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.

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).

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.