---
title: "Social isolation and health behaviors across European generations: findings from ESS Round 11"
id: "plos-one-15-social-isolation-and-health-risks-do-the-younger-generations-behave-differently"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-15-social-isolation-and-health-risks-do-the-younger-generations-behave-differently"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357725"
published_at: "2026-09-10T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Social isolation and health behaviors across European generations: findings from ESS Round 11
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-15-social-isolation-and-health-risks-do-the-younger-generations-behave-differently
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357725)
- **Published At:** 2026-09-10T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This cross-sectional study used data from Round 11 (2023/2024) of the European Social Survey covering individuals from 24 European countries to examine associations between **social isolation** and health-related behaviors across generations. - The authors assessed five indicators of social isolation: **no social meetings**, lack of participation in activities, living alone, lack of emotional support, and unhappiness, and four health outcomes: physical inactivity, tobacco use, alcohol consumption, and body mass index (BMI). - Logistic regression models were applied to estimate associations between each social isolation indicator and each health outcome, stratified by generational cohorts (Silent Generation, Baby Boomers, Generation X, Millennials, Generation Z). - Key reported finding: **non-social meetings** were consistently associated with greater odds of physical inactivity across all generations (reported OR range 1.291–1.789 in the abstract). - The study highlights that social isolation relates meaningfully to unhealthy lifestyles not only in older adults but also among younger cohorts, with some associations in young people exceeding those observed in older groups. - The authors describe Generation X as a transitional group that balances traditional and modern lifestyles in the observed associations. - The paper calls for age-specific policies to address social isolation and mitigate long-term public health consequences of social disconnection in Europe. - Data are available from the ESS Data Portal; the authors report no special data access privileges. The authors declared no funding and no competing interests. - Publication details: received March 16, 2026; accepted August 20, 2026; published September 10, 2026 in PLOS One (open access).
## Clinical Analysis & Structured Key Points
Social isolation and health risks: Do the younger generations behave differently? Findings from the European Social Survey (Round 11 – 2023/2024) | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Social isolation has long been recognized as a determinant of poor health, particularly in older adults. However, its effects on young people remain underexplored. This study investigates how different dimensions of social isolation affect health-related behaviors among different generations (Silent Generation, Baby Boomers, Generation X, Millennials, and Generation Z), including physical inactivity, tobacco and alcohol consumption, and body mass index as dependent variables. Using cross-sectional data from Round 11 of the European Social Survey, we analyze a sample of individuals from 24 European countries. We apply logistic regression models to estimate the association between five social isolation indicators (non-social meetings, lack of participation in activities, living alone, lack of emotional support, and unhappiness) and the above-mentioned four health-related outcomes. The empirical results highlight that social isolation is meaningfully associated with health behaviors, even among younger cohorts (e.g.: non-social meetings [OR = 1.291–1.789] play a key role in contributing to physical inactivity across all generations). It could be said that Generation X represents a transitional group, balancing traditional and modern lifestyles. Our findings emphasize the need for age-specific policies to address social isolation and promote lifelong health and support urgent policy attention to mitigate the long-term public health consequences of social disconnection in Europe. Citation: Blázquez-Fernández C, Lanza-León P, Cantarero-Prieto D (2026) Social isolation and health risks: Do the younger generations behave differently? Findings from the European Social Survey (Round 11 – 2023/2024). PLoS One 21(9): e0357725. https://doi.org/10.1371/journal.pone.0357725 Editor: Deepak Dhamnetiya, Atal Bihari Vajpayee Institute of Medical Sciences & Dr Ram Manohar Lohia Hospital, INDIA Received: March 16, 2026; Accepted: August 20, 2026; Published: September 10, 2026 Copyright: © 2026 Blázquez-Fernández et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The ESS Data Portal is the sole online access point to the ESS data, and users can download the data underlying the results presented in this study though this website: https://ess.sikt.no/en/datafile/242aaa39-3bbb-40f5-98bf-bfb1ce53d8ef.The authors did not have any special privileges, and other interested researchers may download the data in the same manner ( https://doi.org/10.21338/ess11e04_1 ). Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Introduction In contemporary societies, single-person households (without children) have exhibited an increase in recent years within the European Union, rising by 16.90% from 2015 to 2024, compared to 5.8% for all households [ 1 ]. Consequently, social isolation has become a prevalent phenomenon within society over time. However, the question arises as to whether living alone is synonymous with social isolation or loneliness. Individuals can experience social isolation without necessarily feeling lonely, or conversely, feel lonely despite possessing a sufficient number of social relationships [ 2 ]. However, there is still no clear consensus in the literature on this matter. Some researchers consider social isolation and loneliness as two different but connected concepts [ 3 ], with social isolation being more directly associated with the quantity of an individual’s social interactions and contacts rather than the quality of these relationships. Conversely, other authors make no distinction between the two terms, considering loneliness a subjective measure of perceived isolation in the absence of an objective metric for social integration and interaction [ 4 ]. Social isolation can be classified into different types: emotional isolation, which implies the absence of close emotional bonds; structural isolation, involving a lack of integration into existing social networks and the absence of reciprocal friendships; and social loneliness, focusing on the absence of broader social connections [ 5 ]. Thus, while loneliness represents a subjective feeling, social isolation constitutes an objective measure of connections [ 4 , 6 ], often indicated by factors such as living alone, marital status, a reduced social network, infrequent participation in social activities, limited social contacts or feelings of loneliness and lack of support. Integration within social groups is a fundamental determinant of psychological and behavioral health [ 7 , 8 ]. Consequently, social isolation represents a significant concern from a public health perspective, exerting a detrimental impact on health outcomes [ 9 , 10 ]. Furthermore, it poses significant social challenges across all ages, being both a concern and a risk factor for poor health [ 11 ] and adverse lifestyle behaviors, including obesity, alcohol consumption, and tobacco use [ 9 , 12 ]. The prevalence of social isolation has been exacerbated by the 2019 coronavirus disease pandemic and its associated social distancing measures. Social isolation is a phenomenon present across all age groups, demonstrating a consistent increase with ageing [ 13 ]. Nevertheless, younger adults may experience the effects of social isolation differently compared to older adults [ 9 ]. Indeed, early adulthood is characterized by distinct psychosocial challenges, such as the assumption of greater responsibilities and focus on social connections (friendships and romantic partnerships, etc.) other than family relationships [ 7 ]. Despite the predominant focus of research analyzing the health risks associated with social isolation in late adulthood or old age [ 14 , 15 ], attention should also be directed towards young and middle-aged adults. The existing body of literature concerning socially isolated youth (distinct from physical isolation, as experienced during the COVID-19 pandemic) remains limited and often concentrates on specific behaviors, such as alcohol or tobacco consumption [ 16 ]. While much of the literature focuses on the role of social isolation in health behaviors, related work in the economics domain illustrates the broader influence of social networks on life outcomes. Kalfa and Piracha [ 17 ] show that social capital and personal networks can exacerbate labor-market mismatches, highlighting that social structures exert heterogeneous effects on individual outcomes across contexts. Considering the existing evidence, this study aims to investigate the association between social isolation and specific health-related factors, with a particular emphasis on healthy lifestyle behaviors (physical activity and body mass index (BMI)) and the consumption of certain substances (tobacco and alcohol) within the European population. To further this objective, this research seeks to examine whether the associations between social isolation and health-related behaviors vary across different age cohorts, with a primary focus on young adults. This objective posits the hypothesis that greater social isolation will be associated with poorer outcomes in health-related factors among Europeans. Our study contributes to evidence by demonstrating that even in young people, social isolation is strongly associated with unhealthy lifestyles – to a degree that in some cases exceeds that observed in older groups. Recent evidence further supports the idea that the effects of social isolation on health behaviors are not uniform across age groups, concluding that social isolation has significant and heterogeneous impacts on health and socioeconomic outcomes across European generations [ 18 ]. Literature review In light of the social challenges posed by isolation and its significant implications for health, empirical evidence has explored the relationship between social isolation and its effects on various health-related factors and behaviors [ 2 , 9 , 14 ]. Empirical research highlights that the detrimental impact of social isolation on health is mediated, to a large extent, by behavioral mechanisms, particularly during younger stages of life [ 10 , 13 ]. Physical activity is consistently identified as a protective factor for affective well-being. Isolated individuals are more likely to exhibit an unhealthy lifestyle [ 13 ] and less likely to practice regular physical activity [ 11 ]. Benedyk et al. [ 19 ] demonstrate that young adults experiencing affective distress due to social disconnection report improved emotional states when they practice physical exercise. Importantly, the type of isolation (social avoidance, active isolation, and social indifference) significantly influences adolescents’ physical activity patterns [ 20 ]. Furthermore, physical inactivity appears to be one of the mediating factors in the relationship between isolation and increased mortality [ 21 ]. The relationship between isolation and substance use is complex and age-dependent. Adolescents may exhibit differential engagement in healthy behaviors depending on their experience of isolation and their position within a social group. While some socially isolated are more susceptible to detrimental habits, such as smoking or alcohol consumption, to manage anxiety and loneliness [ 2 ], others are less exposed to substance-using peer groups, and isolation could serve as a protective factor against such consumption [ 22 ]. Niño et al. [ 16 ] show that socially indifferent adolescents are more likely to consume alcohol and tobacco, whereas those who actively avoid social contact show lower risk. In adulthood, isolation is linked with elevated risk of substance use disorders. Desai et al. [ 23 ] reported that socially isolated hospitalized patients frequently used tobacco, alcohol, cannabis, and opioids. Similarly, social isolation is associated with increased alcohol consumption, particularly among young adults [ 24 ]. Segrin et al. [ 25 ] demonstrate that negative emotional traits manifested in neuroticism, depression, anxiety, and stress sensitivity indirectly contribute to solitary drinking through heightened social isolation. During the COVID-19 pandemic, socially isolated drinkers exhibited a greater increase in negative effect than non-drinkers [ 26 ]. Social isolation is also linked to poor dietary habits and nutritional outcomes, characterized by low fruit and vegetable intake and reduced diet quality. Socially isolated individuals often consume fewer fruits, vegetables, and fish, leading to reduced diet quality [ 2 , 13 , 27 ]. Social isolation is associated with both undernutrition and food insecurity, defined as limited access to foods necessary for an active and healthy life, especially among older and middle-aged adults [ 28 – 30 ]. These effects are particularly pronounced in individuals who are also obese, representing a vulnerable group [ 31 ]. In a psychological perspective, Tomova et al. [ 32 ] found that acute isolation leads to social craving in a manner analogous to hunger as a response to food deprivation. During the COVID-19 lockdowns, young people reported increases in isolation, pointing to their vulnerability and the long-term implications for lifestyle and mental health [ 33 ]. The impact of social isolation is uneven across generations, shaped by technological fluency, social norms, and life course stages. Members of the Silent Generation (born before 1946) are particularly vulnerable to isolation due to functional limitations and lower levels of digital literacy [ 34 ]. In terms of health behaviors, Ross and de Jager [ 35 ] detail how loneliness among older women is experienced emotionally and socially, often leading to reduced physical activity and poorer dietary habits. In addition, technology-related inequalities may further aggravate sedentary behavior or increased substance use due to isolation [ 7 ]. Although more digitally connected, Baby Boomers (born 1946–1964) remain vulnerable to isolation due to widowhood, retirement, or living alone. They represent a transitional generation, balancing traditional aging with increasing technological engagement. Demey et al. [ 36 ] show that pathways into living alone in mid-life are diverse but frequently lead to social disconnection. Hawkley et al. [ 37 ] identify cohort-specific trends in loneliness, finding that Baby Boomers are experiencing rising levels of social isolation over time. Generation X (born 1965–1980) balances digital literacy with high levels of professional and familial stress. Lissitsa and Kagan [ 38 ] find that self-reliance moderates the link between loneliness and social media addiction among men in Generations X, Millennials, and Z, suggesting that greater self-reliance may protect against the negative effects of isolation on digital well-being. In a related study, Lissitsa and Kagan [ 39 ] show that childhood bullying leads to increased loneliness and social media addiction in adulthood, with self-esteem playing a key mediating role across these generations. Millennials (born 1981–1996) have experienced the effects of digital transition, economic instability, and pandemic-induced isolation [ 33 ]. Their health behaviors reflect this complexity: while they show declining alcohol use partly linked to “generation sensible” health-oriented norms and individualization [ 40 ], their experiences of isolation have intensified derived from the social distancing policies [ 41 ]. Some substitute in-person connection with virtual spaces like the metaverse, which can either mitigate loneliness [ 42 ]. As the most digitally immersed cohort, Generation Z (born after 1996) faces high levels of social isolation, anxiety, and hyperconnectivity-induced stress. Digital surveillance, hyperconnectivity and shifting communication norms exacerbate their vulnerability [ 43 , 44 ]. Emotional intelligence may buffer some of these effects in the workplace [ 45 ], but many still require targeted psychological support in educational and professional contexts [ 8 , 46 ]. These patterns may contribute to unhealthy lifestyle habits, including reduced physical activity, emotional eating, or increased substance experimentation. Methodology and data Data Basic data used in this study is taken from the European Social Survey (ESS). The ESS is an international comparative study conducted every two years since 2001 through different European countries. Indeed, 39 countries have participated in at least one round since 2002–2003, whereas 31 countries participated in ESS Round 11 (latest available when doing this paper). Its main objective is to collect data on the attitudes, beliefs, and behavioral patterns of European populations. The survey is conducted through face-to-face interviews, and its questionnaire includes a fixed core module and rotating modules that address specific topics in each edition [ 47 ]. In this study, cross-sectional data is applied. Precisely data from the Round 11 Survey, for years 2023–2024 (latest available one), is used through 24 European countries. ESS non-substantive response categories, including ‘Refusal’, ‘Don’t know’, and ‘No answer’, were coded as missing values. The empirical analysis was conducted using complete cases only; therefore, respondents with missing information in any of the variables included in each model were excluded from the corresponding regression analysis. Moreover, macro-region clusters, according to EuroVoc (Europe is divided into four subregions: Central and Eastern, Northern, Southern, and Western Europe), are used in our estimates. However, since interest variables are not in all the rounds, non-panel data techniques could be applied here. Variables Because our objective is to analyze the health risks associated with social isolation, dataset covers, following the above-mentioned studies included in the literature review, as main explanatory variables five “social isolation factors” ( non_social_meeting , non_social_act , living_alone , non_emotional_support , and unhappy ) and four population-level health risk factors ( physical_inactivity , smoker , frequent_drinker , and obese_overweight ) as dependent ones. Additionally, other variables associated with both socio-economic and health care factors are used as controls. Table 1 presents the details concerning the description of the variables whereas Table 2 contains the basic descriptive statistics. Download: PNG larger image TIFF original image Table 1. Variables used, description and coding. https://doi.org/10.1371/journal.pone.0357725.t001 Download: PNG larger image TIFF original image Table 2. Descriptive statistics: distribution of the analytical sample, percentages (%). https://doi.org/10.1371/journal.pone.0357725.t002 Statistical analysis Given the nature of the dependent variables (the four are binary ones, and each one could be denoted by ), discrete choice models are particularly well suited to the analysis. Indeed, in this study, logit models are going to be used (1): (1) In the logit model, the conditional (to ) probability is described by the cumulative logistic distribution (that is, conditional to some explanatory variables ) being the predicted probabilities always between zero and one. Besides, results are going to be presented through Odds Ratios (OR), to be understood interdisciplinary, and can be expressed as follows (2): (2) Odds are defined as the ratio of the probability of success and the probability of failure. Then, implies that the variable has no effect on the odds of the event. However, if we obtain and would represent that the variable increases the odds of the event happening, whereas an implied that the variable decreases the odds of the event happening. The correlation matrix was used to identify possible multicollinearity between variables. Based on this matrix, there do not appear to be major multicollinearity issues, and we estimated our models with the abovementioned factors/variables. Given the complex, multi-stage sampling design of the ESS, all regression models were estimated using the ESS analysis weight. All in all, in order to take a look at our data before presenting the empirical results we have done two representative figures. Fig 1 , plots the distribution of health risks factors by generation while Fig 2 , exemplifies the distribution of social isolation factors by generation. Download: PNG larger image TIFF original image Fig 1. Distribution of health risks factors by generation. Source: Authors’ elaboration. https://doi.org/10.1371/journal.pone.0357725.g001 Download: PNG larger image TIFF original image Fig 2. Distribution of social isolation factors by generation. Source: Authors’ elaboration. https://doi.org/10.1371/journal.pone.0357725.g002 From these figures, we can observe that there is not a clear pattern and that each generation behaves differently. However, in Fig 1 , more similarities can be detected between Generation X, Millennials and Generation Z, where the higher percentages are for obese_overweight followed by physical_inactivity , smoker and frequent_drinker whereas Baby Boomers’ frequent_drinker is higher than smoker . Regarding the Silent Generation, physical_inactivity presents the highest results and as for Baby Boomers’ frequent_drinker is higher than smoker . From Fig 2 , it can be highlighted that the higher the age, the higher the
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