Introduction
Individuals leading a more physically active and less sedentary lifestyle have a lower risk of developing several non-communicable diseases (e.g., coronary heart disease and type 2 diabetes) and facing premature death [
1,
2]. However, globally more than one in four adults do not engage in the World Health Organization’s recommended amount of physical activity (PA), namely, at least 150 min of moderate PA, 75 min of vigorous PA, or a combination of these two intensities per week [
3], which has remarkable economic consequences [
4]. Moreover, adults in high-income countries spend the major part of their waking time being sedentary [
5], which further increases the economic burden [
6].
PA involves any bodily movements produced by skeletal muscles, causing the energy expenditure to exceed the basal metabolic rate [
7]. By contrast, sedentary behavior (SB) is any waking behavior that is performed in a sitting, reclining, or lying posture that requires low energy (≤ 1.5 metabolic equivalents) [
8]. The behaviors are interrelated within a 24-hour activity cycle together with sleep, meaning that an increase in one activity results in a decrease in another [
9]. However, PA and SB deserve to be investigated separately for two focal reasons. First, they are independent factors associated with several health-related outcomes, including all-cause mortality [
1,
2], although PA may reduce the health risks caused by SB [
9]. Second, SB may be commonly accumulated a lot even when the recommended amount of PA is met; thus, SB does not equate to physical inactivity [
1].
In the 21st century, worrying trends in the levels of PA and SB have been observed, with the prevalence of physical inactivity and SB increasing in high-income countries [
3,
10]. Nowadays, SB can be difficult to avoid owing to several perpetuating social and environmental factors, such as sedentary jobs [
11] and less physically demanding domestic tasks [
12] that have become more common. PA and SB can indeed occur in the leisure (e.g., exercise), occupational (e.g., manual labor tasks), transportation (e.g., active commuting), and domestic (e.g., housework) domains [
13]. Although current recommendations do not take these domains into account, an increasing amount of literature suggests that while leisure-time PA is beneficial to health [
14], occupational PA is related to adverse health outcomes (e.g., an increased risk of early mortality) [
15]. Hence, information on the domain-specific correlates of PA and SB are needed to develop targeted health-promoting interventions.
Personality characteristics (e.g., socioemotional behavior and temperament) may explain inter-individual differences in the levels of PA and SB even after a long time, as they have been reported to predict multiple behaviors related to health [
16] and work life [
17] after decades. Socioemotional behavior and temperament describe the basic dispositions regarding one’s feelings, reactions, and efforts to regulate arising reactions and the concepts share the same understanding that individual differences arise from the interaction between reactivity and self-regulation [
18,
19]. Specifically, Pulkkinen (originally Pitkänen [
20]) defined
socioemotional behavior as the expression and regulation of one’s emotions in social relationships and characterized it by three higher-order dimensions: behavioral activity, well-controlled behavior, and negative emotionality [
18,
21]. Similarly, Rothbart et al. [
19, p. 123] defined
temperament a few decades later as the relatively persistent “individual differences in reactivity and self-regulation” and conceptualized it as having three higher-order dimensions in childhood: surgency, effortful control, and negative affectivity.
These three dimensions are conceptual counterparts of each other [
18]. Behavioral activity refers to one’s tendency to be actively in contact with others [
18], while surgency refers to one’s tendency to show high activity, prefer high-intensity activities, and not feel uneasy in new social situations [
19]. Well-controlled behavior refers to one’s tendency to act constructively and compliantly when facing a conflict [
18], while effortful control refers to one’s tendency to regulate attention and behavior and prefer low-intensity activities [
19]. Negative emotionality refers to one’s tendency to display both aggressive and anxious behaviors [
18], while negative affectivity refers to one’s tendency to frequently experience feelings of sadness, discomfort, anger, and frustration [
19]. Temperament in adulthood also includes the dimensions of orienting sensitivity and affiliativeness [
22]. Orienting sensitivity refers to one’s tendency to sense cues from the external and internal environment, while affiliativeness refers to one’s tendency to respond empathetically to others’ feelings [
22].
In previous studies, the links to PA and SB were examined using the concept of temperament. Studies investigating the associations of temperament with PA and SB used various measures and assigned different names to the temperament dimensions despite their similarities with existing ones [
19], resulting in the difficulty of drawing definitive interpretations of the literature. Recent evidence, however, suggests that child surgency and related temperamental activity are associated with greater PA [
23‐
26] and lower levels of SB in childhood [
23,
27] as well as predict greater PA in adolescent boys [
28]. However, child temperamental activity predicted lower PA and higher levels of SB in men within a follow-up period of over 20 years [
26]. Negative affectivity is, in turn, linked to lower PA in boys [
24], and a similar association was observed in men in the Jyväskylä Longitudinal Study of Personality and Social Development (JYLS) [
29]. In a follow-up study related to Sharp et al. [
24], this dimension predicted lower PA in girls and greater PA in boys [
30]. Studies on child effortful control and related well-controlled behavior reported the most inconsistent results, wherein these dimensions were associated with lower PA and higher levels of SB in childhood [
23] but predicted greater PA in women in the JYLS [
16]. Orienting sensitivity is, in turn, linked to greater PA in adulthood [
29].
Despite some conflicting findings, temperament has been suggested to be a relevant factor for PA and SB at different ages, and it may have predictive value for these behaviors over decades. However, the majority of the previous studies examined children [
23‐
25,
27,
30] or adolescents [
25,
28], employed cross-sectional [
23,
24,
27] or longitudinal designs that do not extend from childhood into adulthood [
28‐
30], and utilized self- or parent-reported data on PA and SB [
16,
24,
26,
28‐
30]. The domains of PA and SB also varied across studies that focused either on the investigation of leisure time by using questionnaires [
16,
24,
26,
28‐
30] or the assessment of non-domain-specific activities by using accelerometers [
23,
25,
27]. Compared to questionnaires that tend to underestimate SB [
5], accelerometers provide detailed information of intensity, frequency and duration of also habitual and incidental physical movements, which may be difficult to memorize [
13]. None of the previous studies on the links between temperament and PA or SB used accelerometers to assess the PA and SB of adults and, simultaneously, several domains of PA and SB (e.g., leisure and occupational domains).
This study aimed to fill the current research gaps, with the major objective of investigating whether socioemotional behavior in childhood (age 8) and temperament in middle adulthood (age 42) predict PA and SB in late adulthood (age 61). In particular, this study aimed to assess the associations of multiple dimensions of child socioemotional behavior and adult temperament with accelerometer-measured moderate-to-vigorous physical activity (MVPA) and SB. In addition to whole-day MVPA and SB, leisure and occupational domains were investigated. Having data on inter-individual differences from two phases of life enabled the investigation on whether MVPA and SB can already be predicted by personality characteristics in childhood or only in adulthood.
On the basis of previous findings [
23‐
26,
28‐
30], in this study, it was hypothesized that behavioral activity, surgency, and orienting sensitivity are associated with greater MVPA, whereas negative emotionality and negative affectivity are associated with lower MVPA. It was also hypothesized that these links exist from child socioemotional behavior into MVPA and SB in late adulthood but may be stronger when analyzed within adulthood in a shorter time interval. The literature on the association of well-controlled behavior and effortful control with PA [
16,
23] and of all dimensions with SB [
23,
26,
27] remains inconsistent. Setting unambiguous hypotheses is difficult because of these inconsistent findings based on various measures and because none of the previous studies followed their participants for five decades. This study aimed to supplement the previous studies based on the JYLS [
16,
29] by extending the follow-up period to late adulthood (8–50 years old vs. 8–61 years old) and adopting a new method to assess PA (self-reporting vs. accelerometer-based measurement).
Results
The descriptive statistics for all participants and for only the women and men are presented in Table
1. As reported in previous JYLS publications that analyzed slightly different samples [
18,
29], boys scored higher than girls for negative emotionality, while women scored higher than men for negative affectivity and orienting sensitivity. No gender-based differences were observed in terms of MVPA and SB. Among those who reported working hours (N = 99, 62% women), men spent more time at work than did women.
Table 1
Descriptive statistics
Socioemotional behavior (0–3) | | | | | | | | | |
| Behavioral activity | 142 | 2.09 (0.73) | 78 | 2.19 (0.71) | 64 | 1.97 (0.73) | 1.81 | 140 | 0.073 |
| Well-controlled behavior | 142 | 1.50 (0.72) | 78 | 1.58 (0.71) | 64 | 1.40 (0.74) | 1.50 | 140 | 0.135 |
| Negative emotionality | 142 | 0.48 (0.44) | 78 | 0.39 (0.38) | 64 | 0.60 (0.48) | –2.81 | 118 | 0.006 |
Temperament (1–7) | | | | | | | | | |
| Surgency | 131 | 4.30 (0.71) | 73 | 4.33 (0.64) | 58 | 4.26 (0.78) | 0.59 | 129 | 0.555 |
| Effortful control | 131 | 4.87 (0.61) | 73 | 4.89 (0.57) | 58 | 4.85 (0.65) | 0.36 | 129 | 0.722 |
| Negative affectivity | 131 | 3.72 (0.69) | 73 | 3.94 (0.62) | 58 | 3.44 (0.69) | 4.35 | 129 | < 0.001 |
| Orienting sensitivity | 131 | 4.76 (0.79) | 73 | 4.95 (0.73) | 58 | 4.52 (0.80) | 3.19 | 129 | 0.002 |
Physical activity (min/day) | | | | | | | | | |
| Whole-day MVPA | 142 | 55.25 (31.37) | 78 | 54.11 (31.49) | 64 | 56.64 (31.42) | –0.48 | 140 | 0.634 |
| Leisure-time MVPA | 141 | 44.39 (27.87) | 78 | 43.78 (26.52) | 63 | 45.14 (29.65) | –0.29 | 139 | 0.776 |
| Occupational MVPA | 99 | 21.45 (18.81) | 61 | 19.32 (19.03) | 38 | 24.87 (18.17) | –1.44 | 97 | 0.154 |
Sedentary behavior (min/day) | | | | | | | | | |
| Whole-day SB | 142 | 514.32 (104.55) | 78 | 527.65 (93.22) | 64 | 498.09 (115.56) | 1.69 | 140 | 0.094 |
| Leisure-time SB | 141 | 373.59 (130.21) | 78 | 366.01 (131.32) | 63 | 382.97 (129.25) | –0.77 | 139 | 0.444 |
| Occupational SB | 99 | 289.72 (125.33) | 61 | 302.28 (121.97) | 38 | 269.56 (129.62) | 1.27 | 97 | 0.208 |
Wear time (h/day) | | | | | | | | | |
| Whole-day wear time | 142 | 14.69 (1.10) | 78 | 14.70 (1.00) | 64 | 14.67 (1.23) | 0.13 | 140 | 0.898 |
| Leisure-time wear time | 141 | 10.82 (2.82) | 78 | 10.65 (2.74) | 63 | 11.03 (2.91) | –0.78 | 139 | 0.435 |
| Occupational wear time | 99 | 7.84 (1.91) | 61 | 7.50 (1.84) | 38 | 8.37 (1.93) | –2.26 | 97 | 0.026 |
Self-rated health (1–5) | 142 | 3.92 (0.83) | 78 | 3.96 (0.75) | 64 | 3.87 (0.93) | –0.60 | 119 | 0.549 |
| | | |
%
| |
%
| |
%
|
x
2 b
|
df
|
p
|
Season | 142 | | 78 | | 64 | | 0.07 | 1 | 0.866 |
| Summer | 67 | 47.2 | 36 | 46.2 | 31 | 48.4 | | | |
| Fall, winter or spring | 75 | 52.8 | 42 | 53.8 | 33 | 51.6 | | | |
Parents’ occupational status | 142 | | 78 | | 64 | | 9.63 | 2 | 0.007 |
| Blue collar | 99 | 69.7 | 59 | 75.6 | 40 | 62.5 | | | |
| Lower white-collar | 33 | 23.2 | 11 | 14.1 | 22 | 34.4 | | | |
| Upper white-collar | 10 | 7.0 | 8 | 10.3 | 2 | 3.1 | | | |
Occupational status | 142 | | 78 | | 64 | | 43.00 | 2 | < 0.001 |
| Blue collar | 33 | 23.2 | 4 | 5.1 | 29 | 45.3 | | | |
| Lower white-collar | 64 | 45.1 | 52 | 66.7 | 12 | 18.8 | | | |
| Upper white-collar | 45 | 31.7 | 22 | 28.2 | 23 | 35.9 | | | |
The Pearson bivariate correlations for the main and background variables are presented in the Additional file 1 (Table
S1). Based on the correlation matrix, in women, higher scores for negative affectivity correlated with lower whole-day MVPA (r = − 0.28) and leisure-time MVPA (r = − 0.27), while higher scores for surgency correlated with greater leisure-time MVPA (r = 0.28). In men, higher scores for effortful control and lower scores for negative affectivity correlated with higher levels of occupational SB (r = 0.38, r = − 0.42).
Linear regression models were used to examine the associations of personality characteristics with whole-day MVPA and SB. In the analysis of child socioemotional dimensions, after controlling for season, parents’ occupational status and self-rated health, higher scores for behavioral activity predicted higher levels of daily SB in women (Table
2). The association was small (β = 0.24, p = 0.035) and did not remain statistically significant either after the Benjamini–Hochberg correction or the additional adjustment with the participant’s occupational status. In the analysis of adult temperament dimensions, higher scores for negative affectivity predicted lower daily MVPA in women (Table
3). The association was small (β = −0.27, p = 0.028) and remained statistically significant after controlling for season, participant’s occupational status and self-rated health. The associations were not statistically significant after the Benjamini–Hochberg correction. No other statistically significant associations were found.
Table 2
Linear regressions of child socioemotional dimensions predicting whole-day MVPA and SB.
Women | | | 0.08 | | | 0.14 | | | 0.11 | | | 0.08 | | | 0.15 | | | 0.20 | |
| Behavioral activity | 0.04 | 0.714 | | –0.02 | 0.842 | | –0.02 | 0.874 | | 0.19 | 0.100 | |
0.24
|
0.035
| | 0.18 | 0.102 | | |
| Well-controlled behavior | 0.03 | 0.765 | | 0.10 | 0.396 | | 0.10 | 0.407 | | 0.06 | 0.618 | | 0.05 | 0.656 | | 0.06 | 0.624 | | |
| Negative emotionality | –0.04 | 0.752 | | 0.02 | 0.834 | | 0.02 | 0.865 | | –0.01 | 0.956 | | –0.05 | 0.636 | | –0.06 | 0.607 | | |
Men | | | 0.03 | | | 0.15 | | | 0.22 | | | 0.10 | | | 0.13 | | | 0.10 | |
| Behavioral activity | 0.01 | 0.930 | | 0.03 | 0.829 | | –0.04 | 0.774 | | 0.09 | 0.501 | | 0.07 | 0.577 | | 0.07 | 0.585 | | |
| Well-controlled behavior | –0.15 | 0.307 | | –0.21 | 0.134 | | –0.22 | 0.116 | | –0.17 | 0.223 | | –0.12 | 0.405 | | –0.11 | 0.450 | | |
| Negative emotionality | –0.09 | 0.550 | | –0.02 | 0.911 | | –0.06 | 0.657 | | –0.04 | 0.781 | | –0.05 | 0.727 | | –0.05 | 0.765 | | |
Table 3
Linear regressions of adult temperament dimensions predicting whole-day MVPA and SB.
Women | | | 0.19 | | | 0.21 | | | 0.04 | | | 0.12 | |
| Surgency | 0.11 | 0.402 | | 0.15 | 0.276 | | –0.04 | 0.785 | | –0.06 | 0.702 | | |
| Effortful control | –0.05 | 0.686 | | –0.06 | 0.610 | | 0.16 | 0.190 | | 0.09 | 0.451 | | |
| Negative affectivity |
–0.27
|
0.028
| |
–0.27
|
0.034
| | 0.12 | 0.379 | | 0.14 | 0.297 | | |
| Orienting sensitivity | 0.12 | 0.358 | | 0.05 | 0.709 | | 0.01 | 0.921 | | 0.01 | 0.954 | | |
Men | | | –0.01 | | | 0.19 | | | –0.04 | | | 0.14 | |
| Surgency | –0.01 | 0.979 | | –0.13 | 0.484 | | 0.06 | 0.775 | | 0.04 | 0.834 | | |
| Effortful control | –0.10 | 0.617 | | –0.19 | 0.310 | | 0.03 | 0.895 | | –0.03 | 0.872 | | |
| Negative affectivity | –0.07 | 0.731 | | –0.09 | 0.659 | | –0.03 | 0.901 | | –0.16 | 0.440 | | |
| Orienting sensitivity | 0.11 | 0.564 | | 0.07 | 0.696 | | –0.02 | 0.920 | | 0.10 | 0.603 | | |
The associations of personality characteristics with leisure-time and occupational MVPA and SB were further analyzed (Supplementary Material, Tables
S2–
S5). Although behavioral activity was linked to higher levels of daily SB in women (Table
2, Model 2), it was not statistically significantly associated with either leisure-time (Table
S2) or occupational SB (Table
S3). Domain-specific analyses, however, revealed that the inverse association of the women’s negative affectivity with MVPA was apparent during leisure time (β = −0.27, p = 0.040) (Table
S4). The small association remained statistically significant after controlling for season, participant’s occupational status and self-rated health (β = −0.25, p = 0.045) but not after the Benjamini–Hochberg correction. No new associations were detected in the domain-specific analyses. Sensitivity analyses indicated that exclusion of the participants whose accelerometer measurement period overlapped with the declared COVID-19-related state of emergency in Finland did not change the results.
Discussion
This longitudinal study examined whether socioemotional behavior in childhood and temperament in middle adulthood predict accelerometer-measured PA and SB in late adulthood. Overall, behavioral activity at age 8 predicted higher levels of daily SB at age 61 in women. However, the association did not remain statistically significant after controlling for participant’s occupational status. Negative affectivity at age 42 predicted, in turn, lower daily MVPA at age 61 in women. This association was observed particularly during their leisure time. In men, personality characteristics were not associated with MVPA and SB.
Slightly unexpectedly, girls who were not withdrawn or timid and were busy and played eagerly with other children spent more time sedentary in late adulthood compared with their socially more passive peers. Compared to previous studies from Finland, this result is in conflict with those ones reporting a negative association of surgency with SB among preschool-aged children [
23,
27] but in line with the one reporting child temperamental activity as a positive predictor of men’s TV viewing assessed over two decades later [
26]. In that study, unadjusted models were also statistically significant for women [
26]. Even though the effect sizes of the current findings based on standardized betas were small, they are in line with previous studies assessing longitudinal associations between child personality characteristics and adult health behaviors [
16,
26,
40]. In the current study, controlling for parents’ occupational status strengthened the association of behavioral activity with SB. However, the statistically significant association was attenuated following additional adjustment for the participant’s own occupational status. In light of the finding, it seems that socially more active girls have higher occupational statuses in adulthood which are further associated with more SB accumulated during the day. In the previous study based on JYLS, the frequent contacts of girls with other children, namely social activity, was linked to high career orientation, including for example occupational status and education, in adulthood [
41]. Higher occupational class has also been linked to higher levels of accelerometer-measured SB [
42], providing additional support for the possible developmental path. Furthermore, the role of occupational status on the longitudinal association of behavioral activity with SB explains the disparity with the results of cross-sectional studies conducted in early childhood [
23,
27] where social characteristics may also be expressed as active playing with peers.
Consistent with the hypothesis, women who experienced more negative emotions (e.g., frustration) in middle adulthood engaged less in MVPA in late adulthood, particularly during their leisure time, compared with women who experienced less of such emotions. The direction of the observed association is in line with previous findings in childhood [
24,
30] and adulthood [
29] and is further supported by studies reporting a consistent inverse association of equivalent effect size between conceptually similar personality trait neuroticism and MVPA [
43,
44]. As discussed in the literature regarding the associations of personality traits with PA, the findings could be explained by the tendency of women who display high negative affectivity to experience displeasing emotions, such as discomfort, which might, in turn, impact how they enjoy less MVPA or how they prefer other types of lower-stimulus activities [
45]. The fear of embarrassment may also serve as a barrier to engaging in intensive PA [
46], while negative emotions may be associated with less autonomous motivation toward PA which, in turn, leads to less incidental PA [
47]. The latter may also explain why the current findings were observed in women, although Karvonen et al. [
29] found a negative relationship between negative affectivity and self-reported overall and vigorous PA in men participating in the same longitudinal study. In addition to the differences in sample sizes and lengths of follow-up, personality characteristics may be differently related to habitual and incidental PA captured by accelerometers compared to more deliberate PA captured by self-reports [
43].
Although a follow-up period of several decades increases the remarkability of the current findings, it might also be a focal reason why only a few associations were detected in women and none in men. In men, there might be other factors (e.g., social support) associated with PA and SB that are more important, given that they are complex behaviors related to multiple individual, social, and environmental factors [
48,
49]. Compared to the more deliberate PA, habitual and incidental physical movements may also be even more challenging to predict. Overall, only a maximum of one fifth of the variance for daily MVPA and SB was explained by the personality characteristics and covariates.
The strengths of this study include a uniquely long-term longitudinal design, along with the assessment of personality characteristics both in childhood and adulthood and the use of accelerometer-based measurement of PA and SB, with a high compliance rate in the wearing of the device. Accelerometers can monitor temporally accurate information on the intensity, frequency and duration of physical movements [
13]. A limited number of participants who provided detailed diary reports also enabled the extraction of leisure and occupational times from the data.
Several limitations must also be acknowledged. First, there are risks for type I and II errors due to a relatively large number of tests and due to a relatively small sample size, respectively. However, in JYLS, the original sample had no initial attrition and relatively small attrition over the 50-year-long follow-up period [
21,
31] providing a context for sample size considerations. Even though some associations were found in whole-day analyses, a low number of cases might have led to the observed non-significant associations, especially in the analyses of occupational domain. This outcome is unfortunate, since it would have been interesting to determine whether the SB of girls with higher behavioral activity accumulated, especially at work. In this study, conclusions about causal relationships cannot be drawn due to the observational characteristics of the follow-up study. Despite all limitations, the value of this study lies in the uniquely long follow-up period of five decades that provides new insights to the current literature on the links between personality characteristics and PA and SB.
At the conceptual level, although socioemotional behavior and temperament display some similarities, they are two distinct concepts, where the former shifts attention to socialization experiences and is more situation-specific [
21]. However, socioemotional behavior was used as a conceptual counterpart of child temperament in this study because the children’s temperament measures developed by Rothbart et al. [
19] were not available at the time of the first data collection in 1968 [
21].
Despite the multiple strengths of the accelerometer measurement, the device is not optimal for estimating non-step-based activities, such as cycling and gym training [
34]. Additionally, the intensity cut-points are based on absolute intensity, which does not consider individual experiences of physical load even though they are highly correlated with VO
2max [
35]. Information on body posture was not analyzed either. Thus, SB in the current study may involve some activities done in a standing position, though the consensus is that SB refers only to the time spent sitting, reclining, or lying [
8].
It should also be noted that accelerometer data were collected between March 2020 and May 2021, that is, amid the COVID-19 pandemic. During the declared state of emergency in Finland (March 16, 2020–June 16, 2020), gatherings of more than 10 people were restricted, public indoor sports facilities (e.g., swimming halls) were closed, and remote work was strongly recommended, among other measures [
50,
51]. However, data collection was suspended during that period and continued from June 2, 2020 onwards because of the favorable situation for proceeding with the measurements. Sensitivity analyses on the current sample also revealed that exclusion of those participants whose measurement overlapped with the state of emergency at any point (N = 11) did not change the results.
Although the pandemic continued to affect daily life after the state of emergency, the effect on accelerometer-based PA in the present study is likely to be trivial. First, compared internationally, the pandemic situation in Finland during the data collection period was mild in terms of the incidence of COVID-19 and the restrictions [
33]. For example, a curfew was never imposed by Finnish authorities, and thus, restricted indoor activities were replaced by outdoor activities, especially walking, in adults [
50]. Second, international comparisons during the pandemic have suggested that even a partial lockdown did not affect device-based PA and that the effect of restriction orders on PA diminished after a couple of weeks [
52]. Compared to self-reports, accelerometers capturing habitual and incidental movements during the day [
47] are also expected to be less prone to possible changes in deliberate exercise. This standpoint is supported by the relatively similar MVPA and SB levels of the study sample compared to those reported among the Finnish population aged 50–69 years before the pandemic [
53]. Third, although the pandemic-caused pressure related to housework and caregiving on women has been discussed, it is not likely to affect the current sample. In Finland, women living in the transition stage to late adulthood rarely have childrearing duties and their employment rate is equal to that of men [
54].
Conclusions
This study extends the previous literature by suggesting that child socioemotional behavior and adult temperament have predictive value for accelerometer-measured PA and SB in women after decades. The behavioral activity of girls predicted higher levels of daily SB in late adulthood, but the association was attenuated when their own occupational status was taken into account. The negative affectivity of women predicted lower daily and leisure-time MVPA in late adulthood. Although few weak associations of socioemotional behavior and temperament with PA and SB were detected in women, they were observed over several decades, and thus, deserve attention in future studies. In light of these findings, health professionals may also be sensitive to individual characteristics, such as a tendency to experience more negative emotions, when doing health counseling or planning for health-promoting interventions targeting PA and SB. Future research should also address ways of promoting especially leisure-time PA among those high in negative affectivity.
The generalization of the results obtained from a native Finnish sample born in 1959 to other populations, age groups and later-born cohorts should be done with caution. Moreover, future studies should, in general, involve larger and more diverse samples as well as utilize domain-specific approaches and powerful longitudinal designs to investigate causal relationships. Particularly based on this study, it would be a major interest to shed light on the possible mechanisms, such as career-related variables, between child characteristics and later PA and SB. In addition, this research topic has traditionally been examined based on single dimensions. However, since a person can score high or low in several socioemotional or temperament dimensions at the same time, analyzing the combinations of these dimensions would be a more comprehensive approach, consequently gaining a deeper understanding of the complex associations between the variables of interest.
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