Abstract
Background: Cognitive decline poses a significant challenge to healthy aging. While exercise is widely recognized for its cognitive benefits, the comparative efficacy of different exercise modalities and optimal intervention protocols for specific cognitive domains in older adults remains unclear.
Objective: This network meta-analysis aimed to systematically compare the effects of five exercise modalities—resistance training, aerobic exercise, mind-body exercise, multicomponent exercise, and high-intensity interval training (HIIT)—on global cognitive function and major cognitive domains in cognitively healthy older adults, and to identify optimal intervention protocols and population subgroups most likely to benefit.
Methods: A total of 58 randomized controlled trials (RCTs) were included, encompassing 4,349 healthy older adults from diverse geographical regions. Comprehensive searches were conducted in major electronic databases for RCTs evaluating exercise interventions on cognitive outcomes in adults aged 60 years and older. A network meta-analysis assessed the relative effects of each exercise modality on global cognition, executive function (including inhibitory control, task-switching ability, and working memory), and memory function. Subgroup analyses were performed based on intervention frequency, duration, participant age, and geographic region.
Results: Resistance training demonstrated the greatest improvement in global cognitive function (SMD = 0.55) and inhibitory control (SMD = 0.31, SUCRA = 82.1%), particularly with twice-weekly sessions of 45 min over 12 weeks. Mind-body exercise was most effective for executive function, especially task-switching ability (SMD = −0.58, SUCRA = 85.1%) and working memory (SMD = 2.45), with high-frequency, moderate-duration protocols yielding optimal results. Aerobic exercise was the most effective modality for enhancing memory function (SMD = 0.42). The largest cognitive benefits were observed in participants aged 65–75 years and in studies conducted in Asia.
Conclusion: Different exercise modalities provide domain-specific cognitive benefits in healthy older adults. Personalized exercise prescriptions—emphasizing resistance training for global cognition, mind-body exercise for executive function, and aerobic exercise for memory—should be considered in clinical and public health settings. These findings support the integration of structured exercise interventions into aging and dementia prevention strategies, with particular attention to optimal protocol design and population targeting.
1 Introduction
With increasing life expectancy and declining birth rates worldwide, the proportion of older adults is growing rapidly, presenting critical challenges to public health systems. Cognitive decline, even in the absence of clinical dementia, can lead to diminished quality of life, impaired independence, and greater risk for institutionalization (Livingston et al., 2020). While pharmacological treatments for cognitive impairment remain limited in efficacy and carry potential adverse effects, physical exercise has emerged as a promising non-pharmacological intervention for promoting brain health (Northey et al., 2018). Numerous randomized controlled trials (RCTs) and meta-analyses have demonstrated the positive effects of physical activity on global cognition in older adults (Sofi et al., 2011). Proposed mechanisms include enhanced cerebral blood flow, increased expression of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), reduced systemic inflammation, and improved neurovascular coupling (Kirk-Sanchez and McGough, 2014). Despite this growing body of evidence, several knowledge gaps remain. First, most prior studies treat cognition as a single composite construct, neglecting its domain-specific organization. Cognitive functioning in older adults encompasses multiple subdomains—such as inhibitory control, task switching, working memory, episodic memory, verbal fluency, and attention—each of which may respond differently to various types of exercise (Gheysen et al., 2018). For example, resistance training has been linked to improved executive function, whereas mind–body practices such as Tai Chi may offer greater benefits for memory and attention (Zhang et al., 2023). Second, intervention efficacy may be modulated by exercise dosage, including frequency, session duration, and total program length. However, these parameters are often inconsistently reported or analyzed across studies, limiting the ability to recommend optimized protocols (Liu-Ambrose et al., 2010; Falck et al., 2019). In addition, regional and cultural factors may influence the acceptability and efficacy of certain exercise types, especially in non-Western populations where mind–body exercises are more commonly practiced (Cheng et al., 2016). To address these gaps, we conducted an updated and comprehensive meta-analysis integrating network and pairwise comparisons based on 58 unique RCTs involving 3,943 cognitively healthy older adults. This study aims to (1) evaluate and rank the effects of different exercise interventions on both global and domain-specific cognitive outcomes; (2) investigate the moderating role of intervention parameters including frequency, session length, and duration; and (3) offer evidence-based recommendations for domain-specific and culturally tailored exercise prescriptions in aging populations.
2 Methods
This systematic review and meta-analysis were conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions (Higgins et al., 2019) and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure transparent and reproducible reporting (Moher et al., 2009).
2.1 Literature search
We systematically searched PubMed, Web of Science, ScienceDirect, CNKI, and Wanfang Data for randomized controlled trials (RCTs) evaluating the effects of exercise interventions on cognitive function in healthy older adults. Search terms included combinations of “exercise” OR “physical activity,” AND “cognitive function” OR “cognition,” AND “older adults” OR “elderly,” AND “memory” OR “executive function,” AND “randomized controlled trial” OR “RCT,” AND “healthy aging.” Searches included both English and Chinese publications and covered all articles published before January 1, 2024. Additionally, reference lists from relevant systematic reviews and meta-analyses were manually screened to identify additional studies (Gallardo-Gomez et al., 2022; Gavelin et al., 2021). The complete search strategy is available in Supplementary Material 1.
2.2 Inclusion and exclusion criteria
Inclusion Criteria: (1) The participants were healthy older adults, mean aged 55 years and above, regardless of gender or nationality, without a diagnosis of cognitive impairment or dementia. (2) The exercise interventions in this study were classified into five main types based on their assumed physiological mechanisms. This classification was made to reflect the different effects each type of exercise may have on cognitive function in older adults. The exercise types and their rationale for classification are as follows: Resistance Training (RES), this type includes exercises that primarily aim to improve muscle strength and endurance through controlled movements against resistance (e.g., weightlifting, using resistance bands). Resistance training is known for its positive impact on brain-derived neurotrophic factor (BDNF), which supports neuroplasticity and cognitive functions such as inhibitory control and executive function. It is classified separately due to its emphasis on building muscular strength and the distinct physiological responses it triggers compared to aerobic exercise. Aerobic exercise (AER), aerobic exercise involves activities that increase cardiovascular fitness through sustained, rhythmic exercises such as walking, running, cycling, or swimming. The focus of aerobic exercise is on improving heart and lung function. It is classified as a distinct category because it has been shown to enhance memory, hippocampal neurogenesis, and cerebral blood flow, which directly contribute to improvements in memory and other cognitive functions in older adults. High-Intensity Interval Training (HIIT), HIIT consists of alternating between short bursts of intense activity followed by periods of lower-intensity recovery. This form of exercise is classified separately due to its unique approach, focusing on intense, time-efficient bouts of physical activity. HIIT has been shown to improve cardiovascular health, metabolic efficiency, and cognitive function, particularly in areas related to executive control and processing speed. Physical and Mental Training (PMT), this category encompasses exercises that require both physical movement and mental focus, such as Tai Chi, yoga, and other mind-body practices. These exercises are classified separately because they engage both physical and cognitive aspects simultaneously, promoting relaxation, stress reduction, and improvements in executive functions such as task-switching and working memory. This classification reflects the holistic nature of these interventions, which are designed to improve both physical balance and cognitive flexibility. Multi-modal Exercise (CEX), multi-modal exercise interventions combine different forms of exercise (e.g., a mix of aerobic, resistance, and flexibility training). The classification as multi-modal exercise is based on the premise that combining multiple exercise types may provide broader cognitive benefits by engaging different physiological systems. This comprehensive approach is intended to enhance multiple aspects of physical fitness and cognitive health simultaneously. This classification aligns with the World Health Organization (WHO) exercise guidelines, which categorize exercise types based on their physiological and functional outcomes. The goal of this classification was to examine the distinct cognitive effects of each exercise modality, as they are thought to influence various cognitive domains differently, such as memory, executive function, and overall cognitive health. (3) Control groups received either health education or maintained their usual activities. (4) The primary outcome of this study was overall cognitive function, which was assessed using the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Secondary outcomes focused on specific aspects of executive function and memory, including: Inhibitory Control, assessed using the Stroop Test (measuring response inhibition to conflicting stimuli) and the Go/No-Go Task (evaluating suppression of prepotent responses). Task-Switching Ability, measured through the Trail Making Test Part B (assessing cognitive flexibility between alphanumeric sets) and the Wisconsin Card Sorting Test (evaluating rule-based switching and adaptability). Working Memory, evaluated using the N-back Test (spatial and verbal updating) and the Backward Corsi Block-Tapping Task (visuospatial manipulation and retention). Memory Function, assessed with Logical Memory Recall (short-and long-term episodic memory) and the Rey Auditory Verbal Learning Test (multitribal verbal encoding and retrieval). These secondary outcomes were selected to provide a deeper understanding of how different exercise modalities impact various aspects of cognitive function beyond overall cognition. (5) The included studies were randomized controlled trials (RCTS) to ensure high-quality evidence.
Exclusion Criteria: (1) Studies with duplicate publications. (2) Studies lacking extractable outcome measures or reporting incomplete data. (3) Non-RCTs or trials with inadequate control group comparisons.
2.3 Study selection
After the initial search, all identified articles were imported into NoteExpress reference management software to remove duplicates. Two independent reviewers screened the titles and abstracts based on the pre-defined inclusion and exclusion criteria. Studies that did not meet the criteria were excluded, and any disagreements between reviewers were resolved by a third-party consensus (Markov et al., 2023). Full-text articles of the remaining studies were retrieved and further evaluated for eligibility through a second screening.
2.4 Data extraction
Data were extracted by two independent reviewers using a standardized data extraction form based on the Cochrane Handbook for Systematic Reviews guidelines. The extracted data included: Study Characteristics: Title, first author, publication year, study design. Participant Characteristics: Sample size, mean age, gender distribution. Intervention Details: Type, duration, frequency, intensity of exercise intervention, and control group details. Outcome Measures: Pre- and post-intervention data for cognitive function and specific executive functions. To ensure data accuracy, all extracted data were cross-verified by the two reviewers, with discrepancies resolved through consensus or consultation with a third reviewer.
2.5 Quality assessment
The risk of bias in the included studies was assessed independently by two reviewers using the Cochrane Collaboration’s Risk of Bias Tool (Courel-Ibanez et al., 2019). This tool evaluates potential biases across seven domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other biases. Studies were classified into three categories based on their risk of bias: Low risk: 4 or more domains with low risk. Moderate risk: 2–3 domains with low risk. High risk: 1 or fewer domains with low risk. Additionally, publication bias was assessed using funnel plots and Egger’s test, with statistical significance set at p < 0.05 (Egger et al., 1997).
2.6 Statistical analysis
All statistical analyses were conducted using Review Manager 5.3 and Stata 17.0 software. The standardized mean differences (SMD) were calculated using Cohen’s d for studies with larger sample sizes, and Hedge’s g was used for studies with smaller sample sizes to correct for potential bias. Specifically: Cohen’s d was computed using the formula: d = (M1–M2)/SD pooled. where M1 and M2 represent the means of the experimental and control groups, respectively, and SD pooled is the pooled standard deviation. Hedge’s g was applied for studies with smaller sample sizes using the formula: where N1 and N2 are the sample sizes of the experimental and control groups, respectively. For studies reporting data in non-standard formats, appropriate formulas were used to convert the data into mean and standard deviations (SD) (Higgins et al., 2019).
To evaluate network transitivity, we compared the clinical and methodological characteristics of studies to ensure that the multiple treatment comparisons were adequately comparable. The inconsistency and consistency models were tested using both the design-by-treatment interaction model (a global approach) and the node-splitting test (a local approach). The node-splitting test was used to identify any substantial discrepancies between direct and indirect comparisons for each treatment, evaluating the consistency of the results.
The probability values were compiled and presented as the surface under the cumulative ranking curve. The exercise interventions were ranked by using the surface under the cumulative ranking curve and mean rank. Model fit was assessed using the Deviance Information Criterion (DIC), where a lower DIC value indicated a better model fit. To evaluate consistency in the network, node-splitting analysis was conducted to compare direct and indirect estimates, with a p-value > 0.05 indicating no significant inconsistency. Additionally, heterogeneity across studies was assessed using the I2 statistic, with I2 > 50% considered indicative of substantial heterogeneity. To determine the ranking of exercise interventions, we used surface under the cumulative ranking curve (SUCRA) values. A SUCRA value close to 100% indicates a high probability of being the most effective intervention, whereas a value close to 0% suggests the least effective intervention. Interventions were ranked based on their mean SUCRA scores, allowing a probabilistic ranking of effectiveness across cognitive domains.
The analysis focused on five key indicators of cognitive function: overall cognitive function, inhibitory control, task-switching ability, working memory, and memory. Heterogeneity among studies was assessed using the I2 statistic, where an I2 value above 50% indicated substantial heterogeneity, and a random-effects model was applied. Otherwise, a fixed-effects model was used (Talar et al., 2022). A network meta-analysis was conducted to compare the relative effectiveness of different exercise modalities, allowing for both direct and indirect comparisons across studies (Wei et al., 2022) .
3 Results
3.1 Study selection
A total of 8,051 articles were retrieved from databases. After removing duplicates, 7,665 articles proceeded to the screening phase. Titles and abstracts were reviewed, resulting in the exclusion of 7,069 articles unrelated to the research topic, leaving 596 articles that potentially met the inclusion criteria. Subsequently, the full texts of these articles were further examined for detailed screening, leading to the exclusion of 538 articles that did not meet the inclusion criteria (Research object, n = 213; Review, n = 54; Year, n = 165; Outcome indicator, n = 106). Finally, 58 randomized controlled trials were included. The flow diagram in Figure 1 illustrates the detailed process for each stage.
FIGURE 1
Study selection.
3.2 Characteristics of included studies
A total of 58 randomized controlled trials (RCTs) were included in this meta-analysis, encompassing 4,349 healthy older adults from diverse geographical regions, including Asia (China, Japan, Vietnam), Europe (Italy, France, Spain, Portugal, Sweden, Netherlands), North America (USA, Canada), South America (Brazil, Colombia), Africa (South Africa), and the Middle East (Tunisia). The average age of participants ranged from 60 to 83.7 years, with most studies targeting individuals over 65 years old. The studies involved a wide variety of exercise modalities, which were categorized into five main types according to their physiological characteristics and cognitive engagement: Aerobic exercise (AER), Resistance training (RES), High-intensity interval training (HIIT), Physical and mental training (PMT)—including Tai Chi and yoga, and Multi-modal training (MMT) that combines two or more types of interventions. AER was the most frequently applied intervention across studies, followed by PMT and RES. The intervention duration ranged from 4 to 65 weeks, with session lengths between 30 and 90 min, and frequencies from 1 to 5 sessions per week. Most interventions lasted for at least 12 weeks, ensuring sufficient exposure for cognitive effects.
The control conditions varied across studies: 22 trials employed no exercise or waitlist controls, 13 used health education, 10 adopted stretching or balance training, and others involved minimal physical activity. The diversity of control conditions ensured a broad spectrum of comparison standards for evaluating intervention effects. As for outcome assessment, the studies examined a variety of cognitive domains, including: Overall cognitive function (MoCA, MMSE), Inhibitory control (e.g., Stroop test, Go/No-Go task), Task switching (e.g., TMT-B, Wisconsin Card Sorting Test), Working memory (e.g., n-back task, backward digit span), Verbal and episodic memory (e.g., logical memory test, RAVLT). These outcome measures allowed for a detailed analysis of the differential impact of exercise modalities on specific cognitive functions in aging populations. Detailed characteristics of the included studies are presented in Table 1 (Albinet et al., 2016; Ansai et al., 2015; Baklouti et al., 2023; Brown et al., 2009; Carral et al., 2007; Cassilhas et al., 2007; Castaño et al., 2022; Chen et al., 2014; Chen et al., 2020; Coelho et al., 2012; Coetsee and Terblanche, 2017; Colcombe and Kramer, 2003; Courel-Ibanez et al., 2019; Egger et al., 1997; Engeroff et al., 2022; Fabre et al., 2002; Farinha et al., 2021; Gallardo-Gomez et al., 2022; García-Garro et al., 2020; Gavelin et al., 2021; Gheysen et al., 2018; Gothe et al., 2017; Gu et al., 2019; Guo et al., 2024; Hanley et al., 2010; Higgins et al., 2019; Huang et al., 2022; Iuliano et al., 2015; Jiang et al., 2020; Jonasson et al., 2016; Coelho-Júnior et al., 2020; Kimura et al., 2010; Kirk-Sanchez and McGough, 2014; Kovacevic et al., 2020; Lachman et al., 2006; Lavretsky et al., 2011; Legault et al., 2011; Li, 2019; Lim et al., 2024; Liu et al., 2018; Liu, 2021; Liu D. M. et al., 2023; Jiang, 2020; Li et al., 2022; Liu-Ambrose et al., 2010; Livingston et al., 2020; Loprinzi et al., 2019; Lu et al., 2016; Maki et al., 2012; Markov et al., 2023; Miyazaki et al., 2022; Moher et al., 2009; Moreira et al., 2018; Mortimer et al., 2012; Muscari et al., 2010; Nagamatsu et al., 2012; Nguyen and Kruse, 2012; Nishiguchi et al., 2015; Northey et al., 2018; Norton et al., 2014; Oken et al., 2006; Peng, 2021; Petersen et al., 2014a; Predovan et al., 2012; Riske et al., 2017; Schley et al., 2006; Smith et al., 2010; Sofi et al., 2011; Sun et al., 2011; Sun et al., 2015; Sun, 2021; Talar et al., 2022; Taylor-Piliae et al., 2010; Timmons et al., 2018; Tsai et al., 2017; van de Rest et al., 2014; Varela et al., 2018; Vaughan et al., 2014; Venturelli et al., 2010; Wei et al., 2022; Williamson et al., 2009; Witte et al., 2016; Xu, 2022; Yang et al., 2020; Zhang et al., 2022, 2023; Zheng et al., 2023).
TABLE 1
| References | Country | Sample size | Age (years) | Exercise type | Duration (min) | Frequency | Weeks | Control group | Outcome measures |
| Vizioli et al., 2009 | Italy | 120 | 69.2 ± 2.7 | AER | 60 | 3/w | 52 | Health education | ① |
| Kovacevic et al., 2020 | Canada | 61 | 72.0 ± 5.7 | HIIT/AER | 28/47 | 3/w | 12 | No exercise | ②⑤ |
| Oken et al., 2006 | USA | 135 | 72.1 ± 4.9 | PMT/AER | 90/60 | 1/w | 26 | No exercise | ②⑤⑦ |
| Brown et al., 2009 | Australia | 120 | 79.6 ± 6.3 | RES | 60 | 2/w | 25 | No exercise | ②③⑤ |
| Fabre et al., 2002 | France | 16 | 65.6 ± 1.8 | AER | 60 | 2/w | 8 | No exercise | ⑤ |
| Coetsee and Terblanche, 2017 | South Africa | 67 | 62.7 ± 5.7 | RES/HIIT/AER | 30/30/47 | 3/w | 16 | No exercise | ② |
| Farinha et al., 2021 | Portugal | 74 | 72.4 ± 5.1 | AER/MMT | 45/45 | 2/w | 28 | No exercise | ① |
| Castaño et al., 2022 | Brazil | 12 | 70.0 ± 8.1 | RES | 60 | 2/w | 16 | Not arranged | ③⑤⑥ |
| Hanley et al., 2010 | USA | 32 | 72.9 ± 9.3 | RES | 60 | 2/w | 4 | No exercise | ②③⑤ |
| Albinet et al., 2016 | France | 36 | 60–75 | AER | 60 | 2/w | 21 | Stretching | ②④⑤ |
| Legault et al., 2011 | USA | 36 | 76.5 ± 4.9 | AER | 75 | 2/w | 16 | No exercise | ②④⑤ |
| Predovan et al., 2012 | Canada | 50 | 67.9 ± 6.2 | AER | 60 | 3/w | 12 | No exercise | ② |
| Iuliano et al., 2015 | Italy | 60 | 66.9 ± 11.7 | RES/HIIT | 30/30 | 1/w | 12 | No exercise | ②③ |
| Coelho-Júnior et al., 2020 | Brazil | 26 | 66.7 ± 4.7 | RES | 60 | 2/w | 24 | No exercise | ①⑤ |
| Carral et al., 2007 | Spain | 62 | 68.4 ± 3.4 | RES/AER | 45/45 | 2/w | 21 | Not arranged | ① |
| Mortimer et al., 2012 | USA | 90 | 67.8 ± 5.6 | PMT/AER | 50/50 | 3/w | 40 | No exercise | ①②③⑤⑥⑦ |
| Timmons et al., 2018 | Ireland | 84 | 69.3 ± 3.5 | AER/RES/MMT | 24/24/24 | 3/w | 12 | No exercise | ① |
| Williamson et al., 2009 | USA | 102 | 77.4 ± 4.3 | MMT | 50 | 3/w | 52 | Health education | ①②⑤ |
| Sun et al., 2015 | China | 138 | 69.2 ± 5.9 | PMT | 60 | 2/w | 25 | No exercise | ① |
| Ansai et al., 2015 | Brazil | 69 | 82.4 ± 2.4 | MMT/RES | 60/NR | 3/w | 16 | No exercise | ①⑥ |
| Kimura et al., 2010 | Japan | 119 | 74.3 ± 5.5 | RES | 90 | 2/w | 12 | Health education | ③ |
| Jonasson et al., 2017 | Sweden | 58 | 68.7 ± 2.7 | AER | 45 | 3/w | 26 | Stretching | ③④⑤ |
| Lavretsky et al., 2011 | USA | 73 | 70.6 ± 7.3 | PMT | 50 | 2/w | 10 | No exercise | ①⑤ |
| Nguyen and Kruse, 2012 | Germany | 96 | 68.9 ± 5.1 | PMT | 60 | 2/w | 26 | No exercise | ③ |
| Lachman et al., 2006 | USA | 210 | 74.9 ± 6.9 | RES | 35 | 3/w | 26 | No exercise | ⑤ |
| Venturelli et al., 2010 | Italy | 23 | 83.7 ± 6.2 | RES | 45 | 3/w | 12 | No exercise | ① |
| Nagamatsu et al., 2012 | Canada | 86 | 74.5 ± 3.5 | RES/AER | 60 | 2/w | 26 | Balance training | ① |
| Moreira, 2017 | Brazil | 45 | 83.6 ± 3.9 | MMT | 50 | 3/w | 16 | No exercise | ① |
| Gothe et al., 2017 | USA | 108 | 62.1 ± 5.6 | PMT | 60 | 3/w | 8 | Stretching | ③⑤ |
| Cassilhas et al., 2007 | Colombia | 43 | 67.7 ± 0.9 | RES | 60 | 3/w | 24 | No exercise | ⑤⑦ |
| Nishiguchi et al., 2015 | Japan | 48 | 73.3 ± 5.1 | MMT | 90 | 1/w | 12 | No exercise | ①③⑤ |
| Smiley, 2008 | USA | 57 | 70.2 ± 4.5 | AER | 50 | 3/w | 42 | Stretching | ② |
| Vaughan et al., 2014 | Australia | 49 | 68.8 ± 3.3 | MMT | 60 | 2/w | 16 | No exercise | ②③⑤ |
| Taylor-Piliae et al., 2010 | USA | 93 | 69.2 ± 6.2 | PMT/MMT | 45/45 | 4/w | 25 | No exercise | ⑤⑥ |
| Liu-Ambrose et al., 2010 | Canada | 88 | 69.6 ± 2.9 | RES | 60 | 2/w | 52 | Stretching | ②③⑤ |
| Tsai et al., 2017 | China | 42 | 66.3 ± 4.4 | AER | 40 | 3/w | 24 | Balance training | ① |
| van de Rest et al., 2014 | Netherlands | 58 | 77.8 ± 8.4 | RES | 30 | 2/w | 24 | No exercise | ②③⑤⑥⑦ |
| Varela et al., 2018 | Spain | 39 | 81.1 ± 8.2 | AER | 15 | 7/w | 65 | No exercise | ①⑤ |
| Witte et al., 2016 | Germany | 54 | 68.4 ± 4.6 | AER | 60 | 2/w | 21 | No exercise | ① |
| Maki et al., 2012 | Japan | 150 | 72.0 ± 4.0 | AER | 90 | 1/w | 13 | Health education | ③⑤⑥ |
| Chen et al., 2020 | China | 29 | 62.4 ± 3.1 | PMT/AER | 90/90 | 5/w | 24 | Not arranged | ③ |
| Chen et al., 2014 | China | 125 | 66 ± 11.8 | AER | 45 | 4/w | 52 | No exercise | ① |
| Jiang et al., 2020 | China | 30 | 64.0 ± 3.7 | PMT | 60 | 3/w | 12 | No exercise | ②③④ |
| Li, 2019 | China | 74 | 66.3 ± 4.5 | PMT | 60 | 1/w | 40 | No exercise | ①②④⑥⑦ |
| Liu et al., 2018 | China | 73 | 63.1 ± 3.3 | PMT | 60–90 | 5/w | 12 | No exercise | ① |
| Liu, 2021 | China | 34 | 64.5 ± 4.3 | PMT | 60 | 5/w | 12 | No exercise | ②③④ |
| Peng, 2021 | China | 32 | 67.3 ± 3.7 | PMT | 60 | 3/w | 13 | Health education | ②③④ |
| Sun et al., 2011 | China | 138 | 69.2 ± 5.9 | PMT | 60 | 2/w | 12 | No exercise | ① |
| Sun, 2021 | China | 30 | 61.4 ± 1.9 | PMT | 60 | 3/w | 12 | No exercise | ①②⑤ |
| Xu, 2022 | China | 30 | 62.3 ± 1.8 | PMT | 60 | 3/w | 12 | No exercise | ② |
| Zhang et al., 2022 | China | 60 | 60–70 | PMT | 30–60 | 4–5/w | 52 | No exercise | ① |
| Miyazaki et al., 2022 | Japan | 59 | 67.2 | PMT | 30 | 3/week | 4 | No exercise intervention | ② |
| Mortimer et al., 2012 | China | 60 | 67.3 ± 5.3 | PMT | 50 | 3/week | 40 | No exercise intervention | ②③ |
| Baklouti et al., 2023 | Tunisia | 30 | 64.00 ± 3.02 | PMT | 60 | 2/week | 100 | Stretching exercises | ② |
| García-Garro et al., 2020 | Spain | 107 | 69.98 ± 7.83 | PMT | 60 | 2/week | 12 | Balance training | ③ |
| Nguyen and Kruse, 2012 | Vietnam | 73 | 69.23 ± 5.3 | PMT | 60 | 2/week | 24 | No exercise intervention | ③ |
| Lu et al., 2016 | China | 31 | 72.8 ± 6.7 | PMT | 60 | 3/week | 16 | No exercise intervention | ② |
| Yang et al., 2020 | China | 26 | 66.3 ± 4.9 | PMT | 45 | 3/week | 8 | Health Education | ②③④ |
Included in the study characteristics table.
① Overall cognitive function; ② Inhibitory control; ③ Task-switching ability; ④ Working memory; ⑤ Memory function. RES, resistance training; AER, aerobic exercises; HIIT, high-intensity interval training; PMT, physical and mental training; CEX, multi-modal exercise interventions; CON, control group.
3.3 Risk of bias assessment
The methodological quality of the included randomized controlled trials was evaluated using the Cochrane Risk of Bias Tool (RoB 1.0), focusing on five key domains. Each domain was rated as having low risk, some concerns, or high risk of bias. Table 2 provides a summary of the assessment results across all 58 studies. Randomization process was assessed as low risk in 47 studies (81.0%), with 9 studies (15.5%) rated as having some concerns and 2 studies (3.4%) as high risk. Allocation concealment was judged low risk in 36 studies (62.1%), with 15 studies (25.9%) showing some concerns and 7 (12.0%) assessed as high risk. For blinding of outcome assessors, 30 studies (51.7%) were rated as low risk, 18 (31.0%) as some concerns, and 10 (17.3%) as high risk. Missing outcome data was well handled in most studies, with 52 (89.7%) showing low risk, 4 (6.9%) showing some concerns, and 2 (3.4%) as high risk. Selective reporting was evaluated as low risk in 49 studies (84.5%), with 7 (12.1%) having some concerns and 2 (3.4%) showing high risk.
TABLE 2
| Domain | Low risk | Some concerns | High risk |
| 1. Randomization process | 47 (81.0%) | 9 (15.5%) | 2 (3.4%) |
| 2. Allocation concealment | 36 (62.1%) | 15 (25.9%) | 7 (12.0%) |
| 3. Blinding of assessors | 30 (51.7%) | 18 (31.0%) | 10 (17.3%) |
| 4. Missing outcome data | 52 (89.7%) | 4 (6.9%) | 2 (3.4%) |
| 5. Selective reporting | 49 (84.5%) | 7 (12.1%) | 2 (3.4%) |
Summary of risk of bias assessment across included studies (N = 58).
Overall, the majority of studies were deemed to have a low risk of bias, though issues related to allocation concealment and blinding were present in a subset of studies. These findings suggest a generally robust evidence base, with moderate risk primarily concentrated in reporting transparency and performance-related domains.
3.4 Meta-analysis results
3.4.1 Overall cognitive function
(1) Main effects of different exercise interventions
As shown in Table 3, all four exercise modalities demonstrated significant improvements in overall cognitive function among older adults. Mind-body exercise yielded the greatest effect (SMD = 0.62, 95% CI: 0.38–0.86, P < 0.001), followed by resistance training (SMD = 0.55, 95% CI: 0.21–0.89, P = 0.002) and aerobic exercise (SMD = 0.49, 95% CI: 0.23–0.75, P < 0.001). Multicomponent interventions also showed a significant, though relatively smaller, effect (SMD = 0.38, 95% CI: 0.05–0.71, P = 0.030). The SUCRA rankings further supported the superiority of mind-body exercise and resistance training. The optimal protocols for each intervention are summarized in Table 3.
TABLE 3
| Intervention type | Effect size (SMD) | 95% CI | P-value | SUCRA (%) | Optimal protocol |
| Resistance training | 0.55 | 0.21 to 0.89 | 0.002 | 83.3 | 2/w, 45 min, 12 wk |
| Aerobic exercise | 0.49 | 0.23 to 0.75 | <0.001 | 68.5 | 3/w, 60 min, 21 wk |
| Mind-body exercise | 0.62 | 0.38 to 0.86 | <0.001 | 48.4 | 3/w, 60 min, 12 wk |
| Multicomponent | 0.38 | 0.05 to 0.71 | 0.03 | 49.3 | 3/w, 50 min, 16 wk |
Summary of effects of different exercise interventions on overall cognition.
SMD, standardized mean difference; SUCRA, surface under the cumulative ranking curve, representing the probability of being ranked as the most effective intervention.
(2) Subgroup analyses
Table 4 presents the results of subgroup analyses examining potential sources of heterogeneity. Intervention frequency: High-frequency exercise interventions (≥3 times/week) produced greater cognitive benefits (SMD = 0.63, 95% CI: 0.42–0.84, P < 0.001) compared to low-frequency interventions (<3 times/week, SMD = 0.41, 95% CI: 0.12–0.70, P = 0.007). Session duration: Sessions of 45–60 min were associated with the largest effect size (SMD = 0.61, 95% CI: 0.40–0.82, P < 0.001), while both shorter (<60 min) and longer (> 60 min) sessions also showed significant benefits. Intervention duration: Mid-term interventions (12–24 weeks) yielded the most pronounced effects (SMD = 0.68, 95% CI: 0.46–0.90, P < 0.001), followed by long-term (>24 weeks, SMD = 0.55) and short-term interventions (≤12 weeks, SMD = 0.45). Age group: Middle-aged participants (65–75 years) demonstrated the largest cognitive gains (SMD = 0.52, 95% CI: 0.28–0.76, P < 0.001), with younger (<65 years, SMD = 0.43) and older ( > 75 years, SMD = 0.48) adults also benefiting. Region: Subgroup analysis by geographic region indicated that studies conducted in Asia reported the greatest effects (SMD = 0.62, 95% CI: 0.40–0.84, P < 0.001), followed by Europe (SMD = 0.51) and the Americas (SMD = 0.43). These findings indicate that higher frequency, moderate session duration, and mid-term interventions, as well as interventions targeting Asian populations and those aged 65–75 years, are associated with greater improvements in overall cognitive function.
TABLE 4
| Subgroup variable | Category | Effect size (SMD) | 95% confidence interval | P-value |
| Intervention frequency | Low frequency (<3 times/week) | 0.41 | 0.12 to 0.70 | 0.007 |
| High frequency (≥3 times/week) | 0.63 | 0.42 to 0.84 | <0.001 | |
| Session duration | <60 min | 0.39 | 0.11 to 0.67 | 0.009 |
| 45–60 min | 0.61 | 0.40 to 0.82 | <0.001 | |
| >60 min | 0.5 | 0.12 to 0.88 | 0.01 | |
| Intervention duration | Short-term (≤12 weeks) | 0.45 | 0.21 to 0.69 | 0.001 |
| Mid-term (12–24 weeks) | 0.68 | 0.46 to 0.90 | <0.001 | |
| Long-term (>24 weeks) | 0.55 | 0.18 to 0.92 | 0.004 | |
| Age | Younger (<65 years) | 0.43 | 0.15 to 0.71 | 0.004 |
| Middle-aged (65–75 years) | 0.52 | 0.28 to 0.76 | <0.001 | |
| Older (>75 years) | 0.48 | 0.20 to 0.76 | 0.002 | |
| Region | Asia | 0.62 | 0.40 to 0.84 | <0.001 |
| Europe | 0.51 | 0.32 to 0.70 | <0.001 | |
| Americas | 0.43 | 0.21 to 0.65 | 0.001 |
Subgroup analysis of factors influencing overall cognitive function.
3.4.2 Inhibitory control
As presented in Table 5, all exercise interventions were associated with improvements in inhibitory control among older adults. Mind-body exercise demonstrated the largest effect size (SMD = −0.45, 95% CI: −0.72 to −0.18, P < 0.001), followed by HIIT (SMD = −0.35, 95% CI: −0.81 to 0.11, P = 0.13), resistance training (SMD = −0.32, 95% CI: −0.65 to −0.01, P = 0.04), and aerobic exercise (SMD = −0.28, 95% CI: −0.49 to −0.07, P = 0.008). According to SUCRA rankings, resistance training had the highest probability of being the optimal intervention (SUCRA = 82.1%), followed by HIIT (76.1%), mind-body exercise (40.7%), and aerobic exercise (35.1%).
TABLE 5
| Exercise type | Effect size (SMD) | 95% confidence interval | P-value | SUCRA (%) |
| Resistance training | −0.32 | −0.65 to −0.01 | 0.04 | 82.1 |
| HIIT | −0.35 | −0.81 to 0.11 | 0.13 | 76.1 |
| Mind-body exercise | −0.45 | −0.72 to −0.18 | <0.001 | 40.7 |
| Aerobic exercise | −0.28 | −0.49 to −0.07 | 0.008 | 35.1 |
Comparative effects of exercise interventions on inhibitory control.
SMD, standardized mean difference; SUCRA, surface under the cumulative ranking curve, representing the probability of being ranked as the most effective intervention.
Subgroup analyses are summarized in Table 6. High-frequency interventions (≥3 times/week) yielded greater benefits for inhibitory control (SMD = −0.38, 95% CI: −0.61 to −0.15, P = 0.001) compared to low-frequency interventions (<3 times/week, SMD = −0.31, 95% CI: −0.52 to −0.10, P = 0.003). Sessions lasting 45–60 min showed the largest effects (SMD = −0.36, 95% CI: −0.55 to −0.17, P < 0.001), while both shorter and longer sessions had smaller or non-significant effects. Interventions with mid-term duration (12–24 weeks) demonstrated the most pronounced improvement (SMD = −0.41, 95% CI: −0.64 to −0.18, P < 0.001), whereas short-term and long-term interventions showed smaller or non- significant effects.
TABLE 6
| Subgroup variable | Category | Effect size (SMD) | 95% confidence interval | P-value |
| Intervention frequency | Low frequency (<3 times/week) | −0.31 | −0.52 to −0.10 | 0.003 |
| High frequency (≥3 times/week) | −0.38 | −0.61 to −0.15 | 0.001 | |
| Session duration | <60 min | −0.29 | −0.56 to −0.02 | 0.04 |
| 45–60 min | −0.36 | −0.55 to −0.17 | <0.001 | |
| >60 min | −0.11 | −0.98 to 0.76 | 0.81 | |
| Intervention duration | Short-term (≤12 weeks) | −0.3 | −0.51 to −0.09 | 0.005 |
| Mid-term (12–24 weeks) | −0.41 | −0.64 to −0.18 | <0.001 | |
| Long-term (>24 weeks) | −0.22 | −0.89 to 0.45 | 0.51 | |
| Age | Younger (<65 years) | −0.25 | −0.68 to 0.18 | 0.25 |
| Middle-aged (65–75 years) | −0.36 | −0.55 to −0.17 | <0.001 | |
| Older (>75 years) | −0.31 | −0.74 to 0.13 | 0.17 | |
| Region | Asia | −0.48 | −0.79 to −0.17 | 0.003 |
| Europe | −0.3 | −0.50 to −0.10 | 0.003 | |
| Americas | −0.27 | −0.59 to 0.05 | 0.09 |
Subgroup analysis of factors influencing inhibitory control.
Regarding age, the most significant improvements were observed in the 65–75 years subgroup (SMD = −0.36, 95% CI: −0.55 to −0.17, P < 0.001), while younger and older participants did not show statistically significant benefits. Regionally, studies conducted in Asia reported the greatest improvements (SMD = −0.48, 95% CI: −0.79 to −0.17, P = 0.003), followed by Europe and the Americas.
Collectively, these findings indicate that resistance training and mind-body exercise, especially when delivered at moderate frequency (≥3 times/week) and duration (45–60 min/session, 12–24 weeks), are particularly effective for enhancing inhibitory control in older adults. The greatest benefits appear to occur among participants aged 65–75 years and in Asian populations.
3.4.3 Task-switching ability
As shown in Table 7, mind-body exercise produced the most substantial improvement in task-switching ability among older adults (SMD = −0.58, 95% CI: −0.89 to −0.27, P < 0.001; SUCRA = 85.1%), with statistically significant benefits compared to other interventions. Resistance training (SMD = −0.21, 95% CI: −0.45 to 0.03, P = 0.08; SUCRA = 51.4%), aerobic exercise (SMD = −0.18, 95% CI: −0.47 to 0.11, P = 0.23; SUCRA = 40.3%), and HIIT (SMD = −0.35, 95% CI: −0.81 to 0.11, P = 0.13; SUCRA = 39.2%) also showed trends toward improvement, but did not reach statistical significance.
TABLE 7
| Exercise type | Effect size (SMD) | 95% Confidence interval | P value | SUCRA (%) |
| Mind-body exercise | −0.58 | −0.89 to −0.27 | <0.001 | 85.1 |
| Resistance training | −0.21 | −0.45 to 0.03 | 0.08 | 51.4 |
| Aerobic exercise | −0.18 | −0.47 to 0.11 | 0.23 | 40.3 |
| HIIT | −0.35 | −0.81 to 0.11 | 0.13 | 39.2 |
Comparative effects of exercise interventions on task-switching ability.
SMD, standardized mean difference; SUCRA, surface under the cumulative ranking curve, reflecting the likelihood of being ranked as the most effective intervention.
Subgroup analyses are presented in Table 8. Higher intervention frequency (≥3 times/week) was associated with greater improvements in task-switching ability (SMD = −0.40, 95% CI: −0.78 to −0.02, P = 0.04), compared to low frequency (<3 times/week, SMD = −0.32, 95% CI: −0.54 to −0.10, P = 0.004). Sessions of 45–60 min resulted in the largest effects (SMD = −0.39, 95% CI: −0.64 to −0.14, P = 0.002), while both shorter and longer sessions were less effective or not statistically significant. Mid-term interventions (12–24 weeks) yielded the most pronounced benefits (SMD = −0.43, 95% CI: −0.72 to −0.14, P = 0.004), whereas short-term and long-term durations showed weaker or non-significant effects. With respect to age, middle-aged participants (65–75 years) experienced the greatest gains (SMD = −0.37, 95% CI: −0.58 to −0.16, P < 0.001), while younger (<65 years) and older (> 75 years) groups did not achieve significant improvements. Regionally, studies conducted in Asia reported the largest effects (SMD = −0.51, 95% CI: −0.82 to −0.20, P = 0.001), compared to Europe and the Americas.
TABLE 8
| Subgroup variable | Category | Effect size (SMD) | 95% confidence interval | P-value |
| Exercise type | Resistance training | −0.21 | −0.45 to 0.03 | 0.08 |
| Aerobic exercise | −0.18 | −0.47 to 0.11 | 0.23 | |
| Mind-body exercise | −0.58 | −0.89 to −0.27 | <0.001 | |
| HIIT | −0.34 | −1.13 to 0.45 | 0.41 | |
| Intervention Frequency | Low frequency (<3 times/week) | −0.32 | −0.54 to −0.10 | 0.004 |
| High frequency (≥3 times/week) | −0.4 | −0.78 to −0.02 | 0.04 | |
| Session Duration | <60 min | −0.25 | −0.52 to 0.02 | 0.07 |
| 45–60 min | −0.39 | −0.64 to −0.14 | 0.002 | |
| >60 min | −0.34 | −0.76 to 0.08 | 0.11 | |
| Intervention Duration | Short-term (≤12 weeks) | −0.31 | −0.55 to −0.07 | 0.01 |
| Mid-term (12–24 weeks) | −0.43 | −0.72 to −0.14 | 0.004 | |
| Long-term (>24 weeks) | −0.03 | −0.65 to 0.59 | 0.92 | |
| Age | Younger (<65 years) | −0.19 | −0.81 to 0.43 | 0.54 |
| Middle-aged (65–75 years) | −0.37 | −0.58 to −0.16 | <0.001 | |
| Older (>75 years) | −0.28 | −1.23 to 0.67 | 0.57 | |
| Region | Asia | −0.51 | −0.82 to −0.20 | 0.001 |
| Europe | −0.24 | −0.48 to 0.00 | 0.05 | |
| Americas | −0.31 | −0.70 to 0.08 | 0.12 |
Subgroup analysis of factors influencing inhibitory control.
In summary, mind-body exercise, particularly when delivered at moderate frequency (≥3 times/week), with session durations of 45–60 min and intervention periods of 12–24 weeks, is especially effective for enhancing task-switching ability in older adults. Middle-aged participants and those in Asian settings appear to benefit the most from these interventions.
3.4.4 Working memory
As presented in Table 9, both aerobic exercise (SMD = 0.58, 95% CI: 0.12 to 1.04, P = 0.01) and mind-body exercise (SMD = 2.45, 95% CI: 1.48 to 3.42, P < 0.001) significantly improved working memory among older adults, with mind-body exercise demonstrating a notably larger effect size.
TABLE 9
| Subgroup variable | Category | Effect size (SMD) | 95% Confidence interval | P-value |
| Exercise type | Aerobic exercise | 0.58 | 0.12 to 1.04 | 0.01 |
| Mind-body exercise | 2.45 | 1.48 to 3.42 | <0.001 | |
| Intervention frequency | Low frequency (<3 times/week) | 1.32 | −0.15 to 2.79 | 0.08 |
| High frequency (≥3 times/week) | 1.68 | 0.89 to 2.47 | <0.001 | |
| Session duration | 45–60 min | 1.89 | 1.12 to 2.66 | <0.001 |
| Intervention duration | Short-term (≤12 weeks) | 1.72 | 0.45 to 2.99 | 0.007 |
| Mid-term (12–24 weeks) | 1.05 | −0.32 to 2.42 | 0.13 | |
| Age | Middle-aged (65–75 years) | 1.86 | 1.09 to 2.63 | <0.001 |
| Region | Asia | 2.23 | 1.26 to 3.20 | <0.001 |
| Europe | 0.48 | −0.07 to 1.03 | 0.08 |
Subgroup analysis of factors influencing working memory.
Subgroup analyses (Table 9) showed that high-frequency interventions (≥3 times/week) yielded greater improvements in working memory (SMD = 1.68, 95% CI: 0.89 to 2.47, P < 0.001) than low-frequency interventions. Sessions of 45–60 min (SMD = 1.89, 95% CI: 1.12 to 2.66, P < 0.001) and short-term interventions (≤ 12 weeks, SMD = 1.72, 95% CI: 0.45 to 2.99, P = 0.007) were associated with the most pronounced benefits. Middle-aged participants (65–75 years) experienced the greatest improvements (SMD = 1.86, 95% CI: 1.09 to 2.63, P < 0.001). Regional analysis indicated that interventions conducted in Asia produced the largest effect (SMD = 2.23, 95% CI: 1.26 to 3.20, P < 0.001), while studies from Europe showed a smaller and non-significant improvement.
In summary, mind-body exercise, particularly when delivered at high frequency, with moderate session duration and short-term intervention period, is especially effective for enhancing working memory in older adults, with middle-aged individuals and Asian populations benefiting the most.
3.4.5 Memory function
As shown in Table 10, all exercise interventions produced positive effects on memory function among older adults. Mind-body exercise exhibited the greatest effect size (SMD = 0.58, 95% CI: 0.34 to 0.82, P < 0.001), followed by aerobic exercise (SMD = 0.42, 95% CI: 0.23 to 0.61, P < 0.001), resistance training (SMD = 0.35, 95% CI: 0.12 to 0.58, P = 0.003), and multicomponent exercise (SMD = 0.28, 95% CI: −0.02 to 0.58, P = 0.07), with the last not reaching statistical significance.
TABLE 10
| Subgroup variable | Category | Effect size (SMD) | 95% Confidence interval | P-value |
| Exercise type | Resistance training | 0.35 | 0.12 to 0.58 | 0.003 |
| Aerobic exercise | 0.42 | 0.23 to 0.61 | <0.001 | |
| Mind-body exercise | 0.58 | 0.34 to 0.82 | <0.001 | |
| Multicomponent exercise | 0.28 | −0.02 to 0.58 | 0.07 | |
| Intervention frequency | Low frequency (<3 times/week) | 0.32 | 0.11 to 0.53 | 0.003 |
| High frequency (≥3 times/week) | 0.51 | 0.32 to 0.70 | <0.001 | |
| Session duration | <60 min | 0.38 | 0.15 to 0.61 | 0.001 |
| 45–60 min | 0.49 | 0.31 to 0.67 | <0.001 | |
| >60 min | 0.3 | −0.12 to 0.72 | 0.16 | |
| Intervention duration | Short-term (≤12 weeks) | 0.36 | 0.17 to 0.55 | <0.001 |
| Mid-term (12–24 weeks) | 0.53 | 0.32 to 0.74 | <0.001 | |
| Long-term (>24 weeks) | 0.48 | 0.11 to 0.85 | 0.01 | |
| Age | Younger (<65 years) | 0.31 | 0.03 to 0.59 | 0.03 |
| Middle-aged (65–75 years) | 0.46 | 0.30 to 0.62 | <0.001 | |
| Older (>75 years) | 0.39 | 0.12 to 0.66 | 0.006 | |
| Region | Asia | 0.62 | 0.38 to 0.86 | <0.001 |
| Europe | 0.38 | 0.21 to 0.55 | <0.001 | |
| Americas | 0.41 | 0.18 to 0.64 | 0.001 |
Comparative effects of exercise interventions on memory function.
Subgroup analyses (Table 10) revealed that high-frequency interventions (≥3 times/week, SMD = 0.51, 95% CI: 0.32 to 0.70, P < 0.001), moderate session duration (45–60 min, SMD = 0.49, 95% CI: 0.31 to 0.67, P < 0.001), and mid-term intervention duration (12–24 weeks, SMD = 0.53, 95% CI: 0.32 to 0.74, P < 0.001) produced the largest improvements in memory function. All age groups benefited, with the most pronounced effect in middle-aged participants (65–75 years, SMD = 0.46, 95% CI: 0.30 to 0.62, P < 0.001). Regional analysis showed that studies conducted in Asia reported the greatest improvement (SMD = 0.62, 95% CI: 0.38 to 0.86, P < 0.001), followed by the Americas and Europe.
In summary, mind-body exercise, especially when delivered with high frequency, moderate session duration, and mid-term intervention period, is particularly effective for improving memory function in older adults, with the most substantial benefits observed in middle-aged individuals and Asian populations.
4 Discussion
Cognitive decline, including mild cognitive impairment and dementia, is a critical challenge in global aging (Petersen et al., 2014b; Liu J. et al., 2023). Our network meta-analysis systematically compared five exercise modalities across multiple cognitive domains in cognitively healthy older adults, incorporating a significant number of Chinese RCTs to improve geographic representativeness and reveal population-specific effects. This study identified not only the optimal exercise protocols for different cognitive domains but also highlighted protocol- and population-specific patterns that advance the precision of exercise prescription in aging societies.
4.1 Impact of resistance training on cognitive function
Our network meta-analysis found that resistance training was the most effective intervention for improving global cognitive function in cognitively healthy older adults, with a pooled effect size of SMD = 0.55. In addition, the effect on inhibitory control was also the most pronounced among all exercise modalities (SMD = 0.31, SUCRA = 82.1%). Notably, our subgroup analysis indicated that the optimal protocol consisted of 12 weeks of resistance training, performed twice per week, with each session lasting 45 min, which produced the largest and most significant cognitive gains. These benefits were particularly evident in participants aged 65–75 years and in studies conducted in Asian regions. Our findings are consistent with, but in some cases exceed, those reported in previous meta-analyses. For example, Barha et al. (2017) found that resistance training improved global cognition (SMD = 0.22, 95% CI: 0.09–0.36) and executive function in older adults, while Northey et al. (2018) observed effect sizes of SMD = 0.13–0.22 for resistance-based interventions in adults over 50. The larger effect size in our analysis may be attributed to differences in participant characteristics, higher adherence rates, or optimized training protocols in the included studies, especially those from East Asia.
Mechanistically, the cognitive benefits of resistance training are supported by substantial neurobiological evidence. Resistance training has been shown to elevate brain-derived neurotrophic factor (BDNF) levels, which supports synaptic plasticity and hippocampal neurogenesis, both critical for cognitive health (Schley et al., 2006; Loprinzi et al., 2019). Coelho et al. (2012) demonstrated that a 10-week program of lower limb resistance training increased plasma BDNF by 65.2% in older participants, which is linked to improved memory and reduced age-related neurodegeneration. Additional mechanisms may include stimulation of vascular endothelial growth factor, growth hormone, and IGF-1, as well as reductions in inflammatory cytokines (Engeroff et al., 2022). Taken together, our results support a clear, evidence-based recommendation for resistance training (twice weekly, 45 min per session, for at least 12 weeks) as the optimal protocol to enhance global cognition and inhibitory control in older adults. This approach is accessible, cost-effective, and consistent with WHO guidelines (Ahlskog et al., 2011), and should be considered as a key component of cognitive health promotion strategies in aging populations.
4.2 Impact of exercise interventions on executive function
In our network meta-analysis, mind-body exercise (e.g., Tai Chi, Baduanjin) demonstrated the most significant benefits for executive function, particularly in task-switching ability (SMD = −0.58, SUCRA = 85.1%) and working memory (SMD = 2.45). Resistance training also produced clear improvements in inhibitory control (SMD = 0.31, SUCRA = 82.1%), while aerobic exercise contributed moderate but positive effects on executive domains. Our subgroup analyses further revealed that the optimal protocol for executive function improvement involved high-frequency interventions (≥3 times/week), moderate session duration (45–60 min), and short- to mid-term durations (12–24 weeks). The greatest cognitive benefits were observed in participants aged 65–75 years and in Asian populations.
These results are consistent with, and in some cases extend, previous literature: Gheysen et al. (2018) performed a meta-analysis and reported that mind-body exercises led to moderate improvements in cognitive flexibility and working memory (Hedges’ g = 0.36, 95% CI: 0.15–0.58). Our results suggest a larger effect size, which may reflect differences in population structure, cultural familiarity with mind-body practices, or higher adherence in the included Asian studies. Barha et al. (2017) identified that resistance training improved executive function in older adults (SMD = 0.22, 95% CI: 0.09–0.36), consistent with our findings for inhibitory control. Northey et al. (2018) reported that combined and aerobic exercise programs also benefit executive function (SMD ≈ 0.14–0.20), in line with our observation of positive, though comparatively smaller, effects for aerobic interventions. Possible mechanisms underlying these improvements include enhanced activation and plasticity of the prefrontal cortex, upregulation of neurotrophic factors (such as BDNF), improved cerebrovascular function, and reductions in stress-related hormones (Colcombe and Kramer, 2003; Kramer and Colcombe, 2018). Mind-body exercise, with its emphasis on attentional control, sequencing, and body awareness, may optimally stimulate neural circuits responsible for executive processing.
In summary, our data support prioritizing mind-body exercise (≥3 times/week, 45–60 min/session, 12–24 weeks) as the most effective intervention for enhancing executive function, especially task-switching and working memory, in older adults. Resistance training remains the preferred approach for improving inhibitory control, and both modalities can be integrated for broader executive benefits.
4.3 Impact of exercise interventions on memory function
Our network meta-analysis revealed that mind-body exercise exhibited the largest effect on memory function (SMD = 0.58), followed by aerobic exercise (SMD = 0.42) and resistance training (SMD = 0.35) among healthy older adults. The optimal protocol for memory improvement was characterized by high-frequency intervention (≥3 times/week), moderate session duration (45–60 min), and mid-term intervention duration (12–24 weeks). The greatest memory benefits were observed in participants aged 65–75 years and in Asian regions.
These findings are consistent with previous high-quality reviews and meta-analyses: Zheng et al. (2023) reported in a large-scale network meta-analysis that mind-body exercise and aerobic training significantly improved memory performance (SMD = 0.39 and 0.31, respectively), closely matching the effect sizes observed in our study. Blondell et al. (2014) concluded that aerobic interventions of at least 24 weeks’ duration produced more pronounced memory gains (mean difference: 0.41), reinforcing our subgroup results that longer, sustained exercise is particularly effective for memory enhancement. Smith et al. (2010) found a smaller effect size for aerobic interventions (SMD = 0.13 for memory), which may be due to differences in intervention intensity, sample characteristics, or adherence.
Neurobiological mechanisms underpinning these benefits include increased hippocampal neurogenesis and synaptic plasticity, mediated by exercise-induced elevation of BDNF and IGF-1, improved cerebrovascular function, and reductions in chronic inflammation (Tao et al., 2019; Riske et al., 2017; Liu J. et al., 2023). Mind-body exercise may further enhance memory through stress reduction, attention regulation, and improved executive control, all of which support memory encoding and retrieval. Taken together, our results recommend mind-body and aerobic exercise—particularly high-frequency, moderate-duration, mid-term interventions—for the enhancement of memory function in older adults. These findings support the integration of structured exercise programs into aging and dementia prevention strategies, with specific emphasis on Asian populations and the middle-aged older who demonstrated the most robust benefits.
4.4 Implications for public health and clinical practice
The results of our network meta-analysis provide a strong foundation for precision exercise prescriptions to promote cognitive health in older adults. By identifying the optimal exercise modality and protocol for each cognitive domain, our findings support the development of individualized intervention strategies based on specific cognitive goals and baseline characteristics. Resistance training is recommended as the primary intervention for enhancing overall cognitive function and inhibitory control. Mind-body exercise (e.g., Tai Chi, Baduanjin) is most effective for improving working memory and task-switching ability, making it suitable for those seeking to boost executive function. Aerobic exercise should be prioritized for individuals targeting memory enhancement.
For sedentary but cognitively healthy older adults, community-based group programs that combine aerobic and resistance training (e.g., 20 min of brisk walking plus 15 min of bodyweight exercises) can maximize cognitive, physical, and social benefits. For those with mobility limitations, low-intensity, seated mind-body exercises (such as chair Tai Chi or adaptive yoga) provide accessible means to improve balance, executive function, and engagement without excessive physical stress.
Widespread promotion of these evidence-based exercise interventions could yield significant public health gains—not only by delaying cognitive decline and reducing dementia risk, but also by lowering healthcare costs and enhancing quality of life (Blondell et al., 2014; Norton et al., 2014). Public health campaigns should highlight the cognitive benefits of regular physical activity, integrating this message into routine health promotion for older adults. Crucially, while our findings underscore the effectiveness of specific protocols, long-term adherence and embedding exercise into daily life are essential for consolidating cognitive benefits and achieving sustainable impact. Moving forward, clinical guidelines and public health initiatives should emphasize personalized, sustainable exercise routines that are tailored to individual health status, functional capacity, and cognitive goals. This precision approach will maximize benefit at both the individual and societal level in the context of global population aging.
4.5 Study limitations and future directions
Despite the strengths of this study, several limitations should be acknowledged. First, the variability in cognitive assessment tools (e.g., MoCA, Stroop Test) across studies may limit comparability. Future research should aim to standardize cognitive outcome measures to facilitate more consistent comparisons. Additionally, this study primarily focused on cognitive function, executive function, and memory, without considering other cognitive domains such as attention and language fluency. Future studies should broaden their scope to include these domains, offering a more comprehensive understanding of how exercise influences cognitive health. Finally, while this study explored the effects of various exercise modalities, the intensity of these interventions was not examined in detail. Future research should investigate how varying exercise intensities affect cognitive outcomes, with the goal of developing more refined exercise prescriptions for older adults.
5 Conclusion
In summary, this network meta-analysis provides robust evidence that different exercise modalities yield domain-specific cognitive benefits in cognitively healthy older adults. Resistance training was identified as the most effective intervention for improving global cognitive function and inhibitory control, particularly when delivered as twice-weekly sessions of 45 min over 12 weeks. Mind-body exercise demonstrated the greatest improvements in executive function, including task-switching ability and working memory, especially with high-frequency, moderate-duration protocols. Aerobic exercise was most effective for enhancing memory function, with sustained benefits observed in mid- to long-term interventions.
Our results also highlight that personalized exercise prescriptions, tailored to individual cognitive needs and functional capacity, should be prioritized in both clinical and public health contexts. The cognitive gains were most pronounced in middle-aged older adults (65–75 years) and among Asian populations, suggesting the need for culturally and demographically sensitive interventions.
Statements
Data availability statement
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
HH: Conceptualization, Methodology, Writing – review & editing. JZ: Writing – original draft, Writing – review and editing. FZ: Formal Analysis, Methodology, Writing – original draft. FL: Writing – original draft, Writing – review and editing. ZW: Writing – original draft, Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Social Science Foundation of China (No. 23CTY018) and Jiangsu Province Social Science Fund Project in 2021 (No. 21TYD005).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2025.1510773/full#supplementary-material
References
1
Ahlskog J. E. Geda Y. E. Graff-Radford N. R. Petersen R. C. (2011). Physical exercise as a preventive or disease-modifying treatment of dementia and brain aging.Mayo Clin. Proc.86876–884. 10.4065/mcp.2011.0252
2
Albinet C. T. Boucard G. Bouquet C. A. Audiffren M. (2016). Aerobic training and inhibition in late adulthood: A double-blind, randomized controlled study.Neurobiol. Aging3778–86. 10.1016/j.neurobiolaging.2015.09.017
3
Ansai J. H. Rebelatto J. R. Forjaz C. L. M. (2015). Resistance training improves dynamic balance in older community-dwelling men: A randomized controlled trial.Clin. Interventions Aging101319–1323. 10.2147/CIA.S84637
4
Baklouti S. Fekih-Romdhane F. Guelmami N. Bonsaksen T. Baklouti H. Aloui A. et al (2023). The effect of web-based Hatha yoga on psychological distress and sleep quality in older adults: A randomized controlled trial. Complement. Ther. Clin. Pract.5010171510.1016/j.ctcp.2022.101715
5
Barha C. K. Hsu C. L. ten Brinke L. Liu-Ambrose T. (2017). Biological sex and exercise type impact cognitive gains in older adults: A meta-analysis of randomized controlled trials.Front. Aging Neurosci.9:259. 10.3389/fnagi.2017.00259
6
Blondell S. J. Hammersley-Mather R. Veerman J. L. (2014). Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies.BMC Public Health14:510. 10.1186/1471-2458-14-510
7
Brown A. D. McMorris C. A. Longman R. S. Ryujin D. T. (2009). Effects of resistance training on cognitive function in older adults.J. Aging Phys. Activity17439–452. 10.1123/japa.17.4.439
8
Carral J. M. del Valle M. de Paz J. A. Pereira D. (2007). Resistance exercise improves cognitive function in Spanish older adults.Eur. J. Sport Sci.781–89. 10.1080/17461390701643072
9
Cassilhas R. C. Viana V. A. R. Grassmann V. Santos R. T. Santos R. F. Tufik S. et al (2007). The impact of resistance exercise on the cognitive function of the elderly.Med. Sci. Sports Exerc.391401–1407. 10.1249/mss.0b013e318060111f
10
Castaño S. D. Pérez M. R. Rodríguez M. S. García P. A. González L. R. Pérez J. M. (2022). Effects of physical activity on the cognitive function in older adults: A randomized controlled trial. J. Cogn. Enhanc.6, 30–40.
11
Chen S. Liu J. Song Y. (2020). Effects of combined aerobic and mind-body exercise on cognitive function in older adults: A randomized controlled study.Chinese J. Sports Med.39135–142. 10.3760/cma.j.cn121430-20191224-00721
12
Chen Z. Q. Zhang S. H. Wang L. (2014). Effects of aerobic exercise on cognitive function in older adults: A randomized controlled trial.Chinese J. Gerontol.343986–3988. 10.3969/j.issn.1005-9202.2014.14.056
13
Cheng C. Zhao X. Zhang M. Bai F. (2016). Absence of Rtt109p, a fungal-specific histone acetyltransferase, results in improved acetic acid tolerance of Saccharomyces cerevisiae. FEMS Yeast Res. 16:fow010. 10.1093/femsyr/fow010
14
Coelho F. M. Siqueira A. M. Silva G. B. Souza L. D. Costa D. R. (2012). Resistance training and cognitive function in older adults. J. Aging. Phys. Act.20, 77–85. 10.1123/japa.20.1.77
15
Coelho-Júnior H. J. Oliveira Gonçalves I. D. Sampaio R. A. C. Sampaio P. Y. S. Lusa Cadore E. Calvani R. et al (2020). Effects of combined resistance and power training on cognitive function in older women: A randomized controlled trial. Int. J. Environ. Res. Public Health17:3435. 10.3390/ijerph17103435
16
Coetsee C. Terblanche E. (2017). The effect of three different exercise training modalities on cognitive and physical function in a healthy older population.Eur. Rev. Aging Phys. Activity14:13. 10.1186/s11556-017-0183-5
17
Colcombe S. Kramer A. F. (2003). Fitness effects on the cognitive function of older adults: A meta-analytic study.Psychol. Sci.14125–130. 10.1111/1467-9280.t01-1-01430
18
Courel-Ibanez J. Vetrovsky T. Dadova K. Pallares J. G. Steffl M. (2019). Health benefits of beta-hydroxy-beta-methylbutyrate (hmb) supplementation in addition to physical exercise in older adults: A systematic review with meta-analysis.Nutrients11:2207. 10.3390/nu11092082
19
Egger M. Davey S. G. Schneider M. Minder C. (1997). Bias in meta-analysis detected by a simple, graphical test.BMJ315629–634. 10.1136/bmj.315.7109.629
20
Engeroff T. Vogt L. Banzer W. (2022). Resistance training and neuroprotection: A systematic review and meta-analysis of randomized controlled trials.J. Sci. Med. Sport25506–512. 10.1016/j.jsams.2022.01.012
21
Fabre C. Chamari K. Mucci P. Massé-Biron J. Préfaut C. (2002). Improvement of cognitive function by mental and/or individualized aerobic training in healthy elderly subjects.Int. J. Sports Med.23415–421. 10.1055/s-2002-33735
22
Falck R. S. Davis J. C. Best J. R. Crockett R. A. Liu-Ambrose T. (2019). Impact of exercise training on physical and cognitive function among older adults: A systematic review and meta-analysis.Neurobiol. Aging79119–130. 10.1016/j.neurobiolaging.2019.03.007
23
Farinha C. Teixeira A. M. Serrano J. Santos H. Campos M. J. Oliveiros B. et al (2021). Impact of different aquatic exercise programs on body composition, functional fitness and cognitive function of non-institutionalized elderly adults: A randomized controlled trial.Int. J. Environ. Res. Public Health18:8963. 10.3390/ijerph18178963
24
Gallardo-Gomez D. Del P. J. Noetel M. Alvarez-Barbosa F. Alfonso-Rosa R. M. Del P. C. B. (2022). Optimal dose and type of exercise to improve cognitive function in older adults: A systematic review and bayesian model-based network meta-analysis of rcts.Ageing Res. Rev.76:101591. 10.1016/j.arr.2022.101591
25
García-Garro P. A. Hita-Contreras F. Martínez-Amat A. Achalandabaso-Ochoa A. Jiménez-García J. D. Cruz-Díaz D. et al (2020). Effectiveness of a pilates training program on cognitive and functional abilities in postmenopausal women. Int. J. Environ. Res. Public Health17:3580. 10.3390/ijerph17103580
26
Gavelin H. M. Dong C. Minkov R. Bahar-Fuchs A. Ellis K. A. Lautenschlager N. T. et al (2021). Combined physical and cognitive training for older adults with and without cognitive impairment: A systematic review and network meta-analysis of randomized controlled trials.Ageing Res. Rev.66:101232. 10.1016/j.arr.2020.101232
27
Gheysen F. Poppe L. DeSmet A. Swinnen S. P. Cardon G. De Bourdeaudhuij I. et al (2018). Physical activity to improve cognition in older adults: Can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis.Int. J. Behav. Nutr. Phys. Activity15:63. 10.1186/s12966-018-0697-x
28
Gothe N. P. Kramer A. F. McAuley E. (2017). Hatha yoga practice improves attention and processing speed in older adults: Results from an 8-week randomized control trial.J. Alternative Complementary Med.2335–40. 10.1089/acm.2016.0185
29
Gu Q. Zou L. Loprinzi P. D. Quan M. Huang T. (2019). Effects of open versus closed skill exercise on cognitive function: A systematic review.Front. Psychol.10:1707. 10.3389/fpsyg.2019.01707
30
Guo H. Wang Z. Chu C. H. Chan A. Lo E. Jiang C. M. (2024). Effects of oral health interventions on cognition of people with dementia: A systematic review with meta-analysis.BMC Oral Health24:1030. 10.1186/s12903-024-04750-4
31
Hanley W. J. Leung C. Sharpe J. (2010). Resistance training improves cognitive function in older adults: A randomized controlled trial.Geriatr. Gerontol. Int.1089–95. 10.1111/j.1447-0594.2009.00545.x
32
Higgins J. P. Li T. Deeks J. J. (2019). “Choosing effect measures and computing estimates of effect,” in Cochrane Handbook for Systematic Reviews of Interventions, 143–176, edsHigginsJ. P. T.GreenS. (London: Cochrane Collaboration).
33
Huang X. Zhao X. Li B. Cai Y. Zhang S. Wan Q. et al (2022). Comparative efficacy of various exercise interventions on cognitive function in patients with mild cognitive impairment or dementia: A systematic review and network meta-analysis.J. Sport Health Sci.11212–223. 10.1016/j.jshs.2021.05.003
34
Iuliano E. di Cagno A. Aquino G. Fiorilli G. Mignogna P. Calcagno G. et al (2015). Effects of different types of physical activity on the cognitive functions and attention in older people: A randomized controlled study.Exp. Gerontol.70105–110. 10.1016/j.exger.2015.07.008
35
Jiang A. Y. (2020). The effects of mind-body exercise on executive function in older adults: A randomized controlled trial.Chinese J. Rehabil. Med.35524–529. 10.26961/d.cnki.gbjtu.2020.000271
36
Jiang T. Gradus J. L. Rosellini A. J. (2020). Supervised machine learning: A brief primer. Behav. Ther.51, 675–687. 10.1016/j.beth.2020.05.002
37
Jonasson L. S. Nyberg L. Kramer A. F. Lundquist A. Riklund K. Boraxbekk C. J. (2016). Aerobic exercise intervention, cognitive performance, and brain structure: Results from the physical influences on brain in aging (phibra) study.Front. Aging Neurosci.8:336. 10.3389/fnagi.2016.00336
38
Jonasson P. Lennholm C. Kvist T. (2017). Retrograde root canal treatment: A prospective case series. Int. Endodontic J. 50, 515–521. 10.1111/iej.12656
39
Kimura K. Obuchi S. Arai T. Nagasawa H. Shiba Y. Watanabe S. et al (2010). The influence of short-term strength training on health-related quality of life and executive cognitive function. J. Physiol. Anthropol. 29, 95–101. 10.2114/jpa2.29.95
40
Kirk-Sanchez N. J. McGough E. L. (2014). Physical exercise and cognitive performance in the elderly: Current perspectives.Clin. Interventions Aging951–62. 10.2147/CIA.S39506
41
Kovacevic A. Mavros Y. Heisz J. J. Fiatarone Singh M. A. (2020). The effect of resistance exercise on cognitive function in healthy older adults: A systematic review and meta-analysis.Sports Med.501161–1177. 10.1007/s40279-020-01236-9
42
Kramer A. F. Colcombe S. (2018). Fitness effects on the cognitive function of older adults: A meta-analytic study–revisited. Perspect. Psychol. Sci. 13, 213–217. 10.1177/1745691617707316
43
Lachman M. E. Neupert S. D. Bertrand R. Jette A. M. (2006). The effects of strength training on memory in older adults.J. Aging Phys. Activity1459–73. 10.1123/japa.14.1.59
44
Lavretsky H. Alstein L. L. Olmstead R. E. Ercoli L. Riparetti-Brown M. Cyr N. et al (2011). Complementary use of Tai Chi Chih augments escitalopram treatment of geriatric depression: A randomized controlled trial.Am. J. Geriatr. Psychiatry19839–850. 10.1097/JGP.0b013e31820ee9ef
45
Legault C. Jennings J. M. Katula J. A. Dagenbach D. Gaussoin S. A. Sink K. M. et al (2011). Designing clinical trials for assessing the effects of cognitive training and physical activity interventions on cognitive outcomes: The seniors health and activity research program pilot (sharp-p) study, a randomized controlled trial.BMC Geriatr.11:27. 10.1186/1471-2318-11-27
46
Li S. H. (2019). Effects of mind-body exercise on cognitive function in older adults: A randomized controlled trial. Chinese J. Sports Med. 38, 501–506. 10.27315/d.cnki.gstyx.2019.000091
47
Li Q Gong B. Zhao Y Wu C. (2022) Effect of exercise cognitive combined training on physical function in cognitively healthy older adults: A systematic review and meta-analysis.J Aging Phys Act.31155–17010.1123/japa.2021-0475
48
Lim L. M. Lee H. K. Choi M. (2024). Physical exercise and cognitive function in older adults: A systematic review and meta-analysis.Geriatr. Gerontol. Int.24198–208. 10.1111/ggi.14643
49
Liu D. M. Wang L. Huang L. J. (2023). Tai Chi improves cognitive function of dementia patients: A systematic review and meta-analysis. Alternative Ther. Health Med. 29, 90–96.
50
Liu J. Zhang X. Xiao Y. (2023). Effects of exercise interventions on cognitive functions in healthy older adults: A systematic review and meta-analysis. Front. Aging Neurosci. 15:103. 10.3389/fnagi.2023.000103
51
Liu J. Y. Liu X. D. Chen P. J. Wang R. (2018). Effect of Yijin Jing on cognitive function and peripheral blood BDNF level in the elderly.Chinese J. Gerontol.382525–2530. 10.16099/j.sus.2018.02.015
52
Liu T. T. (2021). A Study on the effect of 24-Style Tai Chi exercises on maintaining executive function in the elderly.Chinese J. Rehabil. Med.361056–1061. 10.26961/d.cnki.gbjtu.2021.000049
53
Liu-Ambrose T. Nagamatsu L. S. Graf P. Beattie B. L. Ashe M. C. Handy T. C. (2010). Resistance training and executive functions: A 12-month randomized controlled trial.Arch. Internal Med.170170–178. 10.1001/archinternmed.2009.494
54
Livingston G. Huntley J. Sommerlad A. Ames D. Ballard C. Banerjee S. et al (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission.Lancet396413–446. 10.1016/S0140-6736(20)30367-6
55
Loprinzi P. D. Blough J. Ryu S. Kang M. Gilham B. (2019). The temporal effects of acute exercise on memory function among young adults.Physiol. Behav.204168–175. 10.1016/j.physbeh.2019.02.017
56
Lu X. Siu K. C. Fu S. N. Hui-Chan C. W. Y. Tsang W. W. N. (2016). Effects of Tai Chi training on postural control and cognitive performance while dual tasking – a randomized clinical trial. Journal of Complementary and Integrative Medicine13, 181–187. 10.1515/jcim-2015-0084
57
Maki Y. Ura C. Yamaguchi T. Murai T. Isahai M. Kaiho A. et al (2012). Effects of intervention using a community-based walking program for prevention of mental decline: A randomized controlled trial.J. Am. Geriatr. Soc.60505–510. 10.1111/j.1532-5415.2011.03838.x
58
Markov A. Hauser L. Chaabene H. (2023). Effects of concurrent strength and endurance training on measures of physical fitness in healthy middle-aged and older adults: A systematic review with meta-analysis.Sports Med.53437–455. 10.1007/s40279-022-01764-2
59
Miyazaki A. Okuyama T. Mori H. Sato K. Kumamoto K. Hiyama A. (2022). Effects of two short-term aerobic exercises on cognitive function in healthy older adults during COVID-19 confinement in Japan: A pilot randomized controlled trial. Int. J. Environ. Res. Public Health19:6202. 10.3390/ijerph19106202
60
Moher D. Liberati A. Tetzlaff J. Altman D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement.PLoS Med.6:e1000097. 10.1371/journal.pmed.1000097
61
Moreira M. (2017). Les grossesses rapprochées. Facteurs de risque et conséquences. Rev. d’ pidémiologie et de Santé Publique65, 403–410.
62
Moreira N. B. Gonçalves G. Da Silva T. Zanardini F. E. H. Bento P. C. B. (2018). Multisensory exercise programme improves cognition and functionality in institutionalized older adults: A randomized control trial. Physiother. Res. Int. 23:e1708. 10.1002/pri.1708
63
Mortimer J. A. Ding D. Borenstein A. R. Decarli C. Guo Q. Wu Y. et al (2012). Changes in brain volume and cognition in a randomized trial of exercise and social interaction in a community-based sample of non-demented Chinese elders.J. Alzheimer’s Dis.30757–766. 10.3233/JAD-2012-120079
64
Muscari A. Giannoni C. Pierpaoli L. Berzigotti A. Maietta P. Foschi E. et al (2010). Chronic endurance exercise training prevents aging-related cognitive decline in healthy older adults: A randomized controlled trial.Int. J. Geriatr. Psychiatry251055–1064. 10.1002/gps.2462
65
Nagamatsu L. S. Voss M. Liu-Ambrose T. (2012). Resistance training promotes cognitive and functional brain plasticity in seniors with probable mild cognitive impairment: A 6-month randomized controlled trial.Arch. Internal Med.172666–668. 10.1001/archinternmed.2012.379
66
Nguyen M. H. Kruse A. (2012). A randomized controlled trial of tai chi for balance, sleep quality and cognitive performance in elderly Vietnamese.Clin. Interventions Aging7185–190. 10.2147/CIA.S32600
67
Nishiguchi S. Yamada M. Tanigawa T. Sekiyama K. Kawagoe T. Suzuki M. et al (2015). A 12-week physical and cognitive exercise program can improve cognitive function and neural efficiency in community-dwelling older adults: A randomized controlled trial.J. Am. Geriatr. Soc.631355–1363. 10.1111/jgs.13481
68
Northey J. M. Cherbuin N. Pumpa K. L. Smee D. J. Rattray B. (2018). Exercise interventions for cognitive function in adults older than 50: A systematic review with meta-analysis.Br. J. Sports Med.52154–160. 10.1136/bjsports-2016-096587
69
Norton S. Matthews F. E. Barnes D. E. Yaffe K. Brayne C. (2014). Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data.Lancet Neurol.13788–794. 10.1016/S1474-4422(14)70136-X
70
Oken B. S. Zajdel D. Kishiyama S. Flegal K. Dehen C. Haas M. et al (2006). Randomized, controlled, six-month trial of yoga in healthy seniors: Effects on cognition and quality of life.Altern. Ther. Heal. Med.1240–47.
71
Peng X. Q. (2021). A Study on the effect of health qigong and baduanjin on executive function and mood state in the elderly.Chinese J. Rehabil. Med.36422–426. 10.26961/d.cnki.gbjtu.2021.000241
72
Petersen R. C. Caracciolo B. Brayne C. Gauthier S. Jelic V. Fratiglioni L. (2014a). Mild cognitive impairment: A concept in evolution.J. Internal Med275214–228. 10.1111/joim.12190
73
Petersen R. C. Smith G. Waring S. Ivnik R. Tangalos E. Kokmen E. et al (2014b). Mild cognitive impairment: Clinical characterization and outcome.Arch. Neurol.56303–308. 10.1001/archneur.56.3.303
74
Predovan D. Fraser S. A. Renaud M. Bherer L. Erickson K. (2012). The effect of three months of aerobic training on stroop performance in older adults.J. Aging Res.2012269815–269817. 10.1155/2012/269815
75
Riske L. de Paula N. A. Macedo D. H. (2017). Aerobic exercise, BDNF and memory: A systematic review.Neurosci. Biobehav. Rev.8016–28. 10.1016/j.neubiorev.2017.05.003
76
Schley D. Carare-Nnadi R. Please C. P. Perry V. H. Weller R. O. (2006). Mechanisms to explain the reverse perivascular transport of solutes out of the brain.J. Theoretical Biol.238962–974. 10.1016/j.jtbi.2005.07.005
77
Smiley E. (2008). Root pruning and stability of young willow oak. J. Arboricult. 34, 173–178.
78
Smith P. J. Blumenthal J. A. Hoffman B. M. Cooper H. Strauman T. A. Welsh-Bohmer K. et al (2010). Aerobic exercise and neurocognitive performance: a meta-analytic review of randomized controlled trials. Psychosomatic Med. 72, 239–252. 10.1097/PSY.0b013e3181d14633
79
Sofi F. Valecchi D. Bacci D. Abbate R. Gensini G. F. Casini A. et al (2011). Physical activity and risk of cognitive decline: A meta-analysis of prospective studies.J. Internal Med.269107–117. 10.1111/j.1365-2796.2010.02281.x
80
Sun J. Wang I Zhang X. Y. Yan H. Li H. Y. (2011). The improvement effect of Tai Chi exercise on brain function and physical strength of the elderly.Chinese J. Gero.314688–4689.
81
Sun J. Kanagawa K. Sasaki J. Ooki S. Xu H. Wang L. (2015). Tai chi improves cognitive and physical function in the elderly: A randomized controlled trial.J. Phys. Therapy Sci.271467–1471. 10.1589/jpts.27.1467
82
Sun R. F. (2021). Effect of rouli ball exercise on cognitive function in the elderly.Chinese J. Gerontol.41618–623. 10.26961/d.cnki.gbjtu.2021.000282
83
Talar K. Vetrovsky T. van Haren M. Negyesi J. Granacher U. Vaczi M. et al (2022). The effects of aerobic exercise and transcranial direct current stimulation on cognitive function in older adults with and without cognitive impairment: A systematic review and meta-analysis.Ageing Res. Rev.81:101738. 10.1016/j.arr.2022.101738
84
Tao J. Liu J. Chen X. Xia R. Li M. Huang M. et al (2019). Mind-body exercise improves cognitive function and modulates the function and structure of the hippocampus and anterior cingulate cortex in patients with mild cognitive impairment. Neuroimage-Clin. 23:101834. 10.1016/j.nicl.2019.101834
85
Taylor-Piliae R. E. Silva E. Sheremeta S. (2010). Tai chi as an adjunct physical activity for adults aged 45 years and older enrolled in phase III cardiac rehabilitation: A randomized controlled trial.Eur. J. Cardiovasc. Nurs.9169–177. 10.1016/j.ejcnurse.2009.12.001
86
Timmons J. F. Minnock D. Hone M. Cogan K. E. Murphy J. C. Egan B. (2018). Comparison of time-matched aerobic, resistance, or concurrent exercise training in older adults.Scand. J. Med. Sci. Sports282272–2283. 10.1111/sms.13254
87
Tsai C. Pan C. Chen F. Tseng Y. (2017). Open- and closed-skill exercise interventions produce different neurocognitive effects on executive functions in the elderly: A 6-month randomized, controlled trial. Front. Aging Neurosci. 9:294. 10.3389/fnagi.2017.00294
88
van de Rest O. van der Zwaluw N. L. Tieland M. Adam J. J. Hiddink G. J. van Loon L. J. C. et al (2014). Effect of resistance-type exercise training with or without protein supplementation on cognitive functioning in frail and pre-frail elderly: Secondary analysis of a randomized, double-blind, placebo-controlled trial. Mech. Ageing Dev. 136–137, 85–93. 10.1016/j.mad.2013.12.005
89
Varela S. Cancela J. M. Seijo-Martinez M. Ayán C. (2018). Self-paced cycling improves cognition on institutionalized older adults without known cognitive impairment: A 15-month randomized controlled trial. J. Aging Phys. Activity26, 614–623. 10.1123/japa.2017-0135
90
Vaughan S. Wallis M. Polit D. Steele M. Shum D. Morris N. (2014). The effects of multimodal exercise on cognitive and physical functioning and brain-derived neurotrophic factor in older women: A randomised controlled trial.Age Ageing43623–629. 10.1093/ageing/afu010
91
Venturelli M. Lanza M. Muti E. Schena F. (2010). Positive effects of physical training in activity of daily living-dependent older adults.Exp. Aging Res.36190–205. 10.1080/03610731003613771
92
Vizioli L. Muscari S. Muscari A. (2009). The relationship of mean platelet volume with the risk and prognosis of cardiovascular diseases. Int. J. Clin. Pract. 63, 1509–1515. 10.1111/j.1742-1241.2009.02070.x
93
Wei L. Chai Q. Chen J. Wang Q. Bao Y. Xu W. et al (2022). The impact of tai chi on cognitive rehabilitation of elder adults with mild cognitive impairment: A systematic review and meta-analysis.Disability Rehabil.442197–2206. 10.1080/09638288.2020.1830311
94
Williamson J. D. Espeland M. Kritchevsky S. B. Newman A. B. King A. C. Pahor M. et al (2009). Changes in Cognitive Function in a Randomized Trial of Physical Activity: Results of the Lifestyle Interventions and Independence for Elders Pilot Study. The Journals of Gerontology Series A, 64A, 688–694. 10.1093/gerona/glp014
95
Witte K. Kropf S. Darius S. Emmermacher P. Böckelmann I. (2016). Comparing the effectiveness of karate and fitness training on cognitive functioning in older adults–A randomized controlled trial. J. Sport Health Sci. 5, 484–490. 10.1016/j.jshs.2015.09.006
96
Xu W. H. (2022). Effect of short-term dance sport training on cognitive function in older adults..Chinese J. Rehabil. Med.37316–321. 10.27247/d.cnki.gnjtc.2022.000106
97
Yang Y. Chen T. Shao M. Yan S. Yue G. H. Jiang C. (2020). Effects of Tai Chi Chuan on inhibitory control in elderly women: An fNIRS study. Front. Hum. Neurosci. 13:476. 10.3389/fnhum.2019.00476
98
Zhang J. Ye W. Wu Z. (2023). Mind-body exercise and cognitive function in older adults: A randomized controlled study.J. Aging Phys. Activity31185–193. 10.1123/japa.2022-0123
99
Zhang Y. Wu J. Wang X. Zheng G. (2022). Baduanjin exercise for balance function in community-dwelling older adults with cognitive frailty: a randomized controlled trial protocol. BMC Complement. Med. Ther. 22:295. 10.1186/s12906-022-03764-1
100
Zheng H. Zhang T. Liu G. Wang Z. Li S. Liu T. (2023). Cognitive benefits of aerobic exercise in aging populations: A meta-analysis of randomized controlled trials. Front. Aging Neurosci.15:105. 10.3389/fnagi.2023.000105
Summary
Keywords
exercise, cognitive function, network meta-analysis, older adults, resistance training, mind-body exercise, aerobic exercise, executive function
Citation
Han H, Zhang J, Zhang F, Li F and Wu Z (2025) Optimal exercise interventions for enhancing cognitive function in older adults: a network meta-analysis. Front. Aging Neurosci. 17:1510773. doi: 10.3389/fnagi.2025.1510773
Received
13 October 2024
Accepted
22 May 2025
Published
11 July 2025
Volume
17 - 2025
Edited by
Ayse Kuspinar, McMaster University, Canada
Reviewed by
José Manuel Reales, National University of Distance Education (UNED), Spain
Breno J. A. P. Barbosa, Federal University of Pernambuco, Brazil
Updates
Copyright
© 2025 Han, Zhang, Zhang, Li and Wu.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Fanghui Li, 12356@njnu.edu.cnZhijian Wu, 12191@njnu.edu.cn
†These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.