Prevalence and predictors of smoking, quit attempts and total smoking ban at the University of Turku, Finland

Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: evidence from students at a university in Finland

BACKGROUND

No previous research of university students in Finland assessed lifestyle behavioral risk factors (BRFs), grouped students into clusters, appraised the relationships of the clusters with their mental well-being, whilst controlling for confounders. The current study undertook this task.

METHODS

Students at the University of Turku (n = 1177, aged 22.96 ± 5.2 years) completed an online questionnaire that tapped information on sociodemographic variables (age, sex, income sufficiency, accommodation during the semester), four BRFs [problematic alcohol consumption, smoking, food consumption habits, moderate-to-vigorous physical activity (MVPA)], as well as depressive symptoms and stress. Two-step cluster analysis of the BRFs using log-likelihood distance measure categorized students into well-defined clusters. Two regression models appraised the associations between cluster membership and depressive symptoms and stress, controlling for sex, income sufficiency and accommodation during the semester.

RESULTS

Slightly more than half the study participants (56.8%) had always/mostly sufficient income and 33% lived with parents/partner. Cluster analysis of BRFs identified three distinct student clusters, namely Cluster 1 (Healthy Group), Cluster 2 (Smokers), and Cluster 3 (Nonsmokers but Problematic Drinkers). Age, sex and MVPA were not different across the clusters, but Clusters 1 and 3 comprised significantly more respondents with always/mostly sufficient income and lived with their parents/partner during the semester. All members in Clusters 1 and 3 were non-smokers, while all Cluster 2 members comprised occasional/daily smokers. Problematic drinking was significantly different between clusters (Cluster 1 = 0%, Cluster 2 = 54%, Cluster 3 = 100%). Cluster 3 exhibited significantly healthier nutrition habits than both other clusters. Regression analysis showed: (1) males and those with sufficient income were significantly less likely to report depressive symptoms or stress; (2) those living with parents/partner were significantly less likely to experience depressive symptoms; (3) compared to Cluster 1, students in the two other clusters were significantly more likely to report higher depressive symptoms; and (4) only students in Cluster 2 were more likely to report higher stress.

CONCLUSIONS

BRFs cluster together, however, such clustering is not a clear-cut, all-or-none phenomenon. Students with BRFs consistently exhibited higher levels of depressive symptoms and stress. Educational and motivational interventions should target at-risk individuals including those with insufficient income or living with roommates or alone.

Are behavioural risk factors clusters associated with self-reported health complaints? University students in Finland

OBJECTIVES

No previous research of university students in Finland assessed lifestyle behavioural risk factors (BRFs) and categorized students into clusters, explored the associations of the clusters with self-reported health complaints (HCs), whilst controlling for potential confounders. The current study undertook this task.

METHODS

Students at the University of Turku (1,177) completed an online well-being questionnaire that assessed socio-demographic variables, 5 BRFs - problematic alcohol consumption, smoking, illicit drug use, food consumption habits, moderate-to-vigorous physical activity (MVPA), and 22 HCs. A food frequency questionnaire assessed students' consumption of a range of foods, and a dietary guideline adherence score was computed based on WHO dietary recommendations for Europe. Three separate regression models appraised the associations between the cluster membership and HCs factors, adjusting for sex, income sufficiency and self-rated health.

RESULTS

Mean age was 23 ± 5.2 years, 77% had never smoked and 79% never used illicit drug/s. Factor analysis of HCs resulted in four-factors (psychological, circulatory/breathing, gastro-intestinal, pains/aches); cluster analysis of BRFs identified two distinctive student clusters. Cluster 1 represented more healthy students who never smoked/used illicit drugs, had no problematic drinking, and undertook MVPA on 4.42 ± 3.36 days/week. As for cluster 2 students, half the cluster smoked occasionally/daily, used illicit drug/s, and > 50% had problematic drinking and students undertook MVPA on 4.02 ± 3.12 days/week. More cluster 2 students adhered to healthy eating recommendations, but the difference was not significant between clusters. Regression analysis revealed that females, those with sufficient income, and with excellent/very good self-rated general health were significantly less likely to report all four HCs. Cluster 2 students were significantly more likely to report psychological complaints, circulatory/breathing and gastro-intestinal complaints. There was no significant association between BRFs clusters and pains/aches factor.

CONCLUSIONS

Risk taking students with less healthy lifestyles and behaviour were consistently associated with poorer psychological and somatic health.

Public support for smoke-free policies in outdoor areas and (semi-)private places: a systematic review and meta-analysis

Background

Smoke-free policies are essential to protect people against tobacco smoke exposure. To successfully implement smoke-free policies that go beyond enclosed public places and workplaces, public support is important. We undertook a comprehensive systematic review of levels and determinants of public support for indoor (semi-)private and outdoor smoke-free policies.

Methods

In this systematic review and meta-analysis, six electronic databases were searched for studies (published between 1 January 2004 and 19 January 2022) reporting support for (semi-)private and outdoor smoke-free policies in representative samples of at least 400 respondents aged 16 years and above. Two reviewers independently extracted data and assessed risk of bias of individual reports using the Mixed Methods Appraisal Tool. The primary outcome was proportion support for smoke-free policies, grouped according to location covered. Three-level meta-analyses, subgroup analyses and meta-regression were performed.

Findings

14,749 records were screened, of which 107 were included; 42 had low risk of bias and 65 were at moderate risk. 99 studies were included in the meta-analyses, reporting 326 measures of support from 896,016 individuals across 33 different countries. Support was pooled for indoor private areas (e.g., private cars, homes: 73%, 95% confidence interval (CI): 66-79), indoor semi-private areas (e.g., multi-unit housing: 70%, 95% CI: 48-86), outdoor hospitality areas (e.g., café and restaurant terraces: 50%, 95% CI: 43-56), outdoor non-hospitality areas (e.g., school grounds, playgrounds, parks, beaches: 69%, 95% CI: 64-73), outdoor semi-private areas (e.g., shared gardens: 67%, 95% CI: 53-79) and outdoor private areas (e.g., private balconies: 41%, 95% CI: 18-69). Subcategories showed highest support for smoke-free cars with children (86%, 95% CI: 81-89), playgrounds (80%, 95% CI: 74-86) and school grounds (76%, 95% CI: 69-83). Non-smokers and ex-smokers were more in favour of smoke-free policies compared to smokers. Support generally increased over time, and following implementation of each smoke-free policy.

Interpretation

Our findings suggested that public support for novel smoke-free policies is high, especially in places frequented by children. Governments should be reassured about public support for implementation of novel smoke-free policies.

Funding

Dutch Heart Foundation, Lung Foundation Netherlands, Dutch Cancer Society, Dutch Diabetes Research Foundation and Netherlands Thrombosis Foundation.

Health-Promoting Behavior and Lifestyle Characteristics of Students as a Function of Sex and Academic Level

University students frequently engage in unhealthy behaviors. However, there is a lack of studies examining a wide range of their lifestyle characteristics by sex and academic level of study. This cross-sectional survey of students enrolled in BSc, MSc, or PhD programs at one university in Germany (N = 3389) assessed physical activity (PA), sedentary behavior (SB), nutrition, sleep quality, and alcohol, tobacco, and other drug (ATOD) use by sex and academic level and was conducted with EvaSys version 8.0. Chi-squared tests compared categorical variables by sex, and binary logistic regression analyses adjusted for sex with Bonferroni adjustments evaluated differences across academic level. Although 91% of students achieved the aerobic PA guidelines, only 30% achieved the muscle strengthening exercises (MSE) guidelines, and 44% had high SB. Likewise, <10% met the fruit and vegetable consumption (FVC) recommendations, >40% of students experienced impaired sleep, and >30% had hazardous alcohol consumption. Less than 20% of the sample achieved the guideline/recommendation of all three PA, MSE and SB. Some behaviors exhibited significant sex and academic level differences. The identified at-risk groups included males (lower FVC), females (eating more during stress), and BSc students (poorer nutrition/sleep quality, more ATOD use). Given the above findings, multipronged strategies are needed with an overarching focus highlighting the health−academic achievement links. Behavioral interventions and environmental policies are required to raise awareness and promote student health.

Multi-Substance Use Behaviors: Prevalence and Correlates of Alcohol, Tobacco and Other Drug (ATOD) Use among University Students in Finland

Virtually no studies appraised the co-use of alcohol, tobacco, and other drug (ATOD) among Finn undergraduates. We assessed the associations between sociodemographic, health, academic, policy, and lifestyle characteristics (independent variables); and individual, multiple and increasing ATOD use (dependent variables) using regression analyses. Data were collected by online questionnaire at the University of Turku, Finland (1177 students). Roughly 22% of the sample smoked, 21% ever used illicit drug/s, 41% were high frequency drinkers, and 31.4%, 16.3%, and 6.7% reported 1, 2, or 3 ATOD behaviors respectively. Individual ATOD use was significantly positively associated with the use of the other two substances [adjusted odds ratio (Adj OR range 1.893-3.311)]. Multiple ATOD use was negatively associated with being single (p = 0.021) or agreeing with total smoking or alcohol ban policy on campus (p < 0.0001 for each); but positively associated with not living with parents (p = 0.004). Increasing ATOD behaviors were significantly less likely among those agreeing with total smoking or alcohol ban policy on campus (p range 0.024 to <0.0001). Demographics significant to either individual, multiple, or increasing ATOD use included males, being single, not living with their parents during semesters, and to some extent, religiosity. Age, depressive symptoms, perceived stress, self-rated health, health awareness, income sufficiency, and academic variables were not associated with individual, multiple, or increasing ATOD use. Education and prevention efforts need to reinforce abstinence from ATOD, highlight their harmful outcomes, and target risk groups highlighted above. University strategies should be part of the wider country-wide successful ATOD control policies.