Living Alone and Loneliness in Predicting Health-Related Quality of Life: Results from the Healthy Aging Longitudinal Study in Taiwan (HALST)

doi:10.21203/rs.3.rs-2618842/v1

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Living Alone and Loneliness in Predicting Health-Related Quality of Life: Results from the Healthy Aging Longitudinal Study in Taiwan (HALST)

https://doi.org/10.21203/rs.3.rs-2618842/v1

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Purpose.

This study examined two related but distinct features of social isolation – living alone and loneliness – concerning their unique and joint effects on predicting health-related quality of life (HRQoL). In addition, we examined the moderating and mediating role of loneliness in the associations between living alone and HRQoL.

Method.

Analyses were with a nationally representative sample of 5,012 community-residing adults aged 55 and older (Mage = 59.3, SD = 8.1) and with their follow-up data 3.2 years later.

Results.

At baseline, 9% (n = 508) reported living alone, and 10.3% (n = 586) reported feeling lonely. 2.5% (n = 130) reported both living alone and feeling lonely. A series of regression showed that loneliness was consistently associated with lower physical (PCS) and mental (MCS) components of HRQoL, concurrently and longitudinally, whereas living alone was only associated with lower PCS 3.2 years later. Moderation analysis showed compounded effects of loneliness and living alone in predicting lower MCS. Mediation analyses revealed a cascading effect where living alone was associated with concurrent and subsequent loneliness, which, in turn, was associated with lower PCS and MCS 3.2 years later.

Conclusion.

Loneliness appeared to be a more potent predictor of lower HRQoL in later life. Nevertheless, living alone could indirectly worsen adults’ HRQoL by either exacerbating the negative impacts of loneliness or triggering a sense of prolonged loneliness predicting lower HRQoL. For practice, programs and policies to improve adults’ HRQoL should pay special attention to older adults who report loneliness, especially those who live alone.

aging

social isolation

living alone

loneliness

health-related quality of life (HRQoL)

Population aging is an unprecedented demographic force that brings about changes and challenges worldwide. With an accelerated rate of aging, Taiwan officially became an aged society in 2018 as the population comprised of people aged over 65 years old surpassed 14% [1]. Economic and demographic shifts have inevitably led to a scenario in which the number of older adults aging alone keeps rising [2]. With the rapid population aging, making life healthier and satisfactory in late life has become one of the major public health concerns, especially for those older adults who age alone and are prone to be socially isolated.

Social isolation is an overarching term that encompasses several concepts to describe structural and functional aspects of social interaction and connectedness [3]. Various measures and scales have been developed to assess social isolation, most of which are self-reported. For example, living alone, an objective indicator of structural social isolation is commonly implemented in research surveys. Living alone implies lower social support that an older individual would potentially receive compared to those who live with others [4]. Traditionally, intergenerational co-residence is highly valued by Chinese older adults and thus a desirable living arrangement [5]. Even in highly industrialized and westernized societies like Singapore, Hong Kong, and Taiwan, preferences for living together with adult children and offspring while aging still dominate the societal value of interdependence in a modern Chinese family [6].

On the other hand, loneliness is defined as a subjective, psychologically distressed feeling of being alone or separated [7, 8], which could be quantified as a discrepancy between the levels of desired and actual social contacts [9]. Albeit being relatively abstract and fluctuating over time, loneliness has been evaluated with the single-item question drawn from the Centre for Epidemiologic Studies Depression (CES-D) [10], and much comprehensively with self-reported questionnaires such as the three-item or 20-item UCLA Loneliness Scales [11, 12]. The prevalence of loneliness in community-dwelling older adults ranged from 19.3–34% worldwide [13, 14]. It is important to note that the varying rates of loneliness depend on the studied populations, sample sizes, and measurements of loneliness [15]. Nevertheless, available evidence on the prevalence of loneliness among older adults has warranted researchers’ attention and justified a need to investigate and intervene.

Living Alone And Loneliness As Predictors Of Poorer Health-related Outcomes

Overall, prior research suggests that objective (i.e., living alone) and subjective (i.e., loneliness) social isolation experiences are relevant and substantial to older adults’ health and well-being [16]. In a meta-analysis of 10 + longitudinal studies, the researchers found that older adults who lived alone were 1.32 times higher in mortality rate relative to adults who lived with others. The risk of dying was also 1.26 times higher among lonely older adults than those who did not report feeling lonely [17]. Despite the significance, the analyses were unable to contrast and compare the effects of living alone and feeling lonely on mortality, as these two quantity- and quality-oriented social isolation indicators have been conceptualized interchangeably in this line of research.

In fact, even in the cultural contexts where living alone and loneliness are likely to go hand in hand [18], living alone itself was not a consistent predictor of older adults’ health and subjective well-being. For example, some studies have found that older Chinese adults who lived alone felt more depressed [4, 19] and had lower quality of life [19, 20]. Others reported no evidence for higher levels of depressive symptoms for those who lived alone in Hong Kong [21] and Japan [22]. Likewise, even though the links between loneliness and low subjective well-being were relatively solid in literature (for reviews, see [8]), questions remain as to whether the effects of loneliness could sustain after controlling for the effects of covariates, including living alone [23] and a range of demographic, socioeconomic, and health factors [24].

In summary, the inconclusive findings in literature regarding the impacts of living alone, the commonly used indicator of socially distressing conditions, along with the concerns that living alone and loneliness have been used interchangeability in literature, signified the need to further investigate its unique impact on health after considering the effect of the other. The differentiation between living alone and loneliness has practical implications, too, as to which aspects of social isolation policies and programs should prioritize to effectively improve health and well-being in later life.

Living Alone And Loneliness Interplay To Predict Health-related Outcomes

Although researchers have come to consensus that living alone and loneliness represent distinct aspects of social isolation, it is not until recently that researchers have begun to investigate the mechanisms through which living alone and social isolation conjointly predicted developmental outcomes. With regard to the interactions of loneliness and living alone on health, Lim and Kua (2011) found that the effects of living alone on the mental aspect (PCS) of health-related quality of life (HRQoL) were worse among Singaporean older adults who lived alone. Likewise, Wei et al. (2022) found that living with others significantly buffered the negative impacts of loneliness on cognitive impairments and mortality rates among Chinese community-dwelling adults aged 65 and older. Moreover, Park et al. (2017) found that the effect of living alone on depressive symptoms was mediated by the feeling of loneliness among older Korean Americans. Taken together, these studies corroborate prior findings and extended them by investigate whether and the extent to which living alone and loneliness conjointly determined health-related outcomes. However, replications among older adults of diverse background, as well as evidence drawn from longitudinal research designs and unbiased samples is still warranted.

The Present Study

Summarizing, the available evidence suggested that (a) loneliness would be a more potent predictor of health and well-being relative to the effects of living alone, (b) the effects of loneliness may be contingent upon adults’ living arrangement (a moderation effect), and (c) living alone may create a context that primes older individuals to feel lonelier; thereby resulting in worse outcomes (a mediation effect). The current study built upon prior findings and extended them by further testing this propositions with longitudinal data from a nationally representative sample of Taiwanese older adults living in the community. We set forth the following hypotheses with regard to the unique and conjoint effects of living alone and loneliness on health-related quality of life (HRQoL): First, the effect of living alone would diminish to a significant extent when loneliness was taken into the equation to predict HRQoL. Second, living alone could exacerbate the negative impacts of loneliness on HRQoL. Third, we expected to find support that living alone indirectly predicted lower HRQoL via prolonged feelings of loneliness. Finally, the present study recognized HRQoL as a multidimensional construct, covering physical (PCS) and mental (MCS) aspects of quality of life. Thus, the hypotheses were tested separately for PCS and MCS.

Data and Sample

The present study utilized data from a sample of the Healthy Aging Longitudinal Study in Taiwan (HALST) [25]. HALST is a large, ongoing, nationally representative study which interviewed and followed up community-dwelling adults aged 55 and older since the baseline assessment (2009–2014). Stratified sampling was applied to ensure that the sample distribution of sex and educational attainment in the HALST study represent the sociodemographic distributions of the Taiwanese population in urban and rural areas. In addition, individuals who met the following four conditions were excluded from participating in the study: (1) bedridden, deaf or having difficulty hearing, (3) demented, and (4) too frail. Figure 1 presents a flowchart of the data collection process for the HALST and the sample selection for the current study.

Measurements

Predicting variables

Living arrangement. Living arrangement was assessed with the question “Whom are you living with?” with possible responses of living with someone (coded as 0; including family, friends, and housemaid) and living alone (coded as 1).

Loneliness. Loneliness was assessed with a single-item question “I felt lonely during the past week” with anchors of none to rarely, sometimes or a little of the time, occasionally or a moderate amount of the time, and most or all of the time, derived from the Center for Epidemiologic Studies Depression Scale (CES-D) [10]. For the purpose of statistical analysis, the responses were recoded into a dichotomous variable, such that responses of "sometimes", "occasionally", or "most of the time" was defined as feeling lonely (coded as 1) and none to rarely as not feeling lonely (coded as 0)

Outcome variables

Health-related quality of life. Health-related quality of life was assessed with the SF-12 version 2 [26]. Scoring followed the standard SF-12 algorithms in the user’s manual for the SF-12, yielding two aggregated summary measures: the physical component summary score (PCS) and mental component summary score (MCS). Previous studies have demonstrated the sound psychometric properties [27] and cross-cultural compatibility of the SF-12 [28], and it is considered suitable for application to Asian populations. The traditional Chinese version of the SF-12v2 was obtained through licensing from QualityMetric (http://www.qualitymetric.com/).

Controlled variables

A couple of age-related covariates for HRQoL were selected for the present study based on the results of prior review studies (e.g., [29–31]), including age, partnership status (0 = single, widowed, divorced or separated, 1 = married or partnered), gender (0 = male, 1 = female), educational attainment, social contact frequency (split by the group mean score for a low or high level), residence (0 = rural, 1 = urban), employment (0 = unemployed, 1 = employed), the activity of daily living (ADL) and instrumental activity of daily living (IADL), number of chronic diseases, the Short Physical Performance Battery (SPPB) [32], handgrip strength, and the Mini-Mental State Examination (MMSE) [33]. The rationale behind this was that the broader social context structured by age, sex, and socioeconomic status, as well as physical disabilities, functional needs, and chronic illness, may influence the formation and quality of social relationships as well as self-report quality of life [34]. Therefore, it cannot be assumed that the pathways by which social relationships are associated with health-related quality of life are universal for all. Therefore, the current study used a data-driven approach to determine which sociodemographic variables to be controlled for in further analyses based on univariate and bivariate associations of them with living alone, loneliness, and health-related quality of life.

Analytical Strategies

After obtaining descriptive statistics, the univariate and bivariate associations of PCS and MCS with demographic, socioeconomic, and health-related variables were presented as means (SD). Comparisons between groups of demographic and socioeconomic status (discrete variables) regarding their mean PCS and MCS were tested using F-test. The direction and the strength of the associations of PCS and MCS with functional health (continuous variables) were evaluated with Pearson correlation coefficients. Multiple regression models of PCS and MCS were estimated separately, with the following entry of predicting variables: baseline model (data not shown) includes demographics and socioeconomic status (i.e., age, sex, educational attainment, residence, and employment), general social integration indicators (i.e., partnership status and frequency of social contact), and functional health variables (i.e., handgrip strength, SPPB, ADL, and IADL, and numbers of chronic diseases). Living alone was then entered in Model 1, and loneliness was entered in Model 2. Model 3 evaluated the unique effects of living alone and loneliness on PCS and MCS. Model 4 added the interaction term of living alone and loneliness on top of the variables included in Model 3. The same analyses were applied with the longitudinal data where generalized estimating equations (GEE) method was used to account for potential correlations of repeatedly measured HRQoL for more robust regression coefficients and standard errors, and to maximize the use of available data considering the attrition over time. Cross-sectional and longitudinal structure equation modeling was applied to examine the indirect effects of loneliness in relation to living alone with PCS and MCS assessed 3.2 years later [35, 36]. As hypothesized, living alone at baseline were modeled to predict the mediator (loneliness) at Time 2 (i.e., 3.2 years later) and the outcome (PCS and MCS) at Time 2. The indirect effect in the path were tested for significance at the level of p < .05 with Mplus version 8 [37]. The models were estimated separately for the PCS and MCS, yielding separate estimation of model fit and variance accounted for in these two each outcomes at each measurement occasions. Model fit was evaluated with established goodness of fit indices (RMSEA < .08; SRMR < .05; CFI > .90) [38]. The rest of the analyses were done with SAS software (SAS Institute Inc., Cary, NC, USA).

Descriptive Statistics

Of 5,664 participants, 9% lived alone, and 10.3% reported feeling lonely. Around 2.5% (n = 130) of the sample reported both living alone and feeling lonely. Table 1 presents descriptive and univariate statistics for the variables under consideration in the present study. The mean scores for PCS and MCS in our initial sample (n = 5,664) were 46.8 ± 8.9 and 59.3 ± 8.1, respectively. Prior to conducting inferential statistics, we excluded individuals who had missing value in any of the main variables of interest (Fig. 1; final n = 5,012). Older age, female, lower educational attainment, unemployment, unpartnered, residing in rural areas, and infrequent social contact were all associated with poorer PCS (ps < .0001). Correlations among PCS and an array of functional health variables (i.e., number of chronic diseases, handgrip strength SPPB, MMSE, ADL, and IADL) showed that poorer functional health was significantly related to lower PCS (ps < .0001). While a feeling of loneliness was associated with poorer PCS, older adults' PCS was not significantly different between adults who lived alone and those who lived with others (Ms = 46.8 ± 0.1 and 47.0 ± 0.5, respectively, p = .63). On the other hand, older age, female, unpartnered, infrequent social contact, lower functional health, as well as both of the main social isolation indicators (i.e., living alone and loneliness) were significantly associated with lower MCS, ps < .05.

Cross-sectional And Longitudinal Effects Of Living Alone And Loneliness On Health-related Quality Of Life

Multiple regression analysis was applied to examine the main effects of living alone and loneliness on PCS and MCS. The results from cross-sectional and longitudinal data are shown in Table 2 and Table 3, respectively. With cross-sectional data, the results shows that living alone was not significantly associated with PCS, unstandardized coefficient (B) = 0.26, 95% CI: -0.47,1.00, after controlling for covariates representing important risk and protective factors (Supplemental Table 1, PCS Model 1). In contrast, loneliness was significantly and negatively associated with PCS, B = -1.12, 95% CI: -1.76,-0.47. On average, adults who felt lonely reported a 1.12 unit lower in PCS compared to adults who did not feel lonely (Model 2). The negative impact of loneliness on PCS remained after accounting for the effect of living alone, B = -1.15, 95% CI: -1.08,-0.51 (Model 3). A similar pattern was found for MCS. As shown in Supplemental Table 1, loneliness, but not living alone, was a significant predictor of MCS, B = -7.58, 95% CI: -8.28,-6.88, after accounting for the sociodemographic and health covariates (Models 1 and 2). As predicted, the effect of loneliness on MCS held after accounting for the effect of living alone, B = -7.6, 95% CI: -8.30,-6.89 (Model 3).

Controlled for covariates and the correlations across time, the GEE model showed that living alone and loneliness were both significant longitudinal predictors of PCS, B = 0.59, 95% CI: 0.03,1.14 and B = -0.71, 95% CI: -1.32,-0.11, respectively (Table 2, PCS Models 1 & 2). Their effects remained significant after accounting for the effect of the other, B = 0.67, 95% CI: 0.12,1.22 for living alone, and B = -0.77, 95% CI: -1.38,-0.17 for loneliness (Model 3). Regarding MCS, living alone was no longer a significant longitudinal protective predictor after accounting for the effect of loneliness, B = -0.23, 95% CI: -0.89,0.43. In contrast, the effect of loneliness held after controlling for the effect of living alone, B = -7.28, 95% CI: -8.13,-6.43 (Table 2, MCS Model 3).

Evaluating the Moderating Role of Living Arrangement in the Association of Loneliness with Health-Related Quality of Life

To further investigate the moderation, an interaction between living alone and loneliness was included in the final model (Model 4) of the regression models. With cross-sectional data, the effect of loneliness on PCS was not contingent on adults' living arrangements, B = 0.57, 95% CI: -1.05,2.19 (Supplemental Table 1, PCS Model 4). On the other hand, in predicting MCS, the interaction term involving living alone and loneliness was significant. Furthermore, the coefficient was positive, B = 1.99, 95% CI: 0.23,3.76 (Supplemental Table 1, MCS Model 4), indicating that the effect of living alone amplified the negative impact loneliness had on MCS. Specifically, among adults who reported being lonely, there was a 1.99-unit difference in the average MCS scores between adults who lived alone and those who lived with others. Results from longitudinal data, on the other hand, showed the negative impact of baseline loneliness was not amplified or attenuated by living arrangement in predicting PCS and MCS at Time 2 (Table 2, Model 4).

Evaluating The Mediation Pathways With Longitudinal Structural Equation Modeling

The structural model of the concurrent and longitudinal relations among living alone, loneliness and HRQoL is shown in Fig. 2. The relations were tested separately for PCS and MCS. Regarding the model predicting PCS, the model fit for the full structural model met criteria for a reasonable fit, χ² (3) = 64.24, p < .0001, CFI = 0.96, RMSEA = 0.071 (90% CI: 0.057,0.087), SRMR = 0.028. The path involving cross-sectional data showed that the indirect effect of loneliness was significant for the associations between living alone and PCS, B = -0.54, SE_B = 0.09, p < .001. When it came to predicting PCS at Time 2, the results suggested that loneliness at Time 1 was not a significant mediator for the effect of living alone, B = -0.10, SE_B = 0.07, p = 0.17. Instead, it was the lingering sense of loneliness observed over time (i.e., baseline and Time 2) that significantly mediated the associations of baseline living alone and poorer PCS three years later, B = -0.09, SE_B = 0.02, p < .001 (Table 3). The overall amount of variance accounted for in this model was R² = 0.19, which is a medium effect size according to Cohen (1988) [39].

Regarding the model predicting MCS, the model fit for the full structural model also met criteria for a reasonable fit, χ² (3) = 79.73, p < .001, CFI = 0.95, RMSEA = 0.08 (90% CI: 0.065,0.096), SRMR = 0.03. A similar pattern emerged, such that loneliness significantly mediated the associations of living alone with MCS with all baseline data, B = -1.4, SE_B = 0.15, p < .001. Similarly, loneliness observed across two measurement occasions significantly mediated the associations of baseline living alone with MCS three years later, B = -0.28, SE_B = 0.04, p < .001 (Table 3). The overall amount of variance accounted for in this model was R² = 0.12 and considered a medium effect size.

The mediations models were replicated after controlling for the effects of covariates (Supplemental Table 2). In general, the patterns remained, and the replication emphasized the robust role of loneliness in explaining the impacts of living on lowered HRQoL.

This study was set in the context of growing adult populations who live alone in a rapidly aging society. In recognition of well-documented evidence that social isolation is a significant risk factor for poorer quality of life, the study explored the unique and joint effects of two related but distinct social isolation features (i.e., living alone and loneliness). We tested a series of empirical models to test the independent associations, indirect effects, and conditional processes among the variables of interest. All the models were controlled for contextual factors of demographic, socioeconomic, social contact and partnership, and health-related characteristics.

The rates of living alone (9%) in our sample were relatively low compared to other corners of the world, and the number was in line with a recent survey in which 11% of Asian adults aged 60 years and older lived alone (11%) [40]. Given that loneliness was found to be more prevalent among older adults who lived alone [18, 41], a relatively low prevalence of loneliness in our sample (10.3%) was also expected. It also appeared that the rate of loneliness censored among Taiwanese older adults did not change between the years 2009 and 2015 (10.3% vs. 10.5%) [42]. To put it into perspective, the rates of living alone were also comparatively higher in North America (26%) and Europe (28%) [40]. Coincidentally, the rate of loneliness was also as high as 19.3% among US community-dwelling older adults [13] and 20.1–34% across 25 European countries [14].

The results supported our hypotheses regarding the detrimental consequences of social isolation, in particular, loneliness, on poorer HRQoL. Similar patterns were found cross-sectionally and longitudinally, with PCS and MCS alike. Multiple regression models with baseline and longitudinal data all pointed to the fact that the negative impacts of living alone on HRQoL could be, by and large, explained by concurrent feelings of loneliness. The negative impact of loneliness, on the other hand, was not contingent upon different living arrangements, with the exception that adults who felt lonely and living alone reported even poorer MCS compared to lonely adults who lived with others. In fact, the negative impact of living alone on PCS and MCS was indirect and only significant when prolonged loneliness over time was introduced to the equation.

Taken together, our study extended prior literature examining the bivariate associations among living alone, loneliness, and health-related outcomes [7, 8] by examining concurrent and longitudinal associations, as well as the condition with which the negative impact of loneliness exacerbated [23]. In addition, our results drawn from the structural models corroborated prior findings and added to a growing research interest in the interplays of multiple social isolation indicators concerning health-related outcomes [18, 29, 43].

Limitations And Future Directions

An important contribution of this study is that we illuminate the interplay of living alone and loneliness in predicting HRQoL in late life, using a large-scale probability sample of adults aged 55 and older. In our analyses, an array of covariates were controlled to, to some extent, increase the reliability of our findings. Despite the contributions to the literature, the interpretation and generalizability of the findings would require some caution. One limitation of the study arises from the nature of the measurement of social isolation included in the current study. Loneliness was assessed with a single item rather than a full scale. As powerful as a single item predictor could be in effectively predicting outcomes, using a single item for loneliness inevitably limited our capabilities to capture the varying degree of loneliness across domains [11].

In terms of living alone, the current study could not control its endogeneity. Living alone, in fact, may hold different implications for individuals with various needs, resources, and preferences. In order to understand the deeper meaning of living alone and determine the direct of its effect, qualitative data such as individuals' preference for and satisfaction with living arrangements should be carefully considered in future studies.

To this end, we encourage future research to adopt multi-item scales of loneliness with solid reliability and validity. It will also be critical to investigate factors contributing to the dynamic changes of living alone (e.g., the timing and the length of time that individuals have been living alone) when establishing their unique and joint associations with health-related outcomes. Acknowledging that social relationships and social isolation are multidimensional constructs, each of the indicators' distinctiveness and relatedness may deserve a 'weight.' The weighted combinations of social indicators, in turn, shape various latent typologies of social relationships. These profiles involving multiple characteristics of social isolations could provide a more holistic view of the number of social contacts and how individuals perceive the quality of these social interactions [44–46]. Methodologically, refined analytic strategies such as using latent profile analysis could be applied to disentangle the interplays of a more comprehensive list of social isolation indicators other than living alone and loneliness and their predictive relevance to health-related outcomes in late life.

To note, the present study was not intended to mitigate the potential impact of living alone on HRQoL. In fact, living alone may create a potentially distressing context for prolonged feelings of loneliness over time. Based on these findings, promoting the sense of belongingness could be utilized in the designs of intervention programs for preserving the quality of life in later life. Meanwhile, efforts are also warranted to consider contextual and structural factors, such as living arrangements, when policies and programs are to target higher-risk populations (e.g., adults who lived alone involuntarily) and to alleviate the distressing impact of late-life loneliness on their quality of life.

Statements and Declarations

Competing interest: All authors declare that they have no financial or non-financial interests that are directly or indirectly related to this work submitted for publication.

Acknowledgments: We thank all members and staff of the HALST research group and the participants in the HALST study. We could not wrap up this study without the invaluable mentorship and long-time dedication of Dr. Yen-Feng Chiu, a full researcher at NRHI. The research team pays tribute to Dr. Chiu, who passed away in February 2023.

Funding: This study was supported by the National Health Research Institute (Project No. PH-97~112-SP-01, PH-101~112-PP-04). The National Health Research Institute had no role in the study design, data collection, analysis, interpretation, or writing of the manuscript.

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Tables 1-3 is available in the Supplementary Files section.

No competing interests reported.

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Living Alone and Loneliness in Predicting Health-Related Quality of Life: Results from the Healthy Aging Longitudinal Study in Taiwan (HALST)

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Abstract

Purpose.

Method.

Results.

Conclusion.

Figures

Introduction

Living Alone And Loneliness As Predictors Of Poorer Health-related Outcomes

Living Alone And Loneliness Interplay To Predict Health-related Outcomes

The Present Study

Methods

Data and Sample

Measurements

Predicting variables

Outcome variables

Controlled variables

Analytical Strategies

Results

Descriptive Statistics

Cross-sectional And Longitudinal Effects Of Living Alone And Loneliness On Health-related Quality Of Life

Evaluating The Mediation Pathways With Longitudinal Structural Equation Modeling

Discussion

Limitations And Future Directions

Conclusion And Practical Implications

Declarations

References

Tables

Additional Declarations

Supplementary Files

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