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. 2019 Sep 12;17(3):321–330. doi: 10.1007/s10433-019-00532-y

Sitting time and associated factors among Portuguese older adults: results from Nutrition UP 65

Ana Sofia Sousa 1,2,3, Joana Mendes 1,4,5,✉, Rita S Guerra 1,2,6, Patrícia Padrão 1,7, Pedro Moreira 1,7,8, Alejandro Santos 1,5, Nuno Borges 1,9, Cláudia Afonso 1,7, Cátia Martins 10, Graça Ferro 11, Teresa F Amaral 1,6
PMCID: PMC7458983  PMID: 32904787

Abstract

Older adults are particularly susceptible to sedentary behaviours. Sitting time has been increasingly referred to as a potentially modifiable risk factor in the prevention of chronic diseases. Identifying factors associated with sitting time, particularly those that are modifiable, will allow for more effective public health strategies. This study aimed to describe sitting time among Portuguese older adults and to evaluate associated factors. A cross-sectional study including 1423 older adults ≥ 65 years old was conducted. Sitting time was assessed with the International Physical Activity Questionnaire. Socio-demographic, health, anthropometric and functional variables were collected. Bivariate and multivariate linear regression models were conducted to study the association between these variables and sitting time. The median of sitting time was 300 min/day (interquartile range = 240 min/day), which is equal to 5 h/day. The following factors were directly associated with longer sitting time: being male, age ≥ 80 years, living in Central or Southern Portugal, being retired from work, as well as presenting low physical activity, obesity or longer time to walk 4.6 metres. On the other hand, being married, having higher education and higher household income were inversely associated with longer sitting time. It can be concluded that Portuguese older adults spend a considerable amount of time sitting per day. Potentially modifiable risk factors associated with longer sitting time in this population were related to nutritional status and functional ability.

Keywords: Healthy lifestyle, Older adults, Sedentary behaviour, Time to walk

Introduction

The increasing ageing of the world’s population and the associated negative outcomes, such as the sedentary behaviour, are currently regarded as emerging public health concerns (Owen et al. 2010). Therefore, the evidence for the importance of healthy lifestyles is now overwhelming, particularly in older adults (Harvey et al. 2015). Identifying factors associated with sedentary behaviours in older adults, in particular those that are modifiable, will allow the development of effective strategies to maintain sedentary time within healthy limits.

Sedentary behaviour is distinct from insufficient physical activity, defined by prolonged periods of sitting during activities that entail low levels of energy expenditure (≤ 1.5 metabolic equivalents, METs) performed in a sitting or reclining posture (Tremblay 2012). This behaviour represents on average 65–80% of the waking day of older adults (Harvey et al. 2015) who spent more than 8.5 h/day sitting in some cases (Harvey et al. 2013).

In people aged ≥ 65 years, sitting time has been consistently identified as an independent risk factor for all-cause mortality (de Rezende et al. 2014; Matthews et al. 2012). In older adults, sitting time has been associated with depression, as well as with high waist circumference, overweight and obesity, metabolic syndrome and frailty (de Rezende et al. 2014; Matthews et al. 2012). Thus, sitting time has been increasingly referred to as a potentially modifiable factor in the prevention of chronic diseases and in the improvement of functional ability (Owen et al. 2014). However, regarding the association between functional indicators and sitting time, information is scarce and further research is required. Functional indicators refer to objective measures able to reflect any body function or activity, such as the muscle strength or gait performance (Beaudart et al. 2019). In this context, time to walk is a comprehensive measure of mobility, frequently used in the assessment of functional ability of older individuals (do Carmo Correia de Lima et al. 2019; Studenski et al. 2011), but its association with sitting time is unknown.

As far as Portugal is concerned, knowledge regarding sitting time and associated factors in older adults is absent. Therefore, the present study aimed to describe sitting time among Portuguese older adults and to study factors associated with this sedentary behaviour.

Methods

Study design and sampling

A cross-sectional study was conducted in Portugal in a sample of participants ≥ 65 years old as part of the Nutrition UP 65 project (Amaral et al. 2016).

A random, stratified and cluster sampling method was applied. In order to obtain a representative sample of the country in terms of sex, age and education level, the structure of the Portuguese population, defined in the Nomenclature of Territorial Units for Statistical purposes—NUTS II, was considered. The number of older adults in each strata of region, considering the structure of the Portuguese population in terms of sex, age and education level, was ascertained to fulfil a sample size of 1500 older adults in order to obtain a representative sample. In each regional area, three or more town councils with > 250 inhabitants were randomly selected. The potential participants were contacted directly via town councils and parish centres (Amaral et al. 2016).

Eligibility criteria included being ≥ 65 years old and having only Portuguese nationality with current tax residence in Portugal. Older age was defined as the chronological age of 65 years and older, since it is a definition used in the most developed countries (WHO 2019c). As sitting time was a self-reported variable, individuals presenting cognitive impairment and whose caregiver did not know the answer, were excluded from the analysis (n = 73, 4.9%). Moreover, 4 (0.3%) individuals did not report sitting time; therefore, a total of 1423 (94.9%) older adults were included in the present analysis.

Data collection and variable definition

Data were collected using a structured questionnaire administered by eight registered nutritionists, who were specifically trained in this study for the application of the questionnaires and for the anthropometric measurements.

Socio-demographic data included information on sex, date of birth, marital status, regional area, residence type, education, professional activity and household income.

Age was dichotomized: 65–79 years and ≥ 80 years (Ansah et al. 2015). Marital status was dichotomized as “single, divorced or widowed” and as “married or in a common-law marriage”. The regional areas were defined according to the Nomenclature of Territorial Units for Statistical (NUTS II): North, Centre, Lisbon metropolitan area (Centre), Alentejo (South), Algarve (South), Madeira (islands) and Azores (islands). The residence type was defined as home or care home. Educational level was determined by the number of completed school years, and the following categories were used: no formal education, 1–4 years of education, ≥ 5 years of education. Professional activity was classified in active or inactive. Professionally inactive was defined as being retired from work. Household income was summarized using the following categories: < 500 €, 500–999 € or ≥ 1000 €. From all the included participants, 722 (50.7%) did not know or preferred not to declare their income and thus, they were allocated in a separate category (Amaral et al. 2016).

Health, anthropometric and functional variables included cognitive status, self-perception of health status, body mass index, physical activity and time to walk 4.6 metres.

The Mini-Mental State Examination (MMSE) is a 30-point questionnaire that is extensively used in clinical and research settings to measure cognitive impairment (Folstein et al. 1975). Originally, the value 23/24 was proposed as a universal cut-off for cognitive deterioration/dementia (O’ Connor et al. 1989). This cut-off value is still used in many countries, which negatively affects the classification of the older adults and individuals with low level of literacy, with an opposite effect in individuals with high level of education (Moraes et al. 2010). There is a consensus in the literature indicating that the performance in this test is strongly influenced by several demographic variables, particularly by literacy (Anderson et al. 2007; Matallana et al. 2011). In this sense, Guerreiro el al. developed the adaptation and validation of the original questionnaire for the Portuguese population, considering different levels of education (2010). These validated cut-off scores were used to identify cases of cognitive impairment in the present study as follows: individuals with no education, ≤ 15 points; 1–11 years of school completed, ≤ 22 points; and > 11 years of school completed, ≤ 27 points (Guerreiro 2010).

Self-perception of health was classified as very good/good, moderate, bad/very bad according to a question from the questionnaire used on the Nutrition UP 65 study (Amaral et al. 2016) that was based on SF-12: Surveys to Measure Mental and Physical Health (Ware et al. 1995): “In general, how do you consider your health condition?”

Physical activity levels were assessed by the short form of the International Physical Activity Questionnaire (IPAQ), which covers activities performed during the 7 days prior to the interview (Craig et al. 2003). An original version of IPAQ has been translated into Portuguese to be used in the Portuguese National Health Survey 2005–2006. There are many different ways to analyse physical activity data, but to date there is no consensus on a correct method for describing levels of activity based on self-report surveys. However, IPAQ is being used as an evaluation tool in several intervention studies and was demonstrated that it is a valid and reliable instrument for measuring physical activity in older adults (Rubio Castañeda et al. 2017; Van Holle et al. 2015). Data collected with IPAQ were then converted to MET-minutes (min). Median values were calculated for walking, moderate-intensity activities and vigorous-intensity activities using established formulas. Total physical activity MET-min/week was defined as the sum of walking + moderate + vigorous MET-min/week scores. Kilocalories were computed from MET-min/week scores (Craig et al. 2003). According to Fried el al. (Fried et al. 2001), older men with physical activity per week < 383 kcal and older women with physical activity per week < 270 kcal present low levels of physical activity associated with risk of frailty. Based on this evidence, physical activity levels below those cut-off points were also reported as low in the present study. Consequently, men with physical activity per week ≥ 383 kcal and women with physical activity per week ≥ 270 kcal were classified as presenting normal physical activity levels.

Sitting time, in minutes, was self-reported from the IPAQ question: “How much time, in a regular day, do you spend sitting? This may include the time you spent at a desk, talking with friends, reading, studying or watching TV” (Craig et al. 2003). Sitting time per day was also categorized in tertiles according to the distribution of the sample: 1st tertile: ≤ 230 min; 2nd tertile: (230–360) min and 3rd tertile: ≥ 361 min. Participants with the same value of sitting time were grouped together into the same tertile of distribution; therefore, the number of cases may be different across the three tertiles.

Anthropometric measurements were collected following standard procedures (Stewart et al. 2011). Standing height was obtained with a calibrated stadiometer (Seca 213, Germany) to the nearest 0.1 cm. For participants with visible kyphosis or when it was impossible to measure standing height due to paralysis, mobility or balance limitations, height was obtained indirectly from non-dominant hand length (in centimetres), measured with a calibrated paquimeter (Fervi Equipment, Italy) to the nearest 0.1 cm (n = 37) (Guerra et al. 2014). Body weight (in kilograms) was measured with a calibrated portable electronic scale (Seca 803, Germany) to the nearest 0.1 kg, with the participants wearing light clothes. When it was not possible to weigh a participant, for the same reasons that standing height measurement was unable to be obtained, body weight was estimated from mid-upper arm circumference (MUAC) and calf circumference (CC), (n = 17), through the following equations: (MUAC*1.63) + (CC*1.43) − 37.46, for women, and (MUAC*2.31) + (CC*1.50) − 50.10, for men (Chumlea et al. 1988). Body mass index (BMI) was calculated by the formula: weight (kg)/[height (m)]2 and classified according to the World Health Organization criteria (WHO 2019a).

The time for each participant to walk a distance of 4.6 m on a flat and unobstructed path was registered in seconds (s) (Wang et al. 2012). Several variations in the gait speed measuring procedures have been found in the literature, such as measuring over different distances. However, both Studenski et al. (2011) and Fried et al. (2001) reported a distance of 4.6 m to calculate the speed, dividing the distance by time. This work was used as reference in the present study; particularly, it was based on adults aged 65 years and older. A stopwatch with a resolution of 0.01 s (School Electronic Stopwatch, Dive049, Topgim, Portugal) was used to register the walking time. Normal time to walk 4.6 metres was defined as < 7 s for height ≤ 173 cm (men) and ≤ 159 cm (women), and as < 6 s for height > 173 cm (men) and > 159 cm (women) (Fried et al. 2001).

Ethics

The study protocol was approved by the Ethics Committee of Social Sciences and Health from Faculty of Medicine of University of Porto (nº PCEDCSS—FMUP 15/2015) and by the Portuguese National Commission of Data Protection (nº 9427/2015). This research was conducted according to the Declaration of Helsinki recommendations. Individuals without cognitive impairment were asked to read and sign a duplicated informed consent form. If the participant was considered to have cognitive impairment, two legally authorized representatives were asked to read and sign the duplicated informed consent form.

Statistical analysis

Socio-demographic and health variables of Portuguese older adults were presented according to tertiles of sitting time per day. Categorical variables were summarized as counts and proportions and compared using the Chi-square test. The normality of the distribution regarding quantitative variables was evaluated through Kolmogorov–Smirnov test. Medians and interquartile range (IQR) were presented for variables without normal distribution, which were compared through the Kruskal–Wallis test.

A linear regression was conducted to estimate the association of socio-demographic and health variables with sitting time that was used as the dependent variable (continuous). The following independent variables were considered: sex (dichotomous), age (dichotomous), marital status (dichotomous), regional area (categorical), residence (dichotomous), education (categorical), professional activity (dichotomous), household income (categorical), cognitive status (dichotomous), self-perception of health (categorical), physical activity (dichotomous), BMI (categorical) and time to walk 4.6 metres (dichotomous). Bivariate and multivariate linear regression models were conducted using the enter method. Through bivariate analysis, each variable was put separately into the linear regression, and in the multivariate analysis, the variables were all analysed together. In the multivariate analysis, sex and age were included in the model as they could be potential confounders and the remaining variables were included in the model as they were considered as potential predictors of longer sitting time. Results were considered significant when p < 0.05. The whole statistical analysis was carried out with IBM SPSS Statistics (version 24.0).

Results

Characteristics of the 1423 participants according to tertiles of sitting time are presented in Table 1. Almost 60% of the participants were women (57.5%). Age ranged between 65 and 100 years, with a median of 74 years (IQR: 11 years). The median of sitting time was 300 min/day (IQR: 240 min/day), which is equals 5 h/day.

Table 1.

Socio-demographic and health variables of Portuguese older adults, according to tertiles of sitting time per day

Sitting time per day (n = 1423)
First tertile ≤ 230 min. Second tertile (231–360) min. Third tertile ≥ 361 min. p
(n = 441) (n = 515) (n = 467)
Socio-demographic variables
 Sex, n (%)
  Women (n = 818) 245 (30.0) 280 (34.2) 293 (35.8) 0.018
  Men (n = 605) 196 (32.4) 235 (38.8) 174 (28.8)
 Age (years), n (%)
  (65–79) (n = 1067) 377 (35.4) 409 (38.3) 281 (26.3) < 0.001
  ≥ 80 (n = 356) 64 (18.0) 106 (29.8) 186 (52.2)
 Marital statusa, n (%)
  Married or common-law marriage (n = 683) 267 (39.1) 267 (39.1) 149 (21.8) < 0.001
  Single, divorced or widowed (n = 739) 173 (23.4) 248 (33.6) 318 (43.0)
 Regional area, n (%)
  North (n = 463) 163 (35.2) 172 (37.1) 128 (27.7) < 0.001
  Central (n = 362) 93 (25.7) 103 (28.5) 166 (45.8)
  Lisbon metropolitan area (Central) (n = 356) 133 (37.4) 133 (37.4) 90 (25.2)
  Alentejo (South) (n = 132) 26 (19.7) 63 (47.7) 43 (32.6)
  Algarve (South) (n = 60) 16 (26.7) 18 (30.0) 26 (43.3)
  Madeira (islands) (n = 30) 6 (20.0) 14 (46.7) 10 (33.3)
  Azores (islands) (n = 20) 4 (20.0) 12 (60.0) 4 (20.0)
 Residence type, n (%)
  Home (n = 1360) 428 (31.5) 496 (36.5) 436 (32.0) 0.015
  Care home (n = 63) 13 (20.6) 19 (30.2) 31 (49.2)
 Education (years), n (%)
  0 (n = 201) 33 (16.4)) 52 (25.9) 116 (57.7) < 0.001
  (1–4) (n = 979) 310 (31.7) 355 (36.3) 314 (32.0)
  ≥ 5 (n = 243) 98 (40.3) 108 (44.5) 37 (15.2)
 Professional activityb, n (%)
  Inactive (n = 1389) 425 (30.6) 498 (35.9) 466 (33.5) < 0.001
  Active (n = 30) 16 (53.3) 14 (46.7) 0
 Household income (€), n (%)
  < 500 (n = 232) 48 (20.7) 83 (35.8) 101 (43.5) <0.001
  (500–999) (n = 297) 123 (41.4) 112 (37.7) 62 (20.9)
  ≥ 1000 (n = 172) 85 (49.5) 62 (36.0) 25 (14.5)
  Does not know or does not declare (n = 722) 185 (25.6) 258 (35.7) 279 (38.7)
Health variables
 Cognitive performancec, n (%)
  Without impairment (n = 1398) 437 (31.3) 508 (36.3) 453 (32.4) 0.039
  Impairment (n = 25) 4 (16.0) 7 (28.0) 14 (56.0)
 Self-perception of healthd, n (%)
  Very good/good (n = 453) 167 (36.9) 163 (36.0) 123 (27.1) < 0.001
  Moderate (n = 704) 219 (31.1) 276 (39.2) 209 (29.7)
  Bad/very bad (n = 262) 53 (20.2) 74 (28.2) 135 (51.6)
 Physical activity (kcal/week), median (IQR) 3062 (4826) 1964 (3514) 749 (2154) < 0.001
 Physical activity levels (kcal/week)e,f, n (%)
  Normal (n = 1184) 420 (35.5) 459 (38.8) 305 (25.7) < 0.001
  Low (n = 237) 21 (8.9) 55 (23.2) 161 (67.9)
 Body mass index (kg/m2), median (IQR)
  Women 28.4 (5.9) 29.5 (6.0) 30.5 (7.2) < 0.001f
  Men 27.9 (4.8) 28.0 (4.7) 28.8 (6.0) < 0.036f
 Body mass indexg,h, n (%)
  Underweight (n = 3) 2 (66.7) 0 1 (33.3) <0.001
  Normal (n = 230) 90 (39.1) 73 (31.7) 67 (29.2)
  Overweight (n = 635) 209 (32.9) 250 (39.4) 176 (27.7)
  Obesity (n = 553) 140 (25.3) 192 (34.7) 221 (40.0)
 Time to walk 4.6 m (s), median (IQR)
  Women 4.7 (1.9) 5.3 (2.7) 7.3 (4.0) < 0.001
  Men 4.3 (1.7) 4.7 (1.9) 5.9 (3.2) < 0.001
 Time to walk 4.6 mi,j, n (%)
  Normal (n = 1012) 382 (37.7) 403 (39.8) 227 (22.5) < 0.001
  High (n = 379) 50 (13.2) 104 (27.4) 225 (59.4)
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Participants with the same value of sitting time were grouped together into the same tertile of distribution; therefore, the number of cases is different across the three tertiles

IQR interquartile range, Min minutes. Values may not add up 100.0% due to rounding up

aMissing data points: n = 1; b missing data points: n = 4; c participants were considered to have no cognitive impairment with a score of Mini-Mental State Examination above 15 if they had no formal education, above 22 if they had ≤ 11 years of education, and above 27 if they had > 11 years of education; d missing data points: n = 4; e missing data points: n = 2; f normal physical activity levels were defined as ≥ 383 kcal/week (men) and ≥ 270 kcal/week (women); low physical activity levels were defined as < 383 kcal/week (men) and < 270 kcal/week (women); g missing data points: n = 2; h body mass index was categorized as underweight (< 18.50 kg/m2); normal (18.50–24.99 kg/m2); overweight (25.00–29.99 kg/m2) and obesity (≥ 30 kg/m2); i missing data points: n = 32; j normal time to walk was defined as < 7 s for height ≤ 173 cm (men) and height ≤ 159 cm (women); and as < 6 s for height > 173 cm (men) and height > 159 cm (women)

Participants in the highest tertile of sitting time (≥ 361 min/day) were more likely to be older, unmarried, present lower education level and have lower household income, compared to those in the lowest tertile (Table 1). Participants in the lowest tertile of sitting time (≤ 230 min/day) were more likely to be physically active and have a lower BMI, compared to participants in the highest tertile (Table 1). The time to walk 4.6 metres increased according to tertiles of sitting time, for both women and men (Table 1). The proportion of participants with cognitive impairment and with bad/very bad self-perception of health status was higher in cases of longer sitting time (third tertile) (Table 1).

Results from the linear regression analysis are displayed in Table 2. Through the multivariate model of the linear regression, after adjustment for potential confounders, it was observed that male sex, age ≥ 80 years, living in Central or Southern (Alentejo) region of Portugal, being professionally inactive, presenting low physical activity levels, obesity or longer time to walk 4.6 metres were factors directly associated with longer sitting time. On the other hand, being married, having higher education level and higher household income were factors inversely associated with longer sitting time.

Table 2.

Factors associated with sitting time in Portuguese older adults by linear regression analysis (bivariate and multivariate models)

Variables Bivariate model
Regression coefficient (95% CI)		 p Multivariate model
Regression coefficient1 (95% CI)		 p
Male sex (reference: female sex) − 0.06 (− 0.11; − 0.01) 0.031 0.05 (0.02; 0.10) 0.040
Age ≥ 80 years [reference: (65–79 years)] 0.25 (0.20; 0.30) < 0.001 0.09 (0.04; 0.13) 0.001
Marriage or in common-law marriage (reference: single, divorced or widowed) − 0.24 (− 0.30; − 0.19) < 0.001 − 0.11 (− 0.16; − 0.06) <  0.001
Regional area (reference: North)
 Central 0.16 (0.11; 0.21) < 0.001 0.11 (0.06; 0.16) < 0.001
 Lisbon metropolitan area (Central) − 0.09 (− 0.14; − 0.04) < 0.001 − 0.02 (− 0.07; 0.04) 0.548
 Alentejo (South) 0.03 (− 0.02; 0.08) 0.268 0.08 (0.03; 0.12) 0.002
 Algarve (South) 0.06 (0.01; 0.11) 0.028 0.03 (− 0.02; 0.08) 0.256
 Madeira (island) 0.01 (− 0.05; 0.06) 0.856 0.04 (− 0.01;0.09) 0.055
 Açores (island) − 0.02 (− 0.07; 0.03) 0.455 0 (− 0.05; 0.05) 0.993
Living in care homes (reference: living at home) 0.10 (0.04; 0.15) < 0.001 − 0.02 (− 0.07; 0.03) 0.410
Education (years), (reference: no formal education)
 (1–4) − 0.04 (− 0.09; 0.02) 0.160 − 0.09 (− 0.16; − 0.03) 0.003
 ≥ 5 − 0.16 (− 0.21; − 0.11) < 0.001 − 0.11 (− 0.18; − 0.05) 0.001
Professionally inactive (reference: professionally active) 0.10 (0.05; 0.15) < 0.001 0.06 (0.02; 0.11) 0.008
Household income (€), (reference: household income < 500)
 (500–999) − 0.14 (− 0.19; − 0.09) 0.026 − 0.09 (− 0.15; − 0.03) 0.003
 ≥ 1000 − 0.16 (− 0.22; − 0.11) < 0.001 − 0.06 (− 0.12; − 0.01) 0.047
 Not known or not declared 0.14 (0.09; 0.19) < 0.001 0.02 (− 0.05; 0.08) 0.586
Cognitive impairment (reference: no cognitive impairment) 0.08 (0.02; 0.13) 0.004 0.01 (− 0.03; 0.06) 0.571
Self-perception of health status (reference: very good or good)
 Moderate − 0.06 (− 0.11; − 0.01) 0.020 − 0.03 (− 0.08; 0.03) 0.316
 Bad or very bad 0.20 (0.15; 0.25) < 0.001 0.05 (− 0.03; 0.10) 0.064
Low physical activity levels (reference: normal physical activity levels) 0.36 (0.31; 0.41) < 0.001 0.24 (0.19; 0.29) < 0.001
Body mass index (reference: normal)
 Overweight − 0.09 (− 0.15; − 0.04) < 0.001 0.03 (− 0.03; 0.10) 0.287
 Obesity 0.14 (0.09; 0.19) < 0.001 0.10 (0.04; 0.17) 0.001
High time to walk 4.6 m (reference: normal) 0.38 (0.33; 0.43) < 0.001 0.20 (0.15; 0.26) < 0.001
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Dependent variable: sitting time (min). In the multivariate analysis, sex and age were included in the model as they could be potential confounders and the remaining variables were included in the model as they were considered as potential predictors of longer sitting time.1 Standardized coefficients. CI: confidence interval

Discussion

The present study provided information about sitting time and associated factors in older adults that may allow the development of effective strategies to maintain sedentary time within healthy limits in the future.

Portuguese older adults presented a median of 5 h/day sitting, approximately the same reported among older adults living in Spain (4 h/day) (Balboa-Castillo et al. 2011). These results are in line with a systematic review estimating that almost 60% of older adults present a sitting time of more than 4 h/day (Harvey et al. 2013). However, in the literature, sitting time of older adults varies from 2 h/day, in the case of people living in Japan (Kikuchi et al. 2013), to 9 h/day in older women living in America (Barreira et al. 2016). These differences can be explained by the sedentary lifestyle, which is more prevalent in North America, followed by Europe. On the other hand, people living in Japan grow up with physical activity as a part of the daily routine more so than in the USA (WHO 2019b). In addition, Japan has one of the lowest rates of obesity, while the USA has the highest rates of obesity in the world, and a higher proportion of obese individuals which may explain a longer sitting time (Hawks et al. 2003). In the present study, approximately 80% of older adults presented overweight or obesity which probably increased the sitting time of this older Portuguese population.

In general, sitting time is an increasingly common practice among older adults (Harvey et al. 2015). Moreover, it has been shown that if sitting time was restricted to < 3 h/day, it would increase life expectancy by 0.2 years (Rezende et al. 2016).

The study of factors associated with sitting time is also relevant because available evidence suggests that not all forms of sitting have similar health outcomes. For example, sitting time focused on TV viewing seems to be more consistent with cardiometabolic risk biomarkers than reading, gaming, using a computer or socializing (Wijndaele et al. 2010). Adults and older adults who spend more than 2 h/day watching TV have a 20% increased risk of developing type 2 diabetes and a 15% increased risk of developing cardiovascular disease, regardless of the time spent performing physical activities of moderate–vigorous intensity (Grøntved and Hu 2011). Ekelund et al. also observed that physical activity of moderate intensity performed 60–75 min/day eliminated the negative effect of long overall sitting time, but only attenuated the risks associated with extended TV-viewing time (2016). This is a cause of concern, particularly in a geriatric context, since sitting at home watching television is the most common leisure time described by older adults (Palmer et al. 2018).

The previous studies have shown that indicators of cognitive and nutritional status were associated with sitting time (de Rezende et al. 2014; Matthews et al. 2012). Therefore, variables such as the cognitive performance, levels of physical activity and body mass index, as well as some socio-demographic variables, were considered as potential predictors of longer sitting time in the present study and are detailed below.

In the present study, a positive association between old age and longer sitting time was observed, and according to Milanović et al. individuals tend to be less active with advancing age (2013). Male sex was also independently associated with longer sitting time, although men are generally believed to be more physically active than women (Abel et al. 2001). On the other hand, a study that examined sex differences in patterns of physical activity among older adults, showed that women presented higher physical activity (Amagasa et al. 2017; Matthews et al. 2008). It is known that although men spend more time in vigorous physical activity than women, they also spend more time in sedentary activities (Amagasa et al. 2017). In addition, older men living in Thailand presented 1.65 times higher odds of having longer total sedentary time compared to women (Chang et al. 2018). Present results are in line with this evidence.

In relation to marital status, being married was inversely associated with longer sitting time, compared to single, divorced or widowed status. Several explanations have been proposed through which marital status can affect health behaviours, since sharing habits and responsibilities can influence how people spend their leisure time (Huffman and Szafron 2017). Other studies report that sedentary activities, particularly time spent watching television, are higher among adults who are single (Van Cauwenberg et al. 2014), and that marriage can act as a protective factor against unhealthy behaviour (Huffman and Szafron 2017).

Sitting time also varied according to each regional area. Living in Alentejo region was significantly associated with longer sitting time. These results can be explained by different living conditions and lifestyles according to the different geographical locations of Portugal (Statistics Portugal 2019).

In general, a higher education and household income of participants were inversely associated with longer sitting time. The level of education and income are factors that strongly affect the health status of populations (Farrell et al. 2014). A systematic review presented an inverse association between the level of education and time spent in sedentary activities (Chastin et al. 2015). According to Farrell et al., both low education and low household income are strongly associated with inactivity, particularly in older adults ≥ 85 years old, even when controlling for several related factors (Farrell et al. 2014).

The majority of the participants reported normal levels of physical activity according to the recommended cut-off points (Fried et al. 2001). However, information about the variation is not available. Being retired from work and presenting low physical activity levels were directly associated with longer sitting time. It is expected that with advancing age people become professionally inactive; nevertheless, it was observed in another study that those who have an occupational activity through volunteering presented lower sitting time. This evidence may justify the associations observed in the present study regarding professional inactivity, low physical activity levels and longer sitting time.

Regarding health variables, the study of their association with sitting time is particularly relevant because they are potentially modifiable. Obesity was directly associated with longer sitting time in the present sample, and this association was also found by other authors. According to Bullock et al., adults with a sitting time ≥ 8 h/day had 62% higher odds of being obese compared to those with a sitting time < 4 h/day, after adjustment for physical activity (Bullock et al. 2017). In another study, longer sitting time in adults was associated with abdominal obesity in all analysed body mass index groups (Suliga et al. 2018). Among older adults, individuals who sat ≥ 5 h/day presented higher odds to have obesity and abdominal obesity compared to those who sat < 5 h/day (Sohn et al. 2017).

In the present sample, a longer time to walk 4.6 metres was also directly associated with longer sitting time, suggesting a relation between physical function and sitting time. The time to walk can be easily performed in clinical settings and is highly predictive of negative events (Beaudart et al. 2019); therefore, it was chosen as the functional indicator to be studied in relation to sitting time. On the other hand, no association was found between physical function and sitting time in another study (Reid et al. 2018), which can be due to its smaller sample size (n = 123 community-dwelling older adults). However, longer sitting time was associated with lower proportion of muscle mass and with lower odds of having pre-sarcopenia in community-dwelling adults (Reid et al. 2018). Sarcopenia is associated with deterioration of physical function (Johnson Stoklossa et al. 2017); consequently, the impact of time sitting on the functional ability of older people should be further explored.

Several countries have issued recommendations to reduce sitting time as part of their national physical activity guidelines for older adults; however, much remains to be done for recommendations of the World Health Organization to be fully reflected in national documents across all parts of Europe and all age groups. In addition, recommendations for avoiding extended periods of inactivity were addressed only by a minority of countries (Kahlmeier et al. 2015).

This was the first study conducted in Portugal about factors associated with sitting time in older adults, providing a necessary step to develop effective interventions in public health. Living conditions and lifestyles differ according to cultural customs and geographical location of populations, but the majority of studies about the sedentary behaviour-related causes have been performed in non-European populations (Rezende et al. 2016).

Self-reported sitting time involves an easy and inexpensive data collection and does not alter the habitual behaviour of participants. However, it can be a study limitation if the sitting time is underestimated or overestimated, and objective measures should also be used in future studies. The description of sitting time in domains was not explored in the present study, such as TV viewing, reading or sitting in cars; however, it should be considered in future research since these domains have different impacts on the health of individuals (Wijndaele et al. 2010).

It can be concluded that Portuguese older adults spend a considerable amount of time sitting per day. Potentially modifiable risk factors associated with longer sitting time in this population were related to nutritional status and functional ability.

Funding

Nutrition UP 65 is 85% funded by Iceland, Liechtenstein and Norway through European Economic Area (EEA) Grants (Grant No. PT06) and 15% by Faculdade de Ciências da Nutrição e Alimentação, Universidade do Porto. Iceland, Liechtenstein and Norway sponsor initiatives and projects in various program areas, primarily focusing on reducing economic and social disparities. The European Economic Area Grants are managed by Administração Central do Sistema de Saúde through the Programa Iniciativas em Saúde Pública.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest.

Footnotes

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