Lack of tools to evaluate the quality of diet impedes dietary counselling in healthcare. We constructed a scoring for a validated food intake questionnaire, to measure the adherence to a healthy diet that prevents type 2 diabetes (T2D). The Healthy Diet Index (HDI) consists of seven weighted domains (meal pattern, grains, fruit and vegetables, fats, fish and meat, dairy, snacks and treats). We studied the correlations of the HDI with nutrient intakes calculated from 7-day food records among 52 men and 25 women, and associations of HDI with biomarkers and anthropometrics among 645 men and 2455 women. The HDI correlated inversely with total fat (Pearson’s r = −0.37), saturated fat (r = −0.37), monounsaturated fat (r = −0.37), and the glycaemic index of diet (r = −0.32) and positively with carbohydrates (r = 0.23), protein (r = 0.25), fibre (r = 0.66), magnesium (r = 0.26), iron (r = 0.25), and vitamin D (r = 0.27), (p < 0.05 for all). In the linear regression model adjusted for BMI and age, HDI is associated inversely with waist circumference, concentrations of fasting and 2-h glucose and triglycerides in men and women, total and LDL cholesterol in women, and fasting insulin in men (p < 0.05 for all). The HDI proved to be a valid tool to measure adherence to a health-promoting diet and to support individualised dietary counselling.

Our aim was to validate a 16-item food intake questionnaire (16-FIQ) and create an easy to use method to estimate patients’ nutrient intake in primary health care. Participants (52 men, 25 women) completed a 7-day food record and a 16-FIQ. Food and nutrient intakes were calculated and compared using Spearman correlation. Further, nutrient intakes were compared using kappa-statistics and exact and opposite agreement of intake tertiles. The results indicated that the 16-FIQ reliably categorized individuals according to their nutrient intakes. Methods to estimate nutrient intake based on the answers given in 16-FIQ were created. In linear regression models nutrient intake estimates from the food records were used as the dependent variables and sum variables derived from the 16-FIQ were used as the independent variables. Valid regression models were created for the energy proportion of fat, saturated fat, and sucrose and the amount of fibre (g), vitamin C (mg), iron (mg), and vitamin D (μg) intake. The 16-FIQ is a valid method for estimating nutrient intakes in group level. In addition, the 16-FIQ could be a useful tool to facilitate identification of people in need of dietary counselling and to monitor the effect of counselling in primary health care.

Objectives In a prospective study among workers in an airline company, we explored whether change in work stress symptoms or night shifts was associated with nutrient intake. Methods Participants in a workplace type 2 diabetes (T2D) prevention study completed a questionnaire on lifestyle, work stress symptoms, work schedule, and food intake at baseline and after 2.4-years follow-up (211 men and 155 women, 93% with increased risk for T2D). Multiple linear regression models with covariates were used to explore the associations between change in work stress symptoms or night shifts and change in nutrient intake during the follow-up. Results Among men, an increase in stress and a decrease in perceived workability was associated with a higher proportion of energy (E%) from fat [β 0.6, 95% confidence interval (CI) 0.07-1.11, β 1.3, 95% CI 0.57-2.05] and saturated fat (β 0.3, 95% CI 0.02-0.58, β 0.5, 95% CI 0.14-0.90), respectively. Furthermore, a decrease in workability was associated with lower vitamin C intake (β-9.2, 95% CI -16.56- -1.84) and an increase in sleepiness with higher E% from saturated fat (β 0.7, 95% CI 0.00-0.15). Among women, an increase in work-related fatigue was associated with higher alcohol intake (β 7.5, 95% CI 1.25-13.74) and an increase of night shifts was associated with higher E% from fat (β 0.24, 95% CI 0.00-0.47) and saturated fat (β 0.17, 95% CI 0.04-0.29). Conclusions Work stress symptoms were associated with a reduction in diet quality especially among men. The possible impact of work stress symptoms on workers' dietary habits should be acknowledged and the assessment of dietary habits should consequently be incorporated into occupational health examinations.

Objective: We aimed to examine the association of recovery from work and sleep with workers’ dietary habits. Design: Cross-sectional study. Need for recovery (NFR) from work was assessed with a validated questionnaire. Sleep was assessed with five questions from the Nordic Sleep Questionnaire and sleep quality question. Dietary habits were estimated using a validated sixteen food groups-containing questionnaire. Ordered logistic regression was used to explore the associations of NFR and sleep with dietary habits adjusted for age, education, marital status, work schedule, working full or part time and occupation. Setting: Follow-up visits of type 2 diabetes prevention study cohort in a Finnish airline company Participants: The study included 737 men and 605 women. Results: Poor recovery from work was associated with a higher eating frequency (OR = 1·03, 95 % CI 1·00, 1·06), higher intake of fast food (OR = 1·05, 95 % CI 1·02, 1·08) and sweets (OR = 1·05, 95 % CI 1·02, 1·08) as well as lower intake of vegetables (OR = 0·96, 95 % CI 0·93, 0·98) and fruits (OR = 0·96, 95 % CI 0·93, 0·98) among men. In women, poor recovery from work was associated with higher fast food (OR = 1·06, 95 % CI 1·02, 1·09) and desserts consumption (OR = 1·04, 95 % CI 1·00, 1·07). Among men and women, sleep problems were associated with higher eating frequency (men: OR = 1·04, 95 % CI 1·00, 1·07, women: OR = 1·06, 95 % CI 1·02, 1·11), consumption of fast food (men: OR = 1·07, 95 % CI 1·04, 1·11, women: OR = 1·06, 95 % CI 1·02, 1·10) and sweets (men: OR = 1·05, 95 % CI 1·01, 1·08, women: OR = 1·04, 95 % CI 1·00, 1·08). Conclusions: Poor recovery from work and sleep problems were associated with unfavourable dietary habits especially in men.

Epidemiological studies and animal models suggest that early postnatal nutrition and growth can influence adult health. However, few human studies have objective recordings of early nutrient intake. We studied whether nutrient intake and growth during the first 9 weeks after preterm birth with very low birth weight (VLBW, <1500 g) predict total energy intake, resting energy expenditure (REE), physical activity and food preferences in young adulthood. We collected daily nutritional intakes and weights during the initial hospital stay from hospital records for 127 unimpaired VLBW participants. At an average age 22.5 years, they completed a three-day food record and a physical activity questionnaire and underwent measurements of body composition (dual X-ray absorptiometry; n = 115 with adequate data) and REE (n = 92 with adequate data). We used linear regression and path analysis to investigate associations between neonatal nutrient intake and adult outcomes. Higher energy, protein and fat intakes during the first three weeks of life predicted lower relative (=per unit lean body mass) energy intake and relative REE in adulthood, independent of other pre- and neonatal factors. In path analysis, total effects of early nutrition and growth on relative energy intake were mostly explained by direct effects of early life nutrition. A path mediated by early growth reached statistical significance only for protein intake. There were no associations of neonatal intakes with physical activity or food preferences in adulthood. As a conclusion, higher intake of energy and nutrients during first three weeks of life of VLBW infants predicts energy balance after 20 years. This association is partly mediated through postnatal growth.