Abstract
Background
Loneliness is a public health concern associated with increased morbidity and mortality. Adverse health behaviours and a higher body mass index (BMI) have been proposed as key mechanisms influencing this association. The present study aims to examine the relationship between loneliness, adverse health behaviour and a higher BMI, including daily smoking, high alcohol consumption, physical inactivity, unhealthy dietary habits, and obesity in men and women and across different life stages.
Methods
We conducted a cross-sectional study using data from the 2017 Danish National Health Survey (entitled “How are you?“). Loneliness was assessed using the Three-Item Loneliness Scale. Logistic regression models were employed to analyse the association between loneliness, health behaviour and obesity in a sample of 122,258 individuals (16 + years). The models were adjusted for sex, age, educational attainment, country of origin, and partnership status. Stratified analyses were conducted to investigate differences by sex and life stages.
Results
Loneliness was associated with an increased risk of daily smoking (Adjusted odds ratio (AOR) = 1.30; 95% CI: 1.21–1.40), physical inactivity (AOR = 1.87; 95% CI: 1.75–1.99), unhealthy diet (AOR = 1.58; 95% CI: 1.47–1.70), and obesity (AOR = 1.60; 95% CI: 1.49–1.72). Conversely, loneliness was associated with a reduced risk of high alcohol consumption in men (AOR = 0.82; 95% CI: 0.74-0.0.91).
Conclusions
Our study provides evidence that loneliness is associated with adverse health behaviour and obesity in both men and women and across the lifespan. These findings suggest that health behaviours and obesity may influence the association between loneliness and poor health outcomes. Longitudinal studies are needed to clarify the causal relationships underlying these associations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-21490-4.
Keywords: Loneliness, Health behaviour, Smoking, Alcohol consumption, Physical activity, Diet, Obesity
Introduction
Loneliness is steadily being recognised as a major public health issue due to the growing body of evidence linking loneliness with adverse health [1–3]. Loneliness can be defined as a subjective unpleasant emotional state related to a perceived discrepancy between desired and actual levels of social contact [4]. Longitudinal studies have demonstrated that loneliness is associated with an increased risk of depressive symptoms [5, 6], type 2 diabetes [7, 8], cardiovascular disease [9–11], and all-cause mortality [3, 12]. Health behaviours such as smoking, physical inactivity, and dietary habits have been proposed as important mechanisms through which loneliness influences the risk for morbidity and mortality [11]. To elucidate the negative impact of loneliness on health behaviours, several theoretical assumptions have been proposed. The social control hypothesis rests on the notion that lonely individuals may be more disposed towards adverse and risky health behaviours due to a lack of social support and social cues that encourage healthier habits [13]. The Loneliness Model proposes that a paucity of meaningful social connections can lead to maladaptive social hypervigilance, resulting in a diminished capacity for self-regulation [14]. Additionally, it has been suggested that lonely individuals may be less likely to employ active coping strategies when dealing with adversity, potentially resulting in a state of passivity that manifests as physical inactivity and overeating [15].
While these theoretical assumptions have contributed to our understanding of loneliness and health, the evidence supporting that loneliness is associated with adverse health behaviour and a higher body mass index (BMI) is equivocal [16]. A review investigating the relationship between loneliness and smoking showed that 12 studies did not find any significant association, whereas 13 studies found an association, albeit with a small effect size [17]. The link between loneliness and alcohol consumption is also unclear. Some studies indicate that loneliness is associated with lower alcohol frequency and weekly intake [18, 19], while others indicate no relationship [18–20]. Furthermore, evidence suggests loneliness is associated with solitary drinking but not social alcohol consumption [21]. Likewise, studies investigating loneliness and dietary habits have yielded mixed results [18–20, 22]. Conversely, an association between loneliness and physical inactivity has been demonstrated, and there is evidence in support of a bidirectional relationship [23]. Similarly, studies have demonstrated that loneliness is linked with a higher BMI and that individuals who are lonely are more likely to be obese [24, 25].
The current evidence regarding loneliness, health behaviour and obesity is limited by a lack of comprehensive large-scale population-based studies. Most of the previous studies have focused on specific factors [26–28] or particular age groups [18, 19, 27–29]. Only a few studies have investigated the relationship between loneliness, multiple health behaviours and BMI across sex and age groups [20, 24, 30]. For example, a Swiss population study found that age modified the association between loneliness and smoking. However, the study opted for age groups with a wide span which might not generalise well to life stages [20]. Furthermore, two population-based studies measured loneliness using a direct single-item question that included the word “lonely” [20, 30], which may induce social desirability bias and underreporting due to the social stigma associated with loneliness [31]. Thus, large-scale population-based studies using reliable measures of loneliness are needed to generate detailed knowledge about the associations between loneliness, health behaviour and BMI.
This study aims to provide a comprehensive analysis of the relationship between loneliness, adverse health behaviour and a higher BMI, specifically focusing on daily smoking, high alcohol consumption, physical inactivity, unhealthy dietary habits, and obesity in a nationally representative sample of the Danish population. Furthermore, we aim to investigate sex and age differences in the association between loneliness, health behaviour and obesity.
Methods
Study design and data collection
The present cross-sectional study used data from the 2017 Danish National Health Survey (also labelled “How are you?”) [32]. The survey was conducted using five regional stratified random samples and one national random sample of individuals aged 16 years and older. A standard questionnaire with 52 mandatory questions was used in all six samples, including identical questions on sociodemographic factors, health behaviour, height, and weight [32]. In addition to the mandatory questions, each of the six subsamples had the option to include additional questions of specific interest for their respective region [32]. The present study compiles data from four of the six subsamples, as these four (Central Denmark Region, North Denmark Region, Region Zealand, and Capital Region of Denmark) included identical questions on loneliness. The regions included represented 79% of the Danish population aged 16 years and older. The surveys were based on a stratified random sample drawn from the Danish Civil Registration System using a personal identification number [33]. A total of 228,550 individuals aged 16 years and older were invited to participate through a combination of paper and online questionnaires; 129,319 individuals completed the questionnaire (57%). Individuals with missing information on loneliness (i.e., 2 or 3 items missing on the Three-Item Loneliness Scale (T-ILS) [34, 35]) were excluded before the analysis and the final analytical sample consisted of 122,258 individuals. To enhance the representativeness of the study, calibrated sampling weights were used to account for survey design and non-response in relation to sex, age group, municipality, social background, and healthcare utilisation [32]. These weights were constructed by Statistics Denmark using a model-based calibration approach [36].
Loneliness
Loneliness was measured using T-ILS [34, 35], a brief version of the UCLA Loneliness Scale developed for large-scale population-based surveys [34, 37]. T-ILS contains three items (How often do you feel isolated from others? How often do you feel you lack companionship? How often do you feel left out?) rated on a three-point Likert scale (hardly ever, sometimes, and often) [34]. The scale demonstrated a good internal consistency in this sample with a Cronbach’s α of 0.81. Scores range between 3 and 9, with a higher score indicating greater feelings of loneliness. Following earlier studies, a threshold of 7 was used to identify individuals experiencing loneliness [2, 38]. Correspondingly, those who only experienced loneliness some of the time, that is a score below 7, were treated as “non-lonely” in our analyses.
Health behaviours and obesity
Five dichotomised outcomes were used to assess adverse health behaviours and a higher BMI: Daily smoking, high alcohol consumption, physical inactivity, unhealthy dietary habits, and obesity. Daily smoking was assessed using a single question. Alcohol consumption was examined by asking about the number of standard alcohol units consumed during an average week. In accordance with guidelines outlined by the Danish Health Authority [39], individuals who reported drinking more than ten units per week were categorised as having high alcohol consumption. Physical inactivity was measured using the Nordic Physical Activity Questionnaire-short (NPAQ-short), a brief open-ended questionnaire that monitors adherence to the World Health Organization recommendations for physical activity [40]. The NPAQ-short includes one question regarding weekly time spent on moderate-to-vigorous physical activity (MVPA) and one question regarding weekly time spent on vigorous physical activity (VPA). Individuals who did not meet the recommendation for weekly physical activity (150 min. MVPA, 75 min. VPA, or a combination thereof) were categorised as physically inactive [41]. Dietary habits were measured using the Dietary Quality Score (DQS) [42, 43]. The DQS comprises 24 questions, and respondents were categorised as having unhealthy dietary habits if they had a low intake of fruit, vegetables and fish, and a high intake of saturated fat. BMI was derived from self-reported height and weight. Respondents with a BMI of ≥ 30 kg/m2 were classified as obese.
Socio-demographic factors
Five demographic variables derived from the survey and registers were included in the analysis: Sex, age, educational attainment, country of origin, and partnership status. Sex and age were derived from register data with no missing data. Age was categorised into six distinct age groups reflecting different life stages: 16–24 years being Adolescence/emerging adulthood, 25–34 years being Early adulthood, 35–49 years being Mid-adulthood, 50–64 years being Later adulthood, 65–79 years being Middle old age and 80 + years being Old age. Educational attainment was classified using the Danish version of the International Standard Classification of Education [44] and categorised into four groups based on self-reported data: Low educational level (0–10 years) for primary school with no further education; Medium educational level (11–14 years) for upper secondary education, vocational education or short higher education; High educational level (≥ 15 years) for a bachelor’s degree or higher; and Enrolled in education for respondents currently engaged in an educational program. Country of origin was based on register data and split into three groups: Danish, Other western countries and Non-western countries. Partnership status was derived from both self-reported information and registers. Respondents were categorised as Living with a partner if their status in the registers indicated “married” or “registered partner” or if they had reported living with a spouse, partner, or significant other in the survey. Conversely, respondents were classified as Not living with a partner if their records indicated that they were “divorced”, “widowed”, or “never married” and if they had responded negatively regarding living with a spouse, partner, or significant other.
Statistical analysis
We estimated the relationship between loneliness, health behaviour and obesity using logistic regression models to calculate odds ratios (ORs) and 95% confidence intervals (CIs). The models were adjusted for sex, age group, educational attainment, country of origin, and civil status; all of which were included in the regression models as indicator variables. Moreover, the associations between loneliness and health behaviour and obesity were stratified by sex and six distinct age groups. Finally, to examine the robustness of the results, we performed a sensitivity analysis using a lower threshold for loneliness (T-ILS score ≥ 6; cf. Additional file S1). Statistical analysis was performed using Stata v. 17.
Results
As shown in Table 1, a total of 8.2% of the population were classified as experiencing loneliness (T-ILS score ≥ 7). Those who experienced loneliness were more likely to be women, younger, have lower educational attainment, be enrolled in education, have a non-Danish country of origin, and not live with a partner. In addition, lonely individuals were found to have a higher likelihood of daily smoking (22.7% vs. 15.8%), physical inactivity (41.8% vs. 28.6%), unhealthy dietary habits (22.6% vs. 14.7%), and obesity (21.4% vs. 15.4%) compared to those who were not lonely. Conversely, lonely individuals were slightly less prone to have high alcohol consumption (15.9% vs. 18.5%).
Table 1.
Descriptive statistics of the total population and loneliness status
| Total population (n = 122,258) | Not lonely (n = 114,363) | Lonely (n = 7,895) |
||||
|---|---|---|---|---|---|---|
| n | % | n | % | n | % | |
| Sex (n = 122,258) | ||||||
| Men | 55,849 | 49.1 | 52,680 | 49.5 | 3,169 | 44.3 |
| Women | 66,409 | 50.9 | 61,683 | 50.5 | 4,726 | 55.7 |
| Age (n = 122,258) | ||||||
| 16–24 years | 11,944 | 13.9 | 10,487 | 13.2 | 1,457 | 21.5 |
| 25–34 years | 12,709 | 15.4 | 11,487 | 14.9 | 1,222 | 20.3 |
| 35–49 years | 27,491 | 23.7 | 25,694 | 23.8 | 1,797 | 23.1 |
| 50–64 years | 34,910 | 24.2 | 32,949 | 24.5 | 1,961 | 21.2 |
| 65–79 years | 29,229 | 17.8 | 28,205 | 18.6 | 1,024 | 9.1 |
| 80 + years | 5,975 | 5.0 | 5,541 | 5.0 | 434 | 4.8 |
| Educational attainment (n = 114,552) | ||||||
| Low (≤ 10 years) | 13,594 | 12.0 | 12,366 | 11.7 | 1,228 | 16.0 |
| Medium (11–14 years) | 48,242 | 36.1 | 45,544 | 36.6 | 2,698 | 30.5 |
| High (≥ 15 years) | 38,802 | 29.3 | 37,094 | 30.1 | 1,708 | 20.1 |
| Enrolled in education | 13,914 | 16.3 | 12,321 | 15.5 | 1,593 | 24.8 |
| Country of origin (n = 122,258) | ||||||
| Denmark | 112,688 | 86.4 | 106,016 | 87.2 | 6,672 | 76.4 |
| Other western countries | 4,055 | 5.8 | 3,623 | 5.5 | 432 | 8.8 |
| Non-western countries | 5,515 | 7.9 | 4,724 | 7.3 | 791 | 14.8 |
| Partnership status (n = 120,098) | ||||||
| Living with a partner | 85,844 | 64.4 | 82,414 | 66.5 | 3,430 | 40.3 |
| Not living with a partner | 34,254 | 35.6 | 30,050 | 33.5 | 4,204 | 59.7 |
| Daily smoking (n = 121,366) | ||||||
| Yes | 18,020 | 16.3 | 16,345 | 15.8 | 1,675 | 22.7 |
| No | 103,346 | 83.7 | 97,211 | 84.2 | 6,135 | 77.3 |
| High alcohol consumption (n = 118,654) | ||||||
| Yes | 21,931 | 18.3 | 20,707 | 18.5 | 1,224 | 15.9 |
| No | 96,723 | 81.7 | 90,396 | 81.5 | 6,327 | 84.1 |
| Physical inactive (n = 116,940) | ||||||
| Yes | 34,741 | 29.7 | 31,541 | 28.6 | 3,200 | 41.8 |
| No | 82,199 | 70.3 | 77,992 | 71.4 | 4,207 | 58.2 |
| Unhealthy dietary intake (n = 119,760) | ||||||
| Yes | 17,266 | 15.3 | 15,533 | 14.7 | 1,733 | 22.6 |
| No | 102,494 | 84.7 | 96,621 | 85.3 | 5,873 | 77.4 |
| Obesity (BMI ≥ 30 kg/m2) (n = 120,492) | ||||||
| Yes | 20,111 | 15.9 | 18,350 | 15.4 | 1,761 | 21.4 |
| No | 100,381 | 84.1 | 94,442 | 84.6 | 5,939 | 78.6 |
Note. The percentages shown are weighted based on calibrated weights derived from register data to represent the population of the four Danish regions in 2017
Table 2 shows the crude and adjusted associations between loneliness, adverse health behaviours, and obesity. In the crude analysis (Model 1), loneliness was associated with higher odds of daily smoking (OR = 1.57; 95% CI: 1.47–1.68), physical inactivity (OR = 1.82; 95% CI: 1.72–1.94), unhealthy dietary habits (OR = 1.70; 95% CI: 1.59–1.81) and obesity (OR = 1.49; 95% CI: 1.40–1.59). Conversely, loneliness was associated with lower odds of high alcohol consumption (OR = 0.83; 95% CI: 0.77–0.90). After adjusting for age, sex, educational attainment, country of origin and civil status (Model 2), loneliness remained associated with higher odds of daily smoking (AOR = 1.30; 95% CI: 1.21–1.40), physical inactivity (AOR = 1.87; 95% CI: 1.75–1.99), unhealthy dietary habits (AOR = 1.58; 95% CI: 1.47–1.70), and obesity (AOR = 1.60; 95% CI: 1.49–1.72). On the other hand, loneliness was associated with lower odds of high alcohol consumption (AOR = 0.91; 95% CI: 0.83–0.98).
Table 2.
Odds ratio of adverse health behaviours and obesity in relation to loneliness
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| OR | (95% CI) | p-value | AOR | (95% CI) | p-value | |
| Daily smoking | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.57 | (1.47–1.68) | < 0.0001 | 1.30 | (1.21–1.40) | < 0.0001 |
| High alcohol consumption | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.83 | (0.77–0.90) | < 0.0001 | 0.91 | (0.83–0.98) | 0.0197 |
| Physical inactive | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.82 | (1.72–1.94) | < 0.0001 | 1.87 | (1.75–1.99) | < 0.0001 |
| Unhealthy dietary intake | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.70 | (1.59–1.81) | < 0.0001 | 1.58 | (1.47–1.70) | < 0.0001 |
| Obesity (BMI ≥ 30 kg/m2) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.49 | (1.40–1.59) | < 0.0001 | 1.60 | (1.49–1.72) | < 0.0001 |
Note: Not lonely = T-ILS score < 7
Lonely = T-ILS score ≥ 7
Model 1: Crude. OR = Odds ratio
Model 2: Adjusted. AOR = Adjusted odds ratio. Adjusted for age, sex, educational attainment, country of origin, and partnership status (all included in the regression models as indicator variables)
When stratifying by sex and adjusting for other sociodemographic factors (Table 3; Model 2), loneliness was associated with higher odds of daily smoking, physical inactivity, unhealthy dietary habits and obesity in both men and women. Noteworthy, the association of loneliness on health behaviour and obesity generally appeared stronger among women than men. Moreover, loneliness was associated with lower odds of high alcohol consumption for men (AOR = 0.82; 95% CI: 0.74–0.91), but not women (AOR = 1.00; 95% CI: 0.88–1.14).
Table 3.
Odds ratio of adverse health behaviours and obesity in relation to loneliness. Analyses stratified by sex
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| OR | (95% CI) | p-value | AOR | (95% CI) | p-value | |
| Daily smoking | ||||||
| Men | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.55 | (1.40–1.72) | < 0.0001 | 1.19 | (1.06–1.32) | 0.0021 |
| Women | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.62 | (1.49–1.77) | < 0.0001 | 1.40 | (1.27–1.54) | < 0.0001 |
| High alcohol consumption | ||||||
| Men | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.79 | (0.72–0.88) | < 0.0001 | 0.82 | (0.74–0.91) | 0.0003 |
| Women | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.04 | (0.92–1.17) | 0.5138 | 1.00 | (0.88–1.14) | 0.9574 |
| Physical inactive | ||||||
| Men | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.84 | (1.68–2.02) | < 0.0001 | 1.83 | (1.66–2.02) | < 0.0001 |
| Women | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.80 | (1.67–1.95) | < 0.0001 | 1.88 | (1.73–2.03) | < 0.0001 |
| Unhealthy dietary intake | ||||||
| Men | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.54 | (1.40–1.70) | < 0.0001 | 1.35 | (1.21–1.49) | < 0.0001 |
| Women | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.07 | (1.89–2.27) | < 0.0001 | 1.79 | (1.62–1.98) | < 0.0001 |
| Obesity (BMI ≥ 30 kg/m2) | ||||||
| Men | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.41 | (1.27–1.56) | < 0.0001 | 1.47 | (1.31–1.64) | < 0.0001 |
| Women | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.56 | (1.44–1.70) | < 0.0001 | 1.69 | (1.55–1.85) | < 0.0001 |
Not lonely = T-ILS score <7
Lonely = T-ILS score ≥ 7
Model 1: Crude. OR = Odds ratio
Model 2: Adjusted. AOR = Adjusted odds ratio. Adjusted for age, educational attainment, country of origin, and partnership status (all included in the regression models as indicator variables)
The results from the stratified logistic regression models across the different life stages are shown in Table 4. When examining the association between loneliness and daily smoking, we found that loneliness was associated with higher odds of daily smoking across all life stages in the crude analysis (Model 1; ORs > 1.4), but when adjusting for sociodemographic factors (Model 2), the effects attenuated and loneliness was no longer significantly associated with daily smoking in early adulthood (AOR = 1.14; 95% CI: 0.95–1.38) and middle adulthood (AOR = 1.09; 95% CI: 0.91–1.32). Loneliness was strongly associated with physical inactivity (AORs > 1.7) and unhealthy dietary habits (AORs > 1.4) across all stages of life. Likewise, loneliness was significantly associated with obesity across all groups except for old age. Noteworthy, the association of loneliness with obesity appeared strongest in adolescence/emerging adulthood (AOR = 2.03; 95 CI: 1.63–2.52). Regarding the association between loneliness and high alcohol consumption, loneliness was associated with lower odds of high alcohol consumption in adolescence/emerging adulthood (AOR = 0.76; 95% CI: 0.64–0.90), early adulthood (AOR = 0.77; 95% CI: 0.61–0.97), and in old age (AOR = 0.62; 95% CI: 0.42–0.91).
Table 4.
Odds ratio of adverse health behaviours and obesity in relation to loneliness. Analyses stratified by life stages
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| OR | (95% CI) | p-value | AOR | (95% CI) | p-value | |
| Daily smoking | ||||||
| Adolescence/emerging adulthood (16–24 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.40 | (1.18–1.66) | 0.0001 | 1.35 | (1.13–1.62) | 0.0009 |
| Early adulthood (25–34 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.40 | (1.18–1.67) | 0.0002 | 1.14 | (0.95–1.38) | 0.1520 |
| Middle adulthood (35–49 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.67 | (1.46–1.91) | < 0.0001 | 1.13 | (0.98–1.31) | 0.0917 |
| Later adulthood (50–64 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.79 | (1.59–2.02) | < 0.0001 | 1.32 | (1.16–1.51) | < 0.0001 |
| Middle-old age (65–79 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.70 | (1.41–2.07) | < 0.0001 | 1.34 | (1.10–1.64) | 0.0044 |
| Old age (80 + years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.51 | (1.05–2.17) | 0.0255 | 1.50 | (1.03–2.19) | 0.0330 |
| High alcohol consumption | ||||||
| Adolescence/emerging adulthood (16–24 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.67 | (0.57–0.79) | < 0.0001 | 0.76 | (0.64–0.90) | 0.0017 |
| Early adulthood (25–34 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.90 | (0.73–1.11) | 0.3265 | 0.77 | (0.61–0.97) | 0.0241 |
| Middle adulthood (35–49 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.12 | (0.94–1.34) | 0.2180 | 1.01 | (0.83–1.23) | 0.9045 |
| Later adulthood (50–64 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.90 | (0.78–1.04) | 0.1380 | 0.99 | (0.85–1.15) | 0.8893 |
| Middle-old age (65–79 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.82 | (0.68–0.98) | 0.0324 | 0.99 | (0.81–1.21) | 0.9167 |
| Old age (80 + years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 0.50 | (0.35–0.72) | < 0.0001 | 0.62 | (0.42–0.91) | 0.0139 |
| Physical inactive | ||||||
| Adolescence/emerging adulthood (16–24 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.10 | (1.80–2.44) | < 0.0001 | 1.93 | (1.64–2.27) | < 0.0001 |
| Early adulthood (25–34 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.66 | (1.42–1.92) | < 0.0001 | 1.75 | (1.50–2.05) | < 0.0001 |
| Middle adulthood (35–49 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.96 | (1.74–2.21) | < 0.0001 | 1.83 | (1.61–2.07) | < 0.0001 |
| Later adulthood (50–64 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.97 | (1.76–2.21) | < 0.0001 | 1.83 | (1.62–2.06) | < 0.0001 |
| Middle-old age (65–79 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.26 | (1.91–2.69) | < 0.0001 | 2.13 | (1.79–2.54) | < 0.0001 |
| Old age (80 + years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.64 | (1.97–3.55) | < 0.0001 | 2.44 | (1.81–3.29) | < 0.0001 |
| Unhealthy dietary intake | ||||||
| Adolescence/emerging adulthood (16–24 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.42 | (1.22–1.66) | < 0.0001 | 1.53 | (1.30–1.80) | < 0.0001 |
| Early adulthood (25–34 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.81 | (1.53–2.14) | < 0.0001 | 1.62 | (1.36–1.95) | < 0.0001 |
| Middle adulthood (35–49 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.74 | (1.52–1.99) | < 0.0001 | 1.51 | (1.29–1.76) | < 0.0001 |
| Later adulthood (50–64 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.65 | (1.45–1.89) | < 0.0001 | 1.49 | (1.29–1.73) | < 0.0001 |
| Middle-old age (65–79 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.03 | (1.70–2.43) | < 0.0001 | 1.75 | (1.44–2.11) | < 0.0001 |
| Old age (80 + years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.76 | (1.32–2.34) | 0.0001 | 1.56 | (1.14–2.14) | 0.0057 |
| Obesity (BMI ≥ 30 kg/m2) | ||||||
| Adolescence/emerging adulthood (16–24 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 2.14 | (1.74–2.62) | < 0.0001 | 2.03 | (1.63–2.52) | < 0.0001 |
| Early adulthood (25–34 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.67 | (1.41–1.98) | < 0.0001 | 1.57 | (1.31–1.88) | < 0.0001 |
| Middle adulthood (35–49 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.66 | (1.46–1.89) | < 0.0001 | 1.52 | (1.32–1.74) | < 0.0001 |
| Later adulthood (50–64 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.58 | (1.40–1.78) | < 0.0001 | 1.49 | (1.32–1.69) | < 0.0001 |
| Middle-old age (65–79 years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.71 | (1.45–2.03) | < 0.0001 | 1.61 | (1.35–1.92) | < 0.0001 |
| Old age (80 + years) | ||||||
| Not lonely (ref.) | 1.00 | 1.00 | ||||
| Lonely | 1.25 | (0.90–1.73) | 0.1923 | 1.14 | (0.80–1.64) | 0.4611 |
Not lonely = T-ILS score <7
Lonely = T-ILS score ≥ 7
Model 1: Crude. OR = Odds ratio
Model 2: Adjusted. AOR = Adjusted odds ratio. Adjusted for sex, educational attainment, country of origin, and partnership status (all included in the regression models as indicator variables)
Sensitivity analyses
We performed a sensitivity analysis to assess the robustness of the associations between loneliness, health behaviour and obesity. When utilising a lower threshold (T-ILS score ≥ 6), the estimated ORs in individuals experiencing loneliness were generally lower, but the overall conclusions remained largely unchanged (Additional file 1).
Discussion
Our findings support the notion that loneliness is associated with an increased risk of adverse health behaviours and obesity. Specifically, we observed a robust association between loneliness and daily smoking, physical inactivity, unhealthy dietary habits, and obesity. Conversely, loneliness was associated with a lower risk of high alcohol consumption.
The results in relation to daily smoking are overall in line with the systematic review that examined the relationship between loneliness and smoking, predominantly observing the association between loneliness and smoking in studies with larger sample sizes [17]. As noted by Dewall and Pond, the association between loneliness and smoking corresponds to a small effect size [45], highlighting the need for larger sample sizes to detect this association. In our study, we observed an association between loneliness and daily smoking in both men and women. However, the results varied across the different life stages, and we only identified a significant association in four of the six life stages, that is, adolescence/emerging adulthood, later adulthood, middle-old age, and old age. Furthermore, our results substantially attenuated when we adjusted for confounders, highlighting the possibility for unmeasured or residual confounding. Therefore, some caution is warranted when interpreting the results, as the large sample size enables the detection of minor differences between lonely and non-lonely individuals. Nevertheless, the increased odds of daily smoking in lonely adolescents could be particularly important for public health, as most smokers initiate smoking before the age of 18 [17], and loneliness is highly prevalent among adolescents and young adults [2]. Thus, adolescence could be a key developmental stage for implementing both smoking prevention programs and interventions to reduce loneliness.
In our study, we observed that loneliness was associated with lower odds of high alcohol consumption. Interestingly, we found that loneliness was associated with lower odds of high alcohol consumption in men who are lonely compared to men who are not lonely, yet no detectable difference was observed between lonely and non-lonely women. Furthermore, the lower odds of high alcohol consumption were only significant in adolescence/emerging adulthood, early adulthood, and old age. The lower odds among lonely young individuals could be related to the Danish alcohol culture, which consists of heavy binge drinking in a social setting [46], and lonely individuals may be less likely to participate in those events. This is supported by the finding that loneliness is linked with solitary drinking but not social drinking [21]. However, problematic alcohol behaviour is a complex phenomenon. While our results point to lower odds of high alcohol consumption, we cannot rule out the possibility that a reverse relationship could have been identified if we had focused on other aspects of problematic alcohol behaviour (e.g., binge drinking or alcohol abuse). Therefore, further research utilising alternative measures of problematic alcohol behaviour would be beneficial in enhancing our understanding of the relationship between loneliness and alcohol consumption.
In relation to physical activity, we estimated a strong association between loneliness and physical inactivity which is in agreement with previous research [23]. The association was observed in men and women, and across the different life stages.
For dietary habits, we identified a robust association between loneliness and unhealthy dietary intake across men and women and all life stages. The inconclusive findings [18–20, 22] in previous research may be attributed to variations in the methods used to measure dietary intake. Moreover, many studies have focused on a single dietary component, such as fruit or vegetable intake. To the best of our knowledge, the present study is the first to investigate the relationship between loneliness and overall dietary intake using a comprehensive measure [42, 43]. The consistent observation of a strong association between loneliness and unhealthy diet across all groups in our study reinforces the importance of this relationship. However, given the existing inconsistencies and the scarcity of reliable measures in the scientific literature, further research employing robust dietary measures is warranted to enhance our understanding of this complex association.
Consistent with previous research [24, 25], we observed an association between loneliness and obesity. The association was observed in both men and women and across all stages of life except old age. Noticeably, the association appeared strongest among the adolescent/emerging adulthood group. The relationship between loneliness and obesity is complex, likely involving both behavioural and psychological mechanisms. Loneliness can diminish an individual’s capacity for self-regulation and trigger maladaptive coping strategies that may lead to increased physical inactivity and unhealthy dietary habits [14]. Furthermore, individuals affected by obesity often face weight stigmatization from society, which can increase social isolation, as they may withdraw from social interactions to avoid judgment and discrimination from others [47]. This in turn can further exacerbate feelings of loneliness.
Taken together, the results from our study suggest that formulating intervention strategies that target both social disconnectedness and the promotion of positive health behaviour changes could hold significance for wellbeing, quality of life and disease prevention efforts.
A major strength of our study is the large representative population-based sample which provides us with adequate statistical power to conduct stratified analysis across sex and different life stages. In addition, the use of calibrated weights compensates for both non-response and selection probabilities and thus enhances the representativeness of the study [32]. However, the cross-sectional nature of the study means that no conclusions about the causality between loneliness and adverse health behaviour/obesity can be made. In particular, large-scale population cohorts are needed to explore the directionality between loneliness and adverse health behaviour. In addition, while T-ILS measures a general sense of loneliness, it does not clearly distinguish between prolonged or temporal experiences of loneliness. Lastly, it is important to note that the study was conducted in a Danish population. Therefore, our results may only be generalisable to the Danish and similar western populations, as both loneliness and health behaviour may be culturally conditioned. Hence, more studies using data from different populations are needed, especially studies from low- and middle-income countries.
Conclusion
The present study showed that loneliness is associated with increased odds of adverse health behaviours and obesity in both men and women and across all stages of life. The results from our study underline the importance of health behaviours as a potential pathway between loneliness and adverse health. Longitudinal research based on large population-based data is needed to clarify the directionality of loneliness and health behaviour. Moreover, further investigation is warranted for a deeper understanding of the underlying mechanisms by which health behaviours influence the association between loneliness and health.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The Central Denmark Region is grateful for the support and assistance provided by the Northern Denmark Region, the Danish Capital Region and Region Zealand in conducting the present study.
Author contributions
ML, AH, MK, HT and MJ helped conceptualize the study. MJ, KF and ML devised the analysis plan, and MJ wrote the first draft in close collaboration with KF and ML. MJ, KF, HM, AH, MK, MG and ML contributed to data interpretation, revision and editing of the manuscript. The final manuscript is approved by all authors.
Funding
The North Denmark Region Health Survey was conducted and funded by the North Denmark Region. The Danish Capital Region Health Survey was conducted and funded by the Capital Region. The Central Denmark Region Health Survey was conducted and funded by the Central Denmark Region. The Region Zealand Health Survey was conducted and funded by the Region Zealand.
Data availability
The data that support the findings of this study are available from the four Danish regions - The North Denmark Region (bifrontend@rn.dk), The Danish Capital Region (sundhedsprofil@rh.dk), The Central Denmark Region (hvordanhardudet@rm.dk) and Region Zealand (sundhedsprofilen@regionsjaelland.dk). However, restrictions apply to the availability of these data, which were used under license for the current study, and data may not be shared publicly. The data can be made available upon reasonable request to the authors and with an appropriate, restricted data use agreement from The North Denmark Region, The Danish Capital Region, The Central Denmark Region and Region Zealand.
Declarations
Ethical approval
The present study is registered on the Central Denmark Regions’ internal judicial records of data processing activities (record number 1-16-02-334-19). According to Danish law, no further approval is required from an ethics committee or other research oversight when processing data from a survey (§ 14 Sect. 2) [48]. Individuals invited to participate received a letter of invitation which contained the necessary information about the purpose and content of the survey, emphasizing that participation was voluntary. The letter of invitation stated that by answering the survey, participants gave their full consent for their data to be used in accordance with the purpose of the study, ensuring informed consent from all participants.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the four Danish regions - The North Denmark Region (bifrontend@rn.dk), The Danish Capital Region (sundhedsprofil@rh.dk), The Central Denmark Region (hvordanhardudet@rm.dk) and Region Zealand (sundhedsprofilen@regionsjaelland.dk). However, restrictions apply to the availability of these data, which were used under license for the current study, and data may not be shared publicly. The data can be made available upon reasonable request to the authors and with an appropriate, restricted data use agreement from The North Denmark Region, The Danish Capital Region, The Central Denmark Region and Region Zealand.