Abstract
Background
Physical activity volume (PAV) has been linked to a wide range of health outcomes; however, its association with incident nervous system diseases remains incompletely understood. This study explored their relationship using data from UK Biobank.
Methods
A retrospective cohort study was conducted involving 278,306 participants from the UK Biobank. PAV was quantified as metabolic equivalent of task (MET) minutes per week, derived from self-reported physical activity levels, and categorized into three groups. Incident cases of nervous system diseases were identified through ICD-10 codes obtained from hospital inpatient records, death registries, and self-reports. Cox proportional hazards models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for a series of covariates. Restricted cubic splines were applied to assess potential non-linear associations.
Results
Women and individuals aged 60 years or older exhibited higher incidence rates of nervous system diseases. After multivariable adjustment, higher PAV was significantly associated with a lower risk of overall nervous system diseases (HR < 1). A non-linear dose–response relationship was observed, with the lowest risk occurring at a PAV level of 1,356 MET-min/week. Subgroup analyses indicated that elevated PAV conferred protective effects against several specific conditions. Conversely, higher PAV was associated with an increased risk of meningitis.
Conclusion
Increased levels of physical activity are associated with a reduced risk of numerous nervous system diseases, with optimal protection observed at approximately 1,356 MET-min/week. These findings support the promotion of moderate-to-vigorous physical activity as a preventive strategy for neurological disorders, particularly among high-risk populations.
Keywords: incidence, nervous system diseases, physical activity volume, retrospective study, UK Biobank
1. Introduction
The increasing global burden of nervous system diseases is notable, driven by aging populations. Nervous system diseases, including neurodegenerative diseases (1, 2), cerebrovascular diseases (e.g., stroke) (3), and other neurological disorders (4), have become leading causes of disability and death worldwide (5, 6). With the development of medical technique and pharmacological treatments, the treatments of nervous system diseases are continually evolving (7–9). While clinical treatment often only symptoms management, and some conditions need long-term rehabilitation, the role of exercise in the recovery of the nervous system remains significant (10). As we all know, exercise is a well-established protective factor for some diseases, such as diabetes (11), gastroesophageal reflux disease (12), and even some cancers. Besides, a growing body of evidence suggests that physical activity also contribute to brain health (10). Proposed mechanisms include enhanced cerebral blood flow, reduction of inflammation, improved vascular function, and the promotion of neurotrophic factors. Numerous studies have linked exercise to a reduced risk of individual nervous system diseases, particularly dementia and stroke (13). However, notable gaps in knowledge remain. Current studies either have focused on a single disease outcome or simply illustrate exercise condition using active vs. inactive. The quantitative relationship between the total volume of activity and nervous system disease is unclear (14). Understanding this dose–response relationship is crucial for informing precise public health recommendations.
Physical activity volume(PAV), the total amount of physical activity one performs, is regarded as the comprehensive measure of total exercise “dose” (15). It is a crucial concept in public health and the key factor linked to most health benefit, calculated by manipulating frequency, intensity, and time (16). In order to make sense of association between the volume of physical activity and the subsequent incidence of nervous system disease, we conducted a large-scale retrospective cohort study using date from UK Biobank to explore their relationships.
2. Materials and methods
2.1. Date source
The UK Biobank is a long-term prospective cohort study established to identify the determinants of disabling conditions and data collected from 22 health centers across the United Kingdom (17). At recruitment, participants completed a detailed touch-screen questionnaire which mainly focus on demographic, socioeconomic information, health behaviors and living environment situations were collected by specific measurements. More details about the UK Biobank can be accessed online (http://www.ukbio bank.ac.uk/). The ethical approval was granted by the research ethics committee (11/ NW/0382). Before the research, all participants provided written informed consent and we were permitted to use UK Biobank data under study number 99709. At the beginning, data from 502,428 participants were potentially available. After excluding UK Biobank removed (n = 430), missing data on key covariates (n = 90,935), with no PAV data (n = 86,107), previous diagnosis of Nervous System Diseases at baseline (n = 46,650), data from a total of 278,306 eligible participants, aged from 40 to 69 at recruit in UK Biobank was used in our study. The participants were enrolled between 2006 and 2010, and the follow-up completed on March 31, 2025.
2.2. Calculation of PAV
PAV is the representation of the metabolic equivalent of tasks, and calculated by each participant’s weekly physical activity volume. In this study, the PAV was only used to evaluated physical exercise activity and calculated based on the 2011 Compendium of Physical Activities (18). As the physical activity intensity is usually divided into vigorous, moderate, and light, and each category is assigned eight, five, and three METs. According to the minimum time consumed for each category and its frequencies, the PAV was finally calculated as follows: (8 METs×20 min × times) + (5 METs×30 min × times) + (3 METs×30 min × times). Then, PAV was divided into the following three groups, which, respectively, is <500 group, 500–1,000 group, and ≥1,000 group.
2.3. Ascertainment of nervous system disease incidence
The incident Nervous System disease events was identified in accordance with the ‘first occurrence’ data fields that the UK biobank generated that map the clinical codes from primary care, hospital inpatient admissions, death records, and self-reported medical conditions to 3-digit code of International Classification of Disease (ICD-10) and provide, for each participant, the date that code first occurred in any source.
2.4. Covariates
The selection of covariates in our study reference to a review of several published studies and Global Burden Disease report (19). To mitigate the influence of potential confounding effects, the following five types covariates were considered in multivariable models: Demographic information (Sex, Age, Ethnic background, BMI and MET score), Socioeconomic Status (Employment status and University education), Medical History (Diabetes, Hypertension and Cancer), Health Behaviors (Alcohol drinker status, Smoking status, Sleep duration, Time watch TV, Time use computer, Coffee intake, Tea intake and IPAQ activity group) and Living Environment (PM2.5, PM10, Greenspace percentage in 300 m, Nitrogen dioxide and Nitrogen oxides). Among these, Sleep duration, Time watch TV and Time use compute were self-reported as the number of hours per day or the times per day. BMI or Body Mass Index was calculated using the internationally recognized formula: weight (kg) divided by height (cm) squared. IPAQ (International Physical Activity Questionnaire) is a self-administered questionnaire that was used to assess the physical activity in the past 7 days. As regular PA was defined as performing at least 30 min of moderate PA at least five times a week, we categorized each participant’s weekly PA levels based on that. In this study, we used IPAQ to evaluate the physical activity intensity. Furthermore, considering diabetes, hypertension and cancer are major components of the global burden of disease, this study included these three conditions based on the first reported date in the first occurrence dataset and the date of attending the assessment center.
2.5. Statistical analysis
Statistical analyses used SAS (version 9.4, SAS Institute Inc., Cary, NC, United States) and R (version 4.4.1), with statistical significance at p < 0.05 using t-tests for continuous variables and chi-square for categorical variables. Cox proportional hazards regression models were used to explore the associations between PAV score and total nervous system disorder incidence, and hazard ratios (HRs) and 95% confidence intervals (CIs) were shown. The four models were tested: (1) Model 1 was adjusted for demographic information; (2) Model 2 was adjusted for Model 1 plus socioeconomic status; (3) Model 3 was adjusted for model 2 plus medical history factors; (4) Model 4 further adjusted for health behaviors factors; and (4) Model 5 finally adjusted for living environment factors.
We used restricted cubic spline (RCS) regression to explore potential non-linear associations between PAV score total nervous system disorder incidence. The subgroup analysis was used to further explore associations between PAV score and specific nervous system disorder incidence.
3. Results
3.1. Characteristics of the study population
Data from 502,428 participants were available at beginning, after excluding UK Biobank removed (n = 430), missing data on key covariates (n = 90,935), with no PAV data (n = 86,107), previous diagnosis of Nervous System Diseases at baseline (n = 46,650), data from a total of 278,306 eligible participants in UK Biobank was used in our study.
Among UK biobank participants, a nearly equal gender distribution was shown, participants were almost white (94.8%), and age below 60 were162000 (58.2%). According to each participant’s PAV score, the number of participant in each group were, respectively, 40,780 (MTE < 500 group), 41,976 (500–1,000 group), 195,550 (MTE ≥ 1,000 group). Compared with those whose PAV score was above 1,000, participants in PAV < 500 group tended to be younger, possess a higher BMI, exhibit higher levels of education, and sleep less, smoke more. Additionally, participants in this group spent more time in watching TV and using computer. Furthermore, they also had a higher proportion of Diabetes and Hypertension than the other two groups. While no difference was shown when compared the ratio of Cancer. The characteristics of study participants in the primary overall and by the divided group were presented in Table 1.
Table 1.
Characteristics of study population in UK Biobank.
| Characteristics | PAV <500 group (40780) n (%) | PAV 500–1,000 group (41976) n (%) | PAV ≥1,000 group (195550) n (%) | Total (278306) n (%) | p-value |
|---|---|---|---|---|---|
| Sex | <0.0001 | ||||
| Male | 20,502 (50.3%) | 19,716 (47.0%) | 96,406 (49.3%) | 136,624 (49.1%) | |
| Female | 20,278 (49.7%) | 22,260 (53.0%) | 99,144 (50.7%) | 141,682 (50.9%) | |
| Age at recruitment | <0.0001 | ||||
| <60 | 25,799 (63.3%) | 25,068 (59.7%) | 111,133 (56.8%) | 162,000 (58.2%) | |
| ≥60 | 14,981 (36.7%) | 16,908 (40.3%) | 84,417 (43.2%) | 116,306 (41.8%) | |
| BMI(kg/m2) | <0.0001 | ||||
| <18.5 | 404 (1.0%) | 346 (0.8%) | 1,600 (0.8%) | 2,350 (0.8%) | |
| 18.5–20 | 593 (1.5%) | 698 (1.7%) | 3,864 (2.0%) | 5,155 (1.9%) | |
| 20–25 | 9,925 (24.3%) | 12,607 (30.0%) | 66,937 (34.2%) | 89,469 (32.1%) | |
| 25–30 | 16,801 (41.2%) | 18,037 (43.0%) | 85,241 (43.6%) | 120,079 (43.1%) | |
| ≥30 | 13,057 (32.0%) | 10,288 (24.5%) | 37,908 (19.4%) | 61,253 (22.0%) | |
| Ethnic background | <0.0001 | ||||
| White | 38,142 (93.5%) | 39,599 (94.3%) | 186,182 (95.2%) | 263,923 (94.8%) | |
| African | 359 (0.9%) | 331 (0.8%) | 1,226 (0.6%) | 1916 (0.7%) | |
| Asian | 1,213 (3.0%) | 1,046 (2.5%) | 3,697 (1.9%) | 5,956 (2.1%) | |
| Mixed | 252 (0.6%) | 252 (0.6%) | 1,174 (0.6%) | 1,678 (0.6%) | |
| Other ethnic group | 814 (2.0%) | 748 (1.8%) | 3,271 (1.7%) | 4,833 (1.7%) | |
| Current employment status | <0.0001 | ||||
| Employment | 27,312 (67.0%) | 26,821 (63.9%) | 116,083 (59.4%) | 170,216 (61.2%) | |
| Unemployment | 3,574 (8.8%) | 2,674 (6.4%) | 12,566 (6.4%) | 18,814 (6.8%) | |
| Retired | 9,894 (24.3%) | 12,481 (29.7%) | 66,901 (34.2%) | 89,276 (32.1%) | |
| University education | <0.0001 | ||||
| No | 26,121 (64.1%) | 25,455 (60.6%) | 127,080 (65.0%) | 178,656 (64.2%) | |
| Yes | 14,659 (35.9%) | 16,521 (39.4%) | 68,470 (35.0%) | 99,650 (35.8%) | |
| Alcohol drinker status | <0.0001 | ||||
| Current | 37,521 (92.0%) | 39,307 (93.6%) | 183,436 (93.8%) | 260,264 (93.5%) | |
| Previous | 1,421 (3.5%) | 1,149 (2.7%) | 5,523 (2.8%) | 8,093 (2.9%) | |
| Never | 1838 (4.5%) | 1,520 (3.6%) | 6,591 (3.4%) | 9,949 (3.6%) | |
| Smoking status | <0.0001 | ||||
| Current | 4,941 (12.1%) | 4,066 (9.7%) | 18,037 (9.2%) | 27,044 (9.7%) | |
| Previous | 13,804 (33.9%) | 14,340 (34.2%) | 69,671 (35.6%) | 97,815 (35.1%) | |
| Never | 22,035 (54.0%) | 23,570 (56.2%) | 107,842 (55.1%) | 153,447 (55.1%) | |
| Sleep duration (Hours/day) | <0.0001 | ||||
| <7 | 10,391 (25.5%) | 9,643 (23.0%) | 44,471 (22.7%) | 64,505 (23.2%) | |
| ≥7 | 30,389 (74.5%) | 32,333 (77.0%) | 151,079 (77.3%) | 213,801 (76.8%) | |
| Diabetes | <0.0001 | ||||
| No | 35,839 (87.9%) | 38,098 (90.8%) | 181,052 (92.6%) | 254,989 (91.6%) | |
| Yes | 4,941 (12.1%) | 3,878 (9.2%) | 14,498 (7.4%) | 23,317 (8.4%) | |
| Hypertension | <0.0001 | ||||
| No | 24,156 (59.2%) | 26,149 (62.3%) | 126,220 (64.5%) | 176,525 (63.4%) | |
| Yes | 16,624 (40.8%) | 15,827 (37.7%) | 69,330 (35.5%) | 101,781 (36.6%) | |
| Cancer | 0.5145 | ||||
| No | 31,203 (76.5%) | 32,225 (76.8%) | 150,139 (76.8%) | 213,567 (76.7%) | |
| Yes | 9,577 (23.5%) | 9,751 (23.2%) | 45,411 (23.2%) | 64,739 (23.3%) | |
| Coffee intake (Cups/day) | <0.0001 | ||||
| Not intake | 12,213 (29.9%) | 11,671 (27.8%) | 54,193 (27.7%) | 78,077 (28.1%) | |
| 1 | 7,473 (18.3%) | 8,583 (20.4%) | 41,369 (21.2%) | 57,425 (20.6%) | |
| 2 | 7,033 (17.2%) | 8,062 (19.2%) | 38,618 (19.7%) | 53,713 (19.3%) | |
| 3 | 5,052 (12.4%) | 5,315 (12.7%) | 24,446 (12.5%) | 34,813 (12.5%) | |
| 4 | 3,586 (8.8%) | 3,761 (9.0%) | 16,411 (8.4%) | 23,758 (8.5%) | |
| 5 | 2,411 (5.9%) | 2,106 (5.0%) | 9,492 (4.9%) | 14,009 (5.0%) | |
| ≥6 | 3,012 (7.4%) | 2,478 (5.9%) | 11,021 (5.6%) | 16,511 (5.9%) | |
| Tea intake (Cups/day) | <0.0001 | ||||
| Not intake | 8,078 (19.8%) | 7,322 (17.4%) | 32,769 (16.8%) | 48,169 (17.3%) | |
| 1 | 3,798 (9.3%) | 3,724 (8.9%) | 16,776 (8.6%) | 24,298 (8.7%) | |
| 2 | 5,750 (14.1%) | 6,385 (15.2%) | 28,768 (14.7%) | 40,903 (14.7%) | |
| 3 | 5,912 (14.5%) | 6,499 (15.5%) | 29,870 (15.3%) | 42,281 (15.2%) | |
| 4 | 5,486 (13.5%) | 6,005 (14.3%) | 28,458 (14.6%) | 39,949 (14.4%) | |
| 5 | 4,335 (10.6%) | 4,599 (11.0%) | 22,483 (11.5%) | 31,417 (11.3%) | |
| ≥6 | 7,421 (18.2%) | 7,442 (17.7%) | 36,426 (18.6%) | 51,289 (18.4%) | |
| Time watch TV (Hours/day) | <0.0001 | ||||
| <1 | 3,140 (7.7%) | 3,476 (8.3%) | 16,691 (8.5%) | 23,307 (8.4%) | |
| 1 | 4,978 (12.2%) | 5,772 (13.8%) | 27,764 (14.2%) | 38,514 (13.8%) | |
| 2 | 10,549 (25.9%) | 11,718 (27.9%) | 55,760 (28.5%) | 78,027 (28.0%) | |
| 3 | 9,503 (23.3%) | 9,795 (23.3%) | 45,848 (23.4%) | 65,146 (23.4%) | |
| 4 | 6,331 (15.5%) | 6,307 (15.0%) | 29,465 (15.1%) | 42,103 (15.1%) | |
| ≥5 | 6,279 (15.4%) | 4,908 (11.7%) | 20,022 (10.2%) | 31,209 (11.2%) | |
| Time use computer (Hours/day) | <0.0001 | ||||
| <1 | 17,276 (42.4%) | 18,425 (43.9%) | 91,329 (46.7%) | 127,030 (45.6%) | |
| 1 | 11,633 (28.5%) | 12,818 (30.5%) | 61,116 (31.3%) | 85,567 (30.7%) | |
| 2 | 5,889 (14.4%) | 5,870 (14.0%) | 25,118 (12.8%) | 36,877 (13.3%) | |
| 3 | 2,178 (5.3%) | 1912 (4.6%) | 7,607 (3.9%) | 11,697 (4.2%) | |
| 4 | 1,259 (3.1%) | 1,025 (2.4%) | 3,942 (2.0%) | 6,226 (2.2%) | |
| ≥5 | 2,545 (6.2%) | 1926 (4.6%) | 6,438 (3.3%) | 10,909 (3.9%) | |
| IPAQ activity group | <0.0001 | ||||
| Low | 39,524 (96.9%) | 8,807 (21.0%) | 1,222 (0.6%) | 49,553 (17.8%) | |
| Moderate | 1,256 (3.1%) | 33,169 (79.0%) | 78,983 (40.4%) | 113,408 (40.7%) | |
| High | 0 (0%) | 0 (0%) | 115,345 (59.0%) | 115,345 (41.4%) | |
| PM2.5* | 9.92 [8.2, 21.3] | 9.91 [8.17, 20.1] | 9.90 [8.17, 20.2] | 9.91 [8.2, 21.3] | <0.0001 |
| PM10* | 16.0 [11.8, 30.7] | 16.0 [11.8, 26.3] | 16.0 [11.8, 31.4] | 16.0 [11.8, 31.4] | <0.0001 |
| Greenspace percentage in 300m* | 29.8 [0.3, 99.2] | 29.3 [0.4, 99.1] | 29.8 [0.2, 99.2] | 29.7 [0.2, 99.2] | <0.0001 |
| Nitrogen dioxide* | 26.2 [12.9, 97.1] | 26.2 [12.9, 108] | 26.0 [12.9, 108] | 26.0 [12.9, 108] | <0.0001 |
| Nitrogen oxides* | 42.3 [19.7, 247] | 42.0 [19.7, 265] | 41.9 [19.7, 266] | 41.9 [19.7, 266] | <0.0001 |
*Median [Min, Max].
3.2. Relationships of PAV score and total nervous system disorder incidence
As shown in Table 2, negative associations between PAV score and the incidence of nervous system diseases were shown (HR < 1). Consistently, after adjusted the potential confounders of Socioeconomic Status (Employment status and University education) and Health Behaviors factors (Alcohol drinker status, Smoking status, Sleep duration, Time watch TV, Time use computer, Coffee intake, Tea intake and IPAQ activity group), the association seemed somewhat strengthened in Grouped PAV score. Whereas, after the adjustment of Socioeconomic Status (Employment status and University education) in Continuous PAV score, the association was insignificant, which might be caused by statistical difference. Additionally, adjusting the potential confounders of Medical History (Diabetes, Hypertension and Cancer) and Living Environment (PM2.5, PM10, Greenspace percentage in 300 m, Nitrogen dioxide and Nitrogen oxides), the incidence of total nervous system disorder seemed decrease. The p values for trend were all below 0.05 in above situations except in model 2 for Continuous PAV score.
Table 2.
Associations between PAV score and total nervous system disorder incidence.
| Model | Grouped PAV score | Continuous PAV score | |
|---|---|---|---|
| HR (95% CI) | PCox-regression | PCox-regression | |
| Model 1 | 0.942 (0.929,0.954) | <0.001 | 0.003 |
| Model 2 | 0.931 (0.919,0.944) | <0.001 | 0.497 |
| Model 3 | 0.953 (0.941,0.967) | <0.001 | <0.001 |
| Model 4 | 0.945 (0.925,0.966) | <0.001 | <0.001 |
| Model 5 | 0.946 (0.926,0.967) | <0.001 | <0.001 |
Model 1: Adjust Sex, Age at recruitment, Ethnic background, BMI and MET score.
Model 2: Adjust Variables in Model 1 plus Employment status and University education.
Model 3: Adjust Variables in Model 2 plus Diabetes, Hypertension and Cancer.
Model 4: Adjust Variables in Model 3 plus Alcohol drinker status, Smoking status, Sleep duration, Time watch TV, Time use computer, Coffee intake, Tea intake and IPAQ activity group.
Model 5: Adjust Variables in Model 4 plus PM2.5, PM10, Greenspace percentage in 300 m, Nitrogen dioxide and Nitrogen oxides.
Non-linear relationship between the score of PAV and incidence of nervous system disorder after adjusting potential confounding factors. The risk of nervous system disorder was lowest when the score of PAV was 1,356. Considering the influence of gender and sex, female had higher incidence of Nervous System Diseases and population age above 60 were more likely had Nervous Diseases. More details were presented in Figure 1.
Figure 1.
Restricted cubic spline for testing the hypothesis of nonlinear correlation between MET score and total nervous system disorder incidence in (A). Total population, (B). Population grouped by sex, (C). Population grouped by age.
3.3. Relationships of PAV score and specific nervous system disorder incidence
Subgroup analysis was used to explored the relationships between PAV and some specific nervous system diseases. As shown in Figure 2, PAV had no influence on most nervous system diseases. While it was a risk factor to Meningitis in bacterial diseases classified elsewhere. Additionally, PAV played an protective effect in the following nervous system diseases: other degenerative disease of basal ganglia, other extrapyramidal and movement disorders, multiple sclerosis, transient cerebral ischaemic attacks and related syndromes, sleep disorder, nerve root and plexus compressions in diseases classified elsewhere, mononeuropathies of upper limb, other polyneuropathies, primary disorders of muscles, other myopathies, hemiplegia, paraplegia and tetraplegia, other paralytic syndromes, other disease of spinal cord.
Figure 2.
Associations between PAV score and specific nervous system disorder incidence.
Furthermore, we explored the death ratio of those nervous system diseases to which PAV score showed as protective factors. The detailed death ratio of single nervous system disease and simultaneous two neurological disorders was shown in Figure 3. It shown that the top five death ratio of nervous system diseases are Mononeuropathies of upper limb, Sleep disorders, Nerve root and plexus compressions in diseases classified elsewhere, Transient cerebral ischaemic attacks and related syndromes, and Other extrapyramidal and movement disorders. While participants who simultaneous suffering two different types of nervous system diseases, the death odds of those were no increased.
Figure 3.
UpSet plots for signal-positive nervous system disorders and death intersection. G09, Sequelae of inflammatory diseases of central nervous system; G23, Other degenerative diseases of basal ganglia; G25, Other extrapyramidal and movement disorders; G35, Multiple sclerosis; G45, Transient cerebral ischaemic attacks and related syndromes; G47, Sleep disorders; G55, Nerve root and plexus compressions in diseases classified elsewhere; G56, Mononeuropathies of upper limb; G62, Other polyneuropathies; G71, Primary disorders of muscles; G72, Other myopathies; G81, Hemiplegia; G82, Paraplegia and tetraplegia; G83, Other paralytic syndromes; G95, Other diseases of spinal cord.
4. Discussion
Regular physical exercise, encompassing both aerobic and resistance training, is widely acknowledged as a fundamental component of healthy lifestyle behaviors (20). Its benefits extend across multiple domains of health, particularly in promoting neurological well-being (21).
Physical exercise has been demonstrated to significantly reduce the risk of various neurodegenerative diseases (10), as well as autoimmune and inflammatory neurological disorders, such as multiple sclerosis and demyelinating diseases (22). The vascular protective effects of exercise are particularly noteworthy. These include enhanced cerebral blood flow, improved vascular function, and the promotion of neurotrophic factors such as Brain-Derived Neurotrophic Factor (BDNF) (23). These factors contribute to increased neural plasticity and a reduction in the production of inflammatory mediators, thereby attenuating neuroinflammation. Furthermore, the observed low prevalence of neuromuscular diseases and neurological sequelae—such as hemiplegia, paraplegia, and muscle paralysis—may be partially attributed to selection bias, as individuals with such conditions may be physically unable to engage in regular exercise and thus are underrepresented in the study population (24, 25).
The study also reveals that increased physical activity does not always equate to greater health benefits. Instead, an “optimal activity volume” appears to exist. It is in accordance with the recommendation of WHO about the physical activity intensity in different population (22). Beyond this threshold, the protective effects may plateau or even diminish, underscoring the importance of recommending appropriate exercise levels. Moreover, the incidence of nervous system diseases exhibits sexual dimorphism. The higher incidence rates of neurological diseases observed in women, potentially influenced by hormonal differences and variations in social behaviors. Same phenomenon also appears in other diseases, such as autoimmune diseases (26). The elevated risk among older adults further highlights the need for tailored preventive exercise interventions within this population.
Moderate-to-high-intensity physical activity (PAV) demonstrates a protective effect in the majority of neurological diseases. However, it may be ineffective or even harmful in certain infectious or inflammatory conditions, such as meningitis and nervous system abscess. This may be attributed to the investigation was conducted in the acute stage of diseases, in accordance with the suggestion that absolutely forbidding physical activity in outburn stage of meningitis (27, 28). This result highlights the necessity for disease-specific analyses.
Additionally, this study demonstrated the influence of living environment, which is coordinated with finds of one systematic review which explores the association between greenness exposure and nervous system disease risk and suggests further research focus more attention on the role greenness exposure plays in various nervous system diseases (29). Some previous studies have reported the increasing mortality of circulatory diseases and respiratory diseases with the exposure of PM2.5 (30).
Despite its strengths, this study has several limitations. First, physical activity levels were determined based on baseline health examination data, which may not fully reflect actual activity levels and may lack correlation with objective standard measurements. Due to the voluntary nature of participation, some participants lacked complete diagnostic data. Second, the classification of neurological diseases presents a complex challenge. Etiological and anatomical classifications often overlap, and diagnostic criteria vary across studies. The heterogeneity in disease severity may also introduce variability in the results. Third, there may be a considerable time interval between the health examination and the diagnosis of neurological diseases. Self-reported questionnaires are prone to recall bias. Fourth, the voluntary participation in health check-ups may lead to selection bias. Fifth, as an observational study, the observed associations may not reflect causal relationships. Finally, the sample was predominantly composed of White (94.8%), and the high prevalence of alcohol consumption (over 90%) may have influenced the neurological outcomes. Therefore, caution should be exercised when generalizing these findings to other racial or cultural groups.
Nonetheless, the findings clearly indicate that individuals who do not engage in regular physical activity are at a higher risk of developing neurological diseases. These findings are especially relevant for populations with low PAV levels, including old individuals, those with high body mass index (BMI), and sedentary individuals. Future research should prioritize large-scale prospective cohort studies to validate these results. In summary, physical activity is significantly associated with a reduced risk of neurological diseases, with the risk exhibiting a non-linear relationship with exercise intensity and volume. Excessive exercise may paradoxically increase disease risk. Physical activity demonstrates protective effects against various specific neurological conditions. The study also highlights the influence of gender, age, and activity levels on disease susceptibility, providing a scientific basis for the development of targeted prevention strategies.
5. Conclusion
Increased levels of physical activity are associated with a reduced risk of numerous nervous system diseases, with optimal protection observed in this study. It is advocated that moderate-to-vigorous physical activity need to be considered when referring to daily activity as it is the protective factor for neurological disorders, particularly among high-risk populations such as older adults and those leading sedentary lifestyles. Further mechanistic studies and randomized controlled trials are necessary to confirm the causal relationships suggested by this research. And disease-specific risks and non-linear patterns should be taken into account when formulating future public health recommendations.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by Key Laboratory of Geriatric Long-term Care (Naval Medical University), Ministry of Education and Special Disease Project of Jiading District Health System (grant number: ZB202401).
Footnotes
Edited by: Kajal Sharma, Chitkara University, India
Reviewed by: Francesca Gobbin, University of Verona, Italy
Shirin Pourteymour, University of Oslo, Norway
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: http://www.ukbiobank.ac.uk/.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author contributions
MY: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. YZ: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. KX: Conceptualization, Methodology, Supervision, Writing – original draft. LL: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing. FH: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Supervision, Writing – review & editing.
Conflict of interest
The author(s) declared that this work 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 author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
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.
References
- 1.Singh B, Mahajan S, Abdella S, Khan R, Garg S. Exploring autophagy inducing molecules: targeting diverse pathways in Alzheimer's disease management. Med Res Rev. (2025) 46:70013. doi: 10.1002/med.70013, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Zhou CH, Zhu YC. Imaging of cerebral iron as an emerging marker for brain aging, neurodegeneration, and cerebrovascular diseases. Brain Sci. (2025) 15:944. doi: 10.3390/brainsci15090944, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Xia X, Yu J, Suona D, Zhi H, Hao Y, Wang L. Global burden, trends and health inequalities of stroke attributable to household air pollution, 1990-2021: a decomposition and prediction analysis. Front Public Health. (2025) 13:1625842. doi: 10.3389/fpubh.2025.1625842, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Batchelor R, Violette M. Acceptance and commitment therapy for depression in adolescents with epilepsy. Epilepsy Behav. (2025) 172:110727. doi: 10.1016/j.yebeh.2025.110727, [DOI] [PubMed] [Google Scholar]
- 5.Gooch CL, Pracht E, Borenstein AR. The burden of neurological disease in the United States: a summary report and call to action. Ann Neurol. (2017) 81:479–84. doi: 10.1002/ana.24897, [DOI] [PubMed] [Google Scholar]
- 6.Low JT, Ostrom QT, Cioffi G, Neff C, Waite KA, Kruchko C, et al. Primary brain and other central nervous system tumors in the United States (2014-2018): a summary of the CBTRUS statistical report for clinicians. Neurooncol Pract. (2022) 9:165–82. doi: 10.1093/nop/npac015, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.de Andrade PRA, Motta RC, Fonseca-Santos B. Unlocking ferulic acid for neurological diseases and tailoring of nanoformulations to advance its brain delivery. Int J Pharm. (2025) 684:126173. doi: 10.1016/j.ijpharm.2025.126173, [DOI] [PubMed] [Google Scholar]
- 8.Tao Z, Huo J, Hao X, Liang J. Riboflavin in neurological diseases: therapeutic advances, metabolic insights, and emerging genetic strategies. Front Neurol. (2025) 16:1663136. doi: 10.3389/fneur.2025.1663136, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Mortada I, Farah R, Nabha S, Ojcius DM, Fares Y, Almawi WY, et al. Immunotherapies for neurodegenerative diseases. Front Neurol. (2021) 12:654739. doi: 10.3389/fneur.2021.654739, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Decaix T, Bonnin C, Gotze K, Francois V, Petit C, Riviere C, et al. Benefits of physical activity on cognitive function in patients with neurocognitive disorders: a systematic review. J Frailty Aging. (2025) 14:100069. doi: 10.1016/j.tjfa.2025.100069, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chung C, Lee KN, Han K, Park J, Shin DW, Lee SW. Physical activity intensity and amount are inversely correlated with the risk of tuberculosis in patients with diabetes. Sci Rep. (2025) 15:24701. doi: 10.1038/s41598-025-09593-9, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhai Y, Hu F, Yuan L, Chen D, Cao Y, He J, et al. Association between an energy-adjusted inflammatory diet index and gastroesophageal reflux disease: a retrospective cohort study. Eur J Nutr. (2025) 64:154. doi: 10.1007/s00394-025-03678-x, [DOI] [PubMed] [Google Scholar]
- 13.Garrett J, Chak C, Bullock T, Giesbrecht B. A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults. Commun Psychol. (2024) 2:82. doi: 10.1038/s44271-024-00124-2, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Cabral DF, Rice J, Morris TP, Rundek T, Pascual-Leone A, Gomes-Osman J. Exercise for brain health: An investigation into the underlying mechanisms guided by dose. Neurotherapeutics. (2019) 16:580–99. doi: 10.1007/s13311-019-00749-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Wieloch N, Klostermann A, Kimmich N, Sporri J, Scherr J. Sport and exercise recommendations for pregnant athletes: a systematic scoping review. BMJ Open Sport Exerc Med. (2022) 8:e001395. doi: 10.1136/bmjsem-2022-001395, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ricci C, Gervasi F, Gaeta M, Smuts CM, Schutte AE, Leitzmann MF. Physical activity volume in relation to risk of atrial fibrillation. A non-linear meta-regression analysis. Eur. J Prev Cardiol. (2018) 25:857–66. doi: 10.1177/2047487318768026, [DOI] [PubMed] [Google Scholar]
- 17.Davis KAS, Coleman JRI, Adams M, Allen N, Breen G, Cullen B, et al. Mental health in UK biobank—development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open. (2020) 6:e18. doi: 10.1192/bjo.2019.100, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR, Jr, Tudor-Locke C, et al. 2011 compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc. (2011) 43:1575–81. doi: 10.1249/MSS.0b013e31821ece12, [DOI] [PubMed] [Google Scholar]
- 19.Kaur M, Porel P, Patel R, Aran KR. Kynurenine pathway in epilepsy: unraveling its role in glutamate excitotoxicity, GABAergic dysregulation, neuroinflammation, and mitochondrial dysfunction. Neurotox Res. (2025) 43:18. doi: 10.1007/s12640-025-00738-2, [DOI] [PubMed] [Google Scholar]
- 20.Silva AF, Trybulski R, Trybek G, Moska W, Castro H, Gonzalez-Fernandez FT. Comparing adapted small-sided team sports and aerobic exercise with or without cognitive games: effects on fitness and cognition in older men. J Sports Sci Med. (2025) 24:589–602. doi: 10.52082/jssm.2025.589, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhu H, Tung KT, So HK, Siu PM, Wong ICK, Yam JC, et al. Role of active video games in blood pressure management among children and young adults: systematic review and meta-analysis. J Med Internet Res. (2025) 27:e75000. doi: 10.2196/75000, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Blank M, Israeli D, Shoenfeld Y. Exercise, autoimmune diseases and T-regulatory cells. J Autoimmun. (2024) 149:103317. doi: 10.1016/j.jaut.2024.103317 [DOI] [PubMed] [Google Scholar]
- 23.Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet. (2017) 390:2673–734. doi: 10.1016/S0140-6736(17)31363-6 [DOI] [PubMed] [Google Scholar]
- 24.Vieira TM, Cerone GL, Stocchi C, Lalli M, Andrews B, Gazzoni M. Timing and modulation of activity in the lower limb muscles during indoor rowing: what are the key muscles to target in FES-rowing protocols? Sensors (Basel). (2020) 20:1666. doi: 10.3390/s20061666, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yu JA, Park J. The effect of first-person perspective action observation training on upper extremity function and activity of daily living of chronic stroke patients. Brain Behav. (2022) 12:e2565. doi: 10.1002/brb3.2565, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kim YR, Jung Y, Kang I, Yeo EJ. Understanding sex differences in autoimmune diseases: immunologic mechanisms. Int J Mol Sci. (2025) 26:7101. doi: 10.3390/ijms26157101 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Castro-Marrero J, Saez-Francas N, Santillo D, Alegre J. Treatment and management of chronic fatigue syndrome/myalgic encephalomyelitis: all roads lead to Rome. Br J Pharmacol. (2017) 174:345–69. doi: 10.1111/bph.13702, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.McCrory P, Meeuwisse W, Dvorak J, Aubry M, Bailes J, Broglio S, et al. Consensus statement on concussion in sport-the 5(th) international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med. (2017) 51:838–47. doi: 10.1136/bjsports-2017-097699 [DOI] [PubMed] [Google Scholar]
- 29.Li F, Liu W, Hu C, Tang M, Zhang Y, Ho HC, et al. Global association of greenness exposure with risk of nervous system disease: a systematic review and meta-analysis. Sci Total Environ. (2023) 877:162773. doi: 10.1016/j.scitotenv.2023.162773, [DOI] [PubMed] [Google Scholar]
- 30.Ahn S, Oh J, Yun H, Min H, Kim Y, Kang C, et al. Short-term exposure to fine particulate matter [PM(2.5)], cause specific-mortality, and high-risk populations: a Nationwide time-stratified case-crossover study. Geohealth. (2025) 9:e2024GH001214. doi: 10.1029/2024GH001214, [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: http://www.ukbiobank.ac.uk/.