Relationships between comorbidity, physical activity and frailty: findings from the Southampton Longitudinal Study of Ageing (SaLSA)

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We examined this in the Southampton Longitudinal Study of Ageing. Methods 448 community-dwelling older participants completed a questionnaire which ascertained sociodemographic characteristics (relationship status, educational attainment), number of comorbidities and physical activity (Physical Activity Scale for the Elderly). Frailty components ascertained included: weight loss; self-reported exhaustion; low physical activity; low self-reported walking speed; and difficulty carrying 10 lb. Number of comorbidities in relation to physical activity and number of frailty components was examined using sex-stratified linear and Poisson regression respectively, with additional stratification by relationship status and educational attainment. Analyses were adjusted for age, BMI and smoking status. Results Among females, higher comorbidity was related to lower physical activity and a greater number of frailty components: difference in physical activity (95% CI) and multiplicative increase in number of frailty components (95% CI) per additional comorbidity was − 0.10 (-0.17,-0.03) and 1.25 (1.16,1.34) respectively. Effect sizes for physical activity and number of frailty components were similar among females when stratified according to relationship status and educational attainment. Associations were weak among males in all analyses (p > 0.27 for all associations). Conclusions Comorbidity level may have a sex-specific association with lower physical activity and increased risk of frailty, with stronger relationships observed among females, persisting across different sociodemographic groups. Females with multiple comorbidities may require targeted health interventions, regardless of their social circumstances. Multimorbidity physical activity frailty educational attainment Introduction Multimorbidity is the presence of two or more chronic health conditions in an individual. It is an increasing public health concern and imposes a considerable economic burden on health systems and wider society [ 1 , 2 ]. In a whole population study of over 60 million people in England, the overall prevalence of multimorbidity was 14.8% and increased to 68.2% in those aged 80 years and over [ 3 ]. As life expectancy rises, the burden of multimorbidity is projected to increase considerably, with a projected increase of 84% in the number of people with multimorbidity from 2019 to 2049 [ 4 ]. Individuals with multimorbidity often experience poorer health outcomes, including higher risks of disability, hospital admission, and mortality, as well as reduced quality of life [ 5 ]. Previous systematic reviews have established associations between multimorbidity and both lower physical activity [ 6 ] and increased risk of frailty [ 7 ] among older adults. However, little is known about whether these relationships vary according to sociodemographic factors which may impact lifestyle, such as relationship status and educational attainment. For example, relationship status may influence health behaviours and outcomes through mechanisms such as social support, caregiving, and shared lifestyle habits while educational attainment has been linked to health literacy, access to healthcare resources, and the adoption of health-promoting behaviours [ 8 ]. To address these gaps in knowledge, we examined associations between multimorbidity, physical activity, and number of frailty components in older adults, considering whether these relationships differed according to relationship status and educational attainment. Our study used data from the Southampton Longitudinal Study of Ageing (SaLSA), a population-based cohort of older adults in the United Kingdom. The aim of this work was to establish whether some groups of older adults would be particularly vulnerable, and might benefit from targeted public health interventions. Methods Study design The Southampton Longitudinal Study of Ageing (SaLSA) comprises community-dwelling males and females over the age of 75 years in Southampton, UK, who were recruited between 2021 and 2022. The aim of this study was to create a new local cohort of older adults in the post-pandemic era to investigate the effects of lifestyle and ageing on general and musculoskeletal health. Participants were identified from the general practice database, with the only inclusion criterion being their age at the time of recruitment. Eligibility was determined by their primary care physician. Exclusion criteria included individuals with safeguarding issues, mental health problems, impaired mental capacity, dementia, inability to provide consent, learning disabilities, those on end-of-life care plans, those in residential or nursing homes, and those who were bed-bound. Detailed recruitment methods for SaLSA have been previously described [ 9 ]. Ascertainment of participant information All eligible participants were sent a postal questionnaire to complete. Information ascertained for this study included self-reported height and weight, medical conditions and medical history, smoking status (categorised into current/ex versus never); physical activity (Physical Activity Scale for the Elderly ((PASE)); educational attainment (categorised into university degree/Higher National Diploma/higher professional qualifications versus O Levels/GCSEs/A Levels/vocational training certificates); and relationship status (categorised into married/civil partnership/cohabiting versus single/divorced/separated/widowed). Participants also self-reported the following information: lost more than 10 lb unintentionally in the past year (yes vs no); number of days per week that they felt that everything they did was an effort or that they could not get going ( 4); self-reported walking speed (fast, fairly brisk, normal speed, stroll at an easy pace, very slow, unable to walk); and difficulty carrying 10 lb (none, some, a lot/unable). Participants with ≥ 3 of the following were regarded as living with frailty: lost more than 10 lb unintentionally in the past year; self-reported exhaustion in the past week (≥ 3 days); low physical activity (bottom sex-specific fifth of the distribution); low self-reported walking speed (very slow or unable to walk); or difficulty carrying 10 lb (a lot of trouble or unable). Comorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson Disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol. The resulting number of comorbidities reported was used as a marker of morbidity level. Ethics approval Ethics and HRA approval were obtained from South Central Oxford Research Ethics Committee reference 21/SC/0036, project ID 288051. Informed written consent was obtained from all participants. Statistical methods Summary statistics were used to describe participant characteristics. For each participant, the number of missing PASE components was calculated. Participants with one to three missing components had missing values replaced using mean imputation based on the sample mean of the respective component; the total PASE score was then derived by summing all components. Individuals with four or more missing components were not included in the analyses. The physical activity score was then standardized such that it had a mean of zero and a standard deviation of one to aid interpretation of effect sizes. Number of comorbidities in relation to physical activity and number of frailty components was examined using sex-stratified linear and Poisson regression respectively, with further stratification by relationship status and educational attainment. Analyses were adjusted for age, BMI and smoking status. The analysis sample comprised the 448 participants with complete date regarding all the variables of interest. Analyses were conducted using Stata, version 17; p < 0.05 was regarded as statistically significant. Results Participant characteristics Table 1 illustrates the participant characteristics of the analysis sample. Median age was around 80 years. Median (lower quartile, upper quartile) physical activity scores were 129 (86, 171) among males and 96 (69, 134) among females; corresponding values for number of comorbidities were 2 (1, 3) among both males and females. The number of participants with frailty (3 or more frailty components) was 15 (7%) among males and 36 (14%) among females. Number of comorbidities in relation to physical activity and number of frailty components These associations are presented in Table 2 . Among females, higher comorbidity was related to lower physical activity and a greater number of frailty components: difference in physical activity (95% CI) and multiplicative increase in number of frailty components (95% CI) per additional comorbidity was − 0.10 (-0.17,-0.03) and 1.25 (1.16,1.34) respectively. Effect sizes for physical activity and number of frailty components per additional comorbidity were similar among females when stratified according to relationship status and educational attainment. For example, the multiplicative increase in number of frailty components was 1.39 (1.25,1.56) for females who were married, in a civil partnership or cohabiting, and 1.20 (1.10,1.32) for females who were single, divorced, separated or widowed; corresponding estimates for those who had a university degree, Higher National Diploma or higher professional qualification, and those who did not have such qualifications were 1.33 (1.09,1.62) and 1.25 (1.16,1.35) respectively. In contrast, associations regarding number of comorbidities in relation to physical activity and number of frailty components were weak among males, regardless of how the analysis sample was stratified (p > 0.27 for all associations). Table 1 Participant characteristics of the analysis sample Participant characteristic Men (n = 211) Women (n = 237) Age (years) 79 (77, 83) 80 (77, 84) BMI (kg/m 2 ) 26.0 (3.4) 26.5 (5.1) Ever smoked regularly 132 (63%) 94 (40%) Physical activity score (PASE) 129 (86, 171) 96 (69, 134) Married/cohabiting/civil partnership 148 (70%) 91 (38%) Education (university degree / HND / higher professional qualifications) 51 (24%) 36 (15%) Number of comorbidities* 2 (1, 3) 2 (1, 3) Frailty components Lost more than 10 pounds in weight unintentionally in past year 21 (10%) 16 (7%) Self-reported exhaustion in the past week (≥ 3 days) 39 (18%) 63 (27%) Low physical activity (bottom sex-specific fifth) 42 (20%) 46 (19%) Self-reported walking speed (very slow / unable to walk) 34 (16%) 54 (23%) Carrying 10 pounds (a lot of trouble / unable) 10 (5%) 46 (19%) Frailty status Frailty (3 or more frailty components) 15 (7%) 36 (14%) Number of frailty components 0 127 (60%) 122 (51%) 1 44 (21%) 52 (22%) 2 25 (12%) 31 (13%) 3 9 (4%) 18 (8%) 4 5 (2%) 13 (5%) 5 1 (0%) 1 (0%) PASE: Physical Activity Scale for the Elderly HND: Higher National Diploma BMI was calculated from self-reported height and weight *Comorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson's disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol Table 2 Number of comorbidities in relation to physical activity and number of frailty components, stratified according to various groups Outcome Analysis sample Men Women Estimate (95% CI) P-value Estimate (95% CI) P-value Physical activity (standard deviation difference in physical activity per additional comorbidity) Entire sex-specific sample -0.03 (-0.14,0.07) 0.548 -0.10 (-0.17,-0.03) 0.004 Married, civil partnership, cohabiting -0.06 (-0.19,0.06) 0.325 -0.09 (-0.19,0.02) 0.095 Single, divorced, separated, widowed 0.06 (-0.12,0.25) 0.489 -0.11 (-0.20,-0.03) 0.012 University degree, Higher National Diploma, higher professional qualification 0.05 (-0.21,0.32) 0.681 -0.14 (-0.34,0.07) 0.180 Does not have university degree, Higher National Diploma or higher professional qualification -0.06 (-0.17,0.05) 0.300 -0.10 (-0.17,-0.02) 0.009 Number of frailty components (multiplicative increase in number of frailty components per additional comorbidity) Entire sex-specific sample 1.04 (0.90,1.21) 0.551 1.25 (1.16,1.34) < 0.001 Married, civil partnership, cohabiting 1.12 (0.92,1.35) 0.272 1.39 (1.25,1.56) < 0.001 Single, divorced, separated, widowed 0.93 (0.75,1.15) 0.490 1.20 (1.10,1.32) < 0.001 University degree, Higher National Diploma, higher professional qualification 0.83 (0.55,1.27) 0.396 1.33 (1.09,1.62) 0.004 Does not have university degree, Higher National Diploma or higher professional qualification 1.08 (0.94,1.25) 0.291 1.25 (1.16,1.35) < 0.001 All models were adjusted for age, BMI and smoking status (ever vs never) Physical activity (z-score) was examined using linear regression. Number of frailty components was examined using Poisson regression with robust variance estimation; an estimate of 1.2 corresponds to a 20% increase in the number of components; an estimate of 0.8 corresponds to a 20% decrease. Comorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson's disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol Discussion In this UK study comprising community-dwelling older males and females, greater comorbidity burden was related to lower physical activity and higher numbers of frailty components among females, with similar associations when stratified according to relationship status and educational attainment. In contrast, much weaker associations were observed among males among the whole sample, and when stratified by these sociodemographic characteristics. Our sample size was relatively small, but these observations warrant study and replications in other larger samples. Previous studies have examined sex-specific associations between multimorbidity and physical activity, and to a lesser extent between multimorbidity and frailty [ 6 , 10 ]. Recently, a systematic review of observational studies, examining the association between physical activity and multimorbidity in older adults, reported an inverse association, based on a meta-analysis of more than 77,000 participants [ 6 ]. Of the 15 studies included in this systematic review, nine found an inverse association in both sexes, three found it only in males, and three found no association. In general, relationships were stronger in males, in contrast to our own observations [ 6 ]. Over the past few years, a small number of studies examined this association further providing conflicting evidence [ 11 , 12 ]. Higher chronic disease burden was significantly associated with lower physical activity levels in a US longitudinal study, with no sex differences observed [ 11 ], while a Brazilian study found higher levels of physical activity among women with multimorbidity, compared to their peers without multimorbidity [ 12 ]. Sampling and methodological issues may underlie these differences, and such discrepancies may be attributable to differing sociodemographic factors. In a previous 2019 systematic review that considered the association of multimorbidity with frailty using eight cross-sectional studies, multimorbidity was associated with frailty (odds ratio (95% CI): 2.27 (1.97–2.62)) [ 7 ]. However, all these eight studies pooled males and females, potentially masking sex-differences. Multimorbidity, frailty and low physical activity are reported to be more prevalent in females than males [ 13 – 15 ], who also enjoy longer life expectancy [ 16 ], and important social and biological differences exist between sexes. We observed stronger associations among community-dwelling older females between higher levels of comorbidity, lower physical activity, and increased number of frailty components. Although these observations need testing in larger cohorts, there are many reasons why such sex differences may exist, aside biological differences. For example, the comorbidities described in men and women were different in our sample, with 32% of women and 15% of men reporting a past medical history of osteoarthritis, and hypertension also being more common in women than men (55% vs 48%) while more men reported a past history of cancer than women (24% vs 16%). Patterns of physical activity also differed slightly between men and women, with 25% of men and 14% of women reporting some participation in light sport/recreational activities, and 8% of men and 10% of women reporting some participation in strenuous sport.. Of course, as this study is observational, we cannot determine whether the relationship between number of comorbidities and the outcomes considered is causal. There are several limitations to this study. First, all data were collected through self-reported questionnaires, which may introduce recall bias. As with all cohort studies, participants who agreed to take part were likely healthier than those who declined, which may limit the generalizability of the findings. However, since our analyses were internal, significant bias would only occur if the associations differed markedly between those who participated and those who were invited to participate but declined; this seems unlikely. We adopted the most commonly recommended definition of multimorbidity but there is no consensus on which chronic diseases should be considered when defining multimorbidity [ 17 ]. Similarly, the availability of different ways of assessing physical activity presents challenges in drawing direct comparisons across studies employing different methodologies. We have used PASE which has been validated for use in ageing research and applied across diverse clinical and geographical settings [ 18 ]. In conclusion, this study provides new evidence on the relationship between comorbidity, physical activity, and frailty in older adults, highlighting possible sex differences. The stronger associations in females reinforce the need for targeted interventions to support those at greatest risk, while the weaker associations in males may suggest a complex interplay of factors that warrants further investigation. Future studies should examine these relationships in larger, diverse population-based cohorts, and explore underlying mechanisms to better inform strategies for improving health outcomes in older adults [ 19 ]. Research would benefit from including large population-based samples representative of adults with multimorbidity and incorporating accurate measurement methods to investigate all aspects of PA behaviour. Declarations Acknowledgments Conflict of interest FL has received lecture fees from Alfasigma outside of the submitted work, HPP has received lecture fees from Abbott, Pfizer, and HC-UK conferences outside of the submitted work. EMD is on the Editorial Board for Aging Clinical and Experimental Research , and declares consultancy and speaker fees from Pfizer, UCB and Lilly. The remaining authors declare that they have no conflicts of interest. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FL, LWD, HPP and EMD. The first draft of the manuscript was written by FL and LWD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding FL and HPP are supported by the NIHR Southampton Biomedical Research Centre, Nutrition, and the University of Southampton. This report is independent research and the views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. These funding bodies had no role in writing of the manuscript or decision to submit for publication. For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. 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BMC Geriatr 24(1):761. https://doi.org/10.1186/s12877-024-05332-3 Shepherd J, Gurney S, Patel H (2022) Shared decision making and personalised care support planning: pillars of integrated care for older people. Clin Integr Care 12(100097). https://doi.org/https://doi.org/10.1016/j.intcar.2022.100097 Additional Declarations Competing interest reported. FL has received lecture fees from Alfasigma outside of the submitted work, HPP has received lecture fees from Abbott, Pfizer, and HC-UK conferences outside of the submitted work. EMD is on the Editorial Board for Aging Clinical and Experimental Research, and declares consultancy and speaker fees from Pfizer, UCB and Lilly. The remaining authors declare that they have no conflicts of interest. 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FL has received lecture fees from Alfasigma outside of the submitted work, HPP has received lecture fees from Abbott, Pfizer, and HC-UK conferences outside of the submitted work. EMD is on the Editorial Board for Aging Clinical and Experimental Research, and declares consultancy and speaker fees from Pfizer, UCB and Lilly. The remaining authors declare that they have no conflicts of interest.","formattedTitle":"Relationships between comorbidity, physical activity and frailty: findings from the Southampton Longitudinal Study of Ageing (SaLSA)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity is the presence of two or more chronic health conditions in an individual. It is an increasing public health concern and imposes a considerable economic burden on health systems and wider society [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In a whole population study of over 60\u0026nbsp;million people in England, the overall prevalence of multimorbidity was 14.8% and increased to 68.2% in those aged 80 years and over [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As life expectancy rises, the burden of multimorbidity is projected to increase considerably, with a projected increase of 84% in the number of people with multimorbidity from 2019 to 2049 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Individuals with multimorbidity often experience poorer health outcomes, including higher risks of disability, hospital admission, and mortality, as well as reduced quality of life [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious systematic reviews have established associations between multimorbidity and both lower physical activity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and increased risk of frailty [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] among older adults. However, little is known about whether these relationships vary according to sociodemographic factors which may impact lifestyle, such as relationship status and educational attainment. For example, relationship status may influence health behaviours and outcomes through mechanisms such as social support, caregiving, and shared lifestyle habits while educational attainment has been linked to health literacy, access to healthcare resources, and the adoption of health-promoting behaviours [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address these gaps in knowledge, we examined associations between multimorbidity, physical activity, and number of frailty components in older adults, considering whether these relationships differed according to relationship status and educational attainment. Our study used data from the Southampton Longitudinal Study of Ageing (SaLSA), a population-based cohort of older adults in the United Kingdom. The aim of this work was to establish whether some groups of older adults would be particularly vulnerable, and might benefit from targeted public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eThe Southampton Longitudinal Study of Ageing (SaLSA) comprises community-dwelling males and females over the age of 75 years in Southampton, UK, who were recruited between 2021 and 2022. The aim of this study was to create a new local cohort of older adults in the post-pandemic era to investigate the effects of lifestyle and ageing on general and musculoskeletal health. Participants were identified from the general practice database, with the only inclusion criterion being their age at the time of recruitment. Eligibility was determined by their primary care physician. Exclusion criteria included individuals with safeguarding issues, mental health problems, impaired mental capacity, dementia, inability to provide consent, learning disabilities, those on end-of-life care plans, those in residential or nursing homes, and those who were bed-bound. Detailed recruitment methods for SaLSA have been previously described [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAscertainment of participant information\u003c/p\u003e \u003cp\u003eAll eligible participants were sent a postal questionnaire to complete. Information ascertained for this study included self-reported height and weight, medical conditions and medical history, smoking status (categorised into current/ex versus never); physical activity (Physical Activity Scale for the Elderly ((PASE)); educational attainment (categorised into university degree/Higher National Diploma/higher professional qualifications versus O Levels/GCSEs/A Levels/vocational training certificates); and relationship status (categorised into married/civil partnership/cohabiting versus single/divorced/separated/widowed).\u003c/p\u003e \u003cp\u003eParticipants also self-reported the following information: lost more than 10 lb unintentionally in the past year (yes vs no); number of days per week that they felt that everything they did was an effort or that they could not get going (\u0026lt;\u0026thinsp;1, 1\u0026ndash;2, 3\u0026ndash;4, \u0026gt;\u0026thinsp;4); self-reported walking speed (fast, fairly brisk, normal speed, stroll at an easy pace, very slow, unable to walk); and difficulty carrying 10 lb (none, some, a lot/unable). Participants with \u0026ge;\u0026thinsp;3 of the following were regarded as living with frailty: lost more than 10 lb unintentionally in the past year; self-reported exhaustion in the past week (\u0026ge;\u0026thinsp;3 days); low physical activity (bottom sex-specific fifth of the distribution); low self-reported walking speed (very slow or unable to walk); or difficulty carrying 10 lb (a lot of trouble or unable).\u003c/p\u003e \u003cp\u003eComorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson Disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol. The resulting number of comorbidities reported was used as a marker of morbidity level.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e Ethics and HRA approval were obtained from South Central Oxford Research Ethics Committee reference 21/SC/0036, project ID 288051. Informed written consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eSummary statistics were used to describe participant characteristics. For each participant, the number of missing PASE components was calculated. Participants with one to three missing components had missing values replaced using mean imputation based on the sample mean of the respective component; the total PASE score was then derived by summing all components. Individuals with four or more missing components were not included in the analyses. The physical activity score was then standardized such that it had a mean of zero and a standard deviation of one to aid interpretation of effect sizes. Number of comorbidities in relation to physical activity and number of frailty components was examined using sex-stratified linear and Poisson regression respectively, with further stratification by relationship status and educational attainment. Analyses were adjusted for age, BMI and smoking status. The analysis sample comprised the 448 participants with complete date regarding all the variables of interest. Analyses were conducted using Stata, version 17; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the participant characteristics of the analysis sample. Median age was around 80 years. Median (lower quartile, upper quartile) physical activity scores were 129 (86, 171) among males and 96 (69, 134) among females; corresponding values for number of comorbidities were 2 (1, 3) among both males and females. The number of participants with frailty (3 or more frailty components) was 15 (7%) among males and 36 (14%) among females.\u003c/p\u003e \u003cp\u003eNumber of comorbidities in relation to physical activity and number of frailty components\u003c/p\u003e \u003cp\u003eThese associations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among females, higher comorbidity was related to lower physical activity and a greater number of frailty components: difference in physical activity (95% CI) and multiplicative increase in number of frailty components (95% CI) per additional comorbidity was \u0026minus;\u0026thinsp;0.10 (-0.17,-0.03) and 1.25 (1.16,1.34) respectively. Effect sizes for physical activity and number of frailty components per additional comorbidity were similar among females when stratified according to relationship status and educational attainment. For example, the multiplicative increase in number of frailty components was 1.39 (1.25,1.56) for females who were married, in a civil partnership or cohabiting, and 1.20 (1.10,1.32) for females who were single, divorced, separated or widowed; corresponding estimates for those who had a university degree, Higher National Diploma or higher professional qualification, and those who did not have such qualifications were 1.33 (1.09,1.62) and 1.25 (1.16,1.35) respectively. In contrast, associations regarding number of comorbidities in relation to physical activity and number of frailty components were weak among males, regardless of how the analysis sample was stratified (p\u0026thinsp;\u0026gt;\u0026thinsp;0.27 for all associations).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics of the analysis sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;237)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (77, 83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (77, 84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.0 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver smoked regularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity score (PASE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (86, 171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (69, 134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabiting/civil partnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (university degree / HND / higher professional qualifications)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of comorbidities*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1, 3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrailty components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost more than 10 pounds in weight unintentionally in past year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported exhaustion in the past week (\u0026ge;\u0026thinsp;3 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow physical activity (bottom sex-specific fifth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported walking speed (very slow / unable to walk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrying 10 pounds (a lot of trouble / unable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrailty status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty (3 or more frailty components)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of frailty components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePASE: Physical Activity Scale for the Elderly\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHND: Higher National Diploma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI was calculated from self-reported height and weight\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e*Comorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson's disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of comorbidities in relation to physical activity and number of frailty components, stratified according to various groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnalysis sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePhysical activity (standard deviation difference in physical activity per additional comorbidity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire sex-specific sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03 (-0.14,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (-0.17,-0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried, civil partnership, cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (-0.19,0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09 (-0.19,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle, divorced, separated, widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (-0.12,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11 (-0.20,-0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity degree, Higher National Diploma, higher professional qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (-0.21,0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.14 (-0.34,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not have university degree, Higher National Diploma or higher professional qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (-0.17,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (-0.17,-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNumber of frailty components (multiplicative increase in number of frailty components per additional comorbidity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire sex-specific sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.90,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (1.16,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried, civil partnership, cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.92,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39 (1.25,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle, divorced, separated, widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.75,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 (1.10,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity degree, Higher National Diploma, higher professional qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.55,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (1.09,1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not have university degree, Higher National Diploma or higher professional qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.94,1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (1.16,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAll models were adjusted for age, BMI and smoking status (ever vs never)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysical activity (z-score) was examined using linear regression. Number of frailty components was examined using Poisson regression with robust variance estimation; an estimate of 1.2 corresponds to a 20% increase in the number of components; an estimate of 0.8 corresponds to a 20% decrease.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eComorbidities were ascertained by asking participants whether they were ever diagnosed by a doctor with any of the following conditions: heart attack or angina; stroke or transient ischaemic attack; hypertension; diabetes; asthma, bronchitis, emphysema, or chronic obstructive pulmonary disease (COPD); depression; osteoporosis; anxiety; memory problems or dementia; Parkinson's disease; osteoarthritis or degenerative joint disease; rheumatoid/inflammatory arthritis; cancer; or high cholesterol\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this UK study comprising community-dwelling older males and females, greater comorbidity burden was related to lower physical activity and higher numbers of frailty components among females, with similar associations when stratified according to relationship status and educational attainment. In contrast, much weaker associations were observed among males among the whole sample, and when stratified by these sociodemographic characteristics. Our sample size was relatively small, but these observations warrant study and replications in other larger samples.\u003c/p\u003e \u003cp\u003ePrevious studies have examined sex-specific associations between multimorbidity and physical activity, and to a lesser extent between multimorbidity and frailty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recently, a systematic review of observational studies, examining the association between physical activity and multimorbidity in older adults, reported an inverse association, based on a meta-analysis of more than 77,000 participants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Of the 15 studies included in this systematic review, nine found an inverse association in both sexes, three found it only in males, and three found no association. In general, relationships were stronger in males, in contrast to our own observations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Over the past few years, a small number of studies examined this association further providing conflicting evidence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Higher chronic disease burden was significantly associated with lower physical activity levels in a US longitudinal study, with no sex differences observed [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while a Brazilian study found higher levels of physical activity among women with multimorbidity, compared to their peers without multimorbidity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sampling and methodological issues may underlie these differences, and such discrepancies may be attributable to differing sociodemographic factors.\u003c/p\u003e \u003cp\u003eIn a previous 2019 systematic review that considered the association of multimorbidity with frailty using eight cross-sectional studies, multimorbidity was associated with frailty (odds ratio (95% CI): 2.27 (1.97\u0026ndash;2.62)) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, all these eight studies pooled males and females, potentially masking sex-differences. Multimorbidity, frailty and low physical activity are reported to be more prevalent in females than males [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], who also enjoy longer life expectancy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and important social and biological differences exist between sexes.\u003c/p\u003e \u003cp\u003eWe observed stronger associations among community-dwelling older females between higher levels of comorbidity, lower physical activity, and increased number of frailty components. Although these observations need testing in larger cohorts, there are many reasons why such sex differences may exist, aside biological differences. For example, the comorbidities described in men and women were different in our sample, with 32% of women and 15% of men reporting a past medical history of osteoarthritis, and hypertension also being more common in women than men (55% vs 48%) while more men reported a past history of cancer than women (24% vs 16%). Patterns of physical activity also differed slightly between men and women, with 25% of men and 14% of women reporting some participation in light sport/recreational activities, and 8% of men and 10% of women reporting some participation in strenuous sport.. Of course, as this study is observational, we cannot determine whether the relationship between number of comorbidities and the outcomes considered is causal.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, all data were collected through self-reported questionnaires, which may introduce recall bias. As with all cohort studies, participants who agreed to take part were likely healthier than those who declined, which may limit the generalizability of the findings. However, since our analyses were internal, significant bias would only occur if the associations differed markedly between those who participated and those who were invited to participate but declined; this seems unlikely. We adopted the most commonly recommended definition of multimorbidity but there is no consensus on which chronic diseases should be considered when defining multimorbidity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, the availability of different ways of assessing physical activity presents challenges in drawing direct comparisons across studies employing different methodologies. We have used PASE which has been validated for use in ageing research and applied across diverse clinical and geographical settings [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides new evidence on the relationship between comorbidity, physical activity, and frailty in older adults, highlighting possible sex differences. The stronger associations in females reinforce the need for targeted interventions to support those at greatest risk, while the weaker associations in males may suggest a complex interplay of factors that warrants further investigation. Future studies should examine these relationships in larger, diverse population-based cohorts, and explore underlying mechanisms to better inform strategies for improving health outcomes in older adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research would benefit from including large population-based samples representative of adults with multimorbidity and incorporating accurate measurement methods to investigate all aspects of PA behaviour.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eConflict of interest\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFL has received lecture fees from Alfasigma outside of the submitted work, HPP has received lecture fees from Abbott, Pfizer, and HC-UK conferences outside of the submitted work. EMD is on the Editorial Board for \u003cem\u003eAging Clinical and Experimental Research\u003c/em\u003e, and declares consultancy and speaker fees from Pfizer, UCB and Lilly. The remaining authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FL, LWD, HPP and EMD. The first draft of the manuscript was written by FL and LWD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFL and HPP are supported by the NIHR Southampton Biomedical Research Centre, Nutrition, and the University of Southampton. This report is independent research and the views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. These funding bodies had no role in writing of the manuscript or decision to submit for publication. For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTran PB, Kazibwe J, Nikolaidis GF, Linnosmaa I, Rijken M, van Olmen J (2022) Costs of multimorbidity: a systematic review and meta-analyses. BMC Med 20(1):234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1186/s12916-022-02427-9\u003c/span\u003e\u003cspan address=\"10.1186/s12916-022-02427-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal P, Woolford SJ, Patel HP (2020) Multi-Morbidity and Polypharmacy in Older People: Challenges and Opportunities for Clinical Practice. 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Br J Gen Pract 73(729):e249\u0026ndash;e256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3399/bjgp.2022.0405\u003c/span\u003e\u003cspan address=\"10.3399/bjgp.2022.0405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurostat Life expectancy by age, sex and NUTS 2 region 2025 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ec.europa.eu/eurostat/databrowser/view/demo_r_mlifexp__custom_12630663/bookmark/table?lang=en\u0026amp;bookmarkId=c307c6e0-ed44-4dc3-acd5-929bc11a4aae\u0026amp;c=1724228592019\u003c/span\u003e\u003cspan address=\"https://ec.europa.eu/eurostat/databrowser/view/demo_r_mlifexp__custom_12630663/bookmark/table?lang=en\u0026amp;bookmarkId=c307c6e0-ed44-4dc3-acd5-929bc11a4aae\u0026amp;c=1724228592019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnston MC, Crilly M, Black C, Prescott GJ, Mercer SW (2019) Defining and measuring multimorbidity: a systematic review of systematic reviews. 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BMC Geriatr 24(1):761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-024-05332-3\u003c/span\u003e\u003cspan address=\"10.1186/s12877-024-05332-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShepherd J, Gurney S, Patel H (2022) Shared decision making and personalised care support planning: pillars of integrated care for older people. Clin Integr Care 12(100097). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.intcar.2022.100097\u003c/span\u003e\u003cspan address=\"10.1016/j.intcar.2022.100097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, physical activity, frailty, educational attainment","lastPublishedDoi":"10.21203/rs.3.rs-8700958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8700958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eComorbidity in older adults is linked to lower physical activity and increased risk of frailty, but the role of sociodemographic factors that feed into this relationship, through association with lifestyle modifications, is underexplored. We examined this in the Southampton Longitudinal Study of Ageing.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e448 community-dwelling older participants completed a questionnaire which ascertained sociodemographic characteristics (relationship status, educational attainment), number of comorbidities and physical activity (Physical Activity Scale for the Elderly). Frailty components ascertained included: weight loss; self-reported exhaustion; low physical activity; low self-reported walking speed; and difficulty carrying 10 lb. Number of comorbidities in relation to physical activity and number of frailty components was examined using sex-stratified linear and Poisson regression respectively, with additional stratification by relationship status and educational attainment. Analyses were adjusted for age, BMI and smoking status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong females, higher comorbidity was related to lower physical activity and a greater number of frailty components: difference in physical activity (95% CI) and multiplicative increase in number of frailty components (95% CI) per additional comorbidity was \u0026minus;\u0026thinsp;0.10 (-0.17,-0.03) and 1.25 (1.16,1.34) respectively. Effect sizes for physical activity and number of frailty components were similar among females when stratified according to relationship status and educational attainment. Associations were weak among males in all analyses (p\u0026thinsp;\u0026gt;\u0026thinsp;0.27 for all associations).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eComorbidity level may have a sex-specific association with lower physical activity and increased risk of frailty, with stronger relationships observed among females, persisting across different sociodemographic groups. Females with multiple comorbidities may require targeted health interventions, regardless of their social circumstances.\u003c/p\u003e","manuscriptTitle":"Relationships between comorbidity, physical activity and frailty: findings from the Southampton Longitudinal Study of Ageing (SaLSA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:41:15","doi":"10.21203/rs.3.rs-8700958/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46d6c778-52c5-47f9-b468-ae43d2f8052f","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T07:11:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:41:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8700958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8700958","identity":"rs-8700958","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}