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Effect of non-pharmacological interventions in people with cognitive frailty: a systematic review and network meta-analysis

BMC Public Health volume 24, Article number: 2684 (2024) Cite this article

Absrtact

Objective

To evaluate the effects of various non-pharmacological interventions on patients with cognitive impairment by systematic search and network meta-analysis, and to rank the effects of the included non-pharmacological interventions.

Methods

The databases of PubMed, Cochrane Library, EMbase, Web of Science, CNKI, VIP, WANFANG, and SinoMed were searched by computer. All randomized controlled trials (RCTs) of non-pharmacological interventions for people with cognitive frailty were collected. The search was conducted from 2000 to February 2024. Two reviewers independently screened the studies, extracted data, and assessed the risk of bias of the included studies, and then used Stata15 and R4.3.1 software to conduct network meta-analysis, with physical function and cognitive function as the main outcome indicators.

Results

A total of 19 randomized controlled trials involving 1738 patients were included. The results of network meta-analysis showed that among the non-pharmacological interventions, nutritional support had the best effect on improving frailty scores and cognitive function scores in patients with cognitive frailty. Aerobic training combined with resistance training is best for improving grip strength. For improving the patient's motor status, cognitive training had the best effect on improving TUG test scores. High-speed resistance training is best for improving walking speed.

Conclusion

This review analyses the current study of non-pharmacological interventions to improve physical performance in patients with cognitive frailty. Current evidence suggests that nutritional support is most effective at improving physical frailty and cognitive decline in patients with cognitive frailty, and that exercise and cognitive training interventions significantly improve grip strength and motor ability.

Trial registration

This meta-analysis was prospectively registered with PROSPERO (registration number: CRD42023486881).

Peer Review reports

Introduction

Population aging is becoming a global trend and an important public health issue. According to the United Nations, in 2020, 10% of the population was over 65 years old, and this figure will reach 16% in 2050, when the number of elderly people over 65 years old may exceed 1.6 billion [1, 2]. In the context of population aging, the health needs of the elderly group are gradually increasing. Population ageing will profoundly change the world’s demographic structure and economic status and will pose challenges to the healthcare system in many parts of the world [3]. To fully alleviate medical pressure in the future, we can target the short-term medical needs of the elderly group and improve their current health status. This undertaking can be achieved by maintaining the self-care capacity of the elderly and improving their long-term health. The long-term health problems that plague the elderly population, such as frailty and cognitive impairment, need to be addressed to achieve the goal of maintaining the self-care ability of old people.

The old age group is at high risk of frailty and cognitive impairment [4]. Frailty is defined as a clinical state of reduced reserves and decreased resistance to stressors caused by the cumulative decline in multiple physiological systems [5]. Cognitive impairment is a deficit in one or more cognitive functions, such as memory, learning, attention, and decision-making [6]. Frailty and cognitive impairment have often been studied as different concepts. In 2013, researchers found a link between frailty and cognitive impairment, and some studies were conducted. A study found that frailty is associated with a high risk of mild cognitive impairment [7]. Another study reported that having frailty and cognitive impairment is associated with an increased risk of death [8]. As the research on this topic deepens, researchers continue to find close connections between the two. Frailty and cognitive impairment have similar pathophysiology [9] and may promote each other’s development [10]. On this basis, the concept of cognitive frailty (CF) was proposed.

CF was first introduced in 2013 by the International Academy on Nutrition and Aging and the International Association of Gerontology and Geriatrics as the presence of physical frailty and cognitive impairment in the absence of Alzheimer’s disease and other types of dementia [3]. Since then, the concept of CF has been constantly updated. In 2015, Ruan et al. proposed two subgroups of cognitive impairment, namely, reversible and potentially reversible CF [11]. This proposal further enriched the concept of CF and expanded its clinical importance. However, the most widely accepted definition is still the one proposed in 2013. Research has shown that CF increases the risk of adverse clinical outcomes, such as disability [12], hospitalization [13], reduced quality of life [14], and even death [14, 15]. As a concept that involves physical and cognitive aspects, CF attaches importance to the comprehensive assessment of patients. If left unattended, CF can pose a threat to patients’ health and impair their long-term functioning. Therefore, CF deserves further in-depth research as a direction for maintaining the health of patients, especially elderly ones.

The research on CF has developed considerably. For example, geriatrics and population aging have received increased attention. Meanwhile, the concept of CF has been developed, and an increasing number of researchers have paid attention to this field. These developments have increased the number of studies on CF. Previous studies on CF have mostly been investigative cross-sectional studies that mainly explored the epidemiology of CF in various regions and the related influencing factors. Some population-based surveys have shown that the prevalence of CF is 1%–15.2% [16, 17], whereas systematic reviews have revealed that the prevalence of CF is 6%–16% [15, 18]. CF has many risk factors, including advanced age, female gender, low education level, sleep problems, anxiety, depression, and malnutrition [19,20,21]. Correspondingly, high education level and professional knowledge are factors that protect against CF [22, 23].

CF is generally considered reversible [11], a characteristic that provides theoretical support for early intervention. Early diagnosis can help identify CF patients, and early intervention can help patients restore their normal cognitive function, maintain their self-care ability, and reduce the probability of adverse health events. On this basis, several intervention studies on CF have been conducted, and the interventions involved have been effective.

Interventions can be classified as pharmacological and nonpharmacological. For old people with CF, the use of medications may entail a number of problems. The pharmacodynamic and pharmacokinetic changes brought about by the co-use of multiple drugs may reduce the effectiveness of drug management in patients [24]. Moreover, good drug management requires patients to have a certain cognitive ability [25], so drug management is difficult for CF patients. Nonpharmacological interventions are research methods that do not use drugs to intervene in subjects. Researchers have conducted a series of nonpharmacological intervention studies on CF patients, and these studies mainly involved exercise training, nutritional support, cognitive training, and other aspects [26].

Various studies on non-pharmacological interventions are available, but whether these interventions are effective, which functions are improved in people with CF, and to what extent these interventions can be achieved remain unclear. Clarification of this issue can help investigators determine future research directions and can assist clinicians in identifying the best exercise mode on the basis of the individual characteristics of patients with CF. Although this issue has been explored in previous studies, the results may vary as additional studies are conducted. We wish to update the research in this direction to allow us to enrich the research on CF and recognize its ongoing changes.

Network meta-analyses can compare interventions across multiple studies and rank the effects of multiple interventions on an outcome by means of direct and indirect comparisons. Therefore, we used network meta-analyses to assess and rank the effects of the nonpharmacological interventions being used at present.

Methods

Program and registration

This systematic review and network meta-analysis were conducted according to the PRISMA network meta-analysis [27]. This study is registered on PROSPERO and is dynamically updated in a timely manner. PROSPERO registration number for this study: CRD42023486881. All included studies were based on previously published studies and therefore did not apply ethical approval or informed consent. This study adheres to local and international ethical principles and regulations.

Inclusion criteria

Studies that meet the following criteria at the same time are considered eligible:

a. The patient is diagnosed with cognitive frailty, that is, after ruling out Alzheimer's disease or other dementia diseases, the patient has both physical and cognitive impairment. This diagnosis requires a clear diagnostic tool for both physical frailty and cognitive dysfunction; b. studies involving any non-pharmacological interventions, i.e. an intervention study that does not use any medication for patients; c. The comparator intervention could be placebo, usual treatment, or any other measure. Usual care refers to the treatment that will be used for all patients in the corresponding medical setting, and is a routine measure in medical care; d. The language used is English or Chinese, e. The types of studies were randomized controlled trials (RCTs); f. Outcomes included assessment of physical and cognitive function.

Exclusion criteria

Studies with any of the following were excluded:

a. Studies that were repeatedly retrieved. This includes the same article retrieved in different databases, or studies with the same content published as different forms of paper; b. The participant population was the same batch of studies. This mainly involved studies that used the same intervention at the same time and in the same place, but with different outcomes; c. Studies for which full text is not available or valid data have been extracted.

Studies search strategy

PubMed, Cochrane Library, Embase, Web of Science, CNKI, WANFANG, VIP and SinoMed were searched by computer. The language type is limited to English or Chinese. The concept of CF was systematically introduced in 2013, but before 2013, CF was also studied as a combination of frailty and cognitive impairment. Therefore, for studies up to 2013, we searched for studies that combined frailty and cognitive impairment. Also considering that the results of premature intervention studies may not be of much value, we consider the search to start in 2000. We ultimately limited the search period to 2000 to February 2024. We searched using a combination of MeSH words and text words. The reference lists of included studies were hand searched as well as previous relevant studies to include the target studies as comprehensively as possible. For eligible studies with missing data, we tried contacting the corresponding authors of the studies in an attempt to obtain data.

Studies screening and data extraction

After database and manual search, all search results will be imported into EndNote 9.0. Two researchers (PJJ and WXH) screened the studies according to the title and abstract of the studies in the Endnote software according to the criteria, and the final studies were determined after evaluating the content of the studies. When two researchers disagree, a third researcher (LQ) will be invited to the judging panel and decide based on the opinions of the researchers. The data of the final included studies will be entered into an Excel sheet by two researchers and checked. The extracted data mainly includes: (1) RCT characteristics (e.g. author, year of publication, study site, sample size, intervention and control measures); (2) the characteristics of the study subjects (e.g., gender, age); and (3) outcomes (e.g., physical function, cognitive function, grip strength). For multiple studies in the same RCT, the studies with the longest follow-up time were selected and the remaining studies were supplemented. For multi-group experiments, each set of data was extracted.

The risk of bias of the included studies

Two authors used the Cochrane collaboration risk of bias tool to assess the risk of bias for all final included studies [28, 29]. When there is a disagreement, a third investigator (LQ) will step in and help resolve it. Participants comparability, subject selection, confounding variables, exposure measures, blinding of outcome assessment, outcome evaluation, incomplete outcome data, and selective reporting were evaluated separately for each study, to facilitate the final assessment of the quality grade of the studies. For studies at high risk of deviation, the investigators will discuss whether to include them. Studies with acceptable risk will be retained, while studies with a significant risk of bias will be discarded.

Statistical analysis

We performed a network meta-analysis using R4.3.1 as well as Stata 14. Regardless of the number of arms, each arm of the included studies was considered as a separate intervention. For dichotomous values, OR will be used, while for continuous values, we will use SMD for aggregation and provide 95% confidence intervals. The Q test will be used to assess heterogeneity between studies, with a P > 0.1 considering homogeneity to be significant, using a fixed-effect model, and vice versa using a random-effects model. I2 will also be used to quantify heterogeneity between studies. When I2 < 25% is mildly heterogeneous, 25 ~ 50% is moderately heterogeneous, 50 ~ 75% is large heterogeneity, and 75 ~ 100% is very heterogeneous.

We used the nodal splitting method to evaluate the local inconsistencies of the model. When the results of the node segmentation method show P < 0.05, it indicates that there is a local inconsistency. When the network graph forms a loop, we will use the loop inconsistency test to evaluate the consistency of the loop structure. When the 95% CI of a loop does not contain 0, it indicates that there may be inconsistencies in the loop structure. Sensitivity analysis was performed by excluding inconsistent studies. Comparisons of interventions were made by plotting league tables. We assessed studies for publication bias using funnel plots. When there is an asymmetry of points in the funnel chart, it indicates that there may be some bias. When analyses show inconsistencies in local or loop structures, or when points in the funnel plot are asymmetrical, we will exclude some of the included studies and then analyse the data to identify potential causes.

Results

Screening process and results of studies

A total of 5,504 articles were included in the preliminary screening, among which 5,500 studies were retrieved by the current researchers and 4 were from other sources. A total of 1,599 duplicate studies and 2,297 studies whose design did not meet the requirements were excluded. The titles and abstract sections of the studies were read, which helped us eliminate 2,297 studies. A total of 200 studies were read in full. After reading the full text, we further excluded 181 studies, namely, 9 non-RCT studies, 1 that did not meet the language requirements, 59 that did not have full text access, 14 from which valid data could not be extracted, and 98 that were inconsistent in scope. In the end, 19 studies involving 1,738 patients were used. The flowchart of the screening process of the studies is shown in Fig. 1. The search query and search results are shown in the annex (Table S1).

Fig. 1
figure 1

Flow chart of studies screening

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Basic characteristics of the included studies and results of bias risk assessment

We included a total of 19 studies involving 1,738 patients. The vast majority of the studies were conducted in Asia (18 studies), among which China accounted for the majority (16 studies). In terms of patient source, 8 studies had patients from hospitals, 8 studies had patients from the local community, and 3 studies had patients from nursing centers.

In terms of the tools used to diagnose CF, the Frailty Phenotype Scale (FP) was the most used assessment tool for frailty (16 studies). Among the many tools for diagnosing frailty (e.g., the frailty index and the Fatigue, Resistance, Ambulation, Illness, and Loss of Weight [FRAIL] scale), Fried’s frailty scale is still the most commonly used. It is also preferentially recommended as a frailty diagnostic tool for CF [11].

Two main types of assessment were employed for the diagnosis of cognitive decline. Ten of the studies adopted the Mini-mental State Examination (MMSE), and 7 used the Montreal Cognitive Assessment (MoCA) scale. Although the studies often mentioned Clinical Dementia Rating (CDR) as an important tool for CF [3, 11], in actual research, researchers prefer to use two scales: MMSE and MoCA. MMSE and MoCA have differences in the diagnosis of cognitive impairment. Typically, the criterion is MMSE < 24 points and MoCA ≤ 26 points [30]. Notably, most of the included studies used the Chinese version of MoCA [31]. This version is based on the number of years of education (three levels: < 6 years, 7–12 years, and > 12 years). These results suggest that the differences in diagnostic tools for cognitive impairment may be an important source of variability in CF diagnosis.

Most of the studies reported the length of the investigator’s intervention, which ranged from 12 weeks to 6 months. Most of the studies also set the age criterion to at least 60 years. In this work, usual care was defined as measures, such as standardized medical services, nursing services, and health education, used for all patients in a medical setting. The proportion of usual care in the control groups of the included studies was high. The details are shown in Table 1.

Table 1 Characteristics of included studies
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Quality evaluation

In this assessment, 3 studies were judged to be high risk, and 16 studies were judged to have some concerns. In the randomization process assessment, three were judged to be high risk, and two were found to have some concerns. All studies were rated as low in terms of quality because of deviations from the intended interventions and missing outcome data. In the measurement of the outcomes, five were rated as having some concerns, and the rest were low risk. In the selection of the reported results, all studies were assessed as having some concerns.

The main risk area was the randomization process. In this area, three studies were assessed as high risk because of the use of nonrigorous randomization. For example, investigators grouped patients on the basis of where they lived or when they sought medical care. Another study was assessed as risky because the researchers stated that they followed the principle of randomization but did not specify how the grouping was performed. The details are shown in Fig. 2, Table 2, and the Appendix.

Fig. 2
figure 2

Summary of the quality evaluation of the included studies

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Table 2. Risk of bias assessment using the Cochrane tool
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Meta-analysis

FP

A total of 12 studies involved FP, and 10 interventions were adopted. The 10 interventions were usual treatment, virtual reality (VR) cognitive training combined with aerobic training, aerobic training, aerobic training combined with cognitive training, aerobic training combined with resistance training, cognitive training, dual-task training, high-speed resistance exercise, nutritional support, and social support.

The network diagram is shown in Fig. 3. The results of the global inconsistency test revealed that P = 0.696 > 0.05. The inconsistency model was not statistically significant, so we used the consistency model for analysis. All P values in the local inconsistency test were > 0.05, indicating the absence of local inconsistency. Given that no loop structure was formed between the intervention methods in this result, no loop inconsistency test was performed. The analysis showed the presence of heterogeneity in this study (99.34%). To examine such heterogeneity, we performed subgroup analyses on the basis of the participant source and selection of cognitive assessment tools. However, the decrease in heterogeneity in the final results was not substantial. Therefore, we believe that the heterogeneity of FP was not due to the differences in participant source and cognitive assessment tools. Furthermore, the funnel plot exhibited asymmetry, suggesting that the results may have some bias. To rule out bias, we excluded the two most asymmetrical studies, namely, those of Liu2022 [43] and Chen2022 [42]. The points in the funnel chart became symmetrically distributed after their exclusion. We concluded that the reason for this result may be that the two studies selected a different group of participants compared with the other studies. Specifically, the two studies selected patients on maintenance hemodialysis who received aerobic and resistance exercise during hemodialysis. Our analysis revealed that the physiopathological basis of patients on maintenance hemodialysis is specific compared with that of average participants, which may lead to differences in outcome measures.

Fig. 3
figure 3

Network diagram of different training on FP

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Data analysis showed that among the interventions, nutritional support may be the best for improving the FP score of patients, followed by VR cognitive training combined with aerobic training. The results are shown in Fig. 4. The funnel chart and league table are given in the annex (Figure S1, Figure S2 and Table S2).

Fig. 4
figure 4

Rank chart of FP

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MMSE

A total of 9 studies involved MMSE, and 9 interventions were adopted. These interventions were usual treatment, aerobic training, aerobic training combined with cognitive training, aerobic training combined with resistance training, cognitive training, collaborative nursing, nutritional support, resistance training, and resistance training combined with cognitive training. The network diagram for each study is provided in Fig. 5.

Fig. 5
figure 5

Network diagram of different training on MMSE

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The results of the global inconsistency test showed that P = 0.0035 < 0.05, indicating that the inconsistency test was significant. The inconsistency model was therefore used. All P values in the local inconsistency test were > 0.05, indicating the absence of local inconsistency. Analysis revealed some heterogeneity (78.84%). We performed subgroup analyses on the source of the participants, but no reduction in heterogeneity was observed. Therefore, we conclude that the differences in the participant source may not be the cause of heterogeneity in MMSE. This study was performed in loops, but the results of the loop inconsistency test showed inconsistencies among usual treatment, aerobic training, and aerobic training combined with resistance training. We re-analyzed the studies after excluding the work of Irimia2019 [50]. The second analysis results showed a decrease in annular inconsistency compared with the initial analysis results, but the results were still unsatisfactory. We believe that the inconsistency of the loop structure may be due to various factors, such as the demographic characteristics of the participants and the duration of the intervention. Therefore, we should be objective about the results of MMSE analysis. Moreover, the points in the funnel chart exhibited asymmetry, suggesting that this study may have some bias. We drew the funnel chart again after excluding the study of Irimia2019 [50]. The points in the re-drawn funnel plot are symmetrical. Irimia’s study (2019) was conducted in Spain (Europe). Spain is far from the region where the included studies were conducted (Asia). Hence, the bias in this work may be related to regional factors, such as population ethnicity, geography, climate, and eating habits.

Data analysis showed that among the interventions, nutritional support was the best in improving the MMSE scores of patients with CF, followed by aerobic training combined with cognitive training. The ranking level of MMSE is shown in Fig. 6. The funnel chart and league table are given in the annex (Figure S3, Figure S4 and Table S3).

Fig. 6
figure 6

Rank chart of MMSE

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MoCA

A total of 8 studies involved MoCA, and 9 interventions were adopted. These interventions were usual treatment, VR cognitive training combined with aerobic training, aerobic training, aerobic training combined with cognitive training, aerobic training combined with resistance training, dual-task training, mobile health training combined with aerobic training, nutritional support, and social support. The network diagram for each study is provided in Fig. 7.

Fig. 7
figure 7

Network diagram of different training on MoCA

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The results of the global inconsistency test showed that P = 0.0002 < 0.05, indicating that the inconsistency test was significant. All P values in the local inconsistency test were > 0.05, indicating the absence of local inconsistency. Given that no loop structure was formed between the intervention methods in this result, no loop inconsistency test was performed. Analysis revealed some heterogeneity (99%). We performed subgroup analyses on the source of the participants, but no reduction in heterogeneity was observed. Therefore, we conclude that the differences in the participant source may not be the cause of heterogeneity in MoCA. The points in the funnel plot show symmetry, indicating that there is no bias.

Data analysis showed that among the interventions, nutritional support was the best in improving the MoCA scores of patients with CF, followed by mobile health training combined with aerobic training. The ranking level of MoCA is shown in Fig. 8. The funnel chart and league table are given in the annex (Figure S5 and Table S4).

Fig. 8
figure 8

Rank chart of MoCA

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Grip strength

A total of 8 studies involved Grip Strength (GP), but only 7 were included because network analysis was impossible after the inclusion of the study of Rick2021 [48]. The study of Rick was not included in the network meta-analysis to ensure reliability, so only seven studies were included. Eight interventions were involved: usual treatment, aerobic training, aerobic training combined with resistance training, collaborative nursing, dual-task training, high-speed resistance training, mobile health training combined with aerobic training, and resistance training combined with cognitive training. The network diagram of each study is shown in Fig. 9.

Fig. 9
figure 9

Network diagram of different training on GP

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The results of the global inconsistency test showed that P = 0.0514 > 0.05, which indicates that the inconsistency test was significant. Given the lack of sufficient head-to-head comparisons of the various nodes in the study, the local inconsistencies of the included studies could not be evaluated using the nodal splitting method. No loop inconsistency test was performed because no loop structure was formed between the intervention methods. High heterogeneity (99%) was observed among the included studies. We performed subgroup analyses on the source of the participants, but the analyses did not yield a network structure after differentiation. Hence, accurately distinguishing the source of heterogeneity was impossible. The points in the funnel plot showed symmetry, indicating the absence of bias.

Data analysis revealed that among the interventions, aerobic training combined with resistance training was the best in improving the GP of patients with CF, followed by mobile health training combined with aerobic training. The ranking level of GP is shown in Fig. 10, and the funnel chart and league table are given in the annex (Figure S6 and Table S5).

Fig. 10
figure 10

Rank chart of GP

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Timed Up and Go test

Four studies used the Timed Up and Go (TUG) test, but only three were included because the study of Chen2021 [49] did not perform a network analysis. Five interventions were involved: VR cognitive training combined with aerobic training, aerobic training, aerobic training combined with cognitive training, cognitive training, and high-speed resistance training. The network diagram of each study is given in Fig. 11

Fig. 11
figure 11

Network diagram of different training on TUP

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The results of the global inconsistency test showed that P = 0.8955 > 0.05, so the inconsistency test was not significant. All P values in the local inconsistency test were > 0.05, indicating the absence of local inconsistency. Given that no loop structure was formed between the intervention methods in this result, no loop inconsistency test was performed. The results showed high heterogeneity (99%) among the included studies for this outcome. An effective structure could not be formed between the studies after subgrouping because of the small number of included studies. Therefore, we could not accurately determine the source of consistency. The points in the funnel plot showed symmetry, indicating the absence of bias.

Data analysis revealed that among the interventions, cognitive training was the best in improving the TUG test scores of the patients with CF, followed by high-speed resistance training. The ranking level of TUG is shown in Fig. 12, and the funnel chart and league table are given in the annex (Figure S7 and Table S6).

Fig. 12
figure 12

Rank chart of TUP

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4-m walk test

Four studies used the 4-m walk test. Five interventions were involved: usual treatment, aerobic training, aerobic training combined with cognitive training, nutritional support, and resistance training combined with cognitive training. The network diagram of each study is given in Fig. 13.

Fig. 13
figure 13

Network diagram of different training on 4-m walk test

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The results of the global inconsistency test showed that P < 0.05, so the inconsistency test was significant. All P values in the local inconsistency test were > 0.05, indicating the absence of local inconsistency. Given that no loop structure was formed between the intervention methods in this result, no loop inconsistency test was performed. The results showed high heterogeneity (99%) among the included studies for this outcome. An effective structure could not be formed between the studies after subgrouping because of the small number of included studies. Therefore, we could not accurately determine the source of consistency. The points in the funnel plot showed symmetry, indicating the absence of bias.

Data analysis revealed that among the interventions, resistance training combined with cognitive training was the best in improving the 4-m walk test scores of the patients with CF, followed by aerobic training combined with cognitive training. The ranking level of 4-m walk test is shown in Fig. 14, and the funnel chart and league table are given in the annex (Figure S8 and Table S7).

Fig. 14
figure 14

Rank chart of 4-m walk test

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Discussion

With the expansion of the concept of CF and the deepening of relevant research, many interventional studies (particularly nonpharmacological interventions) have been conducted. The main symptoms associated with CF are physical frailty and cognitive decline, which have motivated scholars to explore nonpharmacological interventions that can substantially improve the physical functions of patients with CF. The results of this study may guide the selection of an appropriate nonpharmacological intervention modality in accordance with the characteristics of patients and may provide ideas for future researchers.

According to this research, current studies on patients with CF prefer to use FP as a diagnostic tool for frailty, and the FRAIL scale is not widely used; FP is preferred possibly because it is suitable for the extensive screening of patients with CF on the basis of their daily work [11]. Among the evaluation tools for cognitive function, MMSE and MoCA have often been used by researchers, whereas CDR has been rarely utilized. Unlike CDR, MMSE and MoCA can diagnose cognitive impairment, and each item is scored using an item, making the scores clear and easy to quantify.

Physical function outcomes

FP is an outcome measure to improve physical functioning. In terms of improving physical frailty in patients with CF, nutritional support was the best among the considered interventions, followed by VR training combined with aerobic training.

The nutritional supplementation methods involved in this study were oral nutrition supplementation with enteral nutrition suspension. In previous studies, nutritional supplementation was also used to improve the health status of frail patients [51]. The reason for the excellent results in this analysis may be that poor nutritional status increases the risk of CF development, and oral nutritional supplementation can effectively manage this condition [18]. Nutritional supplementation is convenient, fast, and easily accepted by a wide range of groups. However, it also has some disadvantages, such as being expensive and imposing requirements on the patient’s gastrointestinal function.

In terms of motor intervention, VR cognitive training is mainly utilized to train patients through VR equipment, and some researchers used VR for neurological rehabilitation training [52]. The main feature of VR cognitive training is its ability to combine a wide range of training methods and entertainment elements through an audio–visual combination, which may motivate patients and enable the subjects to be deeply immersed in cognitive training. Different from the traditional offline cognitive training method, VR, as a new intervention method, mainly relies on the development of information technology. During the 2019 pandemic, many countries imposed quarantine measures, which further increased the importance of electronic devices and information technology in people’s daily lives. Old adults, the main group of CF, learn and become accustomed to the use of electronic devices either voluntarily or involuntarily [53], so they may not be resistant to using VR equipment. This trend may become dominant in the future; for instance, electronic devices, as carriers of Internet technology, could help introduce smart pension into elderly life [54]. However, potential problems exist. Prolonged use of electronic devices can damage vision, so the amount of time for a single VR use must be limited to allow patients’ eyes to rest. The use of VR equipment also requires the environment to be safe [55]. Compared with young patients, old patients may need more time to learn and become accustomed to the use of electronic.

Cognitive function outcomes

Cognitive function improvement in patients with CF can be divided into two aspects: (1) MMSE, where the best improvement effect is provided by nutritional support, followed by aerobic training combined with cognitive training, and (2) MoCA, where nutritional support provides the best improvement, followed by mobile health training combined with aerobic training.

Aerobic exercise is repeatedly mentioned in exercise interventions, and training involving aerobic exercise ranks high in improving the cognitive function. A possible explanation is that aerobic exercise can improve cognitive impairment in old patients by modulating stress and proinflammatory cytokines [56]. It also allows patients to process and respond to external stimuli compared with resistance exercise.

Cognitive training is widely used to improve the cognitive function of subjects [57]. In this study, cognitive training alone was not as effective as the other combined training approaches were in improving the cognitive function. The combination of aerobic and cognitive training may be suitable for people with CF who are physically weak and exhibit cognitive decline, and it can help patients improve their physical and cognitive functions.

Meanwhile, dual-task training ranked low in improving the cognitive function. Dual-task training is a training mode in which patients perform two tasks simultaneously, and it can be divided into two combinations of exercise–cognitive training and exercise–motor training. It can improve patients’ cognitive and physical functions through the complementarity of movement and cognition [41]. The low ranking of dual-task training may be due to several reasons. (1) Although it mobilizes the functions of patients, it increases the pressure on patients’ attention and reduces the training effect. (2) To reduce the interference between different tasks and achieve the goal of performing dual tasks simultaneously, researchers are likely to choose an easy training method, which may not be enough to stimulate the brain of patients with CF.

Grip power outcome measures

The best way to improve grip strength in this study was aerobic training combined with resistance training. Previous studies also showed that aerobic training combined with resistance training can effectively improve grip strength [58]. Ideal results are achieved possibly because resistance and aerobic training enhance the explosiveness and endurance of the patient’s muscles, respectively, and the staggered training schedule gives the patient’s body time to repair the muscles, yielding excellent results.

The effect of high-speed resistance sports ranked low in this study. High-speed resistance training requires a pause of one second and more than two seconds of contraction, and the patient is required to complete this movement as quickly as possible; in addition, the training item is an elastic band [47]. During high-speed resistance training, patients are inclined to complete the training movements as soon as possible, which may make their complete force process incomplete. Moreover, the proportion of grip strength in the exercise regimen chosen by the researchers in the included studies was not as large as that in the other exercises.

Mobility outcomes

The improvement in patients’ mobility ability can be measured using TUG and four-meter-walk tests. The best way to improve TUG test scores is to undergo cognitive training, followed by high-speed resistance training. The best way to increase the scores of the four-meter-walk test is to undergo high-speed resistance training, followed by VR cognitive training combined with aerobic training.

Cognitive training is the most effective intervention for improving TUG test scores. Highly coordinated somatic movements, such as getting up from a chair and turning around in a designated place, are required during the TUG test, and subjects with cognitive training may have some advantages in this area. In this study, high-speed resistance training was the best intervention for improving patient scores in the four-meter-walk speed test. High-speed resistance training requires patients to exercise frequently, and muscle explosiveness, rather than muscle endurance, satisfies this requirement.

Studies not included in the analysis

We excluded two studies in the analysis of GP and TUG as outcomes because the two studies were not linked to the other studies, possibly leading to unreliable results. The study excluded from GP was that of Rick 2021 [48]. The main intervention methods involved were aerobic training combined with cognitive training and VR cognitive training combined with aerobic training. We believe that this study reflects the characteristics of VR as a modern cognitive training tool, so it cannot be simply combined with other tools. The results of a previous study showed that VR training can help improve the upper limb strength of patients after a stroke, and patients are highly motivated to participate [59]. Chen’s study [49], which involved usual treatment and Otago training, was excluded from TUG. Otago training is a multimodal approach that has two parts: balance and strengthening. It improves balance and strength in old patients and is believed to be effective in reducing the risk of falls in old adults [60]. Similar to the included studies, the study of Tiffany et al. confirmed that Otago training improves the motor function of the elderly [61].

Limitations and prospects

Limitations

(1) Regionality: We limited the study language to English and Chinese, which may have introduced a language bias. Moreover, most of the included studies were conducted in China, suggesting the presence of geographical bias. The two points may lead to strong heterogeneity between studies. Therefore, the results of this study may be region-specific, and other regional differences must be considered if the conclusions are to be generalized.

(2) Consistency and homogeneity: Some heterogeneity existed among the included studies. We used a random-effects model in our data analysis, and we performed subgroup analyses. However, the effect was not significant. The loop inconsistency test for MMSE also suggested the existence of inconsistencies. We tried to exclude some studies to determine the causes of these problems. However, we could not identify the exact causes. We believe that two factors are responsible for these results. The first one is the differences among the patients (i.e., the patients were from various countries and regions). Demographic differences, such as age, gender, and race, and differences in geographical and human factors, such as climate, economy, and culture, may have also contributed to the issues. The second factor pertains to the differences in the studies (i.e., the way the studies were conducted). The criteria for determining participants, the assessment tools used, and the duration of the interventions were not completely consistent across the included studies. These factors increased the differences among the included studies and made the results of some tests unsatisfactory.

(3) League tables: The league tables derived from the analysis revealed that the two comparisons of the included studies did not have a statistically significant difference, which may be due to the small sample size of the included studies. After the differentiation of the interventions, the network map included few studies for each intervention modality, and no statistically significant difference was obtained.

Prospects

The overall quality of the studies included in this work was not excellent, and most of them were two-arm studies. We look forward to additional high-quality studies on improving the health of people with CF in the future because the inclusion of such studies will help produce reliable conclusions.

Conclusion

Following a systematic search of individual bibliographies, we performed a network meta-analysis between studies. This review analyses the current study of nonpharmacological interventions to improve physical performance in patients with CF. Current evidence suggests that nutritional support has the best effect on improving physical frailty and cognitive decline in patients with CF, and that exercise training and cognitive training interventions have outstanding effects in improving grip strength and motor ability.In order to improve the status of CF patients, the investigator can choose the best non-pharmacological intervention mode for intervention.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

There are no acknowledgements in this article and therefore do not apply here.

Funding

Supported by: (1)The First Affiliated Hospital of Yunnan University of Traditional Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine) of the second batch of "Outstanding Young Talents" program. (2)Science and Technology Innovation Fund Project K2023, School of Nursing, Yunnan University of Chinese Medicine.

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  1. Peng Junjie and Chang Renjie contributed equally to this work.

Authors and Affiliations

  1. School of Nursing, Yunnan University of Chinese Medicine, Kunming, 650500, China

    Junjie Peng, Xinghong Wei, Zhimin Yin & Qin Liu

  2. Digestive Endoscopy Center, Department of Spleen and Gastroenterology, Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming, 650021, China

    Renjie Chang

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Peng Junjie is responsible for proposing ideas, data analysis, and writing papers; Chang Renjie is responsible for data collection, data analysis, article writing and revision; Wei Xinghong is responsible for data collection; Yin Zhimin is responsible for data collection; Liu Qin is responsible for the revision of the article. The authors Peng Junjie and Chang Renjie have the same contribution.

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Peng, J., Chang, R., Wei, X. et al. Effect of non-pharmacological interventions in people with cognitive frailty: a systematic review and network meta-analysis. BMC Public Health 24, 2684 (2024). https://doi.org/10.1186/s12889-024-20079-7

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