Skip to main content

Association between physical activity, peak expiratory flow, and cognitive function in aging: a cross-sectional analysis

Abstract

Background

The aging global population is experiencing escalating challenges related to cognitive deficits and dementia. This study explored the interplay between pulmonary function, physical activity, and cognitive function in older U.S. adults to identify modifiable risk factors for cognitive decline.

Methods

Utilizing NHANES 2011–2012 data, we conducted a cross-sectional analysis of 729 participants aged ≥ 60 years. Cognitive function, peak expiratory flow (PEF), and physical activity were assessed. Weighted logistic regression and mediation analyses were employed to examine associations.

Results

The sample size was 729 (weighted mean [SD] age, 67.1 [5.3] years; 53.6% female participants). Preliminary correlation analysis indicated a positive correlation between the global cognitive score and physical activity (β = 0.16; p < 0.001), recreational activity (β = 0.22; p < 0.001), and PEF in percent predicted (PEF%) (β = 0.18; p < 0.001). Compared to those with a PEF% >100%, the PEF% (80-100%) group (OR, 2.66; 95% CI, 1.34–5.29; p = 0.005) and PEF% <80% group (OR, 3.36; 95% CI, 1.67–6.76; p = 0.001) were significantly associated with higher cognitive deficits risk. Recreational activity meeting guidelines was linked to a lower risk of cognitive deficits (OR, 0.24; 95% CI, 0.10–0.57; p = 0.001). Mediation analysis demonstrated that PEF mediates the relationship between physical activity and cognitive function.

Conclusion

This study revealed significant associations between lower PEF, diminished physical activity, and increased cognitive deficits in elderly individuals. The results supported the hypothesis that pulmonary function may mediate the connection between activity and cognitive health, emphasizing the importance of respiratory health in cognitive aging. Recognizing these associations is crucial for clinical care and public health policy aiming to mitigate cognitive decline in aging populations. While these findings are intriguing, validation through longitudinal design studies is deemed necessary.

Peer Review reports

Introduction

As the global population ages, the challenges posed by cognitive deficits and dementia have become increasingly prominent in healthcare and public health. Given the current absence of effective treatments, there is an urgent need to identify modifiable risk factors contributing to cognitive decline [1, 2], thereby facilitating the development of effective preventive measures.

Emerging evidence indicates a connection between pulmonary function and cognitive performance [3,4,5]. A decline in pulmonary function typically commences around the age of 25 [5], and nonsmoking adults experience an average annual decline of 1% [6]. It is widely believed that impaired pulmonary function may impact the central nervous system through pathways such as inflammation-mediated vascular diseases, fibrinolysis dysfunction, oxidative stress, and changes in neurotransmitter metabolism caused by hypoxia [7, 8]. Various studies have demonstrated associations between different pulmonary function indicators and cognitive function [3, 9,10,11,12,13,14]. In a community-based cohort study with a 27-year follow-up period, participants experiencing midlife lung disease and impaired pulmonary function exhibited a 58% higher risk of developing dementia or mild cognitive impairment in later life [15]. Additionally, two separate studies have reported that a diagnosis of COPD is associated with an approximately 80% increased risk of experiencing mild cognitive impairment within a 5-year period and developing dementia within a 25-year period, respectively [16, 17]. Moreover, in cross-sectional analyses, poorer pulmonary function has been associated with a smaller brain volume and a greater burden of white matter hyperintensity [18]. Therefore, enhancing pulmonary function may have meaningful implications for cognitive function, irrespective of whether individuals have pulmonary disease.

Furthermore, studies have suggested that elevated levels of physical activity are correlated with a diminished risk of cognitive decline [19, 20] and dementia, including Alzheimer’s disease [21]. Additionally, an association between prolonged sitting and poorer cognitive performance has been noted, although further investigation is required to understand the relationship between sedentary time and overall dementia risk [22,23,24]. Current research generally supports the idea that lifestyle modifications, such as increasing physical activity levels [25,26,27], can contribute to maintaining or enhancing respiratory function. According to a longitudinal study of aging in Canada, the physical activity patterns of older adults were linked to respiratory function, irrespective of smoking status [26]. Furthermore, high levels of physical activity may delay declines in respiratory function [27] and potentially decelerate cognitive decline [15].

We conducted this study to investigate the relationship between physical activity, pulmonary function, and cognitive function in older adults in the U.S. Given the observational and cross-sectional nature of the data, causality cannot be inferred. Nevertheless, by exploring the associations between exercise, pulmonary function, and cognitive deficits, we aimed to provide valuable insights for the prevention of cognitive deficits.

Methods

Participants

National Health and Nutrition Examination Survey (NHANES) is a series of cross-sectional, multi-stage, continuous surveys conducted by the National Center for Health Statistics (NCHS) in the United States. Its primary goal is to assess the health and nutritional status of the U.S. population. The NHANES employs a complex sampling design that takes into account participants’ age, race/ethnicity, and geographic location to obtain a representative sample of the United States’ non-institutionalized population. The research protocol received approval from the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent.

This study is a cross-sectional analysis utilizing data from the 2011–2012 cycle, including data on cognitive performance, pulmonary function, and physical activity. Following the NHANES analysis guidelines [28], the data were weighted to represent the corresponding U.S. civilian population aged ≥ 60 years (eAppendix, eFigure in the Supplement). This study strictly followed the guidelines provided by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Cognitive function assessment

The NHANES study comprises three tests assessing cognitive function. Detailed information can be found online [29] and in the eAppendix of the Supplement. In this study, we standardized the scores for each test (CERAD, AFT, and DSST) and then summed them to obtain a global cognitive score, reflecting cognitive function. We assessed potential clinical relevance by defining the presence of at least mild cognitive deficits as having total composite scores less than 1 standard deviation (SD) below the mean [30,31,32].

Peak expiratory flow (PEF) in percent predicted

Peak expiratory flow (PEF) is a physiological parameter initially proposed for estimating airflow obstruction. Additionally, the PEF in percent predicted (PEF%) proved to be a more accurate and convenient strategy for predicting impaired pulmonary function in older adults [33]. The PEF% is the ratio of the measured PEF to its predicted value. The optimal threshold for detecting airflow limitation is when the PEF% reaches 80% [34]. Jackson et al. analyzed data from the NHANES III and defined a PEF% < 80% as abnormal [35]. In this study, participants were categorized into three groups based on PEF%: < 80%, 80 − 100%, and > 100%.

Physical activity

According to the 2008 Federal Guidelines for Physical Activity [36], we reflected participants’ physical activity by the total exercise time within 7 days, categorized into four levels: No physical activity; Below guideline: moderate physical activity (< 150 min/week), vigorous physical activity (< 75 min/week); Meet guideline: moderate physical activity (150 min/week-300 min/week), vigorous physical activity (75 min/week-150 min/week); Exceed guideline: moderate physical activity (> 300 min/week), vigorous physical activity (> 150 min/week). In this study, physical activity encompasses both work-related and recreational activities, offering a comprehensive measure of individuals’ daily activity levels, regardless of the specific activities involved. The work activity group includes participants who did not engage in recreational activities beyond the Below Guideline level. The recreational activity group includes participants who did not engage in work activity beyond the Below Guideline level.

Covariates

Considering the potential confounding factors among PEF, cognition, and physical activity, we simultaneously investigated several covariates. We enhanced the model fit by including several covariates in the model: age, gender, race/ethnicity, education level, body mass index, smoking history, lung disease, heart disease, hypertension, stroke, and diabetes. These covariates were self-reported by participants or their proxies during NHANES interviews. Age was treated as a continuous variable. Gender was considered a binary variable: male or female. Since widely accepted lung capacity reference values for Asian Americans and other ethnicities are currently lacking, race/ethnicity was limited to three groups: non-Hispanic Whites, non-Hispanic Blacks, and Mexican Americans. Education level was categorized as less than high school, high school, some college, and college degree or higher. Body mass index (BMI) was calculated based on self-reported height and weight. Smoking status included never smoked, former smoker, and current smoker. History of lung disease, heart disease, hypertension, stroke, or diabetes was classified as a binary variable: yes or no.

Statistical analysis

According to the NHANES analysis tutorial, considering the complex sampling design, we applied sampling weights to correct for all the statistical analyses, ensuring that the results possessed national representativeness. We initially conducted a comprehensive case analysis, excluding all participants with missing or invalid data on cognitive performance, pulmonary function, physical activity, and covariates. Continuous variables were described using weighted means (SD), while categorical variables were presented as weighted frequencies (%). The global cognitive score, representing overall cognitive function and following a normal distribution, was obtained by summing the standardized scores of CERAD, AFT, and DSST. Cognitive deficits (yes or no) as a categorical variable were defined as a global cognitive score less than 1 SD below the mean. Missing values for categorical variables were treated as a separate category for comparison.

Spearman correlation analysis was employed in this study to examine the relationships between variables. Weighted logistic regression models were utilized to analyze the associations between PEF and physical activity, respectively, with cognitive deficits. The strength of the associations was expressed as odds ratios (OR) and 95% confidence intervals (95% CI). The mediation analysis involved one independent variable (physical activity/ recreational activity/work activity), one dependent variable (global cognitive score), and one mediating variable (PEF%). Given the multi-categorical nature of the independent variable, relative and overall mediation analysis methods were employed based on previous research [37]. Data analysis was conducted using SPSS 26.0 software, with the PROCESS Macro performing Bootstrap-based mediation effect tests and estimating 95% confidence intervals. The number of bootstrap samples was set at 5000. Control variables, including age, gender, race, and education, were introduced into the model. If the confidence interval included 0, it indicated no significant indirect effect at a 5% significance level.

All the statistical tests were conducted as two-sided, with statistical significance defined at a p-value < 0.05. The analyses were performed using IBM SPSS Statistics version 26.0 for Windows and Stata version 16.0.

Results

Demographics and baseline characteristics

The distribution of participants, as weighted, is presented in Table 1. A total of 729 participants aged 60 years and older were included in the analysis. The mean (SD) age was 67.1 (5.3) years. Among them, 46.4% (weighted proportion) were male. Cognitive deficits were identified in 34.0% (n = 248) of participants. In comparison with participants classified as having cognitive deficits, those with better cognitive function were more likely to be female, possessed higher educational attainment, demonstrated a higher level of PEF%, and engaged in a greater amount of physical activity, particularly recreational activities. Participants classified with cognitive deficits exhibited a significantly higher likelihood of having a history of diabetes, hypertension, coronary heart disease, and stroke.

Table 1 Weighted characteristics of the study populations, NHANES 2011 to 2012

Preliminary correlation analyses

The correlation analysis between variables were analyzed via Spearman correlation analysis and are summarized in Table 2. The global cognitive score was positively correlated with physical activity (β = 0.16; p < 0.001), recreational activity (β = 0.22; p < 0.001), as well as PEF% (β = 0.18; p < 0.001). Furthermore, PEF% demonstrated a significant correlation with physical activity (β = 0.10; p < 0.01) and recreational activity (β = 0.19; p < 0.001). However, the correlation between global cognitive score, PEF%, and work activity was not found to be significant. In addition, a noteworthy negative correlation was identified between age and the global cognitive score.

Table 2 Correlation coefficient matrix of variables

Logistic regression analysis

The prevalence of cognitive deficits varied among PEF percent predicted groups, with rates of 26.8% (n = 64), 34.2% (n = 67), and 39.0% (n = 117) (Table 1). The occurrence of cognitive deficits was lower in groups with higher PEF% than in those with lower PEF%.

The relationship between PEF% and cognitive deficits, evaluated through weighted logistic regression, is presented in Table 3. In Model 1, adjusted for age, gender, race, and education level, the risk of having cognitive deficits was greater in the PEF% (80-100%) and PEF% <80% groups compared to the PEF% >100% group. Specifically, the PEF% <80% group (OR, 3.45; 95% CI, 1.67–7.12) had a significantly greater risk.

Table 3 Weighted logistic regression analysis of PEF and recreational activity in relation to cognitive deficits

In models 2 and 3, additional adjustments were made for BMI, smoking, physical activity, lung disease, heart disease, hypertension, stroke, and diabetes risk factors. The results from Models 2 and 3 were similar to those from Model 1, indicating that, compared to those in the > 100% PEF group, the 80-100% and < 80% PEF groups exhibited a greater risk of cognitive deficits (Table 3). Model 3, which included all the covariates, revealed a significantly greater risk in the PEF% (80-100%) group (OR, 2.66; 95% CI, 1.34–5.29) and in the PEF% <80% group (OR, 3.36; 95% CI, 1.67–6.76).

The relationships between recreational activity and cognitive deficits, evaluated through weighted logistic regression, are also presented in Table 3. In Model 1, adjusted for age, gender, race, and education level, compared to the group with no recreational activity, the group with recreational activity time meeting recommended levels showed a lower risk of cognitive deficits (OR, 0.24; 95% CI, 0.10–0.57), while there were no significant differences for recreational activity time below or above the recommended levels. The results in Model 3, including all covariates, were consistent with those in Model 1, indicating that compared to not engaging in recreational activity, engaging in recreational activity time meeting recommended levels was statistically associated with a lower risk of cognitive deficits (OR, 0.24; 95% CI, 0.10–0.57), with statistical significance.

However, there was no relationship between physical activity and cognitive deficits, as shown in eTable 1 of the Supplement.

Mediation analyses

Mediation analyses with physical activity and recreational activity as independent variables are presented in eTable 2 of the Supplement and Table 4, respectively. Taking the “no physical activity” group as a reference, in the “Exceed guideline” group, the significance of the indirect impact of physical activity through PEF% was confirmed (95% bootstrap CI = 0.001–0.096). A bootstrapped 95% CI confirmed that the indirect impact of physical activity, mediated by PEF%, was 0.039 on the global cognitive score. Furthermore, the indirect effect of PEF contributed to 7.1% of the total variance in global cognitive scores.

Table 4 Mediating model examination by bootstrap

In comparison to the “no recreational activity” group, the recreational activity time in the “meet guideline” group showed a significant indirect effect through PEF% (95% bootstrap CI = 0.013–0.157). PEF% influenced global cognitive score with an indirect effect of 0.075 and accounted for 9.7% of the total variance in global cognitive score. In the “Exceed guideline” group, recreational activity also demonstrated a significant indirect effect through PEF% (95% bootstrap CI = 0.011–0.164). PEF%, as a mediating factor, impacted the global cognitive score with an indirect effect of 0.067 and contributed to 13.0% of the total variance in the global cognitive score.

These findings confirmed our hypothesis that PEF might serve as a mediating factor in the association between activity and cognitive function. Figure 1 illustrates the mediation model along with standardized path coefficients.

Fig. 1
figure 1

Proposed models that investigate mediated effects. Notes: The results of the relative mediation analysis are based on the reference level of “No activity”; (A) Proposed models that investigate mediated effects in the association between physical activity and cognitive function; (B) Proposed models that investigate mediated effects in the association between recreational activity and cognitive function; *P < 0.05, **P < 0.01, ***P < 0.001

Discussion

This study explored the association between physical activity, pulmonary expiratory flow (PEF), and cognitive function in individuals aged 60 years and older in the U.S. Additionally, the mediating role of PEF in the relationship between physical activity and cognitive function was investigated. The findings revealed that higher levels of physical activity, particularly in recreational activities, and a higher percentage of predicted PEF were associated with fewer cognitive deficits. Furthermore, this study found that PEF mediates the relationship between physical activity and cognitive function, although mediation analysis using longitudinal designs would provide more compelling evidence. The results suggested that interventions to improve PEF in older adults and enhance recreational activity might directly or indirectly reduce the risk of cognitive deficits in the elderly.

Previous studies have indicated that PEF serves not only as an indicator of pulmonary function but also as a general marker of health, particularly in the elderly population. The PEF has been associated with chronic conditions in older individuals, such as dementia, muscle atrophy, and frailty, among others [30, 38, 39]. A study utilizing data from the National Health and Aging Trends Study (NHATS) investigated the correlation between PEF and incident dementia in 5935 older adults from 2011 to 2014 [30]. The findings suggested that PEF might be considered a potentially modifiable risk factor for dementia, with higher PEF categories providing greater protection against incident dementia. Consistent with prior research, the present study revealed a significant association between PEF and cognitive function, with a lower PEF% associated with a higher risk of cognitive impairment. The research further provided supporting evidence, indicating that even after adjusting for multiple confounding factors, including age, gender, race, education, smoking, and medical history, the risk of cognitive deficits remained statistically significant when the PEF% decreased to less than 80%. This study might represent the first instance of analyzing the relationship between PEF and cognitive function using data provided by the NHANES database.

However, the pathophysiological mechanisms through which impaired pulmonary function affects cognitive function remain unclear. The prevailing viewpoint suggests that a decline in pulmonary function leads to inadequate brain oxygenation, resulting in neuronal damage and subsequently manifesting as a decline in cognitive ability [40,41,42]. Additionally, there is an alternative perspective proposing a close association between oxygen deficiency-induced inflammation, oxidative stress, and the progression of brain aging [43, 44]. A study on the neurocognitive assessment of chronic obstructive pulmonary disease (COPD) has shown that cognitive impairment is more severe in COPD patients than in healthy controls [45]. In the present study, after excluding 142 participants with pulmonary diseases such as chronic bronchitis, emphysema, or asthma, the direct relationship between PEF% and the global cognitive score remained significant (p < 0.001, eTable 3 in the Supplement).

Physical activity (PA) has long been acknowledged as a widely accepted measure for reducing the risk of age-related cognitive decline. However, due to inconsistencies in the parameters of physical activity used in different studies, variations in cognitive measurement methods, and inconsistent assessments of moderating factors, the dose-response effects of physical activity on cognition and brain health remain unclear [46,47,48]. In the present study, after we adjusted for demographic factors, lifestyle variables, and medical history, we obtained interesting findings. Compared to individuals not engaging in recreational activities, the risk of cognitive deficits was lower in the group meeting the recommended exercise duration, with no statistically significant differences between groups with exercise durations below or exceeding the recommended guidelines. Extended or excessive exercise may result in insufficient recovery time, accompanied by physical stress and the release of cortisol [49], collectively posing potential adverse effects on cognitive functions. Additionally, the study revealed that the duration of work activity was not significantly correlated with cognitive function, a result not previously reported in the literature. The analysis suggested that the disparity in cognitive impact between recreational and work activities stems from the multifaceted nature of the former, embracing diverse experiences, social interactions, and positive emotions. In contrast, the latter, characterized by repetitive tasks and limited social engagement, correlates with inferior emotional experiences, thereby attenuating the cognitive benefits of physical activity.

Physical activity not only enhances cognitive function in older adults but also has been proven effective in safeguarding and improving pulmonary function [26, 27]. Our hypothesis posited that pulmonary function could act as a mediator in the association between physical activity and cognitive function. Further exploration of the associations between physical activity, pulmonary function, and cognitive function may contribute to clarifying causal pathways involved. Mediation analysis demonstrated that PEF served as a mediator in the correlation between physical activity, particularly recreational activity, and cognitive function. This suggested that older adults with poorer pulmonary function might face a greater risk of cognitive impairment when physical activity is reduced. PEF had mediating effects on 9.7% and 13.0% of the variance in the relationship between recommended and exceeded recommended recreational activity time and cognitive function, respectively. This finding suggested that sufficient recreational activity time could not only directly reduce the risk of cognitive impairment but also indirectly reduce the risk through mediating effects.

Limitations

This study has several limitations. First, the pulmonary reference values used in this study were based on samples from the NHANES III covering individuals aged 8 to 80 years in the U.S. However, due to the racial/ethnic of NHANES III individuals classifications, Hankinson et al.‘s research only provided pulmonary measurement reference values for non-Hispanic white, non-Hispanic black, and Mexican American populations [50], lacking reference values for other Hispanic, Asian American, and other ethnic groups. The analysis focused solely on U.S. non-Hispanic white, non-Hispanic black, and Mexican American populations, which might not represent the entire elderly population in the U.S. Second, within the NHANES study, pulmonary function, cognitive performance, and physical activity were simultaneously collected for only the 2011–2012 cycle. Thus, the study relied exclusively on the data from this specific cycle, potentially impacting the accuracy and reliability of the findings. Third, due to the cross-sectional study design and potential residual (unmeasured) confounding factors, the causal relationships were not established in this study. Future research should employ longitudinal designs or clinical trials to further assess the associations among physical activity, pulmonary function, and cognitive function. Forth, it’s important to note that the thresholds for work and recreational activities may differ depending on the intensity and duration of the activities involved. However, it is regrettable that we have not uncovered any studies addressing the question of whether the thresholds for work-related physical activity and recreational activity are consistent. In this study, we initially investigate the influence of diverse activity types on cognitive function, adhering to the conventional 150/75 minutes guideline. We hope this serves as a reference for future research delving deeper into the relationship between different types of activities and health outcomes. Fifth, although comparative analysis of basic characteristic data of participants included and not included in this study (eTable 4 in the Supplement) showed intergroup differences in several variables, the complexity of the sampling design and weight adjustments, coupled with the use of multiple logistic regression analysis to control confounding factors, enhanced the persuasiveness of the results and ensured sample representativeness.

Conclusion

The present study revealed an association in the American elderly population aged older than 60 years, where higher PEF and a longer duration of physical activity, especially recreational activity, were correlated with a reduced risk of cognitive impairment. This research represents the first attempt to evaluate the mediating influence of PEF on the connection between physical activity and cognitive function in older adults. These findings contribute to a better understanding of the interplay among physical activity, pulmonary function, and cognitive function. Importantly, these findings hold significance for the future prevention, treatment, and care of cognitive deficits in the elderly.

Data availability

The data that support the findings of this study are openly available at: https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

PEF:

peak expiratory flow

PEF%:

PEF in percent predicted

NHANES:

National Health and Nutrition Examination Survey

References

  1. Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer’s disease. Lancet (London England). 2016;388(10043):505–17.

    Article  CAS  PubMed  Google Scholar 

  2. Long JM, Holtzman DM. Alzheimer Disease: an update on pathobiology and treatment strategies. Cell. 2019;179(2):312–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Min J-Y, Min K-B, Paek D, Sakong J, Cho S-I. The association between neurobehavioral performance and lung function. Neurotoxicology. 2007;28(2):441–4.

    Article  PubMed  Google Scholar 

  4. Donahue PT, Xue Q-L, Carlson MC, Lipsitz LA. Peak Expiratory Flow Predicts Incident Dementia in a Representative Sample of U.S. older adults: the National Health and Aging trends Study (NHATS). Journals Gerontology: Ser A. 2023;78(8):1427–35.

    Google Scholar 

  5. Emery CF, Pedersen NL, Svartengren M, McClearn GE. Longitudinal and genetic effects in the relationship between pulmonary function and cognitive performance. Journals Gerontol Ser B Psychol Sci Social Sci. 1998;53(5):P311–317.

    CAS  Google Scholar 

  6. Gore CJ, Crockett AJ, Pederson DG, Booth ML, Bauman A, Owen N. Spirometric standards for healthy adult lifetime nonsmokers in Australia. Eur Respir J. 1995;8(5):773–82.

    Article  CAS  PubMed  Google Scholar 

  7. Liao D, Higgins M, Bryan NR, Eigenbrodt ML, Chambless LE, Lamar V, Burke GL, Heiss G. Lower pulmonary function and cerebral subclinical abnormalities detected by MRI: the atherosclerosis risk in communities study. Chest. 1999;116(1):150–6.

    Article  CAS  PubMed  Google Scholar 

  8. Gibson GE, Pulsinelli W, Blass JP, Duffy TE. Brain dysfunction in mild to moderate hypoxia. Am J Med. 1981;70(6):1247–54.

    Article  CAS  PubMed  Google Scholar 

  9. Cook NR, Albert MS, Berkman LF, Blazer D, Taylor JO, Hennekens CH. Interrelationships of peak expiratory flow rate with physical and cognitive function in the elderly: MacArthur Foundation studies of aging. Journals Gerontol Ser Biol Sci Med Sci. 1995;50(6):M317–323.

    Article  CAS  Google Scholar 

  10. Cerhan JR, Folsom AR, Mortimer JA, Shahar E, Knopman DS, McGovern PG, Hays MA, Crum LD, Heiss G. Correlates of cognitive function in middle-aged adults. Atherosclerosis risk in communities (ARIC) Study investigators. Gerontology. 1998;44(2):95–105.

    Article  CAS  PubMed  Google Scholar 

  11. Aleman A, Muller M, de Haan EH, van der Schouw YT. Vascular risk factors and cognitive function in a sample of independently living men. Neurobiol Aging. 2005;26(4):485–90.

    Article  PubMed  Google Scholar 

  12. Sachdev PS, Anstey KJ, Parslow RA, Wen W, Maller J, Kumar R, Christensen H, Jorm AF. Pulmonary function, cognitive impairment and brain atrophy in a middle-aged community sample. Dement Geriatr Cogn Disord. 2006;21(5–6):300–8.

    Article  CAS  PubMed  Google Scholar 

  13. Allaire JC, Tamez E, Whitfield KE. Examining the association between lung functioning and cognitive performance in African American adults. J Aging Health. 2007;19(1):106–22.

    Article  PubMed  Google Scholar 

  14. Singh-Manoux A, Dugravot A, Kauffmann F, Elbaz A, Ankri J, Nabi H, Kivimaki M, Sabia S. Association of lung function with physical, mental and cognitive function in early old age. Age (Dordrecht Netherlands). 2011;33(3):385–92.

    Article  PubMed  Google Scholar 

  15. Lutsey PL, Chen N, Mirabelli MC, Lakshminarayan K, Knopman DS, Vossel KA, Gottesman RF, Mosley TH, Alonso A. Impaired lung function, Lung Disease, and risk of Incident Dementia. Am J Respir Crit Care Med. 2019;199(11):1385–96.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rusanen M, Ngandu T, Laatikainen T, Tuomilehto J, Soininen H, Kivipelto M. Chronic obstructive pulmonary disease and asthma and the risk of mild cognitive impairment and dementia: a population based CAIDE study. Curr Alzheimer Res. 2013;10(5):549–55.

    Article  CAS  PubMed  Google Scholar 

  17. Singh B, Mielke MM, Parsaik AK, Cha RH, Roberts RO, Scanlon PD, Geda YE, Christianson TJ, Pankratz VS, Petersen RC. A prospective study of chronic obstructive pulmonary disease and the risk for mild cognitive impairment. JAMA Neurol. 2014;71(5):581–8.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Frenzel S, Bis JC, Gudmundsson EF, O’Donnell A, Simino J, Yaqub A, Bartz TM, Brusselle GGO, Bülow R, DeCarli CS, et al. Associations of Pulmonary function with MRI brain volumes: a coordinated Multi-study Analysis. J Alzheimers Dis. 2022;90(3):1073–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Erickson KI, Hillman C, Stillman CM, Ballard RM, Bloodgood B, Conroy DE, Macko R, Marquez DX, Petruzzello SJ, Powell KE. Physical activity, cognition, and brain outcomes: a review of the 2018 physical activity guidelines. Med Sci Sports Exerc. 2019;51(6):1242–51.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sofi F, Valecchi D, Bacci D, Abbate R, Gensini GF, Casini A, Macchi C. Physical activity and risk of cognitive decline: a meta-analysis of prospective studies. J Intern Med. 2011;269(1):107–17.

    Article  CAS  PubMed  Google Scholar 

  21. Beckett MW, Ardern CI, Rotondi MA. A meta-analysis of prospective studies on the role of physical activity and the prevention of Alzheimer’s disease in older adults. BMC Geriatr. 2015;15:9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Vaynman S, Gomez-Pinilla F. Revenge of the sit: how lifestyle impacts neuronal and cognitive health through molecular systems that interface energy metabolism with neuronal plasticity. J Neurosci Res. 2006;84(4):699–715.

    Article  CAS  PubMed  Google Scholar 

  23. Voss MW, Carr LJ, Clark R, Weng T. Revenge of the sit II: does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Ment Health Phys Act. 2014;7(1):9–24.

    Article  Google Scholar 

  24. Falck RS, Davis JC, Liu-Ambrose T. What is the association between sedentary behaviour and cognitive function? A systematic review. Br J Sports Med. 2017;51(10):800–11.

    Article  PubMed  Google Scholar 

  25. Dogra S, Good J, Gardiner PA, Copeland JL, Stickland MK, Rudoler D, Buman MP. Effects of replacing sitting time with physical activity on lung function: an analysis of the Canadian longitudinal study on aging. Health Rep. 2019;30(3):12–23.

    PubMed  Google Scholar 

  26. Dogra S, Good J, Buman MP, Gardiner PA, Stickland MK, Copeland JL. Movement behaviours are associated with lung function in middle-aged and older adults: a cross-sectional analysis of the Canadian longitudinal study on aging. BMC Public Health. 2018;18(1):818.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pelkonen M, Notkola IL, Lakka T, Tukiainen HO, Kivinen P, Nissinen A. Delaying decline in pulmonary function with physical activity: a 25-year follow-up. Am J Respir Crit Care Med. 2003;168(4):494–9.

    Article  PubMed  Google Scholar 

  28. Chen TC, Parker JD, Clark J, Shin HC, Rammon JR, Burt VL. National Health and Nutrition Examination Survey: estimation procedures, 2011–2014. Vital Health Stat Ser 2 Data Evaluation Methods Res 2018(177):1–26.

  29. Prevention UCfDCa. National Health and Nutrition Examination Survey: 2011–2012 Data Documentation, Codebook, and Frequencies. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/CFQ_G.htm. Accessed March 2017.

  30. Donahue PT, Xue QL, Carlson MC. Peak Expiratory Flow Predicts Incident Dementia in a Representative Sample of U.S. older adults: the National Health and Aging trends Study (NHATS). Journals Gerontol Ser Biol Sci Med Sci. 2023;78(8):1427–35.

  31. Sasaki M, Kodama C, Hidaka S, Yamashita F, Kinoshita T, Nemoto K, Ikejima C, Asada T. Prevalence of four subtypes of mild cognitive impairment and APOE in a Japanese community. Int J Geriatr Psychiatry. 2009;24(10):1119–26.

  32. Busse A, Hensel A, Gühne U, Angermeyer MC, Riedel-Heller SG. Mild cognitive impairment: long-term course of four clinical subtypes. Neurology. 2006;67(12):2176–85.

  33. Hansen MRH, Schmid JM. Screening for impaired pulmonary function using peak expiratory flow: performance of different interpretation strategies. Respiratory Med Res. 2023;83:101015.

    Article  Google Scholar 

  34. Thorat YT, Salvi SS, Kodgule RR. Peak flow meter with a questionnaire and mini-spirometer to help detect asthma and COPD in real-life clinical practice: a cross-sectional study. NPJ Prim care Respiratory Med. 2017;27(1):32.

    Article  Google Scholar 

  35. Jackson H, Hubbard R. Detecting chronic obstructive pulmonary disease using peak flow rate: cross sectional survey. BMJ (Clinical Res ed). 2003;327(7416):653–4.

    Article  Google Scholar 

  36. Kay MC, Carroll DD, Carlson SA, Fulton JE. Awareness and knowledge of the 2008 physical activity guidelines for americans. J Phys Act Health. 2014;11(4):693–8.

    Article  PubMed  Google Scholar 

  37. Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014;67(3):451–70.

    Article  PubMed  Google Scholar 

  38. Trevisan C, Rizzuto D, Maggi S, Sergi G, Welmer AK, Vetrano DL. Cross-sectional and longitudinal associations between Peak Expiratory Flow and Frailty in older adults. J Clin Med 2019, 8(11).

  39. Ridwan ES, Wiratama BS, Lin MY, Hou WH, Liu MF, Chen CM, Hadi H, Tan MP, Tsai PS. Peak expiratory flow rate and sarcopenia risk in older Indonesian people: a nationwide survey. PLoS ONE. 2021;16(2):e0246179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. van Dijk EJ, Vermeer SE, de Groot JC, van de Minkelis J, Prins ND, Oudkerk M, Hofman A, Koudstaal PJ, Breteler MM. Arterial oxygen saturation, COPD, and cerebral small vessel disease. J Neurol Neurosurg Psychiatry. 2004;75(5):733–6.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Peers C, Dallas ML, Boycott HE, Scragg JL, Pearson HA, Boyle JP. Hypoxia and neurodegeneration. Ann N Y Acad Sci. 2009;1177:169–77.

    Article  CAS  PubMed  Google Scholar 

  42. Antonelli Incalzi R, Marra C, Giordano A, Calcagni ML, Cappa A, Basso S, Pagliari G, Fuso L. Cognitive impairment in chronic obstructive pulmonary disease–a neuropsychological and spect study. J Neurol. 2003;250(3):325–32.

    Article  PubMed  Google Scholar 

  43. Akiyama H, Barger S, Barnum S, Bradt B, Bauer J, Cole GM, Cooper NR, Eikelenboom P, Emmerling M, Fiebich BL, et al. Inflammation and Alzheimer’s disease. Neurobiol Aging. 2000;21(3):383–421.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Janowitz D, Habes M, Toledo JB, Hannemann A, Frenzel S, Terock J, Davatzikos C, Hoffmann W, Grabe HJ. Inflammatory markers and imaging patterns of advanced brain aging in the general population. Brain Imaging Behav. 2020;14(4):1108–17.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Grant I, Heaton RK, McSweeny AJ, Adams KM, Timms RM. Neuropsychologic findings in hypoxemic chronic obstructive pulmonary disease. Arch Intern Med. 1982;142(8):1470–6.

    Article  CAS  PubMed  Google Scholar 

  46. Colcombe S, Kramer AF. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol Sci. 2003;14(2):125–30.

    Article  PubMed  Google Scholar 

  47. Brasure M, Desai P, Davila H, Nelson VA, Calvert C, Jutkowitz E, Butler M, Fink HA, Ratner E, Hemmy LS, et al. Physical activity interventions in preventing Cognitive decline and Alzheimer-Type Dementia: a systematic review. Ann Intern Med. 2018;168(1):30–8.

    Article  PubMed  Google Scholar 

  48. Ciria LF, Román-Caballero R, Vadillo MA, Holgado D, Luque-Casado A, Perakakis P, Sanabria D. An umbrella review of randomized control trials on the effects of physical exercise on cognition. Nat Hum Behav. 2023;7(6):928–41.

    Article  PubMed  Google Scholar 

  49. Hill EE, Zack E, Battaglini C, Viru M, Viru A, Hackney AC. Exercise and circulating cortisol levels: the intensity threshold effect. J Endocrinol Investig. 2008;31(7):587–91.

    Article  CAS  Google Scholar 

  50. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med. 1999;159(1):179–87.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was funded by the Science and Technology Development Plan of Jilin Province (YDZJ202301ZYTS050) to YuehuiWang, the Jilin Provincial Health Commission (2022LC098) to Yuehui Wang.

Author information

Authors and Affiliations

Authors

Contributions

Dr Yuehui Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Dong, Yuehui Wang. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Dong, Yue, Sun. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Dong, Yue, Sun, Zhe Wang.

Corresponding author

Correspondence to Yuehui Wang.

Ethics declarations

Ethics approval and consent to participate

This study received approval from the ethics review board of the National Center for Health Statistics. Written informed consent was provided by all participants.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, B., Yue, Y., Wang, Z. et al. Association between physical activity, peak expiratory flow, and cognitive function in aging: a cross-sectional analysis. BMC Geriatr 24, 460 (2024). https://doi.org/10.1186/s12877-024-05080-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-024-05080-4

Keywords