Skip to main content

Multimorbidity and leisure-time physical activity over the life course: a population-based birth cohort study

Abstract

Background

We aimed to test which life course model best described the association between leisure-time physical activity (LTPA) and multimorbidity at age 55. We analyzed data from birth to age 55 using the database from the 1958 National Child Development Survey.

Methods

Multimorbidity was considered as the presence of more than one chronic condition. LTPA was measured through questionnaires from 1965 (age 7) to 2013 (age 55), which were applied in eight different occasions. We compared the fit of a series of nested adjusted logistic regression models (representing either the critical, accumulation or sensitive period models) with a fully saturated model. Data were reported as odds ratio (OR) and 95% confidence interval (CI).

Results

From an eligible sample of 15,613 cohort members, 9137 were interviewed in the latest sweep (58.5%). Men were more physically active than women at ages 11, 16, and 23 (p < 0.001). LTPA every day in the week was more frequent in women than men in ages 33, 42, and 50 (p < 0.001). The prevalence of multimorbidity at age 55 was 33.0% (n = 2778). The sensitive analysis revealed that LTPA during adolescence (OR: 0.83; 95% CI: 0.70, 0.98) and mid adult life (age 50 and 55; OR: 0.82; 95%CI: 0.69, 0.98) have a stronger effect on the risk for multimorbidity at age 55 considering all other life stages in the model. Also, adolescence showed a critical independent effect on the risk for multimorbidity (OR: 0.82; 95%CI: 0.70, 0.97). No difference was found between those models.

Conclusions

These data support the notion of a protective physical activity “legacy” at early ages of childhood against multimorbidity at older ages. We highlight the need for LTPA promotion through intervention tailored especially on schooling and older ages in order to reduce the burden of multimorbidity.

Peer Review reports

Introduction

Multimorbidity, defined as the co-occurrence of two or more chronic conditions in the same individual [1], affects adults from all age groups, and its burden increases with aging [2]. It is associated with diminished quality of life of both individuals and their families [3]. Likewise, people with multimorbidity have higher frequency of hospitalizations [4, 5], which are usually followed by decreased functional capacity, cognitive function, and increased use of prescribed medications [6]. One important clinical aspect of multimorbidity is its multifactorial etiology. The chronic conditions that are affecting the same individual simultaneously are related to an impairment on different organic systems, which may lead to higher risk of disability and mortality [7,8,9]. Also, the burden of multimorbidity for the patient is increased when primary care systems fail to appropriately address the needs of these patients [9, 10].

Moreover, multimorbidity is associated with increased risk of all-cause mortality in a dose-response effect [11] regardless of age, gender, and economic stratus [12]. Therefore, investigations about how lifestyle factors through life course could be associated with multimorbidity in older ages should be encouraged. Physical activity should be considered an alternative that deserves attention and investment, given its benefits on prevention and treatment of several noncommunicable chronic diseases [13].

Physical activity can be divided into four domains: leisure-time, occupational, commuting, and household activities. Leisure-time physical activity (LTPA) is related to physical activities performed during spare hours or leisure periods, based on individual or collective preferences. LTPDA has been shown an inverse relationship with all-cause, specific-cause mortality [14], and unhealthy ageing (i.e., ageing with no chronic conditions, cognitive impairment, and physical dependency) [15]. However, it is still scantly explored whether (and at which extent) LTPA in early ages or over different life stages could be associated with different rates of non-communicable disease in later life. Our hypothesis is that people who were physically active in early life stages would have a lower risk for multimorbidity than those with low physical activity practice.

Thus, the present study aimed to assess whether there is an association of LTPA at different stages of life with multimorbidity at age 55 and evaluate its cumulative effect during the early life stages on multimorbidity in middle-to-older adults.

Methods

Study design

We analyzed data from the 1958 National Child Development Survey (NCDS). The dataset is freely available upon request at the UK Data Service [16] (https://www.ukdataservice.ac.uk/deposit-data/). Full description of sampling design and methods can be found elsewhere [17]. Briefly, this birth cohort collected data from approximately 94% (N = 17,638) of all births between 3rd and 9th March 1958 in England, Wales, and Scotland. Sociodemographic and behavioral information from both parents besides data regarding to pregnancy and the child were assessed. Further, any child born in the Great Britain in that specific week were identified by school registers and added to cohort sample during second through fourth sweeps (1965, 1969, and 1974; n = 920), reaching a final sample of 18,558 cohort members. Flow diagram about sample composition is shown in Figure S1 (supplementary material).

Fifty-five years after baseline assessments, this cohort remains largely representative of the sample that it was drawn. In the latest sweep (2013) cumulative deaths (n = 1659) and emigrants (n = 1286) through cohort summed 2945. Thus, from an eligible sample of 15,613, 9137 participants were interviewed (response rate: 58.5%) [17]. After baseline measurements, new sweeps occurred in 1965 (cohort age: 7 years), 1969 (11 years), 1974 (16 years), 1981 (23 years), 1991 (33 years), 2000 (42 years), 2002 (44 years), 2004 (46 years), 2008 (50 years), and the latest in 2013 (55 years). The 2002 and 2004 sweeps were not included in the present analysis due to methodological distinctions compared to the others (i.e. self-reported and telephone-based interview, respectively). The next sweep was programed to occur in 2020 and 2021; however, fieldwork was paused in light of COVID-19. The full access to all questionnaires used during follow-up assessments can be found elsewhere (https://cls.ucl.ac.uk/cls-studies/1958-national-child-development-study-2/).

Outcome

We considered multimorbidity as the co-occurrence of more than one of the following morbidities at age 55 [1]: asthma or wheezy bronchitis, cancer, backache, depression, diabetes, coronary heart disease, high blood pressure, obesity (self-reported body mass index ≥30.0), and visual and hearing impairment. Then, based on this classification, a dichotomous variable was created.

Exposure

LTPA was assessed from 1965 to 2013. Data collected in 2002 (age 44 sweep) and 2004 (age 46 sweep) regarding to LTPA were not used in this study because they were assessed by different interview methods (self-administrated and telephone-based interview, respectively), which may decrease significantly both reliability and comparability of measurements [18]. All included LTPA data were assessed by face-to-face interview-administered paper-based questionnaire. Supplementary Table S1 (electronic supplemental material) describes how it was measured in each sweep and how we operationalized it for this study. Briefly, participants were dichotomously classified as physically active when performed LTPA regularly (age 7, 11, and 16) and at least once per week (from age 23 to the latest), as in previous work [19]. Participants who practiced LTPA in level lower than these, they were classified as physically inactive. For age 7, LTPA was assessed based on mother’s perception of how active was active during the day (inactive, normally active, over active). For age 11, the children were asked how often (never or hardly ever, sometimes, often) they played outdoor games or take part in sports outside of school hours. For age 16, a list of indoor and outdoor activities was provided to participants, so they could report the frequency in each activity (not available, never, sometimes, often). Often was scored as 2, sometimes scored as 1, never and not available were scored as 0. The scores were summed across the variables and the resulting categories collapsed to 4. We dichotomized the resulting variable as active (the two most active categories) and inactive (the two least active). For age 23 and older, participants were considered physically active if performed LTPA once a week or more and inactive if practiced LTPA less than once a week. Full description of LTPA operationalization is provided as Supplementary Table S1 and in previous publications [20].

Confounding variables

All multivariate analyses were adjusted for the following variables: gender, marital status, education level, income, country of birth, ethnicity, smoking, alcohol intake, hours of sleep, and LTPA. We considered sex, country of birth, and ethnicity collected at birth sweep. We used LTPA as a possible confounder in analysis where the main exposure was a time-specific sweep and it was adjusted for previous and future LTPA practice. All remaining used variables were assessed in the latest sweep (2013).

Statistical analysis

Descriptive analysis is reported as absolute and relative frequencies. The difference between groups was assessed using chi-square tests. To evaluate the PA effect during earlier life, we stratified the variable in childhood (age 7 and 11 sweeps), adolescence (age 16 sweep), young (age 23 sweep), middle (age 33 sweep), and middle-to-old age (age 50 and 55 sweeps) adults.

Logistic regression was performed as crude analysis (model 1) and using hierarchical model adjusting for gender, marital status, education level, income, country of birth, ethnicity (model 2), as well as smoking, alcohol intake, and hours of sleep (model 3). For regression analyses, PA was categorized as either “inactive” or “active” for each individual. Such categorization was conducted for each life stage considered in the analysis and was based on the daily or weekly frequency of PA. The criteria used for PA categorization for each life stage are presented in Supplementary Table S1.

Then, a structured modeling approach developed by Mishra et al. [21] was used to select the most appropriate life course model for multimorbidity at age 55. This method allows to determine how the changes in PA over the life course might attenuate the risk of multimorbidity in middle-to-late adulthood. Four hypothesized life course models were examined: saturated, critical, sensitive, and accumulation. Saturated model included all possible exposure combinations and interactions and describes all possible trajectories of LTPA throughout life course (childhood, adolescence, young, middle, and middle-to-older adults).

Accumulation model was tested in two versions. First, a strict model (continuous) was assessed by adding the number of times an individual reported being physically active across their life course to form an overall score, which was then used as the exposure. This model assumes that the effect of LTPA at each life period (childhood, adolescence, adulthood) period is the same. Second, a relaxed model (categorical) was examined in which all time periods are contributing to multimorbidity at age 55 but not necessarily in an equal way.

The critical period model assumes that only LTPA in a certain age influences multimorbidity at age 55 regardless of any other time period. Similarly, sensitive model was tested to allow the examination of the varied effect of LTPA across the life course, which can be modelled by simultaneously including all LTPA variables in the model. Finally, a null model was tested with only our outcome at the model [21].

To identify the most appropriate life course model to explain multimorbidity at age 55, likelihood ratio test was conducted comparing each life course model to the saturated model. When nested life course models (critical period, sensitive period, and accumulation) provided similar fit to the fully saturated model (p > 0.05), the one with the lowest Akaike’s information criterion (AIC) was selected. When more than one model presented p-value higher than 0.05 and there is not a large difference in p-values, the simpler model was selected [21].

To minimize data loss, missing data were imputed using multiple imputation chained equations as recommended by the NCDS user guide [22]. We ran imputation models with all variables from our logistic models across 20 imputed datasets. As imputed results were similar to those obtained using observed values, the latest was presented. All statistical analyzes were carried out using STATA 13.1 software (StataCorp, College Station, Texas). A p value of < 0.05 was accepted as statistically significant.

Results

From the initial sample of 18,558 cohort members, 9137 (49.2%) were interviewed in the latest sweep. Excluded participants were more likely to be male (54.8%), have no academic qualification (36.8%), be divorced/separated (24.7%), unemployed (16.5%), and smoker at age 50 (35.9%), as shown in Supplementary Table S2. Most included individuals were born in England (83.5%), female (51.5%), and white (97.8%) (Table 1). At age 55, 27.4% had at least a university or equivalent degree, while 69.9% were married or lived with a partner. Although more men were overweight or obese (71.3%; p < 0.001), alcohol intake were higher among women (p < 0.001).

Table 1 Sociodemographic, behavioral, and clinical characteristics of the sample. N = 9137. United Kingdom, 2013

Our analysis did not detect difference in LTPA between gender (p = 0.087), however, men reported higher work-related physical activity than women (p < 0.001). In addition, 80% of sample self-rated their health as good to excellent with no detectable difference between gender (p = 0.071).

Men were more physically active than women in age 11, 16, and 23 (p < 0.001; Table 2). The proportion of women who engage in in LTPA all days in the week was higher in age 33, 42, and 50. (p < 0.001).

Table 2 Leisure-time physical activity practice from age 7 to 55 among adults aged 55 years. United Kingdom, 2008

From the 8414 participants who reported data about all included morbidities at age 55, 2278 (33.0%) were classified as having multimorbidity. Multimorbidity was more likely among participants with low educational achievement, at lowest quintile interval of income, who were not regular drinkers of alcoholic beverages, and with worse general health perception, as shown in Supplementary Table S3.

Table 3 exhibits results from log-likelihood ratio test to examine which life course model best described the association between LTPA through the life course and multimorbidity. We compared the fit of a series of nested logistic models (critical, accumulation or sensitive period models) with a fully saturated model. Critical periods of LTPA during childhood and adulthood provided an inferior fit compared to the saturated model (p < 0.001). However, critical period model for LTPA during adolescence (p > 0.99) and sensitive model (p > 0.99) provided a fit not worse than saturated model with no clear difference between models based on p-value, log-likelihood, and AIC. Accumulation models provided similar fit compared to saturated model (p > 0.99); however, AIC values were higher than observed in adolescence as a critical period and the sensitive model, as shown in Table 3. Therefore, adolescence as a critical period and the sensitive models were chosen as the two simplest models that best explained the association between LTPA through life stages and the risk of multimorbidity at age 55.

Table 3 Likelihood-ratio test to estimate the best model of physical activity throughout life course and the risk of multimorbidity at age 55. National Child Development Study. United Kingdom (1958–2013). N = 8414

As shown in Table 4, LTPA during adolescence was associated with lower odds for multimorbidity at age 55 (OR: 0.82; 95%CI: 0.70; 0.97) independently of LTPA performed in other stages of life. Based on the sensitive model, being physically active during leisure time at adolescence (OR: 0.83; 95%CI: 0.70; 0.98) and middle-to-late adult life (ages 50 and 55) (OR:0.82; 95%CI: 0.69; 0.98) also promoted a protective effect against multimorbidity at age 55.

Table 4 Odds ratio and 95% confidence interval (CI) for multimorbidity at age 55 by different life course models

Discussion

Our findings confirmed our hypothesis that engage in LTPA through life would reduce the risk for multimorbidity in older ages. Both sensitive and critical periods best explained the association between LTPA through life course and multimorbidity. We revealed that being physically active at adolescence and middle-to-late adult life reduced in about 17 and 18% the odds of multimorbidity at age 55, respectively, even when controlled for LTPA at all other analyzed ages. Similarly, engage in LTPA early in life was revealed as a critical period for decreasing the odds of multimorbidity at age 55.

Adolescence is an important life period where healthy lifestyle promotion (e.g. LTPA) could reduce the risk for some conditions such as obesity [23, 24], diabetes [25], and cognitive impairment [26] in older ages. Nevertheless, physical inactivity is predominant among children and adolescents aged between 6 and 15 years [27,28,29]. This scenario seems to be associated with additional health-related events over the next decades, turning out also to be related to the prevalence of multimorbidity [11]. Given the burden on healthcare systems, that are already poised by elevated prevalence of those conditions [2], it is important to consider physical activity as a continued lifetime strategy for disease prevention instead of a solely rehabilitative method at advanced ages.

In a retrospective study conducted by Fernandes and Zanesco [25], physical activity at childhood (7 to 10 years) and adolescence (11 to 17 years) were related to a decreased risk for arterial hypertension and diabetes in adulthood (18 or more years). In our study, we observed that LTPA at adolescence was associated with odds reduction for multimorbidity at age 55 in the final adjusted model. The fact that this association was statistically significant in model 3 (adjusted for smoking, alcohol intake, and hours of sleep at middle-to-late adult life) show the importance of adolescence as a critical period for reducing the risk of multimorbidity in later life. Taken together, these previous findings support the notion of a protective physical activity “legacy” at early ages of childhood against multimorbidity at older ages. As this population is on school ages, the development of multicomponent school-based interventions promoting healthy lifestyle should be encouraged in order to reduce the likelihood of being affected by multimorbidity [30]. This scenario could also result in an increased quality of life and decreased the burden of multimorbidity in healthcare systems [31, 32].

Physical activity engagement in practice adulthood has been associated with reduced risk for chronic diseases and all-cause mortality [13]. Based on data from the English Longitudinal Study of Aging, Hamer et al. [15] reported that from a sample of older adults aged 50 or more, those who were physically inactive at baseline and became active at older ages had higher odds to have healthier aging than those who remained inactive. Similarly, we reported that LTPA at middle-to-late adult life (ages 50 and 55) had a significant protective effect against multimorbidity at age 55. Although the World Health Organization [33] recommends 150 min per week of moderate-to-vigorous physical activity, some studies have shown that lower levels could lead to reduced risk for chronic diseases and all-cause mortality. In this regard, Ekelund et al. [34, 35] revealed that lower doses of moderate-to-vigorous physical activity (i.e., 24 min per day) could contribute to the risk reduction of premature mortality, with a dose-response pattern in adulthood (20 years or more).

Dregan et al. [26] reported that LTPA sustained through life periods improved cognitive function in older adults. Another study [15] showed that becoming physically active at age 50 was associated with a lower risk for depression symptoms, cognitive impairment, and functional disability at older ages (8-year follow-up). Even though our results corroborate previous findings, cumulative models did not provide a sufficient explanation of the effect of LTPA during life course in multimorbidity. However, we highlight that, whenever feasible, LTPA must be promoted in all age groups especially among those groups with a higher prevalence of physical inactivity (children, adolescents, and older adults) [27, 36, 37]. Interventions tailored from those groups are necessary in order to prevent in the future a scenario with an even higher prevalence of multimorbidity and its burden on the healthcare system [38,39,40,41].

Some limitations of this present study must be acknowledged. First, LTPA was measured by questionnaires. In order to reduce the bias from that measurement, we chose to use in our study only those sweeps with face-to-face interview administrated questionnaire. The 1958 National Child Development Survey is one of the oldest national-based cohort studies, so although the level of LTPA was not examined by devices-based measurements, the information from those sweeps are reliable, comparable, and relevant [20]. Also, as the instrument does not provide details about the weekly volume of LTPA, it was not possible to categorize participants based on the World Health Organization’s guidelines of physical activity. However, the operationalization for LTPA to categorize individuals as physically active or inactive was based on previously published studies from the same birth cohort [20, 42]. Second, participants with multimorbidity were more likely to have no academic qualification and be at the lowest quintile of income, so residual confounding cannot be ruled out. Also, there were no data available on the participants’ family history regarding chronic diseases. Third, although the loss to follow-up may be interpreted as a source of selection bias, the NCDS cohort remains largely representative of the sample that it was drawn with a response rate of sweep (58.5%) [43]. Furthermore, we used multiple imputations to avoid further reductions in the sample due to missing information [22]. Four, although data from other chronic diseases such as stroke were not available, the chronic conditions we considered to identify multimorbidity were highly prevalent among adults aged between 50 and 54 years-old in the United Kingdom in 2019 such as low back pain (18.2%), hearing impairment (17.5%), and diabetes (13.2%) [44]. Fifth, reversal causality cannot be ruled out especially regarding the protective effect of LTPA at age 55 against multimorbidity at the same age.

Conclusions

In conclusion, we identified LTPA during adolescence as a critical period associated with reduced risk for multimorbidity in late adult life. Similar protective status was found in the sensitive model for LTPA during the same period and in middle-to-late adulthood (ages 50 and 55). Although analysis of factors associated with the outcome along lifetime is complex and should be carefully interpreted, our results support the need for LTPA promotion through intervention tailored especially on schooling age and older ages to reduce the burden of multimorbidity.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Data Archive at the University of Essex, https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=5560

Abbreviations

LTPA:

Leisure-time physical activity

NCDS:

National Child Development Study

References

  1. 1.

    Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7(4):357–63. https://0-doi-org.brum.beds.ac.uk/10.1370/afm.983.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pr. 2011;61(582):e12–21. https://0-doi-org.brum.beds.ac.uk/10.3399/bjgp11X548929.

    Article  Google Scholar 

  3. 3.

    World Health Organization (WHO). World report on ageing and health. Geneva: World Health Organization; 2015.

  4. 4.

    Condelius A, Edberg A-K, Jakobsson U, Hallberg IR. Hospital admissions among people 65+ related to multimorbidity, municipal and outpatient care. Arch Gerontol Geriatr. 2008;46(1):41–55. https://0-doi-org.brum.beds.ac.uk/10.1016/j.archger.2007.02.005.

    Article  PubMed  Google Scholar 

  5. 5.

    Freund T, Kunz CU, Ose D, Szecsenyi J, Peters-Klimm F. Patterns of multimorbidity in primary care patients at high risk of future hospitalization. Popul Health Manag. 2012;15(2):119–24. https://0-doi-org.brum.beds.ac.uk/10.1089/pop.2011.0026.

    Article  PubMed  Google Scholar 

  6. 6.

    Clerencia-Sierra M, Calderón-Larrañaga A, Martínez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, et al. Multimorbidity patterns in hospitalized older patients: associations among chronic diseases and geriatric syndromes. PLoS One. 2015;10(7):e0132909. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0132909.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Gruneir A, Bronskill SE, Maxwell CJ, Bai YQ, Kone AJ, Thavorn K, et al. The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study. BMC Health Serv Res. 2016;16(1):154. https://0-doi-org.brum.beds.ac.uk/10.1186/s12913-016-1415-5.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, et al. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015;314(1):52–60. https://0-doi-org.brum.beds.ac.uk/10.1001/jama.2015.7008.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    de Souto BP, Lapeyre-Mestre M, Vellas B, Rolland Y. Multimorbidity type, hospitalizations and emergency department visits among nursing home residents: a preliminary study. J Nutr Health Aging. 2014;18:705–9.

    Article  Google Scholar 

  10. 10.

    Smith SM, Soubhi H, Fortin M, Hudon C, O’Dowd T. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ. 2012;345(sep03 1):e5205. https://0-doi-org.brum.beds.ac.uk/10.1136/bmj.e5205.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Cassell A, Edwards D, Harshfield A, Rhodes K, Brimicombe J, Payne R, et al. The epidemiology of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pr. 2018;68(669):e245–51. https://0-doi-org.brum.beds.ac.uk/10.3399/bjgp18X695465.

    Article  Google Scholar 

  12. 12.

    Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK biobank cohort. BMC Med. 2019;17(1):74. https://0-doi-org.brum.beds.ac.uk/10.1186/s12916-019-1305-x.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(12)61031-9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Samitz G, Egger M, Zwahlen M. Domains of physical activity and all-cause mortality: systematic review and dose–response meta-analysis of cohort studies. Int J Epidemiol. 2011;40(5):1382–400. https://0-doi-org.brum.beds.ac.uk/10.1093/ije/dyr112.

    Article  PubMed  Google Scholar 

  15. 15.

    Hamer M, Lavoie KL, Bacon SL. Taking up physical activity in later life and healthy ageing: the English longitudinal study of ageing. Br J Sport Med. 2014;48(3):239–43. https://0-doi-org.brum.beds.ac.uk/10.1136/bjsports-2013-092993.

    Article  Google Scholar 

  16. 16.

    University of London, Institute of Education C for LS. National Child Development Study Response and Outcomes Dataset, 1958–2013. [data collection]. 6th ed; 2019.

    Google Scholar 

  17. 17.

    Power C, Elliott J. Cohort profile: 1958 British birth cohort (national child development study). Int J Epidemiol. 2006;35(1):34–41. https://0-doi-org.brum.beds.ac.uk/10.1093/ije/dyi183.

    Article  PubMed  Google Scholar 

  18. 18.

    de Bandeira FM, Freitas MP, László M, da Silva MC, Hallal PC, Rombaldi AJ. Mode of administration does matter: comparability study using IPAQ. Motriz Rev Educ Fis. 2015;21:370–4.

    Article  Google Scholar 

  19. 19.

    Pinto Pereira SM, De Stavola BL, Rogers NT, Hardy R, Cooper R, Power C. Adult obesity and mid-life physical functioning in two British birth cohorts: investigating the mediating role of physical inactivity. Int J Epidemiol. 2020;49(3):845–56. https://0-doi-org.brum.beds.ac.uk/10.1093/ije/dyaa014.

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Parsons TJ, Power C, Manor O. Longitudinal physical activity and diet patterns in the 1958 British birth cohort. Med Sci Sport Exerc. 2006;38(3):547–54. http://articles.sirc.ca/search.cfm?id=S-1013878. https://0-doi-org.brum.beds.ac.uk/10.1249/01.mss.0000188446.65651.67.

    Article  Google Scholar 

  21. 21.

    Mishra G, Nitsch D, Black S, De Stavola B, Kuh D, Hardy R. A structured approach to modelling the effects of binary exposure variables over the life course. Int J Epidemiol. 2009;38(2):528–37. https://0-doi-org.brum.beds.ac.uk/10.1093/ije/dyn229.

    Article  PubMed  Google Scholar 

  22. 22.

    Mostafa T, Narayanan M, Pongiglione B, Dodgeon B, Goodman A, Silverwood RJ, et al. Improving the plausibility of the missing at random assumption in the 1958 British birth cohort: A pragmatic data driven approach. 2020.

  23. 23.

    Parsons TJ, Manor O, Power C. Physical activity and change in body mass index from adolescence to mid-adulthood in the 1958 British cohort. Int J Epidemiol. 2006;35(1):197–204. https://0-doi-org.brum.beds.ac.uk/10.1093/ije/dyi291.

    Article  PubMed  Google Scholar 

  24. 24.

    Potter CM, Ulijaszek SJ. Predicting adult obesity from measures in earlier life. J Epidemiol Community Heal. 2013;67(12):1032–7. https://0-doi-org.brum.beds.ac.uk/10.1136/jech-2012-201978.

    Article  Google Scholar 

  25. 25.

    Fernandes RA, Zanesco A. Early physical activity promotes lower prevalence of chronic diseases in adulthood. Hypertens Res. 2010;33(9):926–31. https://0-doi-org.brum.beds.ac.uk/10.1038/hr.2010.106.

    Article  PubMed  Google Scholar 

  26. 26.

    Dregan A, Gulliford MC. Leisure-time physical activity over the life course and cognitive functioning in late mid-adult years: a cohort-based investigation. Psychol Med. 2013;43(11):2447–58. https://0-doi-org.brum.beds.ac.uk/10.1017/S0033291713000305.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(12)60646-1.

    Article  PubMed  Google Scholar 

  28. 28.

    Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sport Exerc. 2008;40(1):181–8. https://0-doi-org.brum.beds.ac.uk/10.1249/mss.0b013e31815a51b3.

    Article  Google Scholar 

  29. 29.

    Friel CP, Duran AT, Shechter A, Diaz KM. Meeting 24-Hour Movement Guidelines Among US Children and Adolescents: Prevalence and Age Trends, 2016–2017. Circulation. 2019;140(Suppl_1):A15418.

    Google Scholar 

  30. 30.

    Kriemler S, Meyer U, Martin E, Sluijs EMF, Andersen LB, Martin BW. Effect of school-based interventions on physical activity and fitness in children and adolescents: a review of reviews and systematic update. Br J Sport Med. 2011;45(11):923–30. https://0-doi-org.brum.beds.ac.uk/10.1136/bjsports-2011-090186.

    CAS  Article  Google Scholar 

  31. 31.

    Bayliss M, Rendas-Baum R, White MK, Maruish M, Bjorner J, Tunis SL. Health-related quality of life (HRQL) for individuals with self-reported chronic physical and/or mental health conditions: panel survey of an adult sample in the United States. Health Qual Life Outcomes. 2012;10(1):154. https://0-doi-org.brum.beds.ac.uk/10.1186/1477-7525-10-154.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Peters M, Kelly L, Potter CM, Jenkinson C, Gibbons E, Forder J, et al. Quality of life and burden of morbidity in primary care users with multimorbidity. Patient Relat Outcome Meas. 2018;9:103–13. https://0-doi-org.brum.beds.ac.uk/10.2147/PROM.S148358.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    World Health Organization (WHO). Global recommendations on Physical Activity for health. Geneva: World Health Organization; 2010.

  34. 34.

    Ekelund U, Dalene KE, Tarp J, et al. Physical activity and mortality: what is the dose response and how big is the effect? Br J Sports Med. 2020;54:1125–6.

  35. 35.

    Ekelund U, Tarp J, Steene-Johannessen J, Hansen BH, Jefferis B, Fagerland MW, et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ. 2019;366:l4570.

    Article  Google Scholar 

  36. 36.

    Andersen LB, Mota J, Di Pietro L. Update on the global pandemic of physical inactivity. Lancet. 2016;388(10051):1255–6. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(16)30960-6.

    Article  PubMed  Google Scholar 

  37. 37.

    Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1· 9 million participants. Lancet Glob Heal. 2018;6(10):e1077–86. https://0-doi-org.brum.beds.ac.uk/10.1016/S2214-109X(18)30357-7.

    Article  Google Scholar 

  38. 38.

    Sichieri R, do Nascimento S, Coutinho W. The burden of hospitalization due to overweight and obesity in Brazil. Cad Saude Publica. 2007;23(7):1721–7. https://0-doi-org.brum.beds.ac.uk/10.1590/S0102-311X2007000700025.

    Article  PubMed  Google Scholar 

  39. 39.

    Allender S, Foster C, Scarborough P, Rayner M. The burden of physical activity-related ill health in the UK. J Epidemiol Community Heal. 2007;61(4):344–8. https://0-doi-org.brum.beds.ac.uk/10.1136/jech.2006.050807.

    Article  Google Scholar 

  40. 40.

    Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–24. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(16)30383-X.

    Article  PubMed  Google Scholar 

  41. 41.

    Katzmarzyk PT, Gledhill N, Shephard RJ. The economic burden of physical inactivity in Canada. CMAJ. 2000;163(11):1435–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Geoffroy M-C, Li L, Power C. Depressive symptoms and body mass index: co-morbidity and direction of association in a British birth cohort followed over 50 years. Psychol Med. 2014;44(12):2641–52. https://0-doi-org.brum.beds.ac.uk/10.1017/S0033291714000142.

    Article  PubMed  Google Scholar 

  43. 43.

    Atherton K, Fuller E, Shepherd P, Strachan DP, Power C. Loss and representativeness in a biomedical survey at age 45 years: 1958 British birth cohort. J Epidemiol Community Heal. 2008;62(3):216–23. https://0-doi-org.brum.beds.ac.uk/10.1136/jech.2006.058966.

    CAS  Article  Google Scholar 

  44. 44.

    Institute for Health Metrics and Evaluation (IHME). GBD Results Tool: University of Washington; 2019.

Download references

Acknowledgements

Not applicable.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. We thank the Centre for Longitudinal Studies (CLS), UCL Institute of Education, for the use of 1958-NCDS data and the UK Data Service for making them available. Neither CLS nor the UK Data Service bear any responsibility for the analysis or interpretation of these data.

Author information

Affiliations

Authors

Contributions

NF, JSL, DU, ELC and AJR conceived the concept and design of the study. NF and JSL wrote the first draft and underwent substantial revision based on the inputs from all other authors. All the authors have read and approved the manuscript.

Corresponding author

Correspondence to Natan Feter.

Ethics declarations

Ethics approval and consent to participate

Multicentre Research Ethics Committee (MREC) approval was sought for NCDS follow-ups from 2000 on, and for the Biomedical Survey. The 1958 and 1965 follow-ups pre-dated the establishment of ethics committees; the 1969, 1974, 1981 and 1991 follow-ups came before the establishment of the MREC system. Internal ethical reviews were undertaken for these waves. Participants in later waves were required to sign informed consent, and ethical approval was obtained from South East and London Multicentre Research Ethics Committee. (Shepherd, P.M. An Introduction to the Background to the Study and Methods of Data Collection in The National Child Development Study. Social Statistics Research Unit. London City University 1985). NCDS sought written parental consent for the 7-year (1965), 11-year (1969) and 16-year (1974) surveys.

Consent for publication

Not applicable.

Competing interests

Authors have no competing interest associated with this paper. Daniel Umpierre is on the editorial board of BMC Public Health.

Additional information

Publisher’s Note

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

Supplementary Information

Additional file 1: Figure S1.

Flow diagram about sampling process through sweeps. 1958 National Child Development Study. United Kingdom.

Additional file 2: Table S1.

Operationalization of physical activity since age 7 sweep and in the present study.

Additional file 3: Table S2.

Sociodemographic, behavioral, and clinical characteristics from the included and excluded sample. N = 18.558. United Kingdom, 2013.

Additional file 4: Table S3.

Sociodemographic, behavioral, and clinical characteristics from the sample. N = 8414. United Kingdom, 2013.

Rights and permissions

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

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Feter, N., Leite, J.S., Umpierre, D. et al. Multimorbidity and leisure-time physical activity over the life course: a population-based birth cohort study. BMC Public Health 21, 700 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-021-10719-7

Download citation

Keywords

  • Physical activity
  • Multimorbidity
  • Cohort
  • Life course