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Meaning in life: bidirectional relationship with depression, anxiety, and loneliness in a longitudinal cohort of older primary care patients with multimorbidity

BMC Geriatrics volume 25, Article number: 195 (2025) Cite this article

Abstract

Background

Depression, anxiety and loneliness are common among older patients. As a potential psychological buffer against these challenges, meaning in life (MIL) remains underexplored in longitudinal studies within this population. This study aims to examine the longitudinal relationship of MIL with depression, anxiety, and loneliness among older adults with multimorbidity in Hong Kong.

Methods

In a prospective cohort of 1077 primary care patients aged 60 or above with multimorbidity in Hong Kong, MIL was assessed using an item from the Chinese Purpose in Life test at baseline, the 1st follow-up (median: 1.3 years), and the 2nd follow-up (median: 3.1 years). Depression, anxiety, and loneliness were assessed using the Patient Health Questionnaire, Generalized Anxiety Disorder, and De Jong Gierveld Loneliness scales, respectively, at each time point. Cross-lagged relationships between MIL and these measures were examined using cross-lagged panel models.

Results

Participants had an average age of 70.0 years, with 70.1% being female. Higher MIL predicted lower depression (β = -0.15), anxiety (β = -0.13), overall loneliness (β = -0.18), emotional loneliness (β = -0.15), and social loneliness (β = -0.16) at the 1st follow-up. Additionally, higher MIL predicted lower overall loneliness (β = -0.12), emotional loneliness (β = -0.11), and social loneliness (β = -0.10) at the 2nd follow-up. At baseline, higher depression (β = -0.21), overall loneliness (β = -0.15), emotional loneliness (β = -0.11), and social loneliness (β = -0.11), but not anxiety, predicted lower MIL at the 1st follow-up. At the 1st follow-up, depression (β = -0.23), anxiety (β = -0.16), overall loneliness (β = -0.10), and emotional loneliness (β = -0.11), but not social loneliness, predicted lower MIL at the 2nd follow-up.

Conclusions

The findings suggest a bidirectional relationship between MIL and mental health outcomes in older patients with multimorbidity in Hong Kong. Emotional loneliness demonstrated a more consistent bidirectional association with MIL than social loneliness. Further research is needed to understand the underlying mechanisms and develop targeted interventions addressing both MIL and mental health problems.

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Introduction

The global population of individuals aged 60 and above is projected to rise from 1 billion in 2019 to 2.1 billion by 2050 [1]. This trend is even more pronounced in Hong Kong, where the older adult population is expected to nearly double from 1.27 million to 2.44 million over the next two decades [2]. Mental health issues are common among older adults, as consistently shown in systematic reviews and meta-analyses. Across studies and regions, the prevalence of depression among older adults ranges from 7.7 to 81.1%, with an average of 31.74% [3]. The prevalence of anxiety disorders in the older population varies from 1.2 to 14% in community settings and from 1 to 28% in clinical settings [4]. Loneliness prevalence among older adults ranges from 5.9 to 43.3% [5].

Amid a global demographic shift towards an aging population, the concept of meaning in life (MIL) assumes a pivotal role in unravelling the intricate dynamics linking aging, health, and overall well-being [6]. MIL is a multifaceted construct encompassing three key dimensions: comprehension, which reflects an individual’s ability to understand life experiences; purpose, which pertains to the pursuit of meaningful goals and aspirations; and significance, the perception of one’s life as valuable and impactful [7]. Together, these dimensions create a sense of coherence, purpose, and worth in life. MIL evolves dynamically across life stages. In adulthood, it often derives from work, hobbies, and personal relationships, contributing to a sense of identity and purpose. Work or occupation serves as a significant source of MIL by providing purpose, achievement, and a sense of contribution to society. As individuals transition into old age, the focus shifts towards reflecting on life experiences and building a legacy. Additionally, MIL is profoundly influenced by cultural and religious worldviews, which shape how individuals interpret and construct meaning in their lives [8]. Importantly, research has shown that MIL is modifiable across different populations [9], indicating its potential as an effective prevention and treatment target for better well-being.

Previous research suggests that MIL plays a significant role in predicting overall well-being among older adults, particularly those dealing with chronic conditions. A strong sense of meaning is associated with better health status and, consequently, less healthcare utilization among older adults [10,11,12]. Its benefits for physical health are demonstrated by reduced risks for all-cause mortality [13,14,15], heart attacks [16], cardiovascular disease [14, 17, 18], stroke [18, 19], and sleep disturbances [20]. MIL is also a protective factor against suicide [21], cognitive decline [21, 22], and limitations in activities of daily living [16], contributing to psychosocial well-being. Furthermore, individuals with higher levels of MIL show higher resilience, represented by better emotional recovery from negative stimuli [23], greater pain tolerance [24], and higher acceptance of their chronic conditions [25]. In general, greater psychological well-being [11, 25,26,27,28] and better adjustment after disease diagnosis [29,30,31] have been observed in individuals with higher MIL in vast cross-sectional studies. Specifically, purpose in life has been negatively correlated with depression [11, 32, 33], anxiety [32,33,34], and loneliness [35]. A more comprehensive investigation of the causal direction and dynamics is provided by a few studies using a longitudinal design. One study showed that MIL appeared to offset the negative impact of traumatic life events on depressive symptoms during the same time period, but these stress-buffering effects were not significant when changes in depressive symptoms over time were examined [36]. Another longitudinal study with over 16,000 middle-aged and older adults in European countries found bidirectional, prospective associations of MIL with depression and loneliness [16].

Despite the growing body of research, there are still many research gaps regarding MIL and mental health. An important one is the prospective associations between MIL and health or quality of life outcomes, as extensive research has relied on cross-sectional data [10, 11, 26,27,28,29,30,31,32, 34, 35]. Only a handful of longitudinal studies have been conducted in this context, leaving the temporal relationships and directions of effects between MIL and mental health outcomes still poorly understood. Furthermore, existing longitudinal studies are marked by limitations such as small sample size [37], a single follow-up [16], and solely unidirectional effects of MIL on depression [27]. In addition, while there is extensive research on older adults in general, there is rarely a longitudinal study focusing specifically on older adults with multimorbidity. This focus is particularly important, as multimorbidity is common in older adults [38], and those with multimorbidity face additional burdens that increase their vulnerability to mental health problems [39,40,41]. By understanding prospective associations, primary care professionals can better enhance MIL and improve mental health outcomes for this specific group. In light of these research gaps, the current study stands as one of the first to use longitudinal data featuring more than two time points to analyse the bidirectional relationship between MIL and mental health in a Chinese older adult population with multimorbidity in primary care.

This study hypothesizes a bidirectional relationship between MIL and depression, anxiety, and loneliness based on several pathways proposed in the literature with some empirical evidence. The first type of pathway involves the consequences of self-regulatory processes that naturally arise from the affective, cognitive, and motivational components of MIL. The presence of meaning often elicits positive feelings, such as enjoyment [42, 43], satisfaction and fulfilment [44], and a sense of connectedness and belongingness [45, 46]. Positive affect can provide feedback regarding satisfactory progress towards one’s goals [47, 48], acting as a buffer against psychological distress. Moreover, MIL provides a cognitive framework that helps individuals perceive patterns, consistency, and significance in their environment [49,50,51,52], reducing negative or distorted thinking patterns that contribute to anxiety and depression. In addition, MIL motivates active engagement in behaviours that benefit mental health, such as health-promoting behaviours [50], initiating and maintaining social relationships [45, 46, 53,54,55], and engaging in prosocial behaviours [56], which are important to older adults with multimorbidity. The belief that life is worth caring for motivates individuals to do something good for themselves and others [45, 53, 57]. Finally, MIL facilitates more adaptive coping and resource mobilization to effectively solve problems and manage stressful life events [50, 52, 58,59,60], playing a crucial role in preventing and reducing anxiety and depression and in strengthening the ability to cope with loneliness [61]. The second type of pathway involves attentional and evaluative processes that shape MIL and may be affected by adverse mental health status. Acting as immediate prompts for appraisal, both affective states and belongingness can influence MIL by altering sensitivity to the relevance of an experience to MIL and its intensity [46, 62, 63]. Furthermore, anxiety, depression, and loneliness are often accompanied by negative cognitive biases that hinder individuals including older adults from finding and recognizing meaning in their lives, such as self-criticism, intrusive rumination, negative thinking, and distorted perception and evaluation of others [64,65,66,67]. Additionally, anxiety and depression reduce engagement in activities through which people can experience meaning in their lives, especially social interactions [68,69,70,71,72,73]. They may also divert individuals from choosing activities that align better with their values or prevent full immersion in activities, thus missing opportunities to enhance their MIL through positive experiences [68, 74,75,76,77]. Similarly, loneliness may make people focus more on social threats and rejection instead of long-term goals that are more important in finding the meaning of their lives [78, 79].

Objectives

This study aimed to examine the longitudinal relationship of meaning in life (MIL) with depression, anxiety, and loneliness among primary care patients with multimorbidity in Hong Kong. We hypothesize that MIL will have a bidirectional relationship with depression, anxiety, and loneliness. Multimorbidity has previously been associated with depression in the Chinese population [80,81,82]. These findings can help in understanding whether MIL may serve as a buffer against the emotional burden caused by multimorbidity and assist in designing effective interventions for better well-being among older individuals with multimorbidity.

Methods

Study design

This prospective cohort study involved primary care patients with multimorbidity (cohort registration number ChiCTR-OIC-16008477). Details of the cohort have been previously published [83]. Ethics approval was obtained from The Joint Chinese University of Hong Kong (CUHK) - New Territories East Cluster (NTEC) Clinical Research Ethics Committee (CREC) (Reference No. CREC2016.204).

Setting and participants

Participants were recruited from four public general outpatient clinics (GOPCs) in the New Territories East Cluster (NTEC) in Hong Kong. These government-funded general outpatient clinics managed by the Hospital Authority are located in a high-density residential region with a population of approximately 800,000 in 2018. Primary care patients aged 60 years or above, with two or more chronic diseases, and who could speak and understand Chinese were eligible for recruitment. Participants were initially screened at the GOPCs for eligibility and then invited for a scheduled face-to-face interview at a teaching clinic for detailed assessments.

At baseline, 1077 participants were recruited between 7 June 2016 and 23 October 2017. The 1st follow-up survey was conducted between 3 April 2018 and 6 March 2019 (n = 736), and the 2nd follow-up survey was conducted via telephone between 24 March and 15 April 2020 (n = 690). The median duration for the 1st follow-up was 484.5 days (IQR: 184.75), and for the 2nd follow-up, it was 1134.5 days (IQR: 258.5).

At baseline and the 1st follow-up, trained research staff conducted assessments face-to-face at the teaching clinic. For the 2nd follow-up assessment in 2020, trained research staff conducted a telephone survey for the convenience of patients due to the COVID-19 outbreak. The baseline survey consisted of questions on physical health, mental health, and demographic factors [83]. Sociodemographic information was collected at baseline, including age, sex, years of education, employment status, and marital status, and information on chronic disease diagnoses was obtained from the electronic clinical management records of the participating clinics.

Measures

Meaning in life

Meaning in life was measured using a single item from the validated and reliable Chinese Purpose in Life test (CPIL), which comprises 20 items and five factors, with a Cronbach’s alpha of 0.84 and Guttman’s split-half reliability coefficient of 0.82 [84]. The item used is from the “Meaning of Existence” subscale and asks participants the question “My personal life is…” on a seven-point Likert scale from 1 (“Not meaningful and purposeful at all”) to 7 (“Very meaningful and purposeful”), with the midpoint 4 as the neutral response. This item has been used in other studies among older adults [12] and in other contexts [85,86,87,88]. It was introduced halfway through the baseline assessment of the cohort. In addition to the baseline assessment, the MIL measure was also assessed at the 1st and the 2nd follow-ups.

Depression

Depression was measured using the 9-item Patient Health Questionnaire (PHQ-9), a self-report questionnaire that assesses the presence and severity of depressive symptoms over the past two weeks. The PHQ-2 and PHQ-9 are valid and reliable tools for detecting depression in primary care [89], with the PHQ-2 designed to be used as an initial screening step [90]. The Chinese version of the PHQ-9 and the shorter PHQ-2 have been validated for the Chinese population [91,92,93]. Each item is rated on a 4-point scale from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 27. Scores between 5 and 9, 10–14, 15–19, and 20 + indicate mild, moderate, moderately severe, and severe depression, respectively. The PHQ-2 was used at baseline to screen for depression, and only those with a PHQ-2 score of at least 3 (n = 208) were asked to complete the PHQ-9, while the full PHQ-9 measure was administered to all participants at the 1st and 2nd follow-ups.

Anxiety

Anxiety was measured using the 7-item Generalized Anxiety Disorder (GAD-7) scale, a self-report questionnaire that assesses the presence and severity of anxiety symptoms over the past two weeks. The GAD-7 is a valid and reliable tool for detecting anxiety disorders in primary care. The shorter GAD-2 has been shown to retain the excellent psychometric properties of the GAD-7 [94] and has been proposed as a first step in screening for anxiety disorders [95]. Each item is rated on a 4-point scale from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 21. Scores between 5 and 9, 10–14, and 15–21 indicate mild, moderate, and severe anxiety, respectively. The GAD-2 was used at baseline to screen for anxiety [94], and only those with a GAD-2 score of at least 3 (n = 184) were asked to complete the GAD-7, while the full GAD-7 measure was administered to all participants at the 1st and 2nd follow-ups.

Loneliness

Loneliness was measured using the 6-item De Jong Gierveld Loneliness Scale (DJGLS) [96]. This scale has been validated for assessing loneliness in Chinese older adults [97]. It contains two subscales assessing social and emotional loneliness, as well as an overall loneliness score. Scores from 0 to 1 indicate little to no loneliness, scores from 2 to 4 indicate moderate loneliness, and scores from 5 to 6 indicate severe loneliness [98]. The DJGLS was assessed at baseline, the 1st follow-up, and the 2nd follow-up. A group of individuals (n = 285) did not complete the DJGLS at baseline because initially, only those with a PHQ-2 score of at least 3 were asked to complete it, as the aim was to measure loneliness levels among those who were depressed at the beginning. In the end, the DJGLS was administered to 792 participants at baseline, with 588 of them having a PHQ-2 score lower than 3.

Statistical analysis

In this study, cross-lagged panel models were used to examine the relationship between meaning in life and another mental health variable of interest (i.e., depression, anxiety, or loneliness) over time. The cross-lagged panel model is a type of structural equation modelling (SEM) that allows testing the directionality of the relationship between two variables by examining the temporal precedence of one variable over the other.

To fit each cross-lagged panel model, the model was specified by defining the cross-lagged paths between the outcome variables across time. The model also included covariances between the residuals of the outcome variables at each time point, as well as variances for each outcome variable. While the main models did not include other covariates, supplementary files include results that were adjusted for age, sex, and the number of chronic diseases at baseline. The SEM was estimated using full information maximum likelihood via the lavaan function from the lavaan package in R, which can handle missing data [99]. The level of statistical significance was set at 5% (two-sided). Standardized coefficients are reported to allow direct comparisons of effect sizes.

Two goodness-of-fit measures were employed to assess the adequacy of model fit: the comparative fit index (CFI) and the standardized root mean square residual (SRMR). A CFI value above 0.95 indicates a good model fit [100], with values above 0.90 acceptable as a rule of thumb [101]. For the SRMR, values lower than 0.06 indicate a good fit, with acceptable values under 0.08 [100]. All data management and statistical analyses were performed using R version 4.3.0 [102].

Results

Participant characteristics

The demographic characteristics of the participants at baseline, the 1st follow-up and the 2nd follow-up are shown in Table 1. Among the 1077 participants at baseline, 341 and 387 were lost to the 1st and 2nd follow-ups, respectively. At baseline, the mean age of participants was 70.0 years (SD = 6.8), and 70.1% were female. The mean number of chronic diseases was 4.0 (SD = 1.8), and the mean number of years of education was 7.6 (SD = 4.3). The majority of participants were either retired (56.5%) or homemakers (35.4%) and were either married (67.3%) or widowed (23.1%). The response rates for T1 and T2 were 68.3% and 64.1% respectively. Those lost to follow-up were not statistically different in sociodemographic characteristics (p > 0.05) from respondents at the two time points, except for being marginally older by about 1 year.

Table 1 Descriptive statistics at baseline, 1st follow-up, and 2nd follow-up
Full size table

The summary statistics of the outcome variables at baseline, the 1st follow-up, and the 2nd follow-up are shown in Table 2. The mean score for meaning in life was 4.85 (SD = 1.2) at baseline, 5.12 (SD = 1.27) at the 1st follow-up, and 5.25 (SD = 1.12) at the 2nd follow-up.

At baseline, the mean PHQ-2 score was 1.16 (SD = 1.49), and 18.9% of participants had a score of 3 or higher, indicating possible depression. Among those who screened positive and completed the PHQ-9 (n = 208), the mean score was 11.25 (SD = 4.42), and 58.2% (n = 121) had a score of 10 or higher, indicating moderate to severe depression. At the 1st follow-up, the mean PHQ-9 score was 4.21 (SD = 4.20), with 11.5% having moderate to severe depression (PHQ-9 ≥ 10). At the 2nd follow-up, the mean PHQ-9 score was 4.49 (SD = 4.41), with 11.0% having moderate to severe depression (PHQ-9 ≥ 10). Severe depression (PHQ-9 ≥ 20) was below 1% at follow-ups.

At baseline, the mean GAD-2 score was 1.21 (SD = 1.46), and 17.0% of participants had a score of 3 or higher, indicating possible anxiety. Among those who screened positive and completed the GAD-7 (N = 184), the mean score was 10.62 (SD = 3.94), and 58.7% had a score of 10 or higher, indicating moderate to severe anxiety. At the 1st follow-up, the mean GAD-7 score was 2.48 (SD = 3.91), with 6.5% having moderate to severe anxiety (GAD-7 ≥ 10). At the 2nd follow-up, the mean GAD-7 score was 2.96 (SD = 3.91), with 6.7% having moderate to severe anxiety (GAD-7 ≥ 10). Severe anxiety (GAD-7 score ≥ 15) was below 3% at follow-ups.

At baseline, the mean DJGLS score for loneliness was 1.76 (SD = 1.82), with 35.2% of participants experiencing moderate loneliness (DJGLS between 2 and 4) and 11.2% experiencing severe loneliness (DJGLS ≥ 5). The proportion of participants with severe loneliness increased considerably to 27.9% at the 2nd follow-up during the initial phase of COVID-19, driven by increases in both emotional and social loneliness.

Table 2 Summary statistics of outcome variables at baseline, 1st follow-up, and 2nd follow-up
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Relationship with depression

The goodness-of-fit measures indicate a good model fit, with CFI between 0.948 and 0.975 and SRMR between 0.030 and 0.049 except for the model with social loneliness (CFI = 0.908, SRMR = 0.050). As shown in Fig. 1, higher MIL at an earlier time point significantly predicted lower levels of depression (PHQ-9) at the 1st follow-up (β = -0.15, p < 0.01), but not at the 2nd follow-up (β = 0.00, p = 0.89). Conversely, higher PHQ-9 scores at an earlier time point predicted lower MIL at both the 1st follow-up (β = -0.21, p = 0.02) and the 2nd follow-up (β = -0.23, p < 0.001). The effect of MIL at baseline on depression at the 1st follow-up becomes statistically insignificant when using the PHQ-2 instead of the PHQ-9 (see Figure S1 in the supplementary file).

Fig. 1
figure 1

Relationship between meaning in life & depression (PHQ-9). CFI = 0.975. SRMR = 0.036. *p < 0.05, **p < 0.01, ***p < 0.001

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Relationship with anxiety

As shown in Fig. 2, higher MIL at an earlier time point was linked to lower levels of anxiety (GAD-7) at the 1st follow-up (β = -0.13, p < 0.01) but not at the 2nd follow-up (β = -0.02, p = 0.57). Conversely, higher GAD-7 scores at an earlier time point only predicted lower MIL at the 2nd follow-up (β = -0.16, p < 0.001) but not at the 1st follow-up (β = -0.17, p = 0.09). The effect of MIL at baseline on anxiety at the 1st follow-up remained statistically significant when using the GAD-2 instead of the GAD-7 (see Figure S2 in the supplementary file).

Fig. 2
figure 2

Relationship between meaning in life & anxiety (GAD-7). CFI = 0.974. SRMR = 0.030. *p < 0.05, **p < 0.01, ***p < 0.001

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Relationship with loneliness

Figure 3 shows that higher MIL at an earlier time point significantly predicted lower levels of loneliness (DJGLS) at both the 1st follow-up (β = -0.18, p < 0.001) and the 2nd follow-up (β = -0.12, p < 0.01). Similarly, higher DJGLS scores at an earlier time point predicted lower MIL at both the 1st follow-up (β = -0.15, p < 0.001) and the 2nd follow-up (β = -0.10, p = 0.01).

Fig. 3
figure 3

Relationship between meaning in life & loneliness (DJGLS). CFI = 0.949. SRMR = 0.049. *p < 0.05, **p < 0.01, ***p < 0.001

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Figure 4 shows that higher MIL at an earlier time point predicted lower levels of emotional loneliness (DJGLS emotional subscale) at both the 1st follow-up (β = -0.15, p < 0.001) and the 2nd follow-up (β = -0.11, p = 0.01). Likewise, higher emotional loneliness scores at an earlier time point predicted lower MIL at both the 1st follow-up (β = -0.11, p < 0.01) and the 2nd follow-up (β = -0.11, p < 0.01).

Fig. 4
figure 4

Relationship between meaning in life & emotional loneliness (DJGLS subscale). CFI = 0.964. SRMR = 0.037. *p < 0.05, **p < 0.01, ***p < 0.001

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Furthermore, Fig. 5 shows that higher MIL at an earlier time point predicted lower levels of social loneliness (DJGLS social subscale) at both the 1st follow-up (β = -0.16, p < 0.001) and the 2nd follow-up (β = -0.10, p = 0.02). However, higher social loneliness scores at an earlier time point were associated with lower MIL at the 1st follow-up (β = -0.11, p < 0.01) but not at the 2nd follow-up (β = -0.06, p = 0.13). These results also show that the outcome variables predict themselves at follow-ups. The only exception is anxiety as measured by GAD-7 due to a lack of samples at baseline, but anxiety at baseline, as measured by GAD-2 with the full sample, also predicts itself at the first follow-up (Figure S2 in the supplementary file). Moreover, these results do not meaningfully change when further adjusted for age, sex, and the number of chronic diseases at baseline (Figures S3 to S9 in the supplementary file).

Fig. 5
figure 5

Relationship between meaning in life & social loneliness (DJGLS subscale). CFI = 0.908. SRMR = 0.050. *p < 0.05, **p < 0.01, ***p < 0.001

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Discussion

This study examined the longitudinal relationship between MIL and mental health outcomes, including depression, anxiety, and loneliness, among primary care patients with multimorbidity in Hong Kong. The results showed that higher MIL predicted lower levels of depression, anxiety, and loneliness at the 1st follow-up and lower levels of loneliness at the 2nd follow-up. Higher levels of depression and loneliness predicted lower MIL at both the 1st and 2nd follow-ups, whereas only anxiety at the 1st follow-up predicted lower MIL at the 2nd follow-up. MIL consistently predicted lower levels of emotional and social loneliness at both time points. Likewise, emotional loneliness predicted lower MIL at both time points, while social loneliness predicted lower MIL only at the 1st follow-up but not at the 2nd follow-up. While previous studies from Hong Kong identified a negative correlation between MIL and depression [103, 104], our study indicates that this relationship may be predominantly characterized as depression influencing MIL rather than vice versa. A 2023 meta-analysis found a stronger negative correlation between MIL and mental health issues, particularly depression, in Asian countries (Pearson correlation coefficient, − 0.48) than in Western countries (− 0.34), though the difference was not statistically significant [105]. Recent research also indicates that MIL plays a crucial role in mediating the effects of loneliness on depressive symptoms among older Korean adults during the COVID-19 pandemic [106]. Similarly, another recent study across Europe and Israel has shown that MIL mediated over 80% of the association between loneliness and depression [107]. Overall, these findings suggest a bidirectional relationship between MIL and depression, anxiety, and loneliness among older patients with multimorbidity in Hong Kong.

The above findings are consistent with previous studies that have found a reciprocal association between MIL and mental health outcomes among middle-aged and older adults [16, 37]. For instance, using different instruments, prospective bidirectional associations were found between MIL and depression and loneliness over a 6-year follow-up in a cohort of over 16,000 middle-aged and older adults from 13 European countries [16]. Despite a smaller sample size and shorter study period, the present study observed reciprocal relationships at more time points, revealing similar patterns among a cohort with multimorbidity. In addition, this study found that emotional loneliness played a more prominent role than social loneliness in predicting MIL.

Notably, some of the bidirectional relationships observed in this study did not remain consistent over time. While MIL showed statistically significant prospective influences on depression and anxiety in the 1st follow-up period, these prospective effects were no longer statistically significant in the 2nd follow-up period. On the other hand, the prospective influence of anxiety on MIL was statistically insignificant in the 1st follow-up period but became statistically significant in the 2nd follow-up period. This inconsistency could result from two possible scenarios — methodological issues or instability of the true associations.

In the first scenario, the true associations are stable over time, but this study failed to detect them due to methodological issues, including changes in the data collection method, measurement error, and potential confounders. First, due to the outbreak of COVID-19, telephone interviews replaced in-person interviews, and the 2nd follow-up period became somewhat longer (with a median interval 31% longer than the 1st follow-up). These changes may have introduced biases and obscured the effects, particularly due to the change in interview mode [108]. Second, the influence of measurement error was unclear and may depend on the specific instrument used. For example, when employing PHQ-2 to measure depression instead of PHQ-9, the statistically significant prospective associations between MIL and depression in the 1st follow-up period became statistically insignificant. In contrast, replacing GAD-7 with GAD-2 to measure anxiety did not affect the statistical significance of the prospective associations between MIL and anxiety. This may indicate that the more comprehensive PHQ-9 scale with higher specificity [89] captures the relationship of depression with MIL better than the screening-oriented PHQ-2, whereas GAD-2 did not appear to substantially attenuate the power of detecting the relationship of anxiety with MIL compared to GAD-7. Third, time-dependent confounders that emerged after the 1st follow-up may have concealed the effects of MIL. Such confounders include life events that are common at older ages and cause overwhelming distress, such as the loss of a significant other, changes in socioeconomic status, and the development of chronic conditions. Older adults generally report a greater sense of purpose and meaning in life, while younger individuals often search for meaning [6, 16]. The sense of purpose in life declines in late adulthood with age acting as a proxy for significant life events such as illness, retirement, and widowhood [16]. However, this progression may not be strictly linear or monotonic; for instance, purpose in life can continue to increase in the oldest old aged 90–105 who reported greater purpose in life than those aged 80–89 [109]. Maintaining high levels of meaningfulness in old age might become more challenging due to age-related losses, such as health issues, retirement, and widowhood.

In the second scenario, the observed inconsistency over time may not be a result of methodological issues but rather reflect instability in the true associations. Such instability could arise from natural developments or external factors. First, the findings may suggest that the protective effects of MIL on mental health outcomes attenuate at a certain age. This aligns with a five-year follow-up study of individuals older than 85, where no predictive or protective link between purpose in life and depression was identified [110]. Second, a major event during the 2nd follow-up period, namely the COVID-19 outbreak, profoundly impacted people’s lives in many aspects, possibly altering the reciprocal relationships between MIL and mental health outcomes. The onset of the COVID-19 pandemic adversely affected the psychosocial health of this cohort, as previously reported [111]. A recent study found that Chinese individuals with greater MIL experienced more stress and anxiety from increased media consumption when bored during the early phase of the COVID-19 outbreak in late January 2020 [112]. Overall, our results suggest that the relationship between MIL and depression or anxiety may have been more susceptible to the impacts of the COVID-19 outbreak, whereas the bidirectional relationship between MIL and loneliness appeared to be less affected. This could be because the extraordinary stressors caused by the pandemic may have overwhelmed older adults, exceeding what individuals could manage through their sense of meaning, thereby disrupting that specific relationship. In contrast, due to the nature of loneliness, individuals may have turned to their sense of meaning as a coping mechanism during times of crisis to navigate feelings of isolation.

In addition to the findings on bidirectional relationships and their changes over time, a new contribution of this study is that emotional loneliness has a stronger and more consistent relationship with MIL than social loneliness. This may reflect the different dimensions of loneliness captured by the DJGLS subscales. Emotional loneliness refers to the absence of close emotional attachments or intimate relationships, while social loneliness refers to the lack of a broader network of social contacts or friends [97]. Although not encompassed within the DJGLS framework, existential loneliness has been identified as a dimension of loneliness distinct from emotional and social loneliness. It reflects a lack of meaning in life and disconnection from a greater purpose, experienced as an intrinsic sense of separateness that cannot be easily remedied through social interactions or relationships [113, 114]. In other words, meaning in life and existential loneliness are interconnected concepts. As recent research suggests that the three dimensions of loneliness are correlated [113], this might explain the more consistent bidirectional relationship between meaning in life and loneliness compared to depression and anxiety. Emotional loneliness may have a greater impact on MIL because it is associated with a lack of meaningful social relationships [113], while social loneliness may be more influenced by situational factors such as the availability of social resources and opportunities. Similarly, it is worth investigating the extent to which existential loneliness is connected to emotional loneliness, as this could explain its stronger bidirectional relationship with MIL compared to social loneliness. Moreover, emotional loneliness may be more challenging to alleviate than social loneliness, as it requires more than simply increasing the quantity or frequency of social interactions. Therefore, interventions targeting emotional loneliness and existential loneliness may be more effective in enhancing MIL than those addressing only social loneliness. Examples of interventions for emotional loneliness include support groups, cognitive behavioural therapy, and emotion-focused therapy [115, 116]. Interventions for existential loneliness include logotherapy, meaning-centred or existential interventions, mindfulness and acceptance-based interventions, and philosophical counselling [117,118,119,120].

Strengths and limitations

This study has several strengths. First, this is one of the first studies with more than one follow-up to examine the bidirectional relationship between MIL and mental health outcomes among older patients with multimorbidity. Second, it used validated measures of MIL and mental health outcomes that have been widely used in previous research. It also has a relatively large sample size and a long follow-up period which increased the statistical power and generalizability of the findings.

However, this study also has a few limitations in addition to the issues already discussed above that could explain the somewhat inconsistent bidirectional relationships (i.e., data collection method, potential confounders, and COVID-19). First, only a single-item measure of MIL was used, which may not capture the full complexity and multidimensionality of the construct. Second, it may be subject to bias from self-report measures and loss to follow-up. Third, a low number of participants who screened positive for depression or anxiety were measured using the full scales at baseline, which may have introduced selection bias and reduced the representativeness of the sample, even though missing data were accounted for using the full information maximum likelihood method. Fourth, as this study only included an older population from Hong Kong, the relationships between MIL and mental health among younger populations should be considered in future studies.

Implications for services and future research

These findings have important implications for services that aim to improve the psychological well-being of older patients with multimorbidity. Services should consider incorporating interventions that enhance MIL among this population, such as meaning-centred psychotherapy [121] and mindfulness [122], along with activities that can facilitate positive mood, self-esteem, autonomy, competence, social connections, and religiosity/spirituality [8]. “Restorying” interventions may also be valuable for increasing MIL, as they help individuals reframe their personal experiences and life stories according to the narrative of an archetypal hero’s journey [123]. Enhancing the presence of MIL may be more effective for improving mental health outcomes in older adults. A 2020 meta-analysis showed that the presence of MIL positively correlates with subjective well-being, whereas the search for meaning often shows a weaker or negative association [124]. Services should also address the negative impact of depression, anxiety, and loneliness on MIL by providing appropriate treatment and support for these conditions. Moreover, services should pay attention to the different types of loneliness that may affect MIL and tailor interventions accordingly. For example, services may provide opportunities for emotional intimacy and attachment for those who suffer from emotional loneliness such as mental health support and establishing peer support networks. Alternatively, they could facilitate social participation and engagement for those who suffer from social loneliness such as through community and intergenerational programs.

Future research should employ a more comprehensive and multidimensional measure of MIL that can capture its different facets, including comprehension, purpose, and significance. Future research should also investigate how and to what extent the COVID-19 pandemic played a role in diminishing the predictive power of MIL on depression and anxiety, as the disruption may have occurred by blocking one or more of the mediating paths mentioned in the last section or caused additional distress that exceeded the capacity of MIL to cope as a psychological resource. Further studies are needed to understand the exact mechanisms, as well as other potential mediators or moderators that may explain or modify the effects of MIL on mental health outcomes, or vice versa, such as positive emotions, optimism, social support, or resilience.

Conclusions

This study investigated the bidirectional relationship between meaning in life (MIL) and mental health outcomes such as depression, anxiety, and loneliness over time in older primary care patients with multimorbidity in Hong Kong. The results showed that MIL and mental health outcomes influenced each other reciprocally, with higher MIL leading to lower levels of depression, anxiety, and loneliness, and vice versa. The psychological burden experienced by older adults may be amplified by multimorbidity, making MIL an even more critical factor in mitigating anxiety, depression, and loneliness in this population. On the other hand, in a healthier population, the relationship between MIL and mental health might be less pronounced, as chronic health issues would not contribute as significantly to psychological distress. Future studies may look more closely at whether there are any differences in these relationships in healthy populations. The results also showed that emotional loneliness exhibited a more consistent bidirectional association with MIL than social loneliness. These findings suggest that MIL is a psychological resource that could act as a buffer against the negative impact of multimorbidity on mental health and that interventions enhancing MIL may improve the psychological well-being of older patients with multimorbidity. Therefore, healthcare professionals should consider referring patients with low MIL to psychologists or mental health specialists. A multidisciplinary approach that integrates insights from psychology, sociology, social work, health sciences, religious studies, and philosophy could lead to more effective interventions targeting both MIL and mental health issues. However, further research is necessary to elucidate the causal mechanisms and moderating factors of this relationship and to develop effective, tailored interventions targeting both MIL and mental health problems.

Data availability

No datasets were generated or analysed during the current study.

References

  1. World Health Organization. Ageing. 2023-07-09]; Available from: https://www.who.int/health-topics/ageing

  2. Wong K, Yeung M. Population ageing trend of Hong Kong. Population. 2019;18:64.

    Google Scholar 

  3. Zenebe Y, et al. Prevalence and determinants of depression among old age: a systematic review and meta-analysis. Ann Gen Psychiatry. 2021;20(1):55.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Remes O, Brayne C, van der Linde R, Lafortune L. A systematic review of reviews on the prevalence of anxiety disorders in adult populations. Brain Behav. 2016;6(7):e00497.

  5. Surkalim DL, et al. The prevalence of loneliness across 113 countries: systematic review and meta-analysis. BMJ. 2022;376:e067068.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Steger MF, Oishi S, Kashdan TB. Meaning in life across the life span: levels and correlates of meaning in life from emerging adulthood to older adulthood. J Posit Psychol. 2009;4(1):43–52.

    Article  Google Scholar 

  7. Steger MF. In: Snyder CR, et al. editors. Meaning in life: a unified model, in the Oxford Handbook of positive psychology. Oxford University Press; 2021. p. 0.

  8. King LA, Hicks JA. The science of meaning in life. Ann Rev Psychol. 2021;72(1):561–84.

    Article  Google Scholar 

  9. Manco N, Hamby S. A Meta-Analytic Review of interventions that promote meaning in life. Am J Health Promotion. 2021;35(6):866–73.

    Article  Google Scholar 

  10. Musich S, et al. Purpose in life and positive health outcomes among older adults. Popul Health Manage. 2018;21(2):139–47.

    Article  Google Scholar 

  11. Pinquart M. Creating and maintaining purpose in life in old age: a meta-analysis. Ageing Int. 2002;27(2):90–114.

    Article  Google Scholar 

  12. Zhang D, et al. Meaning and its association with happiness, health and healthcare utilization: a cross-sectional study. J Affect Disord. 2018;227:795–802.

    Article  PubMed  Google Scholar 

  13. Alimujiang A, et al. Association between life purpose and mortality among US adults older than 50 years. JAMA Netw open. 2019;2(5):e194270–194270.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cohen R, Bavishi C, Rozanski A. Purpose in life and its relationship to all-cause mortality and cardiovascular events: a meta-analysis. Psychosom Med. 2016;78(2):122–33.

    Article  PubMed  Google Scholar 

  15. Krause N. Meaning in life and mortality. Journals Gerontol Ser B: Psychol Sci Social Sci. 2009;64(4):517–27.

    Article  Google Scholar 

  16. Weziak-Bialowolska D, Bialowolski P. Bidirectional associations between meaning in life and the health, emotional ill-being and daily life functioning outcomes among older adults. Psychol Health. 2024;39(6):711–727.

  17. Kim ES, Delaney SW, Kubzansky LD. Sense of purpose in life and cardiovascular disease: underlying mechanisms and future directions. Curr Cardiol Rep. 2019;21:1–11.

    Article  Google Scholar 

  18. Koizumi M, et al. Effect of having a sense of purpose in life on the risk of death from cardiovascular diseases. J Epidemiol. 2008;18(5):191–6.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kim ES, et al. Purpose in life and reduced incidence of stroke in older adults:‘The Health and Retirement Study’. J Psychosom Res. 2013;74(5):427–32.

    Article  PubMed  Google Scholar 

  20. Kim ES, Hershner SD, Strecher VJ. Purpose in life and incidence of sleep disturbances. J Behav Med. 2015;38:590–7.

    Article  PubMed  Google Scholar 

  21. Kleiman EM, Beaver JK. A meaningful life is worth living: meaning in life as a suicide resiliency factor. Psychiatry Res. 2013;210(3):934–9.

    Article  PubMed  Google Scholar 

  22. Kim G, et al. Purpose in life protects against cognitive decline among older adults. Am J Geriatric Psychiatry. 2019;27(6):593–601.

    Article  Google Scholar 

  23. Schaefer SM, et al. Purpose in life predicts better emotional recovery from negative stimuli. PLoS ONE. 2013;8(11):e80329.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Smith BW, et al. The role of resilience and purpose in life in habituation to heat and cold pain. J pain. 2009;10(5):493–500.

    Article  PubMed  Google Scholar 

  25. Dezutter J, et al. Meaning in life: an important factor for the psychological well-being of chronically ill patients? Rehabil Psychol. 2013;58(4):334.

    Article  PubMed  PubMed Central  Google Scholar 

  26. deRoon-Cassini TA, et al. Psychological well-being after spinal cord injury: perception of loss and meaning making. Rehabil Psychol. 2009;54(3):306.

    Article  PubMed  Google Scholar 

  27. Dezutter J, Luyckx K, Wachholtz A. Meaning in life in chronic pain patients over time: associations with pain experience and psychological well-being. J Behav Med. 2015;38:384–96.

    Article  PubMed  Google Scholar 

  28. Scrignaro M, et al. Seeking and experiencing meaning: exploring the role of meaning in promoting mental adjustment and eudaimonic well-being in cancer patients. Palliat Support Care. 2015;13(3):673–81.

    Article  PubMed  Google Scholar 

  29. Schnell T, Krampe H. Meaning in life and self-control buffer stress in times of COVID-19: moderating and mediating effects with regard to mental distress. Front. Psychiatry. 2020;11:582352.

  30. Yıldırım M, Arslan G, Aziz IA. Why do people high in COVID-19 worry have more Mental Health disorders? The roles of resilience and meaning in life. Psychiatria Danubina. 2020;32(3–4):505–12.

    Article  PubMed  Google Scholar 

  31. Arslan G, Yıldırım M. Coronavirus stress, meaningful living, optimism, and depressive symptoms: a study of moderated mediation model. Australian J Psychol. 2021;73(2):113–24.

    Article  Google Scholar 

  32. Almeida VM, Carvalho C, Pereira MG. The contribution of purpose in life to psychological morbidity and quality of life in chronic pain patients. Psychol Health Med. 2020;25(2):160–70.

    Article  PubMed  Google Scholar 

  33. Smith BW, Zautra AJ. The role of purpose in life in recovery from knee surgery. Int J Behav Med. 2004;11:197–202.

    Article  PubMed  Google Scholar 

  34. Dewitte L, Vandenbulcke M, Dezutter J. Meaning in life matters for older adults with Alzheimer’s disease in residential care: associations with life satisfaction and depressive symptoms. Int Psychogeriatr. 2019;31(5):607–15.

    Article  PubMed  Google Scholar 

  35. Greenblatt-Kimron L, Kagan M, Zychlinski E. Meaning in life among older adults: an integrative model. Int J Environ Res Public Health. 2022;19(24):16762.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Krause N. Evaluating the stress-buffering function of meaning in life among older people. J Aging Health. 2007;19(5):792–812.

    Article  PubMed  Google Scholar 

  37. Hedberg P, et al. Purpose in life over a five-year period: a longitudinal study in a very old population. Int Psychogeriatr. 2011;23(5):806–13.

    Article  PubMed  Google Scholar 

  38. Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012;380(9836):7–9.

    Article  PubMed  Google Scholar 

  39. Vancampfort D, et al. The relationship between chronic physical conditions, multimorbidity and anxiety in the general population: a global perspective across 42 countries. Gen Hosp Psychiatry. 2017;45:1–6.

    Article  PubMed  Google Scholar 

  40. Triolo F, et al. The complex interplay between depression and multimorbidity in late life: risks and pathways. Mech Ageing Dev. 2020;192:111383.

    Article  PubMed  Google Scholar 

  41. Hajek A, Kretzler B, König H-H. Multimorbidity, loneliness, and social isolation. A systematic review. Int J Environ Res Public Health. 2020;17(22):8688.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Csikszentmihalyi M. Flow: the psychology of optimal experience. New York: Harper & Row; 1990.

    Google Scholar 

  43. Ryan RM, Deci EL. On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Ann Rev Psychol. 2001;52:141–66.

    Article  CAS  Google Scholar 

  44. Reker GT, Wong PTP. Aging as an individual protowardToward a theory of personal meaning, in Emergent theories of aging. New York, NY, US: Springer Publishing Company; 1988.214–46.

    Google Scholar 

  45. Stavrova O, Luhmann M. Social connectedness as a source and consequence of meaning in life. J Posit Psychol. 2016;11(5):470–9.

    Article  Google Scholar 

  46. Stillman TF, Lambert NM. The bidirectional relationship of meaning and belonging. In: Hicks JA, Routledge C, editors. The experience of meaning in life: classical perspectives, emerging themes, and controversies. Dordrecht: Springer Netherlands; 2013. 305–15.

    Chapter  Google Scholar 

  47. Carver CS, Scheier MF. Origins and functions of positive and negative affect: a control-process view. Psychol Rev. 1990;97(1):19–35.

    Article  Google Scholar 

  48. Carver CS, Scheier MF. On the self-regulation of behavior. On the self-regulation of behavior. 1998, New York, NY, US: Cambridge University Press. xx, 439-xx, 439.

  49. Heintzelman SJ, King LA. (the feeling of) meaning-as-information. Personality Social Psychol Rev. 2014;18(2):153–67.

    Article  Google Scholar 

  50. Hooker SA, Masters KS, Park CL. A meaningful life is a healthy life: a conceptual model linking meaning and meaning salience to health. Rev Gen Psychol. 2018;22(1):11–24.

    Article  Google Scholar 

  51. Steger MF. Meaning in Life, in The Oxford Handbook of Positive Psychology, S.J. Lopez and C.R. Snyder, Editors. 2009, Oxford University Press. p. 0.

  52. Moksnes UK. Sense of Coherence, in Health Promotion in Health Care– Vital Theories and Research, G. Haugan and M. Eriksson, Editors. 2021, Springer International Publishing: Cham.35–46.

  53. Steger MF, et al. Meaning in life and health: proactive health orientation links meaning in life to health variables among American undergraduates. J Happiness Studies: Interdisciplinary Forum Subjective Well-Being. 2015;16(3):583–97.

    Article  Google Scholar 

  54. Steger MF, et al. Understanding the search for meaning in life: personality, cognitive style, and the dynamic between seeking and experiencing meaning. J Pers. 2008;76(2):199–228.

    Article  PubMed  Google Scholar 

  55. Steptoe A, Fancourt D. Leading a meaningful life at older ages and its relationship with social engagement, prosperity, health, biology, and time use. Proc Natl Acad Sci. 2019;116(4):1207–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang S, et al. Psychological capital mediates the influence of meaning in life on prosocial behavior of university students: a longitudinal study. Child Youth Serv Rev. 2022;140:106600.

    Article  Google Scholar 

  57. Ryff CD, Singer B. The contours of positive human health. Psychol Inq. 1998;9(1):1–28.

    Article  Google Scholar 

  58. Sherman AC, Simonton S. Effects of personal meaning among patients in primary and specialized care: associations with psychosocial and physical outcomes. Psychol Health. 2012;27(4):475–90.

    Article  PubMed  Google Scholar 

  59. Shrira A, et al. How do meaning in life and positive affect relate to adaptation to stress? The case of firefighters following the Mount Carmel Forest Fire. Isr J Psychiatry Relat Sci. 2015;52(3):68–70.

    PubMed  Google Scholar 

  60. Skrabski Á, et al. Life meaning: an important correlate of health in the Hungarian population. Int J Behav Med. 2005;12(2):78–85.

    Article  PubMed  Google Scholar 

  61. Folker AP, et al. Does meaning protect against loneliness? Exploring empirical studies and theory. Health Promot Int. 2021;36(2):471–80.

    Article  PubMed  Google Scholar 

  62. King LA, et al. Positive affect and the experience of meaning in life. J Personal Soc Psychol. 2006;90(1):179–96.

    Article  Google Scholar 

  63. Lambert NM, et al. To belong is to Matter: sense of belonging enhances meaning in life. Pers Soc Psychol Bull. 2013;39(11):1418–27.

    Article  PubMed  Google Scholar 

  64. Gortner E-M, Rude SS, Pennebaker JW. Benefits of expressive writing in lowering rumination and depressive symptoms. Behav Ther. 2006;37(3):292–303.

    Article  PubMed  Google Scholar 

  65. Kamijo N, Yukawa S. The role of rumination and negative affect in meaning making following stressful experiences in a Japanese sample. Frontiers in Psychology; 2018;9.

  66. Park CL. Making sense of the meaning literature: an integrative review of meaning making and its effects on adjustment to stressful life events. Psychol Bull. 2010;136(2):257–301.

    Article  PubMed  Google Scholar 

  67. Powers TA, Koestner R, Zuroff DC. Self-criticism, goal motivation, and goal progress. J Soc Clin Psychol. 2007;26(7):826–40.

    Article  Google Scholar 

  68. Blanco JA, Barnett LA. The effects of Depression on Leisure: varying relationships between enjoyment, sociability, participation, and desired outcomes in College Students. Leisure Sci. 2014;36(5):458–78.

    Article  Google Scholar 

  69. Breslin FC, et al. Depression and activity limitations: examining gender differences in the general population. Soc Psychiatry Psychiatr Epidemiol. 2006;41(8):648–55.

    Article  PubMed  Google Scholar 

  70. Holtfreter K, Reisig MD, Turanovic JJ. Depression and infrequent participation in social activities among older adults: the moderating role of high-quality familial ties. Aging Ment Health. 2017;21(4):379–88.

    Article  PubMed  Google Scholar 

  71. Maner JK, Schmidt NB. The role of risk avoidance in anxiety. Behav Ther. 2006;37(2):181–9.

    Article  PubMed  Google Scholar 

  72. Norton J, et al. Anxiety symptoms and disorder predict activity limitations in the elderly. J Affect Disord. 2012;141(2):276–85.

    Article  PubMed  Google Scholar 

  73. Watts AS, Mortby ME, Burns JM. Depressive symptoms as a barrier to engagement in physical activity in older adults with and without Alzheimer’s disease. PLoS ONE. 2018;13(12):e0208581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Donaldson C, Lam D, Mathews A. Rumination and attention in major depression. Behav Res Ther. 2007;45(11):2664–78.

    Article  PubMed  Google Scholar 

  75. Kaiser RH, et al. Distracted and down: neural mechanisms of affective interference in subclinical depression. Soc Cognit Affect Neurosci. 2015;10(5):654–63.

    Article  Google Scholar 

  76. Lapointe M-LB, et al. Attentional bias, distractibility and short-term memory in anxiety. Anxiety Stress Coping. 2013;26(3):293–313.

    Article  PubMed  Google Scholar 

  77. Roberts JW, et al. Does high state anxiety exacerbate distractor interference? Hum Mov Sci. 2021;76:102773.

    Article  PubMed  Google Scholar 

  78. Borawski D. The mediating role of positive orientation in the relationship between loneliness and meaning in life. Int J Environ Res Public Health. 2022;19(16):9948.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Cacioppo JT, et al. Loneliness within a nomological net: an evolutionary perspective. J Res Pers. 2006;40(6):1054–85.

    Article  Google Scholar 

  80. Cheng C, Du Y, Bai J. Physical multimorbidity and psychological distress among Chinese older adults: findings from Chinese longitudinal healthy longevity survey. Asian J Psychiatry. 2022;70:103022.

    Article  Google Scholar 

  81. Yao S-S, et al. Associations between somatic multimorbidity patterns and depression in a longitudinal cohort of Middle-aged and older Chinese. J Am Med Dir Assoc. 2020;21(9):1282–e12872.

    Article  PubMed  Google Scholar 

  82. You L, et al. Association between Multimorbidity and Depressive Symptom among Community-Dwelling elders in Eastern China. Clin Interv Aging. 2019;14(null):2273–80.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Zhang D, et al. Cohort profile: the prospective study on Chinese elderly with multimorbidity in primary care in Hong Kong. BMJ Open. 2020;10(2):e027279.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Shek DTL. Reliability and factorial structure of the Chinese version of the purpose in life questionnaire. J Clin Psychol. 1988;44(3):384–92.

    Article  CAS  PubMed  Google Scholar 

  85. Zhang D, et al. Vaccine resistance and hesitancy among older adults who live alone or only with an Older Partner in Community in the early stage of the Fifth Wave of COVID-19 in Hong Kong. Vaccines. 2022;10(7):1118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Xu Z, et al. Physical and mental health outcomes including behavior and attitudes in people having social contacts with COVID-19 patients. PLoS ONE. 2021;16(2):e0245945.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Zhang D, et al. Association of meaning in life with preventive healthcare use among Chinese adults: are there age and gender differences? BMC Public Health. 2022;22(1):2308.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Xu Z, et al. Association of positive and adverse childhood experiences with risky behaviours and mental health indicators among Chinese university students in Hong Kong: an exploratory study. Eur J Psychotraumatology. 2022;13(1):2065429.

    Article  Google Scholar 

  89. Mitchell AJ, et al. Case finding and screening clinical utility of the Patient Health Questionnaire (PHQ-9 and PHQ-2) for depression in primary care: a diagnostic meta-analysis of 40 studies. BJPsych Open. 2016;2(2):127–38.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Levis B, et al. Accuracy of the PHQ-2 alone and in Combination with the PHQ-9 for screening to detect Major Depression: systematic review and Meta-analysis. JAMA. 2020;323(22):2290–300.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Cheng C, Cheng M. To validate the Chinese version of the 2Q and PHQ-9 questionnaires in Hong Kong Chinese patients. Hong Kong Practitioner. 2007;29(10):381.

    Google Scholar 

  92. Yu X, et al. The Patient Health Questionnaire-9 for measuring depressive symptoms among the general population in Hong Kong. Compr Psychiatr. 2012;53(1):95–102.

    Article  Google Scholar 

  93. Yin L, et al. Validity of the Chinese Language Patient Health Questionnaire 2 and 9: a systematic review. Health Equity. 2022;6(1):574–94.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Sapra A, et al. Using generalized anxiety Disorder-2 (GAD-2) and GAD-7 in a primary care setting. Cureus. 2020;12(5):e8224.

    PubMed  PubMed Central  Google Scholar 

  95. Kroenke K, et al. Anxiety disorders in Primary Care: prevalence, impairment, Comorbidity, and detection. Ann Intern Med. 2007;146(5):317–25.

    Article  PubMed  Google Scholar 

  96. Gierveld JDJ, Tilburg TV. A 6-Item scale for overall, emotional, and Social Loneliness:confirmatory tests on Survey Data. Res Aging. 2006;28(5):582–98.

    Article  Google Scholar 

  97. Leung GTY, de Jong J, Gierveld, Lam LCW. Validation of the Chinese translation of the 6-item De Jong Gierveld loneliness scale in elderly Chinese. Int Psychogeriatr. 2008;20(6):1262–72.

  98. de Jong-Gierveld J, van Tilburg TG. Manual of the loneliness scale. Methoden en technieken; 1999.

  99. Rosseel Y. Lavaan: an R Package for Structural equation modeling. J Stat Softw. 2012;48(2):1–36.

    Article  Google Scholar 

  100. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Modeling: Multidisciplinary J. 1999;6(1):1–55.

    Article  Google Scholar 

  101. Fan X, Thompson B, Wang L. Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Struct Equation Modeling: Multidisciplinary J. 1999;6(1):56–83.

    Article  Google Scholar 

  102. Core Team R. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria; 2023.

  103. Chow EOW, Ho HCY. The relationship between psychological resources, social resources, and depression: results from older spousal caregivers in Hong Kong. Aging Ment Health. 2012;16(8):1016–27.

    Article  PubMed  Google Scholar 

  104. Chow EO. The role of meaning in life: mediating the effects of perceived knowledge of stroke on depression and life satisfaction among stroke survivors. Clin Rehabil. 2017;31(12):1664–73.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Nie S, et al. Meaning in Life and Mental Health issues in older adults: a Meta-analysis. Int J Mental Health Promotion. 2023;25(9):971–84.

    Article  Google Scholar 

  106. Kim C, Chun J. Meaning in Life Mediates the Relationship between Loneliness and Depressive Symptoms among Older Adults during the COVID-19 Pandemic. Healthcare, 2024;12(5):584.

  107. de Maio Nascimento M, et al. The role of meaning in life in the association between loneliness and depression: a mediation study among older adults from 26 European countries. Psychiatr Q. 2024;95(4):599–617.

  108. Krouwel M, Jolly K, Greenfield S. Comparing Skype (video calling) and in-person qualitative interview modes in a study of people with irritable bowel syndrome– an exploratory comparative analysis. BMC Med Res Methodol. 2019;19(1):219.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Bondevik M, Skogstad A. Loneliness, religiousness, and purpose in life in the Oldest Old. J Religious Gerontol. 2000;11(1):5–21.

    Article  Google Scholar 

  110. Hedberg P, et al. Depression in relation to purpose in life among a very old population: a five-year follow-up study. Aging Ment Health. 2010;14(6):757–63.

    Article  PubMed  Google Scholar 

  111. Wong SYS, et al. Impact of COVID-19 on loneliness, mental health, and health service utilisation: a prospective cohort study of older adults with multimorbidity in primary care. Br J Gen Pract. 2020;70(700):e817–24.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Chao M, et al. Psychological distress and state boredom during the COVID-19 outbreak in China: the role of meaning in life and media use. Eur J Psychotraumatology. 2020;11(1):1769379.

    Article  Google Scholar 

  113. van Tilburg TG. Social, Emotional, and existential loneliness: a test of the Multidimensional Concept. Gerontologist. 2020;61(7):e335–44.

    Article  PubMed Central  Google Scholar 

  114. McKenna-Plumley PE, et al. It’s a feeling of complete disconnection: experiences of existential loneliness from youth to older adulthood. BMC Psychol. 2023;11(1):408.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Cacioppo S, et al. Loneliness:clinical import and interventions. Perspect Psychol Sci. 2015;10(2):238–49.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Greenman PS, Johnson SM. Emotionally focused therapy: attachment, connection, and health. Curr Opin Psychol. 2022;43:146–50.

    Article  PubMed  Google Scholar 

  117. Schulenberg SE, et al. Logotherapy for clinical practice. Volume 45. Psychotherapy: Theory, Research, Practice, Training,; 2008.447–63. 4.

    Google Scholar 

  118. Vos J, Craig M, Cooper M. Existential therapies: a meta-analysis of their effects on psychological outcomes. J Consult Clin Psychol. 2015;83(1):115–28.

    Article  PubMed  Google Scholar 

  119. Harris W. Mindfulness-based existential therapy: connecting mindfulness and existential therapy. J Creativity Mental Health. 2013;8(4):349–62.

    Article  Google Scholar 

  120. Knapp S, Tjeltveit AC. A review and critical analysis of philosophical counseling. Prof Psychology: Res Pract. 2005;36(5):558–65.

    Article  Google Scholar 

  121. Vos J. Working with Meaning in Life in Chronic or Life-Threatening Disease: A Review of Its Relevance and the Effectiveness of Meaning-Centred Therapies, in Clinical Perspectives on Meaning: Positive and Existential Psychotherapy, P. Russo-Netzer, S.E. Schulenberg, and A. Batthyany, Editors. 2016, Springer International Publishing: Cham.171–200.

  122. Chu ST-W, Mak WWS. How Mindfulness enhances meaning in life: a Meta-analysis of Correlational studies and Randomized Controlled trials. Mindfulness. 2020;11(1):177–93.

    Article  Google Scholar 

  123. Rogers BA, et al. Seeing your life story as a Hero’s journey increases meaning in life. J Personal Soc Psychol. 2023;125(4):752–78.

    Article  Google Scholar 

  124. Li J-B, Dou K, Liang Y. The Relationship between Presence of meaning, search for meaning, and Subjective Well-Being: A Three-Level Meta-Analysis based on the meaning in Life Questionnaire. J Happiness Stud. 2021;22(1):467–89.

    Article  Google Scholar 

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Acknowledgements

We would like to thank the General Outpatient Clinics (Lek Yuen, Ma On Shan, Shatin (Tai Wai), Yuen Chau Kok) and the Shatin Rhenish Neighbourhood Elderly Centre for providing their support to the project. We also greatly thank all the patients who joined the study.

Funding

The study was nested in a project supported by the Hong Kong Jockey Club Charities Trust (#2016/0024). The funding body had no role in the study design, data collection and analysis, interpretation of data, decision to publish, or manuscript preparation.

Author information

Authors and Affiliations

  1. JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, N.T., Hong Kong

    King Wa Tam, Dexing Zhang, Yiqi Li, Zijun Xu, Qiao Li & Samuel YS Wong

  2. Cumming School of Medicine, University of Calgary, Calgary, Canada

    King Wa Tam

  3. School of Nursing, The Hong Kong Polytechnic University, Hung Hom, KLN, Hong Kong

    Dexing Zhang

  4. The George Institute for Global Health, University of New South Wales, Kensington, Australia

    Yang Zhao

  5. The George Institute for Global Health, Beijing, China

    Yang Zhao

  6. Xiangya School of Public Health, Central South University, Changsha, China

    Lu Niu

Authors
  1. King Wa Tam
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  2. Dexing Zhang
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  3. Yiqi Li
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  8. Samuel YS Wong
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Contributions

KWT contributed to the literature review, analysis and interpretation of data, the original draft, and manuscript revisions. DZ conceived the study, contributed to the study design, the interpretation of data, and manuscript revisions. YL contributed to the literature review and manuscript writing. QL contributed to the literature review. YL, ZX, YZ and LN contributed to the interpretation of data and manuscript revisions. SYSW supervised the study. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Dexing Zhang.

Ethics declarations

Ethics approval and consent to participate

Ethics approval was obtained from The Joint Chinese University of Hong Kong (CUHK) - New Territories East Cluster (NTEC) Clinical Research Ethics Committee (CREC) (Reference No. CREC2016.204). Informed consent was obtained from all participants. We confirm that all methods were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Tam, K.W., Zhang, D., Li, Y. et al. Meaning in life: bidirectional relationship with depression, anxiety, and loneliness in a longitudinal cohort of older primary care patients with multimorbidity. BMC Geriatr 25, 195 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05762-7

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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