Loneliness across time and space

People feel lonely when their social needs are not met by the quantity and quality of their social relationships. Most research has focused on individual-level predictors of loneliness. However, macro-level factors related to historical time and geographic space might influence loneliness through their effects on individual-level predictors. In this Review, we summarize empirical findings on differences in the prevalence of loneliness across historical time and geographical space and discuss four groups of macro-level factors that might account for these differences: values and norms, family and social lives, technology and digitalization, and living conditions and availability of individual resources. Regarding historical time, media reports convey that loneliness is on the rise, but the empirical evidence is mixed, at least before the COVID-19 pandemic. Regarding geographical space, national differences in loneliness are linked to differences in cultural values (such as individualism) but might also be due to differences in the sociodemographic composition of the population. Research on within-country differences in loneliness is scarce but suggests an influence of neighbourhood characteristics. We conclude that a more nuanced understanding of the effects of macro-level factors on loneliness is necessary because of their relevance for public policy and propose specific directions for future research.

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Introduction

People experience loneliness when they feel that their social relationships are deficient in terms of quantity or quality and perceive a gap between their actual and desired relationships 1 . Around the world, people describe loneliness as a painful, sometimes agonizing, experience 2 . Loneliness is conceptually distinct from being alone (a momentary state of objective absence of other people), solitude (when being alone is perceived as pleasant and sought out intentionally) 3 and social isolation 1,3,4,5 (the objective lack of social relationships and social contact 1 ).

Through its adverse effects on sleep, immune functioning and health behaviours, loneliness can lead to long-term health issues such as an increased risk for cardiovascular diseases and reduced longevity 1,6,7,8,9 . The health-related consequences of loneliness are detrimental for individual well-being and come with substantial economic costs for society 10,11 . Loneliness has therefore been recognized as a public health issue that needs to be addressed by public policy 12,13 . Indeed, loneliness is on political agendas in the United Kingdom 14 , Germany 15 , Japan 16 and the European Union 17 . Thus, loneliness has important societal implications, and there is a need for evidence-based recommendations for public policy.

Despite these societal implications, loneliness is a deeply subjective experience and almost all empirically established predictors of loneliness refer to characteristics of the person (Table 1). Loneliness is more common among individuals with low socioeconomic status 18,19 and poor health 19,20 , two individual factors that limit people’s opportunities to participate in everyday social activities. Because poor health is particularly common among the elderly, old age is sometimes considered a critical risk factor for loneliness. However, although studies conducted before the COVID-19 pandemic found that average loneliness was highest in the oldest age group (80 years and older) 18,21,22,23 , increased loneliness has also been reported in younger age groups 18,24 , and a meta-analysis of longitudinal studies found no significant relationship between age and loneliness 25 . Identifying with a group that is marginalized within a society (for example, ethnic/racial 26,27 or sexual orientation/identity 28,29,30,31 minority groups) is associated with higher average levels of loneliness, presumably because these groups are more likely to experience stressors such as discrimination or rejection, which increase the risk of loneliness 29,30,31,32,33 . Loneliness is also correlated with personality traits. Individuals high in extraversion and emotional stability are less prone to loneliness than individuals low on these traits 34 . Finally, the characteristics of one’s social relationships are among the most proximal predictors of loneliness. Having a romantic partner, a large social network, frequent social interactions, and high-quality relationships decrease the risk of loneliness 19,20,35,36 .

figure 1

Second, the strength and even the direction of the effect of a macro-level factor on loneliness might differ among different subgroups (Fig. 1b). In population-level studies, these subgroups are collapsed, so strong effects that exist in only some subgroups might be overlooked. For example, social media use appears to be more beneficial for older adults than for adolescents and young adults 81 , but this differential association would not be detected if these groups were analysed together.

Third, the effects of most macro-level factors might unfold over long timescales 39 , so effects on loneliness might be weak, slow and delayed (that is, only detectable after a certain time lag; Fig. 1c). The exact temporal course of these effects is unclear, but it is possible that many macro-level factors require decades to affect population levels of loneliness in an observable way because their effects are weak initially but accumulate over time 140 .

Implications for policy

A better understanding of how macro-level factors influence loneliness across historical time and geographic space is necessary to develop evidence-based recommendations for public policy measures against loneliness. For researchers, this is an invitation to study these factors more systematically in future research. But loneliness has also become a public policy issue in the past 5 years, and policymakers cannot wait for science to reach some consensus. For those who require guidance now, we offer some tentative policy implications.

First, the impact of macro-level factors should not be overestimated: even on the individual level, the causes of loneliness are complex and idiosyncratic. This is probably even more true for the effects of macro-level factors on population levels of loneliness. Attempts to pin some perceived uptick in loneliness to highly specific macro-level factors such as the introduction of smartphones 141 are likely to overestimate the relevance of a single factor, at the peril of drawing attention away from other factors that are at least as important. Instead, public policy is probably most effective if it targets individual risk factors such as poverty and unemployment and provides funding for the development and dissemination of individual-level evidence-based interventions against loneliness. Several reviews provide overviews of effective interventions for different target populations 142,143,144 .

At the same time, the importance of macro-level factors should not be underestimated. Shifts in macro-level factors such as demographic changes or changes in norms and values can influence the risk of loneliness in a population, albeit through complex and still poorly understood pathways. Geographical differences in the distribution of these macro-level factors can help to identify regions that might be particularly at risk and could serve as model regions for testing specific policies. Macro-level trends can therefore provide some tentative information on whether loneliness might become a greater (or lesser) concern in the future.

Finally, macro-level factors might moderate the effects of individual-level predictors on loneliness 42 . For example, the protective effect of being married might depend on the social norms related to marriage at a particular time period or in a particular geographical region 78 . Thus, the efficacy of policies aiming at reducing loneliness by strengthening marriages will vary across historical time and geographical space. This also means that both individual-level and macro-level measures against loneliness have to fit into the greater context. Policies that are applied in different historical or geographical contexts are not necessarily as effective as in the original setting and therefore need to be re-evaluated and, if necessary, adapted.

Summary and future directions

Systematic effects of macro-level factors on loneliness are theoretically plausible but difficult to detect. Macro-level factors tend to have weak effects on individual-level psychological phenomena, particularly if their effects are directly contrasted against individual-level predictors 140 . However, this does not mean that macro-level factors should be dismissed: the effects of macro-level factors often accumulate over time 140 , influence individual-level constructs through multiple indirect (sometimes contradicting) pathways, and might have divergent effects on different subgroups.

To achieve a more nuanced and complete picture of the association between macro-level factors and loneliness, it is necessary to broaden the available database. Representative samples are key to drawing valid conclusions about differences in population levels of loneliness across time or space. Representativeness can be restricted unintentionally through methodological factors (such as nonresponse bias 56,109 ) and intentionally (such as by excluding certain subgroups from the population of interest). For example, many panel studies deliberately exclude residents of care homes, yet this group faces substantial risk for loneliness 145 . Future research must include individuals from groups, regions and countries that have been underrepresented or completely excluded from previous studies.

To study macro-level factors systematically, researchers must routinely collect multilevel data on the social network, neighbourhood and region in which their participants are embedded. Many individual-level predictors can be aggregated at higher levels. For example, the availability of individual resources can be studied at the individual level (for example, how is individual income related to loneliness) as well as at the local, regional and national level (for example, how are local, regional or national poverty rates related to loneliness levels). In addition, future theoretical and empirical work needs to consider genuine macro-level factors, that is, factors that can only be conceptualized and measured at the macro level (for example, the extent to which mental health is prioritized in a health-care system).

Collecting data repeatedly at regular intervals (for example, annually) over multiple years or even decades would allow systematic investigations into the causal dynamics through which macro-level factors are linked to loneliness. Although most theories and empirical studies treat macro-level factors as predictors of loneliness, the association between macro-level factors and individual-level loneliness is most probably bidirectional (Fig. 2a). The effects of loneliness on individual economic, physical and psychological well-being can translate into population-wide outcomes such as reduced life expectancy 6 , increased health-care costs 10,11 , or reduced political participation 146 . Moreover, trends in macro-level factors might be more relevant than their absolute levels. For example, changes in the demographic composition of a population due to high residential mobility might be more predictive of population loneliness than the demographic composition itself 131 , owing to a cascade of indirect effects across multiple levels (Fig. 2b). Such a cross-level process takes time to unfold and can only be detected with longitudinal data in which factors at all levels are measured repeatedly over long periods of time.

figure 2

A better understanding of the causal relationships between macro-level factors and loneliness is also necessary to identify causal factors that can be targeted by public policy measures to reduce loneliness 147 . Examples of research designs that would allow such causal inferences include randomized control trials on community-level or regional-level interventions. In addition, and contrary to conventional wisdom among psychologists, nonexperimental studies can, under specific circumstances and with specific assumptions, be used for causal inference 148,149 , for example, natural experiments and prospective studies conducted in the context of major historical events 147 , including wars 150 , natural disasters 151 , pandemics 151,152 or economic crises 153 . Indeed, since 2020, researchers have used the COVID-19 pandemic to study the impact of sudden changes in macro-level factors on loneliness 152,154,155,156 .

Finally, a broad database fulfilling these criteria would enable integrative investigations of loneliness across both time and space. The association between a macro-level factor and loneliness always has to be understood in its specific geographic and historical context simultaneously, and, as geographic space or historical time change, so might the relevance of a macro-level factor for changes in loneliness across space and time. In addition, the relationships and interactions among different macro-level factors might also vary across time and space. For example, on the individual level, social class is correlated with the size and function of social networks such that individuals of higher socioeconomic classes tend to view themselves as more independent (rather than interdependent), allowing them to form more diverse and loose social networks 157 . It is possible that similar relationships can be found on the macro level. For example, economic growth could lead to changes in cultural values related to social relationships.

Although there is some overlap between macro-level factors explaining long-term trends in loneliness across historical time and macro-level factors explaining geographical variation in loneliness, few attempts have been made to conceptually or empirically integrate these different perspectives. A recent exception is a spatiotemporal meta-analysis in which historical changes in loneliness among young adults were related to different regional-level characteristics 54 . In general, spatiotemporal meta-analyses expand classic meta-analytic techniques by using spatial and temporal information (that is, considering not only when but also where an included single study was conducted) to explain heterogeneity in effect sizes 158 . Although no significant spatiotemporal associations were found in that particular meta-analysis 54 , this methodological approach might serve as a template for future research examining macro-level factors across time and space simultaneously.

In sum, longitudinal multilevel data from representative samples from multiple countries are necessary to gain a deeper understanding for why loneliness varies across time and space. Collecting such comprehensive data is not feasible for any single laboratory, but with shared resources it is not an impossible goal. In fact, large-scale studies that cover multiple countries across multiple years already exist (for example, the World Happiness Report 159 ), but loneliness is not yet routinely measured in these studies. We therefore call on researchers and funders of large-scale, cross-national panel studies to include standardized measures of loneliness. In addition, we call on researchers around the world to routinely measure loneliness in their studies and thereby contribute to growing the collective database of loneliness across time and space.

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Authors and Affiliations

  1. Faculty of Psychology, Ruhr University Bochum, Bochum, Germany Maike Luhmann, Susanne Buecker & Marilena Rüsberg
  1. Maike Luhmann