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. 2026 Aug 3;21(5):e70096. doi: 10.1111/opn.70096

Comparative Efficacy of Non‐Pharmacological Multicomponent Interventions in Community‐Dwelling Older Adults With Frailty or Prefrailty: A Systematic Review and Network Meta‐Analysis

Huanyu Gao 1, Xuan Li 1, Minmin Leng 2,✉, Zhen Wang 3,✉
PMCID: PMC13433839  PMID: 42547949

ABSTRACT

Aim

The purpose of this study was to compare the efficacy of different non‐pharmacological multicomponent interventions in community‐dwelling older adults with frailty or prefrailty and to determine the most effective non‐pharmacological multicomponent interventions.

Background

Frailty or prefrailty is highly prevalent among community‐dwelling older adults and contributes to significant distress among older adults and increases the caregiver burden. Non‐pharmacological multicomponent interventions are recommended for first‐line management; however, the comparative efficacy among interventions remains unclear.

Methods

A systematic electronic literature search was performed in the PubMed, EMBASE, Cochrane Library, Web of Science, CINAHL, Chinese National Knowledge Infrastructure (CNKI), Wanfang, VIP and Sinomed databases up to July 1, 2025. Randomized controlled trials (RCTs) evaluating the efficacy of non‐pharmacological multicomponent interventions compared with routine care or other interventions in community‐dwelling older adults with frailty or prefrailty were included. A random effects model based on restricted maximum likelihood (REML) estimation was used for the network meta‐analysis. Efficacy was assessed via standardized mean differences with 95% credible intervals, and interventions were ranked via surface under the cumulative ranking curve (SUCRA) probabilities.

Results

Twenty‐two RCTs were included in the analysis. For overall frailty level, exercise + cognitive intervention achieved the highest rank (SUCRA = 84.7%), followed by exercise + cognitive + social support intervention (SUCRA = 83%) and exercise + nutritional intervention (SUCRA = 72%). With respect to motor ability, exercise + nutrition + psychological intervention was the most effective intervention (SUCRA = 77.1%).

Discussion

Non‐pharmacological multicomponent interventions have a positive effect on improving the physical condition of community‐dwelling older adults with frailty or prefrailty. Nurses and care managers should actively prioritize the integration of the above two interventions into personalized frailty or prefrailty care plans, maximizing the efficacy of non‐pharmacological management.

Conclusions

Exercise + cognitive intervention is likely the most effective non‐pharmacological multicomponent intervention for reducing the frailty level, and exercise + nutrition + psychological intervention is likely the most effective non‐pharmacological multicomponent intervention for improving motor ability in community‐dwelling older adults with frailty or prefrailty.

Implications for Practice

The study identified the best non‐pharmacological multicomponent interventions to improve frailty or prefrailty in community‐dwelling older adults, providing a basis for the development of practical interventions in later gerontological nursing practice.

Registration Number

PROSPERO: CRD420251115805.

Keywords: frailty, multicomponent interventions, network meta‐analysis, older adults, prefrailty

Summary

What does this research add to existing knowledge in gerontology?

  • For the older adults with community frailty or prefrailty, exercise combined with cognitive training is the most effective in improving overall frailty, while the combination with psychological support is superior in enhancing motor ability.

  • There is potential for synergy in the non‐pharmacological interventions, and combinations of different types of interventions may benefit community‐dwelling older adults with frailty or prefrailty at the mechanistic level.

What are the implications of this new knowledge for nursing care for and with older adults?

  • Strengthen the education of older adults and nursing staff for the non‐pharmacological multicomponent interventions.

  • Community‐based care centres are advised to develop individualized care plans for the community‐dwelling older adults, combining different non‐pharmacological multicomponent interventions to improve their physical fitness.

  • Implement effective non‐pharmacological multicomponent strategies in community settings through interdisciplinary teams to promote development in relevant areas

How could the findings be used to influence practice, education, research, and policy?

  • Community‐based care centres can specifically promote the benefits of non‐pharmacological multicomponent interventions to improve community‐dwelling older adults' awareness of them.

  • Nurse Managers should update their education of geriatric nurses to include effective non‐pharmacological multicomponent interventions in their training on measures to address frailty or prefrailty.

1. Introduction

The global ageing population has emerged as one of the most significant demographic trends in the 21st century, accompanied by increasingly prominent health challenges associated with ageing. Among these issues, frailty—a geriatric syndrome—is receiving increasing attention. Defined as a clinical condition characterized by an age‐related decline in multisystem physiological reserves, frailty increases the body's vulnerability to stressors. While its clinical manifestations vary, the core features include decreased motor ability, reduced stress resilience and significantly elevated risks of adverse health events such as falls, disability, hospitalization and even death (Fried et al. 2001; Frost et al. 2017; Witham et al. 2020). Therefore, the effective identification and intervention of frailty status hold vital public health significance for improving older adults' quality of life and alleviating the burden of caregiving on both society and families.

Frailty is not a static endpoint but a dynamic developmental process that typically begins with the intermediate stage of prefrailty (Saeed et al. 2022). The prefrailty phase, which is considered reversible, represents a critical window for preventing and delaying the progression of frailty (Sun et al. 2023). Timely and effective interventions during this stage can maximize the restoration of older adults' physiological reserves, thereby preventing their descent into irreversible frailty. Consequently, research on intervention strategies targeting both frail and prefrail older adult populations has become a prominent focus in geriatric medicine.

Currently, non‐pharmacological interventions serve as the primary strategy for preventing and managing frailty or prefrailty owing to their demonstrated advantages in terms of safety, accessibility and sustainability (Nüesch et al. 2013; Sun et al. 2023). Extensive clinical research has validated the effectiveness of various standalone non‐pharmacological interventions, including physical activity (particularly resistance training and mixed physical training) (Bårdstu et al. 2020; Mulasso et al. 2022), nutritional supplementation (such as protein and vitamin D) (Grant 2022), cognitive training (Kouzuki et al. 2020) and personalized management guided by a Comprehensive Geriatric Assessment (CGA) (Yao et al. 2020). However, given that frailty itself constitutes a complex syndrome involving multiple dimensions—physiological, psychological and social—single‐dimensional interventions often prove limited in efficacy. Therefore, non‐pharmacological multicomponent interventions that combine two or more intervention elements are considered to have greater potential to address the multifactorial causes of frailty in a more comprehensive manner (Burton et al. 2021; Hshieh et al. 2015). For these promising interventions to be effectively translated into clinical practice, it is crucial to identify which multicomponent approaches can be feasibly implemented and managed by nursing staff, given that nurses are at the forefront of gerontological nursing.

While numerous randomized controlled trials (RCTs) and traditional pairwise meta‐analyses have investigated the efficacy of non‐pharmacological interventions in alleviating frailty (Eidam et al. 2024; Han et al. 2020), the existing evidence remains significantly limited. First, conventional meta‐analyses can compare only two interventions at a time, making it impossible to evaluate and compare the relative merits of multiple combined multicomponent intervention regimens simultaneously (Bafeta et al. 2014). Second, large‐scale RCTs aimed at direct ‘head‐to‐head’ comparisons of all potential multicomponent interventions are practically unfeasible because of ethical, time and cost constraints. This situation leaves geriatric nurses without high‐quality evidence to determine which combinations of interventions are the ‘optimal’ choices for alleviating frailty or prefrailty states when faced with multiple seemingly effective options.

To address this research gap, network meta‐analysis (NMA) provides an advanced and efficient statistical method (Hutton et al. 2015; Sun et al. 2023). By constructing a network of evidence that includes all relevant interventions, NMA integrates both direct and indirect evidence to enable synchronized comparisons and ranking of multiple treatment efficacy outcomes (Chaimani et al. 2013; Salanti et al. 2014). This approach not only estimates the effectiveness of multicomponent interventions compared with conventional care but also provides relative effectiveness rankings among them, offering more comprehensive and definitive evidence‐based support for clinical practice guideline development. Therefore, this study aims to systematically compare and rank the efficacy of various non‐pharmacological multicomponent interventions for alleviating frailty and improving motor ability among community‐dwelling older adults with frailty or prefrailty through an NMA approach.

2. Methods

This systematic review and NMA was registered at PROSPERO (CRD420251115805). This review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension statement for Network Meta‐Analyses (PRISMA‐NMA) (Hutton et al. 2015).

2.1. Search Strategy

Two authors were involved in developing and carrying out searches across the following databases: PubMed, EMBASE, Cochrane Library, Web of Science, CINAHL, Chinese National Knowledge Infrastructure (CNKI), Wanfang, VIP and Sinomed databases to identify relevant studies that were published up to July 1, 2025. Further relevant publications were identified through the reference lists of the included publications. We also searched existing meta‐analyses related to this topic and downloaded and read the original papers to avoid missing studies that met our inclusion criteria. For each database, the search approach combined both indexing terms and free text terms, utilizing the Boolean operators OR and AND. The detailed search strategies are presented in Supporting Information S1.

2.2. Criteria for Inclusion

Studies were eligible for our NMA if they met all the following criteria:

  1. Participants who were diagnosed with frailty or prefrailty were aged 60 years or older, and whose primary residence was in the community.

  2. Intervention: The intervention group was treated with a non‐drug multicomponent intervention, including two or more interventions in different fields. For example, exercise intervention combined with psychological intervention or cognitive intervention combined with nutritional intervention.

  3. Comparisons: Routine care (maintenance of normal lifestyles or activities, health education, general interventions), multidomain intervention or other nondrug single‐domain interventions different from those in the intervention group.

  4. Outcomes: Frailty or motor ability was used as an outcome measure and was measured using any of the proven frailty assessment tools included in Supporting Information S2.

  5. Study design: Studies with an RCT design were included. In the case of multiple studies with identical data, the study with the largest sample size was given priority.

Studies were excluded if they met the following criteria:

  1. Articles with incomplete data;

  2. Full text not available;

  3. Assessment of debilitating status using only individual indicators (such as grip strength or walking speed);

  4. The study population is debilitated by major diseases (such as cancer);

  5. Articles are reviews, meta‐analyses, systematic reviews, conference papers, academic dissertations, newspapers and letters;

  6. Republished literature.

2.3. Study Selection and Data Extraction

All of the retrieved records were imported into EndNote X21 to exclude duplicate studies. The two authors then worked independently to identify the RCTs that met the inclusion criteria and separately extracted the data. To further assess the eligibility of potential studies, the authors obtained the full text and discussed any disagreements with another author. Data extraction was conducted using a standardized form to collect essential information, including the first author, publication year, study country, setting, type of participants, mean age, intervention and comparison group content/dosage and outcome measures/assessment tools. In addition, we also extracted reports of adverse event occurrence from the included studies.

2.4. Risk of Bias Assessment

The risk of bias was evaluated using the Revised Cochrane risk‐of‐bias tool for randomized trials (RoB 2) (Sterne et al. 2019). This tool evaluates studies across five domains: selection bias resulting from the randomization process, performance bias resulting from variations from planned interventions, detection bias resulting from missing outcome data, attrition bias resulting from the measurement of the outcome and overall biases. The risk of bias was classified as low, high or unclear, as described in the Cochrane Handbook. The risk of bias assessment was performed independently by two authors, and any discrepancies between the two authors were agreed upon after discussion with the third author.

2.5. Statistical Analysis

First, we performed a traditional pairwise meta‐analysis of all of the direct comparisons. We extracted and calculated the mean and standard deviation of the post‐intervention data to measure the effect size. Specifically, standardized mean differences (SMDs) with 95% confidence intervals (95% CIs) were used to express the effect size because not all of the studies utilized the same scale for their specific assessments. The effect size was rated as small (0.2), moderate (0.5) or large (0.8) (Wang et al. 2019). Heterogeneity was evaluated via the I 2 test. I 2 values of 0%, 25%, 50% and 75% correspond to no, low, medium and high heterogeneity, respectively (Wang et al. 2019). A fixed effects model was used to calculate the pooled effect sizes if the data were not significantly heterogeneous. Otherwise, a random effects model was used.

The NMA model was used to compare the effects among different interventions. Direct and indirect evidence from any pair of interventions was combined to generate pooled effect sizes expressed as SMDs with 95% credibility intervals (CIs). The node‐splitting method was used to locally test the consistency assumption (Bucher et al. 1997; Dias et al. 2010). This method involved assessing inconsistency between direct and indirect evidence for specific treatment comparisons within the network. An NMA was conducted on the basis of the consistency model to comprehensively compare the effects of different nondrug interventions. The surface under the cumulative ranking curve (SUCRA) was used to separately rank the different nondrug interventions (Salanti et al. 2011). A larger SUCRA value corresponded to a higher ranking. A p value of < 0.05 indicated statistical significance. Furthermore, visual inspection of the comparison‐adjusted funnel plots was used to assess publication bias.

All of the statistical analyses were conducted in Stata 14.0 via the mvmeta, metan, and network packages.

3. Results

3.1. Study Selection

The electronic literature search yielded 7027 potentially relevant articles. After the removal of duplicates and obviously irrelevant articles, we retrieved 4515 full‐text studies to further evaluate their eligibility; subsequently, 4395 articles were excluded because they did not meet the inclusion criteria. Ultimately, a total of 22 studies involving 11 different interventions were included in this NMA. The detailed screening process is illustrated in Figure 1.

FIGURE 1.

FIGURE 1

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Flow diagram of the process used to search for and select studies.

3.2. Characteristics of the Included Studies

The included studies were published between 2013 and 2025 and were conducted in Australia (n = 2), China (n = 9), Canada (n = 1), Europe (n = 2), Japan (n = 2), Korea (n = 2), Singapore (n = 3) and the United Kingdom (n = 1). The studies involved a total of 6796 participants, and the sample size ranged from 23 to 1520. There were 14 types of interventions for frailty or prefrailty among the included RCTs: exercise + nutrition intervention (EN, n = 7); exercise + cognitive + nutrition + health education intervention (ECNH, n = 5); exercise + cognitive intervention (EC, n = 4); nutrition intervention (N, n = 3); exercise + nutrition + psychological intervention (ENP, n = 2); exercise + health education intervention (EH, n = 2); exercise + cognitive + psychological + health education intervention (ECPH, n = 1); exercise + nutrition + health education intervention (ENH, n = 1); exercise + nutrition + electrotherapy intervention (ENE, n = 1); exercise + cognitive + social support intervention (ECS, n = 1); cognitive + social support intervention (CS, n = 1); exercise + social support intervention (ES, n = 1); exercise intervention (E, n = 1); and cognitive intervention (C, n = 1). Eighteen studies compared non‐pharmacological multicomponent interventions with routine care, and four studies compared non‐pharmacological multicomponent interventions and single interventions of different types. The intervention duration ranged from 8 weeks to 36 months. Regarding the outcome measures and assessment tools, all instruments used for the primary outcome—frailty alleviation—were validated frailty scales, as detailed in Supporting Information S2. For the secondary outcomes, motor ability improvement was assessed using walking speed, gait speed and the number of arm curls completed within 30 s. The characteristics of the included studies are summarized in Supporting Information S3.

3.3. Risk of Bias of Included Studies

In general, five studies were assessed as ‘high risk’, eight studies were assessed as ‘some concerns’, and nine studies were assessed as ‘low risk’. Figure 2 describes the details of the risk of bias.

FIGURE 2.

FIGURE 2

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The result of the risk of bias assessment. (a) Risk of bias graph. (b) Risk of bias summary.

3.4. Traditional Pairwise Meta‐Analysis

3.4.1. Primary Outcome: Frailty Alleviation

Given the significant heterogeneity in the data, we employed a random effects model. ECNH (SMD = −0.26, 95% CI [−0.42, −0.11]), ENP (SMD = −0.25, 95% CI [−0.49, −0.02]), ENE (SMD = −1.14, 95% CI [−1.92, −0.37]), EC (SMD = −2.56, 95% CI [−4.78, −0.35]), EN (SMD = −2.46, 95% CI [−4.83, −0.09]) and ECS (SMD = −2.48, 95% CI [−2.95, −2.02]) significantly alleviated frailty compared with the control group. The remaining interventions did not yield significant changes in terms of frailty alleviation compared with the control group. Details can be found in Supporting Information S4a–d.

3.4.2. Secondary Outcome: Motor Ability Improvement

Direct comparisons between the intervention groups and the control group are presented in Supporting Information S4e–h. Given the significant heterogeneity in the data, a random effects model was implemented. ECNH (SMD = 0.27, 95% CI [0.13, 0.40]), ENE (SMD = 1.60, 95% CI [0.77, 2.43]), ENP (SMD = −0.44, 95% CI [−0.66, 0.22]) and EH (SMD = 0.64, 95% CI [0.21, 1.07]) demonstrated significantly superior effects relative to the control group. However, compared with the control group, neither EN (SMD = 1.12, 95% CI [−0.50, 2.74]) nor EC (SMD = 1.96, 95% CI [−0.72, 4.65]) significantly improved motor ability among community‐dwelling older adults with frailty or prefrailty.

3.5. Network Meta‐Analysis

3.5.1. Primary Outcome: Frailty Alleviation

A total of 22 RCTs involving 6796 participants evaluated the alleviation of frailty associated with different non‐pharmacological multicomponent interventions among community‐dwelling older adults with frailty or prefrailty. The global inconsistency test results demonstrated that there was no significant inconsistency in the overall network (p = 0.5603). The p values of all of the comparisons in the local inconsistency test were greater than 0.05, thus indicating that there was no significant inconsistency at the local level. To combine effect sizes and account for interstudy heterogeneity, we employed a random effects model. The heterogeneity variance (τ 2) in the model was estimated using restricted maximum likelihood (REML) estimation. The degree of heterogeneity was quantified by τ 2 and the I 2 statistic, with τ 2 = 0.18 and I 2 = 99%. The details are shown in Supporting Information S5. The network map is shown in Figure 3a. Each node represents an intervention, and the size of the node is proportional to the number of participants. Furthermore, the lines link the direct comparisons, and the width of the lines is proportional to the number of trials comparing the two interventions.

FIGURE 3.

FIGURE 3

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The network diagrams for all interventions. C, cognitive intervention; CS, cognitive + social support intervention; E, exercise intervention; EC, exercise + cognitive intervention; ECNH, exercise + cognitive + nutrition + health education intervention; ECPH, exercise + cognitive + psychological + health education intervention; ECS, exercise + cognitive + social support intervention; EH, exercise + health education intervention; EN, exercise + nutrition intervention; EN, exercise + nutrition intervention; ENE, exercise + nutrition + electrotherapy intervention; ENH, exercise + nutrition + health education intervention; ENP, nutrition + exercise + psychological intervention; ES, exercise + social support intervention; N, nutrition intervention; RC, routine care.

The relative effects of the different interventions are presented in Figure 4a. The analysis revealed that EC (SMD = −2.23, 95% CI [−3.49, −0.96]), ECS (SMD = −2.47, 95% CI [−4.91, −0.02]) and EN (SMD = −1.66, 95% CI [−2.69, −0.62]) were more effective than routine care was. There was no statistically significant difference observed between the other two comparisons. The results revealed that EC was the most likely intervention to be ranked first (84.7%), followed by ECS (83%) and EN (72%), and the detailed rank probability of the efficacies of different interventions is shown in Figure 4a. The SUCRA details are presented in Supporting Information S5c.

FIGURE 4.

FIGURE 4

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Relative effects of different interventions across all outcome measures. Treatments are ranked according to their probability of being the best treatment. The numbers in grey boxes are SUCRA (surface under the cumulative ranking curve) values and their crnls (credible intervals), which represent the ranks of the treatments. Significant pairwise comparisons are highlighted in dark yellow bold font. (a) Relative effect sizes of efficacy of interventions for alleviating frailty according to network meta‐analysis. (b) Relative effect sizes of efficacy of interventions for improving motor ability according to network meta‐analysis.

3.5.2. Secondary Outcome: Motor Ability Improvement

A total of 15 RCTs, encompassing 3844 participants, evaluated the improvement in motor ability associated with different non‐pharmacological multicomponent interventions among community‐dwelling older adults with frailty or prefrailty. The global inconsistency test indicated no significant inconsistency across the entire network (p = 0.6477). All comparisons in the local inconsistency tests yielded p > 0.05, indicating that there was no significant inconsistency at the local level. To combine effect sizes and account for interstudy heterogeneity, we employed a random effects model. The heterogeneity variance (τ 2) in the model was estimated using restricted maximum likelihood (REML) estimation. The degree of heterogeneity was quantified by τ 2 and the I 2 statistic, with τ 2 = 0.11 and I 2 = 97%. Detailed results are available in Supporting Information S6. The network map (Figure 3b) represents each intervention as a node. Node size is proportional to the number of participants assigned to the respective intervention. Lines link the direct comparisons, and the width of the lines is proportional to the number of trials comparing the two interventions.

The relative effects of the different interventions are presented in Figure 4b. ENP (SMD = −2.17, 95% CI [−3.92, −0.43]) demonstrated a significant advantage over RC (SMD = −1.96, 95% CI [−3.20, −0.72]) in improving motor ability in community‐dwelling older adults with frailty or prefrailty. N (SMD = −1.75, 95% CI [−3.28, −0.23]), ECNH (SMD = −1.74, 95% CI [−3.23, −0.25]) and S (SMD = −1.67, 95% CI [−3.32, −0.02]) had no significant advantage over RC. There was no statistically significant difference observed between the other two comparisons. The results revealed that ENP was the most likely intervention to be ranked first (77.1%), followed by CG (73.1%) and ECPH (67.3%), and the detailed rank probabilities of the efficacies of the different interventions are shown in Figure 4b. The SUCRA details are presented in Supporting Information S6c.

3.6. Publication Bias

In general, the comparison‐adjusted funnel plot generated via visual estimation did not exhibit substantial asymmetry. The effect estimates of each treatment group were approximately symmetrically distributed around the centerline, and most of the data points were localized within the expected area of the comparison‐adjusted funnel plot. No obvious asymmetry or blank area was observed, indicating that there was no evidence of publication bias. The comparison‐adjusted funnel plot is shown in Supporting Information S7.

3.7. Certainty of Evidence Assessment

The evidentiary certainty of network meta‐analysis results was assessed using the CINeMA framework. Detailed results are available in Supporting Information S8. For the primary outcome‐frailty alleviation, there were 105 evidence‐based comparisons with established certainty. Of the 25 direct comparisons, 14 comparisons (RC versus EN, RC versus ENE, RC versus N, RC versus C, EN versus ENE, EN versus E, ECNH versus E, N versus E, C versus E, EN versus N, ECNH versus N, ENE versus N, C versus N and ECNH versus C) were rated as low evidence, whereas the remaining 11 comparisons were rated as very low evidence. Of the 80 indirect comparisons, only two comparisons (RC versus CS, ENP versus ECS) were rated as low evidence, whereas the remaining comparisons were rated as very low evidence. For secondary outcome‐motor ability improvement, there were 66 evidence‐based comparisons with established certainty. Of the 22 direct comparisons, 15 comparisons (RC versus EN, RC versus ENE, RC versus E, RC versus N, RC versus C, EN versus ENE, EN versus E, ECNH versus E, N versus E, C versus E, EN versus N, ECNH versus N, ENE versus N, C versus N and ECNH versus C) were rated as low evidence, whereas the remaining seven comparisons were rated as very low evidence. Of the 44 indirect comparisons, all comparisons were rated as very low evidence.

3.8. Adverse Events and Serious Adverse Events

Of the 22 included RCTs, safety‐related information was reported in 13 studies (59.1%). Adverse events or serious adverse events were explicitly documented in seven studies (31.8%). Falls were the most frequently reported adverse events. All serious adverse events were deemed unrelated to the intervention. Detailed information is available in Supporting Information S9.

4. Discussion

Owing to the high incidence of frailty or prefrailty among community‐dwelling older adults and the increased utilization of non‐pharmacological multicomponent interventions, we integrated data from RCTs as comprehensively as possible. Finally, we compared the effects of 11 different non‐pharmacological multicomponent interventions on frailty and motor ability in community‐dwelling older adults with frailty or prefrailty. These findings provide high‐level evidence for geriatric nurses to select optimal intervention approaches and offer scientific foundations for developing evidence‐based care targeting older adults in frail populations.

4.1. Summary and Interpretation of Findings

Traditional paired meta‐analyses revealed that compared with the control group, the ECNH, ENE, ECS, EC, EN and ENP groups were associated with significant alleviation of frailty. For the NMA, the rank probability demonstrated that EC is most likely to be ranked first (84.7%), ECS is most likely to be ranked second (83%), and EN is most likely to be ranked third (72%). On the basis of the paired analysis and NMA, our study revealed that EC was the most effective intervention for ameliorating frailty, followed by ECS and EN. The results also revealed that for the outcome of motor ability, ENP may be the most effective delivery format for interventions (77.1%).

Non‐pharmacological multicomponent treatments have been recommended for the basic clinical management of frailty; moreover, various non‐pharmacological intervention measures have been implemented. The probability of efficacy ranking demonstrated that among the various non‐pharmacological multicomponent interventions, EC showed the largest effect size for frailty alleviation (SMD = −2.23, 95% CI [−3.49, −0.96]) and had the highest SUCRA probability (84.7%), though the certainty of this evidence was rated as very low. Our results align with those of prior studies and highlight the benefits of EC in alleviating frailty among frail older adults. There are several potential mechanisms underlying this positive effect. From the perspective of neurobiology, exercise can effectively combat neurodegeneration associated with ageing and frailty by stimulating the secretion of neurotrophic factors and optimizing structural and functional connections in the brain. Exercise, particularly as a core component of multicomponent interventions, has been proven to significantly increase the levels of various neurotrophic factors. The level of brain‐derived neurotrophic factor (BDNF), a key protein that regulates neuronal survival, growth and synaptic plasticity, increases in response to exercise—a mechanism considered crucial for the cognitive‐enhancing effects of exercise (Erickson et al. 2011; Kirk‐Sanchez and McGough 2013). Combined cognitive training may further amplify this effect by increasing the brain's cognitive demands (Ledreux et al. 2019). Both resistance training and aerobic exercise increase insulin‐like growth factor‐1 (IGF‐1) levels (Kirk‐Sanchez and McGough 2013; Vints et al. 2024). Since IGF‐1 can cross the blood–brain barrier to promote neurogenesis and angiogenesis—mechanisms that may mediate the regulation of BDNF (Stein et al. 2018) by exercise—these effects collectively alleviate older adult frailty.

Chronic low‐grade inflammation serves as a key driver in the progression of ageing and frailty (Eustáquio et al. 2020; Kong et al. 2024). Exercise activates the prefrontal and parietal networks, enhances multitasking capabilities and neural resource allocation efficiency, promotes functional compensation and strengthens the brain network (De Bruin et al. 2015; Kirk‐Sanchez and McGough 2013) and significantly reduces the expression of pro‐inflammatory factors such as TNF‐α. This reduction in chronic inflammation helps slow muscle loss and cognitive decline (Ibrahim et al. 2023; Tan et al. 2023). In summary, exercise interventions directly increase muscle strength, balance and cardiovascular function in frail older adults, effectively slowing physical decline.

Through targeted training, cognitive interventions improve neurocognitive functions, reducing secondary factors such as reduced mobility and psychological stress caused by cognitive impairment. By working synergistically, these approaches not only address physical limitations but also increase seniors' active engagement through cognitive stimulation. This creates a ‘body–cognition’ bidirectional cycle that comprehensively alleviates frailty symptoms. For gerontological nursing practice, this suggests that integrating brief cognitive training modules into routine exercise sessions led by nurses could maximize frailty reduction.

The rank probability of the curative effect demonstrated that among the interventions, ECS ranked second in terms of effect size for frailty alleviation (SMD = −2.47, 95% CI [−4.91, −0.02]), with an SUCRA probability of 83% (ranking second), but the certainty of evidence was very low. In contrast to EC, ECS integrates social support interventions. First, research has indicated that social support interventions such as emotional and instrumental support can reduce depressive scores, improve life satisfaction and enhance treatment adherence. Individuals with strong social support demonstrated a 2.07‐fold lower risk of frailty than those with poor social support did (Aughterson et al. 2024; Barghouth et al. 2024; Wilhelmson and Eklund 2013). Second, EC synergistically activates both motor and cognitive cortex regions, improving cerebral blood flow perfusion (Rieker et al. 2022) and thereby alleviating neurasthenia. Moreover, social support provides monitoring and motivation, reduces intervention dropout rates, enhances treatment adherence and further reinforces the effectiveness of frailty improvement (Li et al. 2024; Tan et al. 2023). Gerontological nursing professionals should recognize that promoting community resource access and social network strengthening constitute vital elements of frailty management, complementing physical and cognitive training approaches.

The rank probability of the curative effect demonstrated that among the interventions, EN ranked third in terms of effect size for frailty alleviation (SMD = −1.66, 95% CI [−2.69, −0.62]), with an SUCRA probability of 72% (ranking third), but the certainty of evidence was low. Compared with EC, EN primarily replaced cognitive intervention with nutritional intervention. First, from a single perspective, nutritional interventions such as protein (especially leucine) not only activate the mTORC1 pathway to promote muscle protein synthesis but also increase satellite cell proliferation, accelerating muscle injury repair (Drummond et al. 2009). Substances such as vitamin D and calcium regulate calcium–phosphorus metabolism, improve bone density and reduce fracture risk. Vitamins C/E and zinc can scavenge free radicals and minimize DNA/protein oxidative damage (Fekete et al. 2022), thereby improving the nutritional status of frail older adults and reducing complication risks. Second, at the molecular level, exercise‐induced microdamage to muscle fibres provides raw materials through nutritional supplementation. This synergistically activates the mTOR pathway, enhances insulin sensitivity, promotes nutrient transport into muscle tissue, reduces systemic inflammatory factors, strengthens muscle strength and improves gait speed—a key indicator of frailty—and ultimately facilitates the alleviation of frailty in older adult populations (Choi et al. 2021; Smith 2011). This finding highlights the need for nurses to integrate systematic nutritional screening and dietary monitoring into standard exercise‐based care plans, ensuring that nutritional support is tailored to optimize muscle repair and functional recovery in frail older adults.

The results of motor ability analyses indicate that ENP demonstrated the greatest improvement in motor ability (SMD = −2.17, 95% CI [−3.92, −0.43]) with an SUCRA of 77.1%, but the certainty of this evidence was very low. The mechanisms of exercise and nutritional interventions have been previously described. Psychological intervention primarily serves as a safeguard for exercise and nutritional interventions, which can enhance self‐efficacy and manage fear, thereby ensuring that exercise and nutritional programmes are accepted and sustained by older adults in the long term, ultimately allowing for the continuous accumulation of positive physiological changes (Azizan et al. 2013; Meyer et al. 2025). A case study involving an older adult female comprehensively integrated psychological skills training—including goal setting, self‐instructional training and positive reinforcement—alongside physical exercise, which resulted in significant improvements not only in physical parameters but also in exercise motivation, self‐efficacy and quality of life (Cordoba et al. 2022). This finding suggests that in gerontological nursing, nurses should develop individualized care plans that integrate psychological support strategies into exercise and nutritional programmes, thereby leveraging the synergistic effects of these three components to enhance older adults' motor ability.

4.2. Limitations

Several limitations of this review should be noted. (1) The secondary outcome of motor ability demonstrated unstable results because of the absence of standardized criteria for motor ability in the included studies. In the future, standardized tools for measuring motor ability can be developed and validated according to actual conditions to reduce heterogeneity among studies and improve the reliability and accuracy of the research. (2) The current rankings rely solely on indirect evidence, as few studies directly compared nonpharmacological multicomponent interventions, necessitating future research to bridge this knowledge gap through direct comparison of different multicomponent interventions. (3) Therapeutic efficacy data were extracted from baseline to post‐intervention phases, with conclusions primarily reflecting short‐term outcomes rather than long‐term effects. Subsequent studies should focus on long‐term efficacy across implementation approaches and conduct follow‐up observations. (4) This review included only Chinese and English literature from 9 databases and excluded studies in other languages and grey literature, which may introduce publication bias. (5) The very low certainty of evidence for most comparisons limits the reliability of SUCRA rankings; nurses should implement these interventions with caution and monitor individual responses closely.

4.3. Implications for Practice and Research

This NMA clarifies the effectiveness of non‐pharmacological multicomponent interventions in alleviating frailty and improving motor ability in community‐dwelling older adults with frailty or prefrailty. These results can provide evidence‐based practice guidance for clinical staff, gerontological nurses, and family caregivers. Specifically, by applying non‐pharmacological multicomponent interventions in a targeted manner, personalized care plans for frail older adults can be optimized, and their frailty level and motor ability can be improved. The results of this study can also assist rehabilitation teams in quickly identifying targeted and efficient intervention tools and simultaneously providing support for improving smart service systems for the care of older adults, along with promoting the integrated application of non‐pharmacological multicomponent interventions and traditional care.

5. Conclusion

In this study, the effects of non‐pharmacological multicomponent interventions on frailty level and motor ability in community‐dwelling older adults with frailty or prefrailty were systematically evaluated via an NMA. The results demonstrated that EC is likely the most effective non‐pharmacological multicomponent intervention for alleviating frailty and that ENP is likely the most effective non‐pharmacological multicomponent intervention for improving motor ability in community‐dwelling older adults with frailty or prefrailty. This study provides high‐quality evidence for the precise application of non‐pharmacological multicomponent interventions in the management of frailty, which can not only reduce the risk of drug use but also alleviate the burden of care on families and institutions by increasing older adults' autonomy and social participation.

Author Contributions

Study design: M.L., Z.W. Data collection: H.G., X.L. Data analysis: H.G., X.L. Study supervision: M.L., Z.W. Manuscript writing: H.G., X.L. Critical revisions for important intellectual content: M.L., Z.W.

Funding

This work was supported by Natural Science Foundation of Shandong Province (Grant No. ZR2024QG032) and Postdoctoral Innovation Project of Shandong Province (Grant No. SDCX‐ZG‐202303057).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting Information: S1 Search strategy.

Supporting Information: S2 A list of pre‐specified eligible instruments for measuring frailty.

Supporting Information: S3 Characteristics of the included studies.

Supporting Information: S4 The results of traditional pairwise meta‐analysis.

Supporting Information: S5 The results of network meta‐analysis related to frailty improvement.

Supporting Information: S6 The results of network meta‐analysis related to motor ability improvement.

Supporting Information: S7 Comparison‐adjusted funnel plot of all studies.

Supporting Information: S8 Certainty of evidence assessment results.

Supporting Information: S9 Adverse events and serious adverse events of included randomized controlled trials.

OPN-21-e70096-s001.docx (2MB, docx)

Acknowledgements

This systematic review and network meta‐analysis has been both a challenging and enriching journey. We sincerely thank all those who provided guidance, support and encouragement, making the completion of this project possible.

Contributor Information

Minmin Leng, Email: lengmm1992@126.com.

Zhen Wang, Email: wangz@sdfmu.edu.cn.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information: S1 Search strategy.

Supporting Information: S2 A list of pre‐specified eligible instruments for measuring frailty.

Supporting Information: S3 Characteristics of the included studies.

Supporting Information: S4 The results of traditional pairwise meta‐analysis.

Supporting Information: S5 The results of network meta‐analysis related to frailty improvement.

Supporting Information: S6 The results of network meta‐analysis related to motor ability improvement.

Supporting Information: S7 Comparison‐adjusted funnel plot of all studies.

Supporting Information: S8 Certainty of evidence assessment results.

Supporting Information: S9 Adverse events and serious adverse events of included randomized controlled trials.

OPN-21-e70096-s001.docx (2MB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from International Journal of Older People Nursing are provided here courtesy of Wiley

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