Dataset Information

Patterns of multiple lifestyle risk factors and their link to mental health in the German adult population: a cross-sectional study.

ABSTRACT: OBJECTIVES:Lifestyle risk factors, such as drinking or unhealthy diet, can expotentiate detrimental health effects. Therefore, it is important to investigate multiple lifestyle risk factors instead of single ones. The study aims at: (1) identifying patterns of lifestyle risk factors within the adult general population in Germany and (2) examining associations between the extracted patterns and external factors. DESIGN:Cross-sectional study. SETTING:General German adult population (aged 18-64 years). PARTICIPANTS:Participants of the 2015 Epidemiological Survey of Substance Abuse (n=9204). PRIMARY OUTCOME MEASURES:Lifestyle risk factors (daily smoking, at-risk alcohol consumption, unhealthy diet, low physical activity, weekly use of pharmaceuticals, as well as consumption of cannabis and other illicit drugs). RESULTS:A latent class analysis was applied to identify patterns of lifestyle risk factors, and a multinomial logistic regression was carried out to examine associations between the extracted classes and external factors. A total of four classes were extracted which can be described as healthy lifestyle (58.5%), drinking lifestyle (24.4%), smoking lifestyle (15.4%) and a cumulate risk factors lifestyle (1.7%). Individuals who were male, at younger age and single as well as individuals with various mental health problems were more likely to show multiple lifestyle risk factors. CONCLUSIONS:Healthcare professionals should be aware of correlations between different lifestyle risk factors as well as between lifestyle risk groups and mental health. Health promotion strategies should further focus especially on younger and single men.

SUBMITTER: Atzendorf J

PROVIDER: S-EPMC6303616 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Patterns of multiple lifestyle risk factors and their link to mental health in the German adult population: a cross-sectional study.

Atzendorf Josefine J Apfelbacher Christian C Gomes de Matos Elena E Kraus Ludwig L Piontek Daniela D

BMJ open 20181219 12

<h4>Objectives</h4>Lifestyle risk factors, such as drinking or unhealthy diet, can expotentiate detrimental health effects. Therefore, it is important to investigate multiple lifestyle risk factors instead of single ones. The study aims at: (1) identifying patterns of lifestyle risk factors within the adult general population in Germany and (2) examining associations between the extracted patterns and external factors.<h4>Design</h4>Cross-sectional study.<h4>Setting</h4>General German adult popu ...[more]

PMID: 30573479

Similar Datasets

Project description:BackgroundThere is now evolving data exploring the relationship between depression and various individual lifestyle factors such as diet, physical activity, sleep, alcohol intake, and tobacco smoking. While this data is compelling, there is a paucity of longitudinal research examining how multiple lifestyle factors relate to depressed mood, and how these relations may differ in individuals with major depressive disorder (MDD) and those without a depressive disorder, as 'healthy controls' (HC).MethodsTo this end, we assessed the relationships between 6 key lifestyle factors (measured via self-report) and depressed mood (measured via a relevant item from the Patient Health Questionnaire) in individuals with a history of or current MDD and healthy controls (HCs). Cross-sectional analyses were performed in the UK Biobank baseline sample, and longitudinal analyses were conducted in those who completed the Mental Health Follow-up.ResultsCross-sectional analysis of 84,860 participants showed that in both MDD and HCs, physical activity, healthy diet, and optimal sleep duration were associated with less frequency of depressed mood (all p < 0.001; ORs 0.62 to 0.94), whereas screen time and also tobacco smoking were associated with higher frequency of depressed mood (both p < 0.0001; ORs 1.09 to 1.36). In the longitudinal analysis, the lifestyle factors which were protective of depressed mood in both MDD and HCs were optimal sleep duration (MDD OR = 1.10; p < 0.001, HC OR = 1.08; p < 0.001) and lower screen time (MDD OR = 0.71; p < 0.001, HC OR = 0.80; p < 0.001). There was also a significant interaction between healthy diet and MDD status (p = 0.024), while a better-quality diet was indicated to be protective of depressed mood in HCs (OR = 0.92; p = 0.045) but was not associated with depressed mood in the MDD sample. In a cross-sectional (OR = 0.91; p < 0.0001) analysis, higher frequency of alcohol consumption was surprisingly associated with reduced frequency of depressed mood in MDD, but not in HCs.ConclusionsOur data suggest that several lifestyle factors are associated with depressed mood, and in particular, it calls into consideration habits involving increased screen time and a poor sleep and dietary pattern as being partly implicated in the germination or exacerbation of depressed mood.

Project description:The aim of this study was to examine the associations of dietary-lifestyle patterns (DLPs) with adiposity and metabolic abnormalities in adult Polish men that were under 40. The cross-sectional study included 358 men that were 19-40-year-old. Dietary and lifestyle data were collected with multicomponent food frequency questionnaire (KomPAN®). DPLs were derived with Principal Component Analysis (PCA) using 25 dietary and six lifestyle as the input variables. Adiposity was determined with the use of: overweight (body mass index 25-29.9 kg/m2), central obesity (waist-to-height ratio ? 0.5), general obesity (body fat ? 25%), excessive visceral fat tissue (? median), and increased skeletal muscle mass (? median). The metabolic abnormalities were characterised by elevated: fasting blood glucose (FBG ? 100 mg/dL), triglycerides (TG ? 150 mg/dL), total cholesterol (TC ? 200 mg/dL), or systolic or diastolic blood pressure (? 130 or ? 85 mmHg, respectively). Four PCA-driven DLPs were derived and labelled accordingly to the most characteristic dietary or lifestyle behaviours that were correlated with each pattern. Multivariate logistic regression revealed that higher adherence (upper vs. bottom tertile as referent) to "Protein food, fried-food, and recreational physical activity" pattern was associated with higher odds of overweight and increased skeletal muscle mass, and lower odds of: general obesity, excessive visceral fat tissue, and elevated TC. Higher adherence to "Healthy diet, active, past smokers" pattern was associated with higher odds of overweight and lower odds of: general obesity, excessive visceral fat tissue, and elevated FBG. Higher adherence to "Sandwiches and convenient diet" pattern was associated with higher odds of: central obesity, general obesity, excessive visceral fat tissue, elevated TC, elevated TG, occurrence at least two metabolic abnormalities, and lower odds of increased skeletal muscle mass. A higher adherence to "Fast foods and stimulants" pattern was associated with higher odds of central obesity, general obesity, excessive visceral fat tissue, and lower odds of increased skeletal muscle mass. The interrelations between diet and lifestyle behaviours were reflected in three out of four patterns. Healthy diet attempts combined with active lifestyle was associated with reduced risk of adiposity and metabolic abnormalities despite some unhealthy components, like former smoking or fried-food consumption. In contrary, patterns that were composed of undesirable dietary behaviours solely, as well as poor diet combined with stimulant use, were associated with higher adiposity and worse metabolic health, despite the relatively young age of the study participants. Accurate mapping of dietary-lifestyle behaviours can serve as a tool for formulating evidence-based recommendations.

Project description:A healthy lifestyle can be beneficial for one's mental health. Thus, identifying healthy lifestyle choices that promote psychological well-being and reduce mental problems is useful to prevent mental disorders. The aim of this longitudinal study was to evaluate the predictive values of a broad range of lifestyle choices for positive mental health (PMH) and mental health problems (MHP) in German and Chinese students.Data were assessed at baseline and at 1-year follow-up. Samples included 2991 German (Mage?=?21.69, SD?=?4.07) and 12,405 Chinese (Mage?=?20.59, SD?=?1.58) university students. Lifestyle choices were body mass index, frequency of physical and mental activities, frequency of alcohol consumption, smoking, vegetarian diet, and social rhythm irregularity. PMH and MHP were measured with the Positive Mental Health Scale and a 21-item version of the Depression Anxiety and Stress Scale. The predictive values of lifestyle choices for PMH and MHP at baseline and follow-up were assessed with single-group and multi-group path analyses.Better mental health (higher PMH and fewer MHP) at baseline was predicted by a lower body mass index, a higher frequency of physical and mental activities, non-smoking, a non-vegetarian diet, and a more regular social rhythm. When controlling for baseline mental health, age, and gender, physical activity was a positive predictor of PMH, smoking was a positive predictor of MHP, and a more irregular social rhythm was a positive predictor of PMH and a negative predictor of MHP at follow-up. The good fit of a multi-group model indicated that most lifestyle choices predict mental health comparably across samples. Some country-specific effects emerged: frequency of alcohol consumption, for example, predicted better mental health in German and poorer mental health in Chinese students.Our findings underline the importance of healthy lifestyle choices for improved psychological well-being and fewer mental health difficulties. Effects of lifestyle on mental health are comparable in German and Chinese students. Some healthy lifestyle choices (i.e., more frequent physical activity, non-smoking, regular social rhythm) are related to improvements in mental health over a 1-year period.

Project description:Background: The development of evidence-based interventions for delaying or preventing cognitive impairment is an important challenge. Most previous studies using self-report questionnaires face problems with reliability and consistency due to recall bias or misclassification among older people. Therefore, objective measurement of lifestyle components is needed to confirm the relationships between lifestyle factors and cognitive function. Aims: The current study examined the relationship between lifestyle factors collected with wearable sensors and cognitive function among community-dwelling older people using machine learning. Methods: In total, 855 participants (mean age: 73.8 years) wore a wristband sensor for 7.8 days on average every 3 months. Various lifestyle parameters were measured, including walking steps, conversation time, total sleep time (TST), sleep efficiency, time awake after sleep onset, awakening count, napping time, and heart rate. Random forest (RF) regression analysis was used to examine the relationships between total daily sensing data and Mini-Mental State Examination (MMSE) scores. Confounding factor analysis was conducted with models that were adjusted and unadjusted for demographic and vascular risk factors, and selected variables were assessed as risk and protective factors using partial dependence plots (PDPs). Results: Lifestyle data were collected for 31.3 ± 7.1 days per year using wristband sensors. RF regression analysis adjusted for age, gender, and education levels selected four variables, including number of walking steps, conversation time, TST, and heart rate. Moreover, walking steps, conversation time, and heart rate remained after RF regression analysis adjusted for demographic and vascular risk factors. Number of walking steps, conversation time, and heart rate were categorized as protective factors, whereas TST was categorized as a risk factor for cognitive function. Although PDPs of number of walking steps and heart rate revealed continuously increased MMSE scores, those of conversation time and TST and revealed that the tendency in the graph was reversed at the boundary of a particular threshold (321.1 min for conversation time, 434.1 min for TST). Conclusions: Lifestyle factors, such as physical activity, sleep, and social activity appear to be associated with cognitive function among older people. Physical activity and appropriate durations of sleep and conversation are important for cognitive function.

Dataset Information

Patterns of multiple lifestyle risk factors and their link to mental health in the German adult population: a cross-sectional study.

Publications

Patterns of multiple lifestyle risk factors and their link to mental health in the German adult population: a cross-sectional study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets