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Languagesfinnish, english, swedish
Publications (12) View all
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Article: A 26-Year Follow-Up Study of Heavy Drinking Trajectories from Adolescence to Mid-Adulthood and Adult Disadvantage.
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ABSTRACT: AIMS: The aim of the study was to identify heavy drinking trajectories from age 16 to 42 years and to examine their associations with health, social, employment and economic disadvantage in mid-adulthood. METHODS: Finnish cohort study's participants who were 16 years old in 1983 were followed up at age 22, 32 and 42 (n = 1334). Heavy drinking was assessed at every study phase and based on these measurements trajectories of heavy drinking were identified. The trajectory groups were then examined as predictors of disadvantage at age 42. RESULTS: Five distinct heavy drinking trajectories were identified: moderate (35%), steady low (22%), decreasing (9%), increasing (11%) and steady high (23%). Frequencies of the trajectory groups differed by gender. Using the moderate trajectory as a reference category, women in the steady high trajectory had an increased risk of experiencing almost all disadvantages at age 42. In men, increasing and steady high groups had an increased risk for experiencing health and economic disadvantage. CONCLUSION: Steady high female drinkers and steady high and increasing male drinkers had the highest risk for disadvantage in mid-adulthood. By identifying heavy drinking trajectories from adolescence to mid-adulthood we can better predict long-term consequences of heavy alcohol use and plan prevention and intervention programmes.Alcohol and Alcoholism 03/2013; · 2.95 Impact Factor -
Article: Leisure Time Physical Activity in a 22-Year Follow-Up among Finnish Adults.
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ABSTRACT: BACKGROUND: The aim of this study was to explore long-term predictors of leisure time physical activity in the general population. METHODS: This study comprised 718 men and women who participated in the national Mini-Finland Health Survey from 1978-1980 and were re-examined in 2001. Participants were aged 30-80 at baseline. Measurements included interviews, health examinations, and self-administered questionnaires, with information on socioeconomic position, occupational and leisure time physical activity, physical fitness, body mass index, smoking, alcohol consumption, and physical functional capacity. Analyses included persons who were working and had no limitations in functional capacity at baseline. RESULTS: The strongest predictor of being physically active at the follow-up was participation in physical activity at baseline, with an OR 13.82 (95%CI 5.50-34.70) for 3 or more types of regular activity, OR 2.33 (95%CI 1.22-4.47) for 1-2 types of regular activity, and OR 3.26 (95%CI 2.07-5.15) for irregular activity, as compared to no activity. Other determinants for being physically active were moving upwards in occupational status, a high level of baseline occupational physical activity and remaining healthy weight during the follow-up. CONCLUSIONS: To prevent physical inactivity among older adults, it is important to promote physical activity already in young adulthood and in middle age and to emphasize the importance of participating in many types of physical activity.International Journal of Behavioral Nutrition and Physical Activity 10/2012; 9(1):121. · 3.83 Impact Factor -
Article: Occupational class differences in leisure-time physical inactivity--contribution of past and current physical workload and other working conditions.
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ABSTRACT: Our aim was to examine the contribution of past and current physical workload to occupational class differences in leisure-time physical inactivity. Data were taken from the Finnish population-based Health 2000 Survey of employees aged >or=30 years (N=3355). We assessed physical activity during leisure time using a questionnaire and dichotomized responses to inactive versus active. Occupational class was classified into white- and blue-collar worker. Adjustments were made for current work-related factors, other measures of socioeconomic position, clinically diagnosed chronic diseases, other health behaviors, and history of physical workload. We applied sequential logistic regression to the analyses. Inactivity during leisure time was more common in blue-collar employees than in their white-collar counterparts [women odds ratio (OR) 1.50, 95% confidence interval (95% CI) 1.12-2.00; men OR 1.66, 95% CI 1.30-2.12]. These occupational differences were not due to working hours, work schedule, or chronic diseases. Among women, current job strain decreased the occupational differences in leisure-time physical inactivity slightly (OR 1.37, 95% CI 0.99-1.04). Education and household income contributed to occupational differences for men (OR 1.45, 95% CI 1.02-2.07), but had no additional effect among women. The occupation differences in leisure-time physical inactivity disappeared after adjusting for smoking and body mass index in women (OR 1.33, 95% CI 0.97-1.83) and men (OR 1.27, 95% CI 0.88-1.82) and were further attenuated after adjusting for history of physical workload among men (OR 1.07, 95% CI 0.67-1.72). Having a long history of exposure to physical work (among men) and a high current job strain (among women) contributed to occupational class differences in leisure-time physical inactivity.Scandinavian journal of work, environment & health 12/2009; 36(1):62-70. · 3.12 Impact Factor -
Article: Effects of childhood socio-economic conditions on educational differences in leisure-time physical activity.
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ABSTRACT: Our purpose was to assess the direct and indirect contribution of childhood socio-economic conditions to educational differences in leisure-time physical activity among women and men. Population-based data were derived from a representative sample of Finns aged >or=30 years (N = 7112) as part of Health 2000 Survey. Multinomial logistic regression analysis was applied. Leisure-time physical activity was divided into inactivity, moderate and high activity. Childhood socio-economic conditions had both a direct and indirect effect through adulthood socio-economic conditions on educational differences in leisure-time physical activity. The direct effect of childhood socio-economic conditions on educational differences in inactivity was stronger than its indirect effects through adulthood socio-economic conditions and other health behaviours and related factors. Adulthood socio-economic conditions had a considerable indirect effect on educational differences in leisure-time physical activity through other adulthood health behaviours and related factors among men. In order to narrow educational differences in leisure-time physical activity, we should secure a childhood environment that enables a physically active lifestyle, support leisure-time physical activity in diverse occupational groups, guarantee equal physical activity possibilities across different educational careers and support those with insufficient material resources.The European Journal of Public Health 11/2009; 20(3):346-53. · 2.73 Impact Factor -
Article: The contribution of childhood circumstances, current circumstances and health behaviour to educational health differences in early adulthood.
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ABSTRACT: The life course approach emphasises the contribution of circumstances in childhood and youth to adult health inequalities. However, there is still a lot to know of the contribution of living conditions in childhood and youth to adult health inequalities and how later environmental and behavioural factors are connected with the effects of earlier circumstances. This study aims to assess a) how much childhood circumstances, current circumstances and health behaviour contribute to educational health differences and b) to which extent the effect of childhood circumstances on educational health differences is shared with the effects of later living conditions and health behaviour in young adults. The data derived from the Health 2000 Survey represent the Finnish young adults aged 18-29 in 2000. The analyses were carried out on 68% (n = 1282) of the sample (N = 1894). The cross-sectional data based on interviews and questionnaires include retrospective information on childhood circumstances. The outcome measure was poor self-rated health. Poor self-rated health was much more common among subjects with primary education only than among those in the highest educational category (OR 4.69, 95% CI 2.63 to 8.62). Childhood circumstances contributed substantially (24%) to the health differences between these educational groups. Nearly two thirds (63%) of this contribution was shared with behavioural factors adopted by early adulthood, and 17% with current circumstances. Health behaviours, smoking especially, were strongly contributed to educational health differences. To develop means for avoiding undesirable trajectories along which poor health and health differences develop, it is necessary to understand the pathways to health inequalities and know how to improve the living conditions of families with children.BMC Public Health 02/2009; 9:164. · 2.00 Impact Factor
