerable variation in the types and difficulty levels of cognitive tasks; and 3) the effects of AE and RE may be underestimated due to limited sample sizes. Fourth, the study did not conduct subgroup analyses based on different population characteristics, such as age stratification, comorbidities, or cognitive status. Fifth, the inclusion of only English-language publications may have introduced selection bias. Finally, due to the limited number of included studies, the effect estimates for certain interventions may be subject to bias. These limitations suggest that future research should: 1) conduct large-scale studies with more rigorous methodologies; 2) refine reporting standards for intervention protocols; 3) enhance the retrieval of non-English literature; and 4) further investigate the interaction between population characteristics and intervention effects to provide a basis for precision fall prevention.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research employed a Bayesian network meta-analysis to comprehensively assess the preventative effects of nine non-pharmaceutical interventions on falls in older individuals.The results indicated that Exer+Cog demonstrated the best overall preventive efficacy, with the highest level of evidence supporting its effectiveness in fall prevention. MBE and Education, as secondary effective interventions, hold significant substitutive value in specific populations and under resource-limited conditions. The study recommends that clinical practice should develop individualized prevention intervention systems based on regional resource accessibility, the physiological and psychological characteristics of target populations, and fall risk stratification. Future research should focus on precise exploration of the dose\u0026ndash;response relationship of interventions, as well as cost\u0026ndash;benefit evaluations supported by long-term follow-up data, to provide more robust evidence for the standardized practice of fall prevention in older persons.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNPIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-pharmacological interventions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNetwork Meta-Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExer+cog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExercise+cognitive training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultimodal exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMind-body exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAerobic exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResistance exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsual care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRelax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRelax training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSUCRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurface Under the Cumulative Ranking curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Science and Technology Major Project (Project Title: National Multicenter Randomized Controlled Trial of Lifestyle Intervention for High-Risk Populations with Type 2 Diabetes; Project No.: 2024ZD0531803).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhiyuan Sun: Responsible for proposing research ideas, data analysis, and manuscript revision.\u003c/p\u003e\n\u003cp\u003eMing Gao: Responsible for data collection, data analysis, manuscript writing, and revision.\u003c/p\u003e\n\u003cp\u003eDewei Mao: Responsible for data collection.\u003c/p\u003e\n\u003cp\u003eXuewen Tian: Responsible for data collection.\u003c/p\u003e\n\u003cp\u003eQinghui Shang: Responsible for data collection and manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eShandong Sport University, 10600 Century Avenue, Jinan, Shandong 250102, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDivision of Physical Education, The Chinese University of Hong Kong, 2001 Longxiang Avenue, Shenzhen 518172, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMontero-Odasso M, van der Velde N, Alexander NB, Becker C, Blain H, Camicioli R, Close J, Duan L, Duque G, Ganz DA\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eNew horizons in falls prevention and management for older adults: a global initiative\u003c/strong\u003e. \u003cem\u003eAge Ageing\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e50\u003c/strong\u003e(5):1499-1507.\u003c/li\u003e\n \u003cli\u003eBhasin S, Gill TM, Reuben DB, Latham NK, 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\u003cstrong\u003e176\u003c/strong\u003e(4):524-535.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Falls, Non-Pharmacological Interventions, Network Meta-Analysis, Older Adults","lastPublishedDoi":"10.21203/rs.3.rs-6880677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6880677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNon-pharmacological interventions (NPIs), due to their high safety profile, cost-effectiveness, and ease of implementation, have become a research focus for preventing falls among older persons. This study aims to systematically evaluate the differential preventive effects of various NPI strategies, providing evidence-based guidance to optimize clinical practice and public health policy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA systematic search was conducted in PubMed, Web of Science, Embase, and the Cochrane Library for English-language randomized controlled trials (RCTs) published up to March 10, 2025. Bayesian network meta-analysis was performed using a random-effects model with R version 4.4.3 and Stata 16.0 software. Heterogeneity was assessed using the I² statistic. Publication bias was evaluated through funnel plots combined with Begg’s and Egger’s tests. The effect sizes were reported as odds ratios (OR) with 95% confidence intervals (CIs). Model consistency was verified using node-splitting analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 45 RCTs (n = 17,671) were included. Between-study heterogeneity was low (I² = 17%). Network meta-analysis showed that compared to Usual, Exer+Cog (OR = 0.64, 95% CI: 0.34–0.84), MBE (OR = 0.64, 95% CI: 0.47–0.87), and Education (OR = 0.65, 95% CI: 0.45–0.90) demonstrated superior fall prevention effects. Subgroup analyses revealed: 1) Temporal effects: within intervention periods ≤4 months, Exer+Cog showed the best effect (OR = 0.34, 95% CI: 0.09–0.81), while MBE was significantly effective during the 4–8 month period (OR = 0.41, 95% CI: 0.23–0.73); 2) Gender specificity: in populations with 50–80% female participants, Exer+Cog (OR = 0.50, 95% CI: 0.27–0.81) and Education (OR = 0.62, 95% CI: 0.38–0.97) showed more pronounced effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eExer+Cog may represent the optimal strategy for preventing falls in older adults, while MBE and Education can serve as effective alternative interventions. It is recommended that individualized fall prevention programs be developed based on the availability of resources and the characteristics of the population. Future research should focus on optimizing intervention dosage and long-term benefits.\u003c/p\u003e","manuscriptTitle":"Comparative efficacy of Nine Non-Pharmacological Interventions for Fall Prevention in Older Adults: A Systematic Review and Network Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:20:25","doi":"10.21203/rs.3.rs-6880677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56074405-d6e1-4c5a-84d7-af8bb1ec86a2","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T13:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:20:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6880677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6880677","identity":"rs-6880677","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}