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Medicine & Science in Sports & Exercise:
doi: 10.1249/MSS.0000000000000246
Epidemiology

Midlife Determinants Associated with Sedentary Behavior in Old Age

VAN DER BERG, JULIANNE D.1; BOSMA, HANS1; CASEROTTI, PAOLO2; EIRIKSDOTTIR, GUDNY3; ARNARDOTTIR, NANNA YR3,4; MARTIN, KATHRYN R.5; BRYCHTA, ROBERT J.6; CHEN, KONG Y.6; SVEINSSON, THORARINN4; JOHANNSSON, ERLINGUR7; LAUNER, LENORE J.5; GUDNASON, VILMUNDUR3,8; JONSSON, PALMI V.9,10; STEHOUWER, COEN D. A.11; HARRIS, TAMARA B.5; KOSTER, ANNEMARIE1

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Author Information

1Department of Social Medicine/CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, THE NETHERLANDS; 2Institute of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, DENMARK; 3Icelandic Heart Association, Kopavogur, ICELAND; 4Research Center of Movement Science, University of Iceland, Reykjavik, ICELAND; 5Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD; 6Diabetes Endocrinology and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD; 7Center for Sport and Health Sciences, Iceland University of Education, Laugarvatn, ICELAND; 8University of Iceland, Reykjavik, ICELAND; 9Faculty of Medicine, University of Iceland, Reykjavik, ICELAND; 10Department of Geriatrics, Landspitali National University Hospital, Reykjavik, ICELAND; and 11Department of Internal Medicine/Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, THE NETHERLANDS

Address for correspondence: Julianne D. van der Berg, MSc, P.O. Box 616, 6200 MD Maastricht, The Netherlands; E-mail: j.vanderberg@maastrichtuniversity.nl.

T.B.H. and A.K. contributed equally.

Submitted for publication October 2013.

Accepted for publication December 2013.

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Abstract

Background: Sedentary behavior is associated with adverse health effects. Insights into associated determinants are essential to prevent sedentary behavior and limit health risks. Sedentary behavior should be viewed as a distinct health behavior; therefore, its determinants should be independently identified.

Purpose: This study examines the prospective associations between a wide range of midlife determinants and objectively measured sedentary time in old age.

Methods: Data from 565 participants (age 73–92 yr) of the AGESII-Reykjavik Study were used. Participants wore an accelerometer (ActiGraph GT3X) on the right hip for seven consecutive days. On average, 31 yr earlier (during midlife), demographic, socioeconomic, lifestyle, and biomedical factors were collected. Linear regression models were used to examine prospective associations between midlife determinants and sedentary time (<100 counts per minute) in old age.

Results: After adjustment for sex, age, follow-up time, minutes of moderate to vigorous physical activity, body mass index, health status, mobility limitation, and joint pain in old age, the midlife determinants not being married, primary education, living in a duplex or living in an apartment (vs villa), being obese, and having a heart disease were associated with, on average, 15.3, 12.4, 13.5, 13.3, 21.8, and 38.9 sedentary minutes more per day in old age, respectively.

Conclusions: This study shows that demographic, socioeconomic, and biomedical determinants in midlife were associated with considerably more sedentary time per day in old age. These results can indicate the possibility of predicting sedentariness in old age, which could be used to identify target groups for prevention programs reducing sedentary time in older adults.

During the last years, there has been growing interest in sedentary behavior as a risk factor for adverse health effects, independent of physical activity. Sedentary behavior, such as sitting, lying down, watching TV, and using the computer, has been associated with mortality (8,20,21,23,31) and metabolic and cardiovascular risk factors (2,9,13,18,19,32).

Sedentary time increases with age, and older adults spend up to 80% of their waking time being sedentary (1,6,11,24). It is important to identify determinants that contribute to a (highly) sedentary lifestyle among older adults to prevent this lifestyle and limit its health risks.

Determinants of physical activity and, in particular, moderate to vigorous physical activity (MVPA), such as health status and self-efficacy, have been studied extensively (3). However, sedentary behavior should be viewed as a distinct health behavior that differs from a lack of MVPA, and therefore, its determinants should be independently identified. To date, only a few studies have prospectively examined the determinants of self-reported (7,25) or objectively measured sedentary time (10,14). The number of determinants investigated in these studies was limited, and the study populations were middle age.

To develop prevention programs for sedentary behavior, it is of importance to study a broad spectrum of determinants over the lifetime that affects sedentary time. Therefore, this study examines over a period of three decades the prospective associations between a wide range of midlife determinants (demographic, socioeconomic, lifestyle, biomedical) and objectively measured sedentary time in old age, in a subsample of the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study.

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METHODS

Study Population

For this study, a subsample of the AGES-Reykjavik Study cohort was used. The AGES-Reykjavik Study originates from the Reykjavik Study, which was established in 1967 and comprised a random sample of 30,795 participants born in 1907–1935 and residing in Reykjavik, Iceland (17). Measurements for the Reykjavik Study were conducted in the period between 1967 and 1992. From 2002 to 2006, the measurements for the AGES-Reykjavik Study took place in 5764 participants of the original cohort (17). Measurements for the follow-up AGESII-Reykjavik Study took place in 2007–2011 among 3411 participants. From April 2009 to June 2010, an accelerometry substudy was performed in which 658 participants who did not have severe cognitive dysfunction (Mini-Mental State Examination ≤20) were asked to wear an accelerometer for seven consecutive days (1). After excluding participants who did not record at least one valid day (at least 10 h of monitoring) (n = 15) or had missing data of the measurements during the Reykjavik Study, AGES-Reykjavik Study, or AGESII-Reykjavik Study (n = 78), a total of 565 participants (age 73 to 92 yr) were included in the current analyses (Fig. 1). All participants gave written informed consent.

Figure 1
Figure 1
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The AGES Reykjavik Study was approved by the institutional review boards of the National Institute on Aging, the National Bioethics Committee (VSN: 00-063), and the Data Protection Authority.

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Measurements
Sedentary time in old age.

Sedentary time was assessed using an accelerometer (ActiGraph GT3X, Ft. Walton Beach, FL), which was attached to a belt and was worn on the right hip. Participants were instructed to wear the accelerometer for seven consecutive days and only remove the monitor before going to bed and during showering, bathing, swimming, and other water activities. The triaxial ActiGraph recorded movement on the vertical, anteroposterior, and mediolateral axes. Raw accelerometry data were processed and averaged in minutes using customized software written in MATLAB R2006a (The MathWorks, Inc., Natick, MA) to obtain relevant outcome variables. Nonwear time was defined as any interval ≥60 consecutive minutes of zero counts in all three axes, in which a period up to 2 min of nonzero counts under 100 in the vertical axis was allowed (1). A valid day was defined as at least 10 h of monitoring. The outcome measure used in this analysis was the percentage of wear time minutes per day spent sedentary (0–99 counts per minute (cpm) in the vertical axis).

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Midlife determinants.

A variety of midlife determinants collected as part of the Reykjavik Study were used for the analysis, including demographic characteristics, socioeconomic factors, lifestyle factors, and biomedical factors. Data were collected by questionnaires (detailed medical history and health-related behavior), physical examinations, and laboratory tests.

Demographic characteristics of sex, age, and marital status were derived from a questionnaire. Marital status was categorized as married/not married, with the categories single, widow(er), divorced, and separated collapsed into not married, because of the small numbers in these categories. Because of the large number of individuals without this information (n = 178), this group was included as a separate category.

Socioeconomic factors included level of education, housing type, and occupation, which were derived from a questionnaire. Level of education was categorized into college/university, secondary, and primary. Housing type was categorized into villa, duplex, and apartment, and occupation was categorized into professional work, light work, manual labor, and homemaker.

Lifestyle factors included smoking status, physical activity, active commuting, and occupation activity and were derived from a questionnaire. These factors were categorized into never smoker, previous smoker, and current smoker; active/inactive; active commuting/not active commuting; and for occupational activity “on the move,” standing, and sitting, respectively.

Biomedical factors included body mass index (BMI), weight status, hypertension, cholesterol levels, triglyceride levels, heart disease, type 2 diabetes, arthritis, lung disease, and lung function (FVC, FEV1, FEV1/FVC ratio). Height and weight were measured during the physical examinations, and BMI was calculated as kilogram per meter squared. Weight status was categorized as BMI <25, BMI 25–30, and BMI ≥30. Hypertension was defined as self-reported hypertension, and/or a systolic blood pressure ≥140 mm Hg and/or a diastolic blood pressure ≥90 mm Hg (5). Cholesterol and triglyceride levels in blood were obtained by laboratory tests. The presence of heart disease, diabetes, arthritis, and lung disease were derived from a questionnaire about whether participants had ever sought a doctor or were treated in a hospital for a disease. Lung function was obtained by spirometry. The spirometric reference values were calculated using the NHANES III/Hankinson equations (16), and the FEV1/FVC ratio was calculated using the observed values.

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Covariates.

Covariates were measured using questionnaires and accelerometry in old age as part of the AGESII-Reykjavik Study and included sex, age, follow-up time, daily minutes of MVPA (MVPA ≥2020 cpm), BMI (kg·m−2), health status, mobility limitation, and joint pain. Follow-up time was calculated as the time between midlife measurements and AGES II measurements. Health status was defined as the number of the following conditions: cancer (measured as part of AGESI-Reykjavik Study), arthritis, osteoporosis, Parkinson disease, heart disease, asthma, chronic obstructive pulmonary disease, and depression. Mobility limitation was defined as any difficulty walking 500 m or climbing 10 steps without resting. Joint pain was defined as pain, aching, or stiffness in one or more of the following joints: back (upper, middle, and under), hips, knees, ankles, and toes.

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Statistical analysis.

Descriptive characteristics were summarized as mean and SD or percentages. Chi-square tests and ANOVA were used to examine differences between men and women in the categorical and continuous midlife determinants. Gender differences in the accelerometry variables were examined using ANOVA. Linear regression analysis was used to study whether the midlife determinants predicted the amount of sedentary time in old age. For each determinant, the analyses were run separately, with the determinant as the exposure variable and the percentage of minutes per day spent sedentary as the outcome. All models were adjusted for sex, age, and follow-up time (model 1). Model 2 was additionally adjusted for MVPA (min·d−1) to understand whether the midlife determinants were related to sedentary time in old age independent of MVPA. Model 3 was additionally adjusted for BMI, health status, mobility limitation, and joint pain in old age to exclude the effects of these conditions on sedentary time. Interactions of all midlife determinants with gender were not statistically significant (P > 0.10), and therefore, the main analyses were conducted for men and women together. All analyses were conducted with IBM SPSS Statistics 20.0. Significance level was set at <0.05.

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RESULTS

Descriptive characteristics of the study sample are presented in Table 1. In midlife, women were significantly older than men and were less often married. Women also had a lower educational level, lived more often in an apartment, were less often employed, and reported more being “on the move” during work than men. Women smoked less than men, had a lower BMI, were less overweight, and had less hypertension and lower triglyceride levels (all P < 0.05). Self-reported physical activity and active commuting did not significantly differ between men and women.

Table 1
Table 1
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Table 2 shows the physical activity characteristics assessed by accelerometry of the participants in old age (mean age is 80 yr (SD, 4.7)). Almost 50% of the participants (49.9%) provided seven valid days of data (>10 h of monitoring), with 28.5% providing 6 d; 9.6%, 5 d; 4.1%, 4 d; and 3.9%, 3 d. Participants spent most of their wear time (75%) being sedentary, more than 10 h a day in both sexes. Men were significantly more sedentary than women but recorded also more minutes in MVPA.

Table 2
Table 2
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In Table 3, the results of the linear regression analyses with the midlife determinants of sedentary time are presented. After adjusting for sex, age, and follow-up time (model 1), level of education, living in an apartment, current smoker status, BMI, obesity (BMI ≥30), heart disease, and lung function (FVC and FEV1) were significantly associated with a higher amount of sedentary time per day in old age. After additionally adjusting for minutes of MVPA (model 2), primary education, living in a duplex or apartment, BMI, obesity, heart disease, and FVC remained statistically significant associated with more sedentary time. These variables except BMI and FVC remained significantly associated with a higher percentage of sedentary time after adjusting for BMI, health status, mobility limitation, and joint pain in old age (model 3). Not being married was also significantly associated with more sedentary time in this model.

Table 3
Table 3
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The percentages of sedentary time (B) shown in Table 3 correspond with an average of 15.3 (95% CI, 0.3–30.4) sedentary minutes more per day in old age for participants who were not married at midlife compared with those who were married; 12.4 (0.7–24.0) sedentary minutes more for participants with primary education compared with college/university educated; 13.5 (2.6–24.5) and 13.3 (4.4–22.2) sedentary minutes more for participants living in a duplex or apartment, respectively, compared with those living in a villa; 21.8 (5.4–38.2) sedentary minutes more for obese participants compared with those with BMI ≤25; and 38.9 (0.9–76.9) sedentary minutes more for participants with a heart disease.

In additional analyses, weight status and heart disease were further adjusted for marital status, level of education, and housing type at midlife; this did not alter the results (data not shown).

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DISCUSSION

To our knowledge, this is the first study that examined the prospective associations between midlife determinants from four different domains (demographic, socioeconomic, lifestyle, and biomedical) and objectively measured sedentary time in old age over a period of three decades. The midlife determinants marital status, level of education, housing type, weight status, and heart disease were statistically significantly associated with a higher amount of sedentary time per day in old age, even after adjusting for sex, age, follow-up time, MVPA, BMI, health status, mobility limitation, and joint pain.

The demographic factor not being married and the socioeconomic factors, primary education, living in a duplex, or living in an apartment, were associated with, on average, 15.3, 12.4, 13.5, and 13.3 min more sedentary time in old age, respectively. Smoking at midlife was the only lifestyle factor that was associated with sedentary time, but exclusively in the unadjusted model. Spending most working hours sitting was not significantly associated with more sedentary time in old age. Finally, two biomedical factors, being obese and having a heart disease, were associated with, on average, 21.8 and 38.9 min more sedentary time per day in old age.

To date, only a few studies have prospectively examined the association of determinants of objectively measured sedentary time. A study among middle-age participants (mean, 54 yr) investigated associations between two demographic factors (age and sex), two socioeconomic factors (level of education and employment grade), two lifestyle factors (smoking status and self-reported physical activity), two biomedical factors (BMI and general perceived health), and sedentary time and found that age and education were associated with sedentary time after 13 yr of follow-up (14). Ekelund et al. (10) examined the associations between sedentary time and biomedical factors (body weight, BMI, fat mass, and waist circumference) in middle-age participants (mean, 49 yr). After 5.6 yr of follow-up, body weight, BMI, fat mass, and waist circumference predicted an increase in the amount of sedentary time.

BMI was also described as a determinant of a sedentary lifestyle in a study that measured BMI and a sedentary lifestyle at the ages of 41, 44, 46 and 54 yr, although sedentariness was not measured objectively in that study (25). Obesity was prospectively associated with self-reported time spent watching TV per week in a cohort of middle-age active employees (29). Ding et al. (7) examined individual, social, and environmental correlates of change in self-reported TV viewing time, which was used as a measure for sedentary time. They described associations between education, self-reported physical activity, living in a low-walkable neighborhood, and TV viewing time after 4 yr of follow-up in a population age 20–65 yr.

Recently, several studies have been conducted in which determinants of self-reported physical inactivity were examined (4,12,22,28). However, sedentary behavior should be assumed as a distinct health behavior with its own nature and physiology, which differs from a lack of physical activity and should therefore be studied independently (15,27,30). More studies examining the determinants of sedentary behavior are warranted to better advise the development of public health guidelines and prevention programs, as emerging evidence shows the adverse health consequences of sedentary behavior (26).

A major strength of the present study was the objective measurement of sedentary time with accelerometry in a large sample of older adults of which 92% provided at least four valid days. The amount of sedentary time (75%) presented in this study is comparable with the amounts described in other studies with older adults, which also used the same cut point to define sedentary time (6,11,24). Using a hip-worn accelerometer to define sedentary time may, however, not accurately separate sitting time from standing time. Another strength of this study was adjustment for important confounders such as health status, mobility limitation, and joint pain in the final analyses, which exclude the possibility that these factors account for or contribute to a higher amount of sedentary time, although this could have resulted in overadjustment, as the confounders may be part of the pathway between the midlife determinants and sedentary time or could be a result of sedentary time. Furthermore, the longitudinal design of the study with a follow-up time of approximately 31 yr is an important strength.

Some limitations of the study should also be considered. First, the midlife data were collected between 1967 and 1992, although this limitation was in part addressed by adjusting all analyses for follow-up time. Furthermore, the determinants could easily have changed during that period. The measurements in midlife were predominantly collected using questionnaires, which could possibly have resulted in misclassification of the lifestyle or biomedical factors.

Second, the associations between the biomedical factors and the amount of sedentary time are possibly bidirectional because, for example, obesity leads to a more sedentary lifestyle, but more sedentary time could also increase BMI. In this study, sedentary time was measured exclusively in old age; therefore, it might be possible that obesity or heart disease resulted from a more sedentary lifestyle. However, an extra analysis in which these factors were additionally adjusted for physical activity in midlife (a proxy for sedentary time in midlife) did not change the results (data not shown). Furthermore, studies have shown that BMI at baseline predicted sedentary time at follow-up although the reverse association was not established (10,25). These findings support the hypothesis that the direction of the association is mainly from the biomedical factors to sedentary time.

Third, although this study adjusted for a broad spectrum of confounders, it is possible that some unmeasured confounders, such as psychosocial factors (anxiety, isolation), account for some of the presented associations, which could be explored in further research. Also, this study was conducted in a relatively healthy subsample of the AGESII-Reykjavik Study, and therefore, the results might not be representative for the general older population. Finally, because sedentary time was collected for 1 wk, the data may not truly reflect habitual behavior.

To conclude, this study shows that midlife determinants across several domains, demographic and socioeconomic factors—not being married, lower educational level, and poorer housing—and biomedical factors, including being obese and having a heart disease, were associated with considerably more sedentary time per day in old age. These results can indicate the possibility of predicting sedentariness in old age, years before this behavior manifests. This information could be used to identify target groups for prevention programs aimed at reducing sedentary time and decreasing the risk of sedentary-related adverse health effects.

The researchers are indebted to the participants for their willingness to participate in the study.

This study has been funded by the National Institutes of Health contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). A Koster has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2011-CIG) under grant agreement PCIG09-GA-2011-293621.

The authors report no conflicts of interest.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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Keywords:

ACCELEROMETRY; SEDENTARY LIFESTYLE; OLDER ADULTS; LONGITUDINAL STUDIES; SOCIOECONOMIC FACTORS; BIOMEDICAL FACTORS

© 2014 American College of Sports Medicine

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