What is wellness? Investigating the importance of different domains of wellness among laypeople and experts: A survey study.

AIMS: Lack of consensus on wellness has led to a vast number of different conceptualisations, which hinder international efforts to monitor individual-level wellness and social progress comparably. This study aimed to aid in the harmonisation of the concept by contributing to the scarce research on laypeople's views on wellness. The study investigates whether the importance of different areas of wellness varies depending on age, gender, education or socio-economic position. Furthermore, considering that wellness models are often constructed by expert panels, this study aimed to shed light on how experts' and laypeople's views on wellness vary. METHODS: Altogether, 1152 laypeople and 23 Finnish experts rated the importance of 61 systematic review-based wellness domains. Each domain received an ordinal number, which, together with the Mann-Whitney U-test or Kruskal-Wallis test, was used to examine the differences between the groups. RESULTS: Thirteen wellness domains were found at the top of the lists, regardless of whether the results were analysed based on gender, age, education or socio-economic position. When looking at the priority order of different domains, we were able to identify several differences between the expert panel and laypeople. CONCLUSIONS: To ensure the relevance of wellness models, it is vital to understand the areas that laypeople consider to be important for their comprehensive wellness. This study offers 13 domains that could be combined with an expert view on wellness and used as a starting point for creating a more comprehensive, inclusive and better-suited wellness instrument.

Building Consensus on Domains of Wellness Using Finnish and International Expert Panels: A Delphi-Method Study.

PURPOSE: The paper investigates whether we can build consensus on wellness domains and create a more universal conceptual framework for wellness. DESIGN: A modified ranking type of Delphi method. PARTICIPANTS: Two separate panels consisting of 23 Finnish and 11 international experts. METHODS: Panels were asked to rate the importance of 61 systematic review-based wellness domains and to eventually form a wellness model in both panels. The similarities between the resulting models were investigated and a new conceptual framework for wellness was created. RESULTS: The Finnish model included 8 themes and 20 domains, and the international model 5 themes and eleven domains. Eight of the eleven domains were an exact match for the Finnish model (namely mental health, cognitive health, exercise, nutrition, community, life satisfaction, meaningfulness, work-life balance). There were also 2 similar domains that could be found in both models (namely self-care and lifestyle habits, social networks). A new conceptual framework for wellness was created based on these ten domains. CONCLUSION: The lack of consensus on the wellness construct has made it difficult to find comparable measures that could assess and improve the level of wellness of individuals, organizations, and society. This study offers a conceptual framework that can be further validated and turned into a more universal measurement instrument.

Using online wellness assessment to screen for risk of lowered work ability, burnout, depression and anxiety in occupational health: A cross-sectional study.

Objectives: An increasing prevalence of disability and sickness absences related to mental health highlights the need to find scalable measures to identify common occupational health challenges early on. This study (1) investigates how well current work ability measures capture psychosocial occupational health challenges, (2) examines how online wellness questionnaire data are linked to these challenges and (3) suggests a limited set of questions for screening employees. Methods: A total 709 employees filled out a wellness survey, the Work Ability Index, the Bergen Burnout Indicator and screening questions for generalized anxiety disorder and depression. The survey question clusters and previously identified domains of wellness were used to examine the correlations between the domains and occupational health indicators. Linear models and stepwise Akaike information criterion model reduction were used to identify questions that most explained variation in each challenge. The strongest questions were combined into a set, and recursive partitioning was used to form a screening tool for occupational health. Results: Despite over 80% of participants having good perceived work ability, we found a simultaneous anxiety risk in 22%, depression risk in 30%, some burnout symptoms in 7% and presenteeism in 36% of the participants. Correlations between several wellness domains and occupational health indicators were found. We identified eight questions that could be used to screen for a combined risk of lowered work ability, burnout, anxiety or depression. Conclusions: Our results demonstrate current measures not being sufficient to capture employees' mental health and suggest a brief set of questions to identify employees at risk.

Utility of an online well-being assessment in targeting employee well-being programmes: a cross-sectional survey study in Finland.

OBJECTIVES: Occupational health challenges are changing, emphasising the need for a more comprehensive approach. This study examines how a subjective well-being assessment can be used to identify target groups for work well-being interventions and brings insight into how survey-based well-being evaluations are linked to clinical health indicators (ie, anthropometric measurements and blood tests). DESIGN: A cross-sectional survey study using results from the Virta1 randomised controlled trial and a third-party well-being questionnaire database. SETTING AND PARTICIPANTS: Online well-being survey data from 2990 respondents was used to identify target groups for work well-being interventions and clinical health indicator data from 713 respondents was used to examine how subjective evaluations are linked to physical health. RESULTS: We identified five groups of employees with different well-being challenges and presenteeism levels: Good well-being, Hard on oneself, Lifestyle challenges, Recovery challenges and Multiple challenges. The subjective evaluations correlated with clinical health indicators, showing that the well-being groups differed significantly in their average clinical health profiles. Especially people in the Multiple challenges group had multiple physical health challenges, while people in the Good well-being and Hard on oneself groups did not. CONCLUSIONS: Our results show that a subjective well-being assessment can identify different groups with distinct characteristics and health risks and that subjective evaluations of well-being correlate strongly with physical health. Online well-being assessment offers potentially a cost-effective way for occupational health providers to screen large populations to target physical health examinations to groups that need them the most and simultaneously get a better understanding of their well-being needs.

The Effect of Healthy Lifestyle Changes on Work Ability and Mental Health Symptoms: A Randomized Controlled Trial.

OBJECTIVE: The effects of lifestyle interventions on the prevention of a decline in work ability and mental health are not well known. The aim of this randomized controlled trial was to examine the effects of healthy lifestyle changes on work ability, sleep, and mental health. METHODS: Workers aged 18-65 years, who were free from cardiovascular diseases, diabetes, and malignant diseases, and did not use medication for obesity or lipids were included (N = 319). Based on their cholesterol balance, participants were classified into medium-risk and high-risk groups and were randomized into four arms: group lifestyle coaching (N = 107), individual lifestyle coaching (N = 53), the control group for group coaching (N = 106), and the control group for individual coaching (N = 53). The intervention groups received eight sessions of mostly remote coaching for 8 weeks about healthy diet, physical activity, other lifestyle habits, and sources/management of stress and sleep problems, and the control groups received no intervention. In individual coaching, the coach focused more on individual problem solving and the possibilities for motivation and change. The intention-to-treat principle was applied, and missing data on the outcomes were imputed using multiple imputation. RESULTS: After the completion of the intervention, the risk of depressive symptoms was lower by 53% (95% CI 1-77%) in participants who received individual lifestyle coaching compared with the control group. The intervention had no beneficial effects on anxiety, work ability, sleep duration, or daily stress. In subgroup analyses, group lifestyle coaching had beneficial effects on depressive symptoms and work ability in participants with less tight schedules or less stretching work, whereas individual lifestyle coaching lowered the risk of depressive symptoms in those with fewer overlapping jobs, less tight schedules, or less stretching work. CONCLUSION: Short but intensive remote lifestyle coaching can reduce depressive symptoms and improve work ability, and time-related resources at work may improve mental health in the context of individual lifestyle intervention. However, further randomized controlled trials are needed to confirm the findings.

Association of the IL6-174(G/C) polymorphism with C-reactive protein concentration after weight loss in obese men.

Elevated plasma concentration of C-reactive protein has emerged as an important predictor of future cardiovascular diseases and metabolic abnormalities in apparently healthy individuals. Obese individuals tend to have elevated C-reactive protein concentrations. Weight loss induces a change in this protein, and single nucleotide polymorphisms in regulating genes might affect this change, since C-reactive protein concentration is known to be approximately 40-50% heritable. Our aim was to study the association between the IL6 -174(G/C), IL1B +3,954(C/T) and CRP +1,059(G/C) single nucleotide polymorphisms, and CRP concentrations in obese men during a weight reduction program. We genotyped 72 obese men who had participated in a weight reduction program. Their C-reactive protein concentrations, interleukin-6 levels and fat mass were determined at two time points: at baseline and after weight reduction (after 2 months). After weight reduction, the mean weight loss was 14.3 kg. Median C-reactive protein concentrations decreased, after weight reduction, from 1.72 to 1.22 mg/l (p < 0.02). The baseline C-reactive protein concentration did not differ between the IL6-174(G/C) genotypes, but after weight loss, concentrations differed (p = 0.03 Kruskal-Wallis test); the highest concentration was found in the CC genotype (CC 1.01 versus GG 1.93 mg/l, p = 0.007 ANOVA post-hoc test). This change in concentration was associated with the IL6-174(G/C) genotype (p = 0.01, Kruskal-Wallis test), being least in the CC genotype. The other single nucleotide polymorphisms studied were not associated with CRP concentrations. Our results show that, at baseline, there is no difference in C-reactive protein concentrations among the different IL6-174(G/C) genotypes, but after weight loss the CC genotype is associated with highest C-reactive protein concentrations, resulting from the fact that C-reactive protein seems not to decrease with weight loss in this genotype.

Physical inactivity and obesity: a vicious circle.

OBJECTIVE: Physical activity (PA) begins to decline in adolescence with a concomitant increase in weight. We hypothesized that a vicious circle may arise between decreasing PA and weight gain from adolescence to early adulthood. METHODS AND PROCEDURES: PA and self-perceived physical fitness assessed in adolescents (16-18 years of age) were used to predict the development of obesity (BMI > or =30 kg/m(2)) and abdominal obesity (waist >/=88 cm in females and > or =102 cm in males) at age 25 in 4,240 twin individuals (90% of twins born in Finland, 1975-1979). Ten 25-year-old monozygotic (MZ) twin pairs who were discordant for obesity (with a 16 kg weight difference) were then carefully evaluated for current PA (using a triaxial accelerometer), total energy expenditure (TEE, assessed by means of the doubly labeled water (DLW) method), and basal metabolic rate (BMR, assessed by indirect calorimetry). RESULTS: Physical inactivity in adolescence strongly predicted the risk for obesity (odds ratio (OR) 3.9, 95% confidence interval (CI) 1.4-10.9) and abdominal obesity (4.8, 1.9-12.0) at age 25, even after adjusting for baseline and current BMI. Poor physical fitness in adolescence also increased the risk for overall obesity (5.1, 2.0-12.7) and abdominal obesity (3.2, 1.5-6.7) in adulthood. Physical inactivity was both causative and secondary to the development of obesity discordance in the MZ pairs. TEE did not differ between the MZ co-twins. PA was lower whereas BMR was higher in the obese co-twins. DISCUSSION: Physical inactivity in adolescence strongly and independently predicts total (and especially) abdominal obesity in young adulthood, favoring the development of a self-perpetuating vicious circle of obesity and physical inactivity. Physical activity should therefore be seriously recommended for obesity prevention in the young.

Effects of a weight maintenance program with or without exercise on the metabolic syndrome: a randomized trial in obese men.

BACKGROUND: Weight maintenance (WM) after weight reduction (WR) is difficult, but increased physical activity may help. We studied whether adding exercise to diet counseling decreases the occurrence of the metabolic syndrome (MBO). METHODS: Ninety voluntary middle-aged men with a BMI range of 30-40 and a waist girth >100 cm were recruited to the research institute's clinic in 1997. After a very-low-energy diet for 2 months (WR), the men were randomized into a walking, resistance training or control group for 6 months (WM). All groups received similar dietary advice. After WM, there was a 23-month follow-up. Diagnosis of MBO was based on >or= 3 components. RESULTS: After WR, the mean weight loss was 14.2 kg. At the end, the weight decrease was 4.8 kg (n = 68) with no statistically significant difference between the groups. All groups had improved some components (insulin, HDL cholesterol, body composition) of MBO. When the groups were combined, the odds ratio for the occurrence of MBO (vs. baseline) was 0.10 after WR, 0.08 after WM and 0.29 at the end. CONCLUSIONS: Adding structured exercise to diet counseling did not alleviate MBO better than diet only. However, the occurrence of MBO was reduced in all groups at the end.