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Population-based projections of sitting and physical activity (PA) help to guide PA programs. We aimed to project total and context specific sitting and PA until year 2028 in adults aged 46–74 years in Finland.
Subject and methods
The population based DILGOM Study in 2007 and 2014 provided longitudinal data on self-reported weekday sitting in five contexts (work, vehicle, at home in front of TV, at home by computer, elsewhere), total sitting, and PA in three domains (occupational, commuting and leisure time). Projections until 2028 were generated using a Markovian multistate model and multiple imputation techniques by gender, age and education.
Results
Total weekday sitting was projected to increase until 2028 only in the 64–74-year-olds and the low educated (+ 24 and + 32 min/day, p < 0.05, respectively). Sitting at home by computer was projected to increase on average 30 min/weekday (p < 0.05) and occupational PA decrease by 8 to 20%-units (p < 0.05) in all midlife and older adults. Further, sitting at home by TV and sitting elsewhere were projected to decrease in many, although not all groups.
Conclusion
Projected changes suggest increase in sitting by computer and decrease in occupational PA, which indicate the growing importance of leisure-time as the potential mean to increase PA.
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Introduction
Sitting is the most common type of sedentary behavior as adults spend a considerable proportion of their day sitting (Aadahl et al. 2013; Yang et al. 2019). Sitting for work contributes largely to daily sitting, but a fair share is also spent in leisure time sitting in front of screens such as TV and computer (Wennman et al. 2019). In many countries, the population levels of daily total sitting have been merely unchanged or increased somewhat since the early 2000 (Aadahl et al. 2013; Jelsma et al. 2019; López-Valenciano et al. 2020; Yang et al. 2019). In the European Union countries, the proportion of adults who sit more than 4 h 30 min daily has increased from 49.3% in 2000 to 54.3% in 2017 (Lopez-Valenciano et al. 2020). Several observations exist that the prevalence of sitting by a computer or a screen has increased among adults (Smith et al. 2014; Wennman et al. 2019; Yang et al. 2019). At the same time, leisure time physical activity has also been increasing (Aadahl et al. 2013; Biernat and Piatkowska 2020; Borodulin et al. 2016). Especially in younger generations of adults, this kind of modern lifestyle, including high leisure time physical activity and prolonged leisure sedentary time, seems prevailing (Aadahl et al. 2013).
In 2020, the COVID-19 pandemic changed our lives, impacting the health and wellbeing of people worldwide (Chakraborty and Prasenjit 2020). The impacts were also seen in people’s lifestyles, where many adults decreased their physical activity and increased their sedentary time during the pandemic (Charreire et al. 2022; Loef et al. 2022; Stockwell et al. 2021). Almost half of adults in Finland decreased their leisure time physical activity and more than a third reduced commuting physical activity during the COVID-19 pandemic (Jääskeläinen et al. 2022).
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The development of sedentary time and physical activity in adult populations has not altogether been as strived for in terms of public health. Low physical activity, as well as prolonged sitting may have several negative consequences for health and wellbeing (Borodulin et al. 2014; Dempsey et al. 2020; Lee et al. 2012), and guidance to reduce time spent sedentary or sitting has now also been included in the global physical activity guidelines (Dempsey et al. 2020). Estimations on how physical activity and time spent sedentary are changing in the population, can be used as a basis to inform public health work and interventions. There are so far few studies that have projected the development of sedentary time and physical activity in adult populations. According to Ng and Popkin (2012) adults’ weekly sedentary time in the United States (U.S.), the United Kingdom (UK), Brazil, China, and India, will increase approximately by 2 (India) to 10 (the UK) hours per week from the years 2005–2009 until the year 2030. Along with the projected increase in sedentary time, the weekly amount of physical activity will decrease by 27 (India) to 65 (Brazil) MET-hours per week, with large decreases estimated especially in occupational physical activity. Similarly, in Russian adults aged 18 – 60 years, total physical activity has been projected to decrease by almost 40 MET-hours per week until the year 2030 (Dearth-Wesley et al. 2014). In Norway, the frequency and the intensity of physical activity in both men and women were projected to increase until year 2025, whereas duration of physical activity was projected to decrease (Rossi and Calogiuri 2018).
The aim of this study was to contribute to the sparse literature with projections about changes in total and context specific sitting time and prevalence of physical activity in midlife and older adults in Finland.
Methods
Data comprise the DILGOM Study (Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome), which is a longitudinal cohort with baseline in 2007 and a follow-up in 2014, consisting of a subpopulation of the National FINRISK 2007 Study which represents 25- to 74-year-old adults from five different areas in Finland (Borodulin et al. 2018). Altogether, 9957 persons were invited to the National FINRISK 2007 Study and 63% (n = 6258) participated. All these participants were invited to take part in the DILGOM Study conducted later the same year (Kanerva et al. 2018). In total, 80% of the eligible participants (n = 5024) took part in the DILGOM Study in 2007 and 74% of them (n = 3735) also participated in the 2014 follow-up. The DILGOM Study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa. All participants provided written informed consent.
Sitting time and physical activity were assessed using self-administered questionnaires. Participants reported time spent sitting in five different contexts on a typical weekday (for work in office or equivalent; in vehicle; at home in front of TV; at home by computer; and elsewhere). Total sitting was calculated as the sum of context specific sitting times. Volume of occupational, commuting and leisure time physical activity were assessed in three different questions with pre-defined answer options ranging from none to high (Borodulin et al. 2016). All domains were dichotomized into no physical activity or some physical activity (physically active), respectively.
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Information about education, occupational status and self-rated health were assessed on the questionnaire. Education was assessed as total years of schooling, and further divided into birth-cohort stratified thirds representing low, mid and high education (Borodulin et al. 2018). Participants reported their current occupational status as one of the following: at work; student; unemployed; retired; housewife; other. The first two were classified as working and the rest as not working. Ratings on self-rated health status were grouped into good (very good and good), moderate (rating fair), and poor (poor and very poor).
Statistical methods
The projections were generated for the years 2021 and 2028 using a Markovian multistate model (Commenges 1999), which allows the transitions for all time dependent variables. The transition probabilities were based on the observed individual transitions between 2007 and 2014. Based on these transition probabilities, the projected states in 2021 were generated using the states in 2014. The states in 2028 were generated sequentially, based on the 2021 states. The transitions were modeled using the classification and regression trees (CART) method, which automatically incorporates possible nonlinearities and interactions. The missing values in the DILGOM Study 2007 and 2014 datasets were multiply imputed and the projected values for 2021 and 2028 were generated using the chained equations method implemented in the mice package (van Buuren and Groothuis-Oudshoorn 2011) of the R software (R Core Team, 2019, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.). We applied the bootstrap method (Efron 1979) with 360 samples to account for the sampling uncertainty. More details on the methods have been presented in Härkänen et al. (2019). We present the results using 95% confidence intervals although for the projections these are prediction intervals, which incorporate the prediction uncertainty in addition to the sampling uncertainty in the observed survey data.
To be able to report the changes in the same age groups at different calendar years, based on the attained age over time, we report the results starting at age 46, as the youngest individuals in 2007 were 25 years old, and in 2028 they will be 46 years old. Similarly, the upper age limit 74 years in 2007 is used in the reporting until 2028. Results are presented separately on the attained age over time for age groups of 46–55 years, 56–64 years, and 66–74 years.
Results
The mean context specific and total weekday sitting time and prevalence of physical activity in 2014 are presented in Table 1. Men had higher total sitting time than women (393 vs. 355 min/weekday), sat more in vehicle (53 vs. 29 min/weekday) and at home by computer (38 vs. 26 min/weekday). Men were also less active in commuting (18% vs. 31%) compared to women. The youngest age group (46–55 years) had largest total daily sitting time and the oldest (66–74 years) had the lowest. Sitting at home in front of the TV increased with age, the youngest age group sitting on average 125 min/weekday and the oldest 182 min/weekday. Highest educated adults spent most time in total sitting (420 min/weekday), work related sitting (155 min/weekday) and sitting at home by computer (46 min/weekday), when compared to mid and low educated. Other background characteristics of the DILGOM Study 2014 sample are presented in online resource Table 1.
Table 1
Means and corresponding lower and upper 95% confidence intervals of total and context-specific weekday sitting and prevalence of physical activity in 2014, as well as the projected means and prevalence in 2028, by gender, attained age, and education
Total sitting
(min/weekday)
Work-related sitting
(min/weekday)
Vehicle sitting
(min/weekday)
TV sitting at home
(min/weekday)
Computer sitting at home
(min/weekday)
Elsewhere sitting
(min/weekday)
Occupational physical activity
(%)
Commuting physical activity
(%)
Leisure-time
physical activity
(%)
Gender
Men 2014
393 (383–403)
107 (99–115)
53 (49–58)
156 (151–160)
38 (35–42)
39 (36–42)
42 (39–44)
18 (16–20)
83 (81–85)
Men 2028
391 (368–413)
106 (87–126)
44 (37–52)
144 (135–153)
67 (60–75)
31 (25–36)
26 (22–30)
21 (18–25)
82 (79–85)
Women 2014
355 (347–364)
114 (105–122)
29 (27–31)
149 (144–153)
26 (24–28)
38 (35–42)
38 (35–40)
31 (29–33)
82 (80–84)
Women 2028
370 (350–390)
110 (90–132)
28 (24–32)
143 (135–150)
61 (54–67)
28 (24–35)
26 (22–31)
25 (21–28)
81 (78–84)
Age groups
46–55 2014
435 (423–447)
193 (182–203)
53 (50–58)
125 (120–130)
36 (32–39)
28 (25–32)
60 (57–63)
35 (32–38)
80 (77–83)
46–55 2028
429 (396–461)
173 (139–206)
46 (39–53)
119 (109–130)
65 (57–74)
26 (20–34)
40 (32–49)
29 (24–35)
82 (77–86)
56–65 2014
390 (378–403)
127 (117–136)
44 (40–48)
146 (141–151)
34 (30–38)
40 (35–46)
41 (38–44)
28 (25–30)
83 (81–85)
56–65 2028
405 (375–433)
140 (110–166)
39 (34–46)
134 (124–143)
63 (56–71)
28 (22–35)
33 (27–39)
27 (23 –33)
82 (77–85)
66–74 2014
304 (294–314)
23 (19–28)
26 (24–29)
182 (177–187)
27 (23–30)
47 (42–52)
20 (18–22)
14 (12–16)
84 (82–86)
66–74 2028
328 (308–354)
41 (24–71)
25 (20–30)
167 (158–178)
62 (56–70)
32 (25–40)
12 (8–20)
16 (13–21)
81 (78–85)
Education level
Low 2014
322 (308–334)
64 (55–74)
41 (36–46)
163 (158–169)
21 (18–24)
34 (30–39)
44 (41–48)
26 (23–28)
81 (79–84)
Low 2028
353 (331–378)
81 (64–02)
34 (29–42)
154 (145–164)
57 (50–65)
27 (22–33)
29 (24–34)
22 (18–26)
80 (76–83)
Mid 2014
369 (357–382)
104 (95–113)
41 (38–46)
160 (154–165)
27 (24–29)
38 (33–42)
41 (39–44)
23 (21–26)
82 (80–85)
Mid 2028
376 (353–398)
103 (82–124)
36 (31–42)
148 (138–158)
59 (52–66)
30 (24–38)
26 (22–31)
22 (18–26)
81 (78–84)
High 2014
420 (408–431)
155 (144–165)
39 (36–42)
136 (132–141)
46 (42–50)
43 (38–48)
34 (31–36)
26 (23–29)
84 (82–86)
High 2028
406 (378–431)
137 (111–165)
35 (30–40)
130 (120–139)
73 (65–83)
31 (25–38)
24 (20–28)
25 (22–29)
83 (80–86)
Projections are indicated in italics. The education levels correspond to the following: Low lowest birth-cohort-stratified third; Mid middle birth-cohort-stratified third; High highest birth-cohort-stratified third
The projected total and context specific sitting times and prevalence of physical activity in 2028 are also presented in Table 1. The projected changes in total and context specific sitting until 2028 are presented in Fig. 1a-b by gender, in Fig. 2a-c by attained age and in Fig. 3a-c by education, and in detail in online resource Table 2. Men (Fig. 1a) are projected to significantly increase sitting at home by the computer (+ 28.6 min/weekday) and decrease vehicle sitting (-9.7 min/weekday), sitting at home in front of TV (-12.0 min/weekday) and sitting elsewhere (-8.3 min/weekday). Women (Fig. 1b) are projected to significantly increase sitting at home by computer (+ 34.5 min/ weekday) and to decrease sitting elsewhere (-9.7 min/ weekday).
Fig. 1
a Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 in men. b Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 in women
a Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 among those with attained age 46–55 years. b Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 among those with attained age 56–65 years. c Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 among those with attained age 66–74 years
a Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 in those with low education. b Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 in those with mid education. c Projected changes and corresponding lower and upper 95% confidence intervals of total and context specific weekday sitting until 2028 in those with high education
In the 46–55-year-olds (Fig. 2a) based on the attained age, only sitting at home by computer is projected to increase significantly by 28.9 min/ weekday. The 56–65-year-olds (Fig. 2b) are projected to significantly increase sitting at home by computer (+ 28.9 min/ weekday) and decrease sitting at home in front of TV (-12.2 min/ weekday) as well as sitting elsewhere (-11.7 min/day). Significant increases in total sitting (+ 24.1 min/ weekday), work-related sitting (+ 18.2 min/ weekday) and sitting at home by computer (+ 35.9 min/ weekday), as well as decreases in sitting at home in front of TV (-14.4 min/ weekday) and sitting elsewhere (-14.1 min/ weekday) are projected in the 66–74-year-olds (Fig. 2c).
The projections by educational group (Fig. 2a-c) show significant increase in total sitting (+ 31.5 min/ weekday) and sitting at home by computer (+ 36.3 min/ weekday) in the low educated; increase in sitting at home by computer (+ 32.2 min/ weekday) and decrease in sitting at home in front of TV (-11.7 min/ weekday) in mid educated; and increase in sitting at home by computer (+ 27.1 min/ weekday) and decrease in sitting elsewhere (-11.9 min/ weekday) in the high educated.
Regarding physical activity, the projected prevalence in 2028 is presented in Table 1 and the projected changes in Table 2. Occupational physical activity is projected to decrease significantly until 2028 in men and women, all age groups, and all education groups. The projected change varies between -7.9%-units (in 56–66-year-olds) and -19.9%-units (in 46–55-year-olds). Commuting physical activity is projected to decrease by 6.0%-units in women, but not significantly change in any other group. No significant changes are projected for leisure time physical activity.
Table 2
Projected mean changes (%-units) and lower and upper 95% confidence intervals [CI] of physical activity prevalence until 2028, by gender, attained age, and education
Occupational physical activity
Mean (CI) %-units
Leisure time physical activity
Mean (CI) %-units
Commuting physical activity
Mean (CI) %-units
Gender
Men
−15,6* (−20,1; −10,6)
−1,0 (−4,6; 2,6)
3,1 (−0,6; 6,9)
Women
−11,2* (−15,7; −6,2)
−1,1 (−4,9; 2,3)
−6,0* (−10,3; −2,1)
Age
46–55
−19,9* (−27,7; −10,5)
1,7 (−2,7; 6,6)
−5,4 (−11,4; 0,2)
56–65
−7,9* (−14,9; −0,9)
−1,7 (−6,1; 2,4)
−0,3 (−5,6; 5,2)
66–74
−8,2* (−12,7; 0,0)
−2,9 (−1,3; 1,1)
2,6 (−1,3; 6,8)
Education Level
Low
−15,5* (−21,6; −9,3)
−1,5 (−5,8; 2,8)
−3,2 (−7,9; 0,7)
Mid
−15,2* (−19,7; −10,0)
−1,1 (−4,8; 2,8)
−0,9 (−5,5; 4,0)
High
−9,8* (−14,7; −4,5)
−0,5 (−4,2; 3,1)
−0,8 (−5,3; 3,6)
* = p < 0.05 Note: The education levels correspond to the following: Low Lowest birth-cohort stratified third; Mid Middle birth-cohort stratified third; High Highest birth-cohort stratified third
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Discussion
According to these projections, time spent sitting at home by computer will increase on average around 30 min/ weekday, and the prevalence of physically active work decrease with 8 to 20% -units until the year 2028 in midlife and older adults in Finland, if the behaviors continue developing similarly as between 2007 and 2014. It seems that, among those older than 66 years, and in low educated, weekdays may become even more sedentary as total weekday sitting was projected to increase by 24 to 32 min/ weekday. Most changes in other contexts were observed for sitting at home in front of TV, which was projected to decrease by approximately 10 min/ weekday in men, older age groups (based on the attained age) and mid educated. Also sitting elsewhere was projected to decrease (by 8 to 14 min/ weekday) in several groups (men, women, 56–65- and 66–74-year-olds, and high educated). In women, prevalence of physically active commuting was projected to decrease by 6%-units, as compared to year 2014.
The current projections of changes in total and context specific weekday sitting until 2028 add to the so far sparse literature regarding future views of sitting time in adult populations. Ng and Popkin (2012) used both repeated cross-sectional and longitudinal time-use data and projected the development of adults’ (≥ 18 years) weekly sedentary time in five big countries (the U.S., the U.K., Brazil, China and India) accounting for almost 50% of the world’s population. They projected sedentary time to increase in all countries, and range between 20 h per week in India to 42 and 51 h per week in the U.S. and the U.K. in 2030, respectively. Our current projections estimated men to sit on average 6.5 h/ weekday and women to sit on average 6.1 h/ weekday in 2028, roughly meaning 43 to 46 h/week, a corresponding amount as projected for adults in the U.K. (Ng and Popkin 2012). However, the current projections did not suggest any significant increases in total weekday sitting, except in the oldest age group and the lowest educated.
Projections in the current study, based on individual level data from the early twenty-first century, suggest that sitting at home by the computer is the sole context where time spent sitting will increase among all midlife and older adults. Previous longitudinal studies have observed significant increases in computer time and sitting by the computer (Yang et al. 2019; Wennman et al. 2019; Smith et al. 2014) and it is obvious, that the technological development has changed and is changing our behaviors. Indeed, Ng and Popkin (2012) suggest that their projections of increasing weekly sedentary time already are a result, and will continue to be a result, of growth in media technologies. Also, the relatively low projected sedentary time in India for year 2030, as compared to for example the US and the UK, was suggested to be due to the less advanced technological reformation in India compared to the other countries (Ng and Popkin 2012). Finding ways to combine technology use and physical activity can be a future solution to tackle sedentary lifestyle.
Increased sitting by the computer may also have other reasons. Time from sitting in front of TV may be reallocated to sitting by computer but, this is not altogether supported by the current projections. There was a decreasing trend for sitting by TV until 2028 in all midlife and older adults, but the projected change was significant only for some population groups. Another cause for increases in sitting at home by computer may be more remote work. Recently, due to the COVID-19 pandemic remote work has increased and working remote was associated with a more sedentary lifestyle (Loef et al. 2022). The current analyses cannot distinguish between the reasons for sitting at home by computer, even if work-related sitting was separately assessed. Thus, some people may have reported also work-related sitting by the computer.
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This study suggests that the prevalence of occupational physical activity is to decrease largely among midlife and older adults in Finland. The finding is expected because of retirement in the studied age group. However, repeated cross-sectional data on all-aged adults have shown similar trend for decreasing occupational physical activity levels during the past decades (Borodulin et al. 2016). Whether or not all decrease in occupational physical activity is related with increased sedentary time in work, it is important to acknowledge that the amount of sedentary time at work contributes largely to the total daily sedentary time of working adults (Clemes et al. 2014; Prince et al. 2019). To combat sedentariness of work even if the physical strain otherwise is reduced, strategies such as standing and treadmill desks could be considered more routinely (Shrestha et al. 2018; MacEwen et al. 2014).
Compared to earlier projections regarding population-level changes in physical activity (Dearth-Wesley et al. 2014; Ng and Popkin 2012; Rossi and Calogiuri 2018), current results cannot provide information about changes in total physical activity. However, based on the domain-specific results, total physical activity can be assumed to rather decrease than increase. The main reason would be the decrease in occupational physical activity, as no significant changes were projected for leisure time physical activity, and only little for commuting physical activity. In Russian (Dearth-Wesley et al. 2014), as in U.S., U.K., Chinese, Brazilian and Indian adults (Ng and Popkin 2012), projections until 2030 suggest a decrease of 30 to even 65 MET-hours per week in physical activity. In Norway, Rossi and Calogiuri (2018) projected men and women of all ages to increase their total physical activity frequency and intensity, but to reduce the duration of physical activity, resulting in an overall limited increase in physical activity by the year 2025. Common for these projections regarding physical activity, except the Norwegian one, is that the decrease in total physical activity is mainly driven by reductions in occupational physical activity, and to some extent also commuting physical activity. All studies indicate leisure time physical activity to remain quite stable.
The COVID-19 outbreak in 2020 changed the way of living of people worldwide. Large restrictions and lockdowns forced people into their homes, and this had consequences also on sedentary time and physical activity behavior (Charreire et al. 2022; Loef et al. 2022; Stockwell et al. 2021). Many adults increased their sedentary time and while some reported increasing physical activity, a large proportion became less physically active during the pandemic (Charreire et al. 2022). These changes are unfortunate in terms of individual and public health and together with previously documented development and future scenarios they emphasize the need to support and promote healthy lifestyle of people.
Taken together, the projected changes in sitting and physical activity, as well as other findings on the topic, call for actions to actively avoid increases in sedentary time and reductions in physical activity. The updated WHO physical activity guidelines 2020 now also includes recommendations on sedentary time as the evidence clearly shows that sedentary time relates with several health outcomes (Dempsey et al. 2020). If we want to reduce sedentary time, the most promising approaches from the literature suggest focusing specifically on reducing sedentary time, or to undertake lifestyle interventions in general. Interventions trying to increase physical activity show less effect on sedentary time. (Martin et al. 2015). Physical activity promotion on the other hand, benefits from multisite and multicomponent approaches, intervening on policy, environmental, behavioral, and social levels (Kahn et al. 2002; World Health Organization 2018). Self-monitoring elements, group components, and point-of-decision prompts are promising community-based intervention strategies to increase physical activity among the general adult population (Brand et al. 2014). In such community-based interventions, particular attention needs to be paid on widely reaching the population to increase the effectiveness of the interventions (Baker et al. 2015; 2018 Physical Activity Guidelines Advisory Committee 2018).
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Strengths and limitations
Self-report methods used to assess sitting and physical activity have advantages and disadvantages. Self-report enables assessing context specific information, but the precision at which sitting or physical activity is reported may suffer. Sitting is only one form of sedentary behavior which constitutes all waking behaviors with low energy expenditure while in seated or reclining position (Sedentary Behaviour Research N, 2012). A recent review showed that self-report measures of sitting typically underestimate the total time spent sedentary as compared to device-based assessment on average 1.74 h/day (Prince et al. 2020). Device-based information on sedentary time could be able to capture also smaller and significant changes in total sedentary time. Having both device-based and self-reported data would add value to the reporting. Likewise, we also lack more detailed information about physical activity volumes and intensities, which would have enriched the results and enabled for example calculation of MET-hours as in earlier studies.
These projections are based on data from a population-based longitudinal cohort allowing use of individual-level modelling. Even though the data was collected in the early twenty-first century and do not show the recent trends in population sitting and physical activity, the data is valuable for projections in many ways. The projection model predicts not only the outcomes but also changes in the predictors, which can influence the changes in the outcome later. This is important when making projections, as, for example, changes in the working status has often a considerable impact on the sedentary behavior. Most applications in creating health projections are only based on a small number of socio-demographic factors, which are assumed to remain constant or to change deterministically, such as in ageing. Also, there can be interactions of the predictors, for example, changes in the outcome can differ between education classes at different ages, which our method can account for. Our method can provide projections for the 7-year fold, and this may seem a restriction, but the time points between the projection times can be interpolated linearly as it is likely that changes on the population level take place smoothly during such relatively short time periods. As typical, there were drop-out of participants in the DILGOM Study, but the participation rate remained on a good level (80% for baseline and 74% for follow-up). The drop-out in such population-based studies more often concern men, younger subjects, and the less healthy (Mindell et al. 2015; Tolonen et al. 2017), suggesting that for example estimates of weekday sitting time could to some extent be more concerning.
In the future, projections that demonstrate the potential consequences of changes in sitting and physical activity would have upon population health are important. But we still need more knowledge from intervention studies and randomized controlled trials to better understand causes and consequences of sedentary behavior and time spent sitting, and the interplay between sedentary behaviors, physical activity and health in order to perform such projections. Considering the interrelated nature of sedentary time and physical activity (Pedisic et al. 2017), it would also be interesting to provide more detailed projections that include both ends and different nuances of the 24-h movement continuum.
Conclusions
If the previously observed trend in total and context specific sitting and physical activity continues, sitting by computer will have increased by almost 100% (i.e., 30 min) per weekday and occupational physical activity prevalence decreased by 10–20%-units in midlife and older adults in Finland until 2028. Interventions and programs that focus on reducing sitting time, and on the other hand, increasing physical activity, are needed to change the sedentary lifestyle among adults.
Declarations
Ethics approval
The DILGOM Study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa.
Consent to participate
All participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors have no competing interests to declare that are relevant to the content of this article.
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2018 Physical Activity Guidelines Advisory Committee (2018) 2018 Physical Activity Guidelines Advisory Committee Scientific Report. U.S. Department of Health and Human Services, Washington
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