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. 2024 Mar 27;7(3):e243861. doi: 10.1001/jamanetworkopen.2024.3861

Adolescent Cardiorespiratory Fitness and Future Work Ability

Perttu T T Laakso 1,✉, Francisco B Ortega 1,2, Pertti Huotari 1, Asko J Tolvanen 3, Urho M Kujala 1, Timo T Jaakkola 1
PMCID: PMC10973892  PMID: 38536172

Key Points

Question

Is health-related physical fitness during adolescence associated with work ability at the middle and end of working life?

Findings

In this cohort study of 1207 individuals in Finland, higher cardiorespiratory fitness, but not musculoskeletal fitness or body mass index, in adolescence was associated with higher work ability and lower sickness absence in adulthood.

Meaning

These findings suggest that low cardiorespiratory fitness in youth may be an early indicator of impaired work ability throughout working life, and enhancing cardiorespiratory fitness in the first decades of life might contribute to better work capacity and productivity in the labor force.

This cohort study examines the associations of adolescent health-related physical fitness with future work ability among individuals in Finland.

Abstract

Importance

Although research indicates that low fitness in youth is associated with a higher risk of chronic disability in men, the association of fitness in adolescence with work ability in working men and women remains unknown.

Objective

To examine the associations of adolescent health-related physical fitness with future work ability.

Design, Setting, and Participants

This 45-year observational cohort study, conducted in Finland, examined the direct and indirect longitudinal associations of objectively measured health-related fitness in adolescence (in 1976) with self-reported work ability and sickness absence in early middle age (in 2001) and the Work Ability Index in late middle age (in 2021). A countrywide stratified random baseline sample included fitness measurements for cardiorespiratory fitness (CRF) (running 1.5 km for girls and 2 km for boys), musculoskeletal fitness (MF; standing broad jump and sit-ups for both sexes, pull-ups for boys, and flexed-arm hang for girls), and height and weight, from which body mass index (BMI) was calculated. Structural equation modeling–based path analysis, adjusted for age and sex at baseline and for educational level, work-related physical strain, and leisure-time physical activity in late middle age, was conducted. Data analysis was performed from January to July 2023.

Main Outcomes and Measures

Self-reported work ability was measured with structured questions in early middle age and with the validated Work Ability Index in late middle age.

Results

The final sample from longitudinal analyses (1207 individuals; 579 [48%] male individuals) consisted of participants with fitness measurement from age 12 to 19 years, and work ability assessment from age 37 to 44 years and/or age 57 to 64 years. Higher adolescent CRF was associated with higher work ability (839 participants; β = 0.12; 95% CI, 0.01 to 0.22; P = .03) and lower sickness absence (834 participants; β = −0.07; 95% CI, −0.12 to −0.02; P = .004) in early middle age and, indirectly, mediated by work ability in early middle age, with a higher work ability at the end of working age (603 participants; β = 0.04; 95% CI, 0.001 to 0.08; P = .04). The results remained consistent in both sexes and after adjustment for the confounders. Neither MF nor BMI was associated with work ability (MF, 1192 participants; β = −0.07; 95% CI, −0.17 to 0.03; BMI, 1207 participants, β = 0.09; 95% CI, −0.004 to 0.19) or sickness absence (MF, 1185 participants, β = 0.02; 95% CI, −0.03 to 0.06; BMI, 1202 participants, β = −0.03; 95% CI, −0.09 to 0.03) in early middle age or with late middle age work ability, mediated by work ability in early middle age (MF, 603 participants, β = −0.02; 95% CI, −0.06 to 0.01; BMI, 603 participants, β = 0.03; 95% CI, −0.004 to 0.07).

Conclusions and Relevance

These findings suggest that low CRF in youth is associated with poor work ability at the middle and end of working life, which highlights the informative and prognostic value of CRF assessment early in youth. Enhancing CRF in the first decades of life might contribute to better work capacity and productivity in the labor force, which would have implications for health, quality of life, society, and the economy.

Introduction

Work ability refers to individuals’ work-related physical, mental, and psychosocial resources and factors associated with the working environment.1 Low educational level, older age, a high physical and mental workload, and impaired health status have been identified as negatively affecting work ability.2,3,4 Maintaining good work ability is essential at all societal levels. For the individual, it increases well-being, quality of life, and employment,2,5,6 and for the work organization it prevents disability,7,8 sickness absence,8,9 and presenteeism10 (ie, working while ill11), which have been associated with substantial costs.12,13 At the community level, work ability is positively associated with gross domestic product.14,15

Health-related physical fitness (hereafter fitness) is a dimension of physical fitness defined as the ability to perform physically demanding tasks acceptably. The components of fitness are cardiorespiratory fitness (CRF), flexibility, body composition, and musculoskeletal fitness (MF).16 CRF, MF, and healthy body composition are positively associated with both current17,18,19,20 and future21,22,23,24,25 health. Assessing fitness in childhood and adolescence is important, because it has been shown to be an important marker of noncommunicable diseases.22,25,26

The association of fitness with work ability has been demonstrated in cross-sectional studies. Evidence from experimental27,28 and observational4,29 studies has shown that sufficient levels of CRF27,28,29 and MF4,30 and a healthy body weight3,4,31 benefit work ability. However, studies using objective fitness measurement have relied on small samples, which limits their generalizability. In turn, longitudinal studies on the benefits of youth fitness in association with work ability have only investigated the association for adulthood chronic disability and mostly in men.32,33,34,35 It is, therefore, unknown whether lower youth fitness levels are associated with lower work ability in persons in the labor force (ie, those not receiving a disability pension) and the possible economic consequences of this. The corresponding associations in women also remain unknown.

Public debate has raised concern on whether deteriorating fitness36 in youth will lead to a future labor force with greatly eroded work ability. At the same time, the working-age population in high-income countries is decreasing,37 prompting calls to maximize the output of the existing labor force. This cohort study investigated the associations of adolescent fitness with work ability in early and late middle age among male and female participants. Specifically, we aimed to provide novel evidence on the associations of fitness in adolescence with future work ability in both sexes and ascertain the informative value of fitness based on a uniquely long follow-up study.

Methods

Study Population

This cohort study used data collected at 3 time points for the Liikuntaharrastuksen Seurantatutkimus (ie, Physical Activity Follow-Up Study) project, which was launched in 1976: (1) baseline was in April to May 1976, (2) follow-up 1 was in April to May 2001, and (3) follow-up 2 was in March 2021. The baseline sample comprised children and adolescents aged 9 to 21 years, of whom 12- to 19-year-old adolescents (mean [SD] age, 14.9 [1.7] years) with eligible fitness data were qualified for inclusion in the final sample (up to 1803 participants). The baseline sample was recruited using stratified random sampling, from 56 schools in the eastern, western, central, and northern parts of Finland, including urban and rural districts. The follow-up 1 data, which were collected with a postal questionnaire when the participants were aged 37 to 44 years, provided information on early middle age work ability (up to 1577 participants, 87.5% of baseline sample). The follow-up 2 data, comprising 769 participants (43% of baseline sample), then aged 57 to 64 years, provided information on late middle age Work Ability Index (WAI) data. The Liikuntaharrastuksen Seurantatutkimus data acquisition method has been described in detail elsewhere.38 Because of participant dropout and missing data in the baseline fitness test results and follow-up item responses, the number of participants varied depending on the longitudinal association examined. The exact number of participants in each analysis is presented in eTable 1 in Supplement 1, and the data structure is shown in Figure 1. The Human Sciences Ethics Committee of the University of Jyväskylä approved the study. Written informed consent was obtained before study entry. The study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Figure 1. Data Structure of the Study.

Figure 1.

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The path model used in this study analyzed all the associations of at least 1 exposure in baseline (1976) with at least 1 outcome in the first (2001) and/or the second (2021) follow-up. Because of the possibility of participating in 1976 and 2021 without participating in 2001, the missing data are not cumulative. The numbers for a particular association analyzed are presented in eTable 1 in Supplement 1. BMI indicates body mass index; CRF, cardiorespiratory fitness; MF, musculoskeletal fitness; WAE, Work Ability Estimate; WAI, Work Ability Index.

Assessment of Physical Fitness

The baseline physical fitness tests were administered in schools by a trained research team using an identical measurement protocol. The tests used were based on the International Fitness Test guidelines.39 Before tests, participants completed a short health inquiry,40 and only healthy adolescents were allowed to participate. Participants conducted an identical warm-up routine, and their height (centimeters) and weight (kilograms) were measured by their school health care professional. CRF was assessed by running tests (2000 m for boys and 1500 m for girls) performed on an outdoor 400-m running track. MF tests, conducted in an indoor sport facility, included standing broad jump (centimeters), sit-ups (repetitions in 30 seconds), and pull-ups (maximum number of repetitions) and/or flexed-arm hang (seconds). The standing broad jump and sit-up tests were identical for both sexes, whereas the upper-body strength tests were sex specific, with pull-ups for boys and flexed-arm hang for girls. The tests are given in detail in eTable 2 in Supplement 1. For statistical analyses, the test scores were adjusted for age and sex by calculating standardized z scores.

Assessment of Work Ability

In late middle age (2021), work ability was self-assessed using the WAI41 (eTable 3 in Supplement 1). Participants who were retired (17 participants), disabled (2 participants), or otherwise permanently outside working life (12 participants) were excluded. The total WAI score ranges between 7 and 49. Scores can subsequently be categorized according to the original classification4 as poor (7-27), average (28-36), good (37-43), and very good (44-49).

Because the WAI was not used in the follow-up 1 questionnaire in 2001, we used the Work Ability Estimate2,42,43 and sickness absence as compensatory indicators of work ability. The Work Ability Estimate (hereafter, work ability) is a single-question self-assessment method used previously in the Finnish population-based Mini-Finland study.42,43 The question is formulated as, “Do you have any disease or injury which decreases your work ability and overall functioning (no/yes)?” The variable was subsequently inverted by multiplying it by −1 to align it with the WAI so that for both outcomes the higher the score represented the higher the level of work ability. Sickness absence, which has shown a negative reciprocal association with work ability,7,8,9,10 was assessed as the number of full days off work over the past 12 months. Both indicators are included in the WAI.41

Covariates

At baseline (1976), the fitness test results were adjusted for age and sex by calculating standardized z scores. Educational level, work-related physical strain, and leisure-time physical activity (LTPA) in late middle age (2021) were added into the path model to act as cross-sectional variables and as covariates in the longitudinal associations. Formal educational level was obtained by asking, “How many years in total have you studied full-time, including all levels of education?” Work-related physical strain by asking the structured question, “What kind of work do you do most of the year (information, information and physical, or physical work)?” LTPA was assessed as frequency (“How often do you participate in physical activity?”), duration (“What is the duration of your average LTPA session?”), and intensity (“How would you describe the intensity of your average LTPA session?”),35 from which metabolic equivalent of task (MET) hours per day was calculated.

Statistical Analysis

Data analysis was performed from January to July 2023. Longitudinal associations between the adolescent fitness components, early middle age work ability indicators, and the late middle age WAI were investigated using structural equation modeling–based path analysis, conducted with Mplus version 8.0 (Muthen & Muthen). On the basis of the results of the sensitivity analyses described in the eAppendix in Supplement 1, a single group model combining sex and age groups was adopted as a final model. The mediation effect was tested via the indirect effect estimate using a robust SE estimate.

Model fit was evaluated using the χ2 test, root mean square error of approximation, comparative fit index, Tucker-Lewis index, and standardized root mean square residuals. Models were estimated using the weighted least squares mean and variance estimator with robust SE and scale-corrected χ2 value. Missing data analysis was conducted with the Little missing completely at random test using SPSS statistical software version 28 (IBM). All P values were 2-sided, and the significance threshold was set at P < .05.

Results

Descriptive Statistics

Table 1 shows the participants’ baseline characteristics, and Table 2 shows the follow-up characteristics. The study was well-balanced by sex (1207 individuals; 579 male [48%]). The prevalence of decreased work ability in early middle age was 18.0% (134 participants) in men and 16.8% (140 participants) in women, and the mean (SD) sickness absence rate was 10.6 (44.2) full days per year among men and 9.4 (35.1) full days per year among women. In late middle age, 56.7% of the participants (436 participants) worked in low and 14.1% (108 participants) worked in high physically demanding jobs. The mean (SD) WAI value (39.1 [7.0] for men; 37.9 [7.3] for women) was defined as good, according to the original categorization. A significant decrease in the WAI by age was observed among women (mean [SD], 38.6 [7.3] for age 57-60 years vs 36.4 [7.2] for age 61-64 years; t415 = 8.18; P = .004) but not among men (mean [SD], 39.1 [7.0] for age 57-60 years vs 39.2 [7.2] for age 61-64 years; t374 = 0.00; P = .95). The mean (SD) LTPA value was 3.5 (3.4) MET hours per day among men and 2.8 (2.5) MET hours per day among women, and those with higher LTPA tended to have higher WAI. One-way analysis of variance with categorized LTPA variable (high LTPA, >3.50 MET hours per day; middle LTPA, >1.57 to ≤3.50 MET hours per day; low LTPA, ≤1.57 MET hours per day) showed that the groups of high (mean [SD] WAI, 40.1 [6.6]) and middle (mean [SD], 39.3 [6.7]) LTPA had a significantly higher mean WAI (analysis of variance F2,754 = 9.59; P < .001 for high vs low; P = .02 for middle vs low) than the low LTPA group (mean [SD], 37.6 [7.1]) (Scheffe pairwise comparison). Correlations between the study variables are presented in Table 3.

Table 1. Baseline Characteristics of the Study Sample.

Characteristic Male participants Female participants
12-19 y 12-15 y 16-19 y 12-19 y 12-15 y 16-19 y
Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a
Age, y 14.9 (1.7) NA 13.8 (1.0) NA 16.8 (0.9) NA 14.9 (1.7) NA 13.9 (1.0) NA 16.8 (0.9) NA
Height, cm 168.2 (11.2) 872 163.4 (10.6) 562 176.6 (6.4) 310 162.0 (6.5) 931 160.7 (6.4) 615 164.6 (5.8) 316
Weight, kg 56.2 (12.4) 872 51.1 (10.9) 562 65.2 (9.4) 310 52.5 (8.2) 931 50.7 (8.5) 615 56.1 (6.8) 316
Body mass indexb 19.6 (2.7) 872 18.9 (2.5) 562 20.9 (2.6) 310 20.0 (2.5) 931 19.6 (2.6) 615 20.7 (2.2) 316
Sit-ups in 30 s 20.4 (4.2) 866 19.6 (4.2) 551 21.7 (3.8) 315 16.6 (3.8) 934 16.5 (3.7) 611 16.7 (4.0) 323
Standing broad jump, cm 213.2 (30.1) 868 200.5 (25.4) 551 235.5 (24.0) 317 174.1 (21.4) 940 171.9 (21.2) 613 178.4 (21.2) 327
Flexed arm hang, s NA NA NA NA NA NA 13.9 (9.9) 939 13.5 (10.0) 612 14.6 (9.7) 327
Pull-ups 5.7 (4.0) 862 4.6 (3.5) 548 7.6 (4.2) 314 NA NA NA NA NA NA
Running test
1500 m, s NA NA NA NA NA NA 495.6 (87.0) 587 501.0 (87.2) 422 480.0 (85.0) 165
2000 m, s 582.5 (121.1) 660 601.4 (124.1) 438 545.7 (106.2) 222 NA NA NA NA NA NA
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Abbreviation: NA, not applicable.

a

Refers to number of participants.

b

Body mass index is calculated as weight in kilograms divided by height in meters squared.

Table 2. Follow-Up Characteristics of the Study Sample.

Characteristic Male participants Female participants
25-y Follow-up 45-y Follow-up 25-y Follow-up 45-y Follow-up
37-44 y 37–40 y 41-44 y 57-64 57-60 y 61-64 y 37-44 y 37-40 y 41-44 y 57-64 y 57-60 y 61-64 y
Mean (SD) No.a Mean (SD) No.a Mean (SD) No. a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a Mean (SD) No.a
Age, y 39.9 (1.7) NA 38.3 (1.0) NA 41.8 (0.9) NA 59.9 (1.7) NA 58.3 (1.0) NA 61.8 (0.9) NA 39.9 (1.7) NA 38.9 (1.0) NA 41.8 (0.9) NA 59.9 (1.7) NA 58.9 (1.0) NA 61.8 (0.9) NA
Height, cm 179.6 (6.4) 739 179.6 (6.5) 471 179.7 (6.2) 268 176.7 (8.8) 366 176.5 (9.0) 234 177.1 (8.5) 132 165.9 (5.7) 814 165.9 (5.8) 531 165.8 (5.4) 283 167.7 (7.1) 403 167.8 (7.2) 269 167.6 (7.1) 134
Weight, kg 83.1 (12.0) 739 83.3 (12.2) 471 83.0 (11.8) 268 83.5 (14.5) 366 83.7 (15.0) 234 83.1 (13.4) 132 66.4 (11.6) 814 66.7 (12.4) 531 65.8 (10.0) 283 76.2 (16.5) 403 76.7 (17.4) 269 75.1 (14.5) 134
BMIb 25.7 (3.3) 739 25.8 (3.4) 471 25.7 (3.2) 268 26.7 (3.9) 366 26.8 (4.2) 234 26.4 (3.5) 132 24.1 (3.9) 814 24.2 (4.1) 531 24.0 (3.7) 283 27.0 (5.3) 403 27.2 (5.6) 269 26.7 (4.9) 134
Decreased work ability, No. (%) 134 (18.0) 745 82 (17.3) 473 52 (19.1) 272 NA NA NA NA NA NA 140 (16.8) 832 99 (18.2) 544 41 (14.2) 288 NA NA NA NA NA NA
Sickness absence, d/y 10.6 (44.2) 740 10.9 (44.2) 471 10.1 (44.2) 269 NA NA NA NA NA NA 9.4 (35.1) 832 9.1 (33.9) 546 9.8 (37.4) 286 NA NA NA NA NA NA
WAI NA NA NA NA NA NA 39.1 (7.0) 365 39.1 (7.0) 237 39.2 (7.2) 128 NA NA NA NA NA NA 37.9 (7.3) 404 38.6 (7.3) 271 36.4 (7.2) 133
LTPA, MET-h/d NA NA NA NA NA NA 3.5 (3.4) 358 3.4 (3.4) 229 3.7 (3.3) 129 NA NA NA NA NA NA 2.8 (2.5) 399 2.9 (2.7) 265 2.6 (2.1) 134
Strain, No. (%)
Low NA NA NA NA NA NA 210 (58.2) 361 130 (56.6) 231 80 (61.2) 130 NA NA NA NA NA NA 226 (55.4) 408 157 (57.3) 273 69 (51.4) 135
Middle NA NA NA NA NA NA 88 (24.5) 57 (24.4) 31 (24.6) NA NA NA NA NA NA 136 (33.3) 77 (28.3) 59 (43.5)
High NA NA NA NA NA NA 63 (17.3) 44 (19.0) 19 (14.2) NA NA NA NA NA NA 46 (11.3) 39 (14.3) 7 (5.1)
Education, No. (%)
Basic (0-9 y) NA NA NA NA NA NA 13 (3.5) 365 9 (3.7) 247 4 (3.0) 118 NA NA NA NA NA NA 5 (1.2) 404 3 (1.1) 273 2 (1.4) 131
Secondary (10-12 y) NA NA NA NA NA NA 69 (18.9) 49 (19.8) 20 (17.2) NA NA NA NA NA NA 64 (15.9) 56 (20.5) 8 (6.5)
Higher (≥13 y) NA NA NA NA NA NA 283 (77.7) 189 (76.4) 94 (79.9) NA NA NA NA NA NA 335 (82.9) 214 (78.4) 121 (92.0)
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Abbreviations: BMI, body mass index; LTPA, leisure-time physical activity; MET-h/d, metabolic equivalent of task hours per day; NA, not applicable; WAI, Work Ability Index.

a

Refers to number of participants.

b

Body mass index is calculated as weight in kilograms divided by height in meters squared.

Table 3. Correlations Between the Study Variablesa.

Year and variable 1976 2001 2021
BMI MF CRF Work abilityb Absencec WAI LTPA Straind
1976
MF −0.226e NA NA NA NA NA NA NA
CRF −0.116e 0.413e NA NA NA NA NA NA
2001
Work abilityb 0.089f −0.041 0.078 NA NA NA NA NA
Absencec −0.022 −0.006 −0.060f −0.350e NA NA NA NA
2021
WAI −0.041 0.030 −0.004 −0.303e −0.029 NA NA NA
LTPA −0.019 0.106f 0.111f −0.055 −0.012 0.135e NA NA
Straind 0.065 0.002 0.015 −0.016 −0.094 −0.191e −0.095f NA
Education level −0.025 0.039 −0.021 0.113 −0.003 0.175e 0.033 −0.269e
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Abbreviations: BMI, body mass index; CRF, cardiorespiratory fitness; LTPA, leisure-time physical activity; MF, musculoskeletal fitness; NA, not applicable; WAI, work ability index.

a

Data are correlation coefficients. Correlations are biserial if work ability in 2001 is included, polyserial if strain in 2021 is included, and Pearson product moment for all others.

b

Coefficient is an inverted (multiplied by −1) version of the variable decreased work ability and overall functioning due to health impairment in 2001.

c

Refers to absenteeism due to sickness or injury.

d

Refers to work-related physical strain.

e

P < .01.

f

P < .05.

Baseline body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), CRF, and MF z scores between dropouts and nondropouts were evaluated using the Little missing completely at random test. The test indicated that nondropouts had healthier characteristics than dropouts (eTable 4 in Supplement 1); that is, the missing values were not completely missing at random. Using weighted least squares mean and variance estimator in the path analysis, the missing completely at random assumption could be relaxed, and it can be assumed that the missing values were missing at random.

Longitudinal Associations of Health-Related Fitness in Adolescence With Work Ability in Early and Late Middle Age

The model fit information indicated that the path model fitted the data well (χ210 = 8.84; P = .55; root mean square error of approximation = 0.00; comparative fit index = 1.00; Tucker-Lewis index = 1.00, standardized root mean square residuals = 0.02). The results (Figure 2 and eTable 1 in Supplement 1), adjusted for late middle age LTPA, educational level, and work-related physical strain, showed that CRF in adolescence was associated with the work ability indicators in both early and late middle age. Between adolescence and early middle age, adolescent CRF showed a direct positive association with work ability (839 participants; β = 0.12; SE = 0.05; 95% CI, 0.01 to 0.22; P = .03) and a negative association with sickness absence (834 participants; β = −0.07; SE = 0.02; 95% CI, −0.12 to −0.02; P = .004). However, although statistically significant, the associations were low in magnitude. Neither MF nor BMI was associated with early middle age work ability (MF, 1192 participants; β = −0.07; 95% CI, −0.17 to 0.03; BMI, 1207 participants, β = 0.09; 95% CI, −0.004 to 0.19) or sickness absence (MF, 1185 participants, β = 0.02; 95% CI, −0.03 to 0.06; BMI, 1202 participants, β = −0.03; 95% CI, −0.09 to 0.03) or late middle age work ability, mediated by work ability in early middle age (MF, 603 participants, β = −0.02; 95% CI, −0.06 to 0.01; BMI, 603 participants, β = 0.03; 95% CI, −0.004 to 0.07). Although no direct association between adolescent fitness and late middle age WAI was detected, a significant indirect association (603 participants; β = 0.04; SE = 0.02; 95% CI, 0.001 to 0.08; P = .04) was found between adolescent CRF and late middle age WAI mediated by full (ie, not impaired) work ability in early middle age. Analysis of the associations between early and late middle age showed that work ability in early middle age, which was concurrently a significant mediator in the indirect association between adolescent CRF and work ability in late middle age, had a medium-sized positive association (β = 0.35; SE = 0.06) with the WAI in late middle age.

Figure 2. Path Diagram .

Figure 2.

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Diagram shows statistically significant (P < .05) standardized regression coefficients (straight arrow), correlations (curved arrow), and SEs (in parentheses). Absence 2001 refers to absenteeism due to sickness or injury in 2001. Strain 2021 refers to work-related physical strain in 2021. Education 2021 refers to education level in 2021. BMI indicates body mass index; CRF, cardiorespiratory fitness; LTPA, leisure-time physical activity; MF, musculoskeletal fitness; WAE, work ability estimate; WAI, Work Ability Index.

aRefers to an inverted (multiplied by −1) version of the variable decreased work ability and overall functioning due to health impairment in 2001.

Discussion

This 45-year prospective cohort study contributes 4 major findings to the literature. First, higher adolescent fitness, particularly CRF, was directly associated with higher work ability and lower sickness absence in early middle age and was indirectly associated with higher WAI toward the end of working life (ages 57-64 years). Second, unlike previous studies that have investigated the associations of fitness with a disability pension and only among men, our study provides novel evidence on the prognostic value of CRF for future work ability in both men and women. Third, although small, the associations found were consistent and independent of potentially important confounders, such as BMI in adolescence and LTPA, educational level, and work-related physical strain in late middle age. Fourth, when CRF, MF, and BMI were all included in the model as exposure variables and therefore adjusted for each other, only CRF was associated with future work ability outcomes.

Our finding that CRF in adolescence was positively associated with the work ability indicators in early and late middle age is unique owing to its 45-year-long follow-up and responds to concerns raised in the public domain about young people’s decreased fitness36 in relation to their future health and work ability. We cannot directly compare our results with those of previous studies, because we found no similar studies with work ability as the primary outcome. However, our findings are supported by previous longitudinal studies32,34,35 with chronic disability as an outcome measure that have found an association between late adolescent CRF and an adulthood disability pension due to cardiovascular disease34 and all causes,32 independently of MF and BMI. In those studies, BMI was also a factor associated with a disability pension, whereas the role of MF remained smaller and more inconsistent.

The association of adolescent CRF with work ability in adulthood is likely due to the interrelationship between CRF and chronic cardiometabolic conditions21,44,45,46 that negatively influence work ability.2,47 It is widely accepted that in adulthood CRF is an important individual factor associated with cardiometabolic disease,17,48 which is a major burden for public health globally.49,50 Moreover, CRF in youth may contribute to the risk for metabolic syndrome,21,51 cardiovascular disease,44,45 and type 2 diabetes52 later in life. This pattern was clearly visible in our results, where adolescent CRF, mediated by work ability in early middle age, was positively associated with late middle age work ability. Although fitness status was not controlled for in middle age, the association of CRF with health conditions was present, as was that of health conditions with work ability. However, although the association of adolescent CRF with adulthood cardiometabolic health has been established,21,26,44,45,51,52 its mechanism remains unclear. It may be related to genetic regulation, which has been demonstrated not only to determine maximal oxygen uptake in youth53 but also likely contributes to a lower risk for cardiometabolic diseases in adulthood.54 In turn, Henriksson et al32 found an interesting association of youth CRF with not only disability due to circulatory causes but also tumors, injuries, and psychiatric, musculoskeletal, and nervous system disorders, indicating a possible broader role for CRF as a contributor to work ability. In addition to its genetic component, CRF is known to be associated with environmental factors, especially physical activity, meaning that higher CRF is also indicative of long-term exposure to an active life, which can also directly and indirectly positively influence health and, thus, work ability. Furthermore, better CRF is a marker of better brain (mental and cognitive) health,55 which is another important component of work ability.

We found no significant associations of adolescent MF or BMI with early or late middle age work ability after adjusting for CRF. Regarding MF, the result is mainly in line with previous findings. Despite Henriksson et al33 finding a significant association of late adolescent MF with all-cause disability, the association with disability pension due to cardiovascular disease was small and inconsistent.34 Similarly, the positive association of adulthood cardiometabolic health with youth MF23 has been smaller than that with CRF.45,52,56,57,58 Unlike previous corresponding cross-sectional3,31 and longitudinal32,34 studies, and studies on the role of youth body composition in future adulthood cardiometabolic disease risk,22,25,44,45 we observed no association of BMI with work ability after adjusting for CRF. Presumably, the conflicting finding might be related to methodological differences between our and the reference studies, as well as the relatively small sample size in our study. We assessed CRF by a running test, unlike the bicycle test used in the reference studies. Because running is a more weight-dependent activity than stationary biking, it might be that CRF and BMI were more interdependent in our study, a possibility that could partially explain the results. Moreover, only 2 of the reference studies controlled for CRF,32,34 indicating that further research on CRF adjustment is needed.

In addition to the WAI across 45 years, our path analysis yielded another major finding. Adolescent CRF, but not BMI or MF, was inversely and significantly associated with early middle age sickness absence. Because sickness absence, aside from being a main component of work ability,41 is a significant independent factor threatening productivity,12,13 this is an important finding that supplements the evidence gathered from studies with shorter follow-ups.29,59

The main strength of this prospective study is its 45-year-long follow-up, connecting pediatric age with age at the end of working life. The end-point age (57-64 years) is highly relevant from the work productivity viewpoint. This finding is an important addition to the literature32,33,34,35 on the longitudinal associations of fitness with chronic disability and early pensions. Along with economic impact, work ability is associated with quality of life.5,6 Hence, the results of this study highlight the role of physical activity in youth, a main lifestyle contributor to a better CRF, as an important factor to enhance well-being in adulthood. Other strengths are a sex-balanced geographically representative baseline sample that allowed us to investigate potential sex differences and similarities, and the use of sophisticated statistical techniques to address survivor bias, which is a common threat to validity in longitudinal studies. Moreover, the baseline fitness data were gathered using an objective measure, thereby enhancing their validity. Another strength is that work ability at the end point was measured with the widely used and validated WAI,41,47,60 which has been shown to be valid, especially among older workers,2 as was also the case in this study. Finally, measurements were conducted at 3 time points instead of just 2, allowing us to examine potential mediators in early middle age that influence work ability at the end of working life.

Limitations

This study has its limitations. First, the WAI was not used in 2001, compelling us to resort to the simpler and discriminant measure of Work Ability Estimate in early middle age. However, despite the measure’s limitations, particularly in terms of lack of work-specific and prognostic perspective on work ability, it has been used widely42,43 and showed good concurrent validity with the WAI.2 Not surprisingly, the greatest association in our model was found between the Work Ability Estimate and the WAI. Second, the lack of fitness measurements in the follow-ups prevented us from investigating changes in CRF in relation to work ability, a task that remains for future studies. Third, the baseline measurement did not include in-depth health screening with assessment of all preexisting medical conditions (even though adolescents at these ages are mostly healthy) or family health history, which might have affected the associations between fitness and work ability. Fourth, the possible confounders were not controlled for in early middle age, which might have influenced the results concerning the first 25 years. Fifth, participant dropout, which is unavoidable in such a long follow-up, and the subsequent decline in sample representativeness should be noted. Moreover, the observational design means that causal relationships cannot be established.

Conclusions

The results of this 45-year prospective cohort study provide novel findings that low CRF in adolescence is associated with poorer work ability at the end of working life, and, hence, with productivity and other economic factors. This association was independent of potentially relevant confounders, including BMI and MF, and was mediated by work ability in early middle age. An additional important contribution of this study is that we include both sexes, unlike earlier cohorts studying disability only in men, and our findings support that the long-term association between adolescent CRF and future work ability is consistent in male and female individuals. On the other hand, a significant association between BMI or MF with work ability was not observed later in life, after adjustment for CRF. Altogether, our findings support the notion that assessing CRF in adolescence is relevant and informative from a clinical, public health, and economic value. Furthermore, our findings suggest that promoting CRF through physical activity and exercise in childhood and adolescence could potentially benefit the work ability of the future labor force.

Supplement 1.

eTable 1. Associations in the Path Analysis, Adjusted for All Covariate

eTable 2. Descriptions of the Fitness Tests in Adolescence

eTable 3. The Work Ability Index, Items and Instructions for Calculation

eAppendix. Sensitivity Analysis (Estimation of the Path Model Using Multigroup Model)

eTable 4. Missing Value Analysis of Follow-Up and Non-Follow-Up Participants

jamanetwopen-e243861-s001.pdf (488.5KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e243861-s002.pdf (14KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. Associations in the Path Analysis, Adjusted for All Covariate

eTable 2. Descriptions of the Fitness Tests in Adolescence

eTable 3. The Work Ability Index, Items and Instructions for Calculation

eAppendix. Sensitivity Analysis (Estimation of the Path Model Using Multigroup Model)

eTable 4. Missing Value Analysis of Follow-Up and Non-Follow-Up Participants

jamanetwopen-e243861-s001.pdf (488.5KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e243861-s002.pdf (14KB, pdf)

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