An investigation of the relationship between subjective sleep quality,loneliness and mood in an Australian sample: Can daily routine explain the links?

Abstract

Background: Loneliness and low mood are associated with significant negative health outcomes including poor sleep, but the strength of the evidence underlying these associations varies. There is strong evidence that poor sleep quality and low mood are linked, but only emerging evidence that loneliness and poor sleep are associated.

Aims: To independently replicate the finding that loneliness and poor subjective sleep quality are associated and to extend past research by investigating lifestyle regularity as a possible mediator of relationships, since lifestyle regularity has been linked to loneliness and poor sleep.

Methods: Using a cross-sectional design, 97 adults completed standardized measures of loneliness, lifestyle regularity, subjective sleep quality and mood.

Results: Loneliness was a significant predictor of sleep quality. Lifestyle regularity was not a predictor of, nor associated with, mood, sleep quality or loneliness.

Conclusions: This study provides an important independent replication of the association between poor sleep and loneliness. However, the mechanism underlying this link remains unclear. A theoretically plausible mechanism for this link, lifestyle regularity, does not explain the relationship between loneliness and poor sleep. The nexus between loneliness and poor sleep is unlikely to be broken by altering the social rhythm of patients who present with poor sleep and loneliness.

Keywords

subjective sleep quality loneliness depression lifestyle regularity daily routines

Introduction

Loneliness includes feelings of isolation and disconnectedness, but particularly reflects dissatisfaction with the quality of social interactions (Cacioppo et al., 2003; Hawkley et al., 2003; Heinrich and Gullone, 2006). Recent literature differentiates loneliness from depression, suggesting it is a separate and potentially important clinical construct (Heinrich and Gullone, 2006). Loneliness is associated with significant health impairment and is predictive of health problems including Alzheimer’s disease (Wilson et al., 2007), depressive symptoms (Cacioppo et al., 2006), daytime dysfunction (Hawkley et al., 2010), impaired neuroendocrine, cardiovascula, and inflammatory stress responses (Steptoe et al., 2004) and elevated blood pressure (Hawkley et al., 2006). Studies by Cacioppo and Hawkley and colleagues have shown that loneliness is specifically associated with impaired subjective sleep quality and reduced objective sleep efficiency, but not reduced sleep duration (Cacioppo, Hawkley, Berntson et al., 2002; Cacioppo, Hawkley, Crawford et al., 2002; Hawkley et al., 2010), although these effects have not been demonstrated by independent research groups. There is a relationship between disrupted sleep and impairment of basic physiological processes, including impairment to glucose tolerance, sympathetic tone and cortisol regulation (Knutson et al., 2007; Van Cauter et al., 2007), and this association has led to the suggestion that subjective sleep quality may be an important mechanism through which loneliness undermines general health (Cacioppo and Hawkley, 2003; Hawkley and Cacioppo, 2003).

Loneliness is important to sleep quality, as sleep complaints are more prevalent among people who are socially alienated or otherwise dissatisfied with their social relations (Carney et al., 2006; Heinrich and Gullone, 2006; Ohayon and Roth, 2001). However, the mechanism that links loneliness and poor sleep has yet to be demonstrated. One potential mechanism is that of lifestyle regularity, assessed by the daily routine or social rhythm. Social rhythms reflect the timing of meals, social or interpersonal interaction, work, and play (Monk et al., 1990). The rhythm of such daily activities has been shown to influence a wide variety of human factors, including the sleep–wake cycle, sleep quality, mood, cognition and alertness (Adan and Sánchez-Turet, 2001; Cajochen et al., 2001; Magalhães et al., 2005; Spiegel et al., 2003; Zisberg et al., 2010). As such, the social rhythm reflects not only the regularity of social interactions, but also the regularity of social time cues (zeitgebers). Light provides the most potent zeitgeber for the circadian clock; however, social cues may still have a direct role (Grandin et al., 2006), and social behaviour can also determine the timing and extent of light exposure (Roenneberg et al., 2003). Further, habitual light exposure may have direct effects on emotional well-being (Grandner et al., 2006). Several studies have shown that sleep quality is related to daily activity level, such that poor sleep quality arises from inactive or sedentary lifestyles (Morgan, 2003; Ohayon et al., 2001; Sherrill et al., 1998). Irregular daily routines are associated with poor sleep quality and strategies that regulate bed and wake times can improve sleep in patients with insomnia (Edinger and Means, 2005; Espie et al., 2001). Limited, unsatisfactory or irregular social contact may result in poor sleep quality through circadian disruption. Consistent with this proposition, an association between circadian phase irregularities and insomnia has been reported (Lack et al., 2007; Wright et al., 2006). A relationship between daily routine assessed by the Social Rhythm Metric (SRM) and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) has been also been reported, supporting this argument (Carney et al., 2006; Monk et al., 2003; Zisberg et al., 2010). The potential association between social rhythm and loneliness remains to be assessed.

An alternative explanation of the relationship between these factors is suggested by the ‘social zeitgeber theory’ (Ehlers et al., 1988). This theory proposes that the disruption of an individual’s social routine by a critical stressor leads to further disruption of an individual’s circadian rhythm, resulting in a mood disorder (Grandin et al., 2006). One prediction from this theory is that regularity in social rhythm should predict level of mood through the mechanism of sleep disruption. The expected relationship between insomnia and subsequent depression has been demonstrated (Chang et al., 1997; Perlis et al., 1997; Riemann and Voderholzer, 2003), although the role of social rhythm in this alternative model is not clear.

This study aimed to replicate previous findings that demonstrate the association between loneliness and poor sleep quality, since this association has yet to be independently replicated or investigated outside of North America, and to test two models of the association between loneliness, social regularity, sleep quality and mood. It was predicted that first, lifestyle regularity (social rhythm) would mediate the relationship between loneliness and subjective sleep quality, and second, sleep quality would mediate the relationship between social rhythm regularity and level of mood.

Method

Participants

Participants responded to an advertisement placed in university environs about a study examining ‘lifestyle, social contact and sleep quality’. Ninety seven (97) participants (28 male, 69 female) met the age criteria (18–40 years), and completed and returned all the study materials.

Measures

The revised UCLA Loneliness Scale (R-UCLA; Russell, 1996) is a 20-item self-report questionnaire with positively and negatively worded items that address feelings of loneliness. Participants are asked to rate how often they feel the way described by the items on a four-point Likert scale, ranging from 1 (never) to 4 (often). R-UCLA scores are summed to produce a total score, with scores potential ranging from 20 to 80. Higher scores indicate a higher level of perceived loneliness. Mean scores have been extrapolated from earlier studies (Ponizovsky and Ritsner, 2004) and range from 40 to 50 for non-clinical populations. The scale is reliable (Cronbach’s α = 0.88; Ponizovsky and Ritsner, 2004) and is the most commonly used tool for assessing loneliness (Heinrich and Gullone, 2006).

The Social Rhythm Metric (SRM17; Monk et al., 1990) is a daily diary that requires the timing of 17 activities to be noted (e.g. out of bed, first contact with a person, lunch, etc.). Most of the activities on this scale are regarded as ones that would be likely to be undertaken during an ordinary day (Frank et al., 2005). The SRM17 measure was completed at the end of each day (Monk et al., 2002). The regularity of activities is calculated using a validated algorithm (Monk et al., 1990, 1991) to provide an index between 0 (no apparent regularly) and 7 (perfectly regular). The reliability and validity of the SRM for daily activity monitoring has been demonstrated previously (Monk et al., 2003).

The Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989) is a 19-item measure that gauges sleep quality and circumstances that might affect sleep quality, such as coughing, snoring or feeling too hot. Participants are asked to indicate how the questions refer to their usual sleeping habits during the preceding month. Scores are then summed to form an overall sleep disturbance score; a higher score indicates greater pathology. The PSQI is extensively used and has good reliability and validity (Backhaus et al., 2002; Buysse et al., 1989). Monk and colleagues provide a thorough description of both the PSQI and the SRM17 (Monk et al., 2003).

The Depression Anxiety and Stress Scales (DASS-21; Lovibond and Lovibond, 1995b) is a self-report measure on which participants rate the frequency and severity of experiencing negative emotions over the previous week. Participants rate 21 statements on a four-point Likert scale, from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). DASS depression subscale scores (DASS-D) are calculated by summing responses to the seven items on this scale. Higher scores indicate greater pathology. The internal consistency of the DASS-D scales have all been estimated to be good to excellent, and normative data are available for the Australian population (Lovibond and Lovibond, 1995a, 1995b). The DASS-D has high concurrent validity when compared to the Beck Depression Inventory (Bieling et al., 1998) and the Hospital Anxiety and Depression Scale (Nieuwenhuijsen et al., 2003).

Procedure

Participants read the participant information sheet and provided written consent to participate. Participants completed the SRM17 daily for two weeks at home (returned directly or via stamped and addressed envelopes provided), and completed the R-UCLA, PSQI and the DASS-D in the laboratory. This study was approved by the institutional ethics committee.

Results

Descriptive statistics for scale scores are reported in Table 1. More than half of the participants (n = 57) had PSQI scores indicative of sleep pathology (a score of ≥ 5) (Monk et al., 2003). Sixty three of the participants fell within the ‘normal’ symptom severity range on the DASS-D (Lovibond and Lovibond, 1995b), with six participants falling within the ‘severe’ and ‘very severe’ symptom severity range. Mean score for the R-UCLA was 39 (SD = 10). Table 1 also shows bivariate correlations between measures. Significant positive associations were found between loneliness and sleep quality, loneliness and depression, and depression and sleep quality.

Table 1.

Mean and standard deviation of measures, and bivariate correlations between measures

	Mean ± SD	Age	R-UCLA	PSQI	SRM17	DASS-D
Age	21.6 ± 5.1	–
R-UCLA	39.3 ± 10.3	0.13	–
PSQI	5.7 ± 3.3	−0.04	029**
SRM17	3.4 ± 0.7	0.19	−0.08	0.07	–
DASS-D	9.4 ± 8.7	−0.02	0.49**	0.35**	−0.13	–

p < 0.01

R-UCLA = revised UCLA Loneliness Scale; PSQI = Pittsburgh Sleep Quality Index; SRM17 = Social Rhythm Metric; DASS-D = Depression Anxiety and Stress, Depression Subscale.

Three regression analyses were conducted to test the first prediction, that daily routine would mediate the relationship between loneliness and subjective sleep quality, and to address conventional criteria for mediation (Baron and Kenny, 1986). The R-UCLA was a significant predictor of PSQI (R² Δ = 0.085, FΔ (1, 95) = 8.85, p = 0.004; β = 0.29, p = 0.004) but did not predict SRM17 scores (R² Δ = 0.007, FΔ (1, 95) = 0.65, p = 0.42; β = −0.08, p = 0.42). Further, SRM17 responses did not predict PSQI score (R² Δ = 0.004, FΔ (1, 95) = 0.41, p = 0.52; β = −0.07, p = 0.52). Finally, social rhythm did not meet the criteria for mediation when entered with R-UCLA (that is, the relationship between R-UCLA and PSQI did not reliably reduce or become non-significant when SRM17 was entered) (R² Δ = 0.09, FΔ (1, 95) = 4.83, p = 0.010; β = −0.29, p = 0.003). Sobel’s test of the difference in these equations was therefore not necessary (Holmbeck, 1997).

To test the second prediction, that sleep quality would mediate the relationship between social rhythm and level of mood, a further three regression analyses were conducted. The SRM17 was not a significant predictor of DASS-D score (R² Δ = 0.017, FΔ (1, 95) = 1.68, p = 0.197; β = −0.132, p = 0.197) and was not a significant predictor of PSQI scores (R² Δ = 0.004, FΔ (1, 95) = 0.408, p = 0.524; β = − 0.065, p = 0.24). However, PSQI responses did predict DASS-D score (R² Δ = 0.120, FΔ (1, 95) = 12.98, p = 0.001; β = 0.347, p = 0.001).

The DASS-D and R-UCLA scores were regressed onto the PSQI to directly assess the relative impact of loneliness and level of mood on subjective sleep quality (Table 2). In this case, DASS-D explained 15% of the variance in PSQI score, but the R-UCLA score did not explain further variance.

Table 2.

Regression coefficients for hierarchical regression of DASS-D (Step 1) and R-UCLA (added in Step 2) onto the PSQI score

	B	SE b	B
Step 1
Constant	4.42	0.47
DASS-D	0.13	0.04	0.35**
Step 2
Constant	2.66	1.30
DASS-D	0.10	0.04	0.27*
R-UCLA	0.05	0.04	0.16

R² = 0.14 for Step 1, ΔR² = 0.12 for Step 2 (p = 0.001)

p < 0.05, ** p < 0.01

DASS-D = Depression Anxiety and Stress, Depression Subscale; R-UCLA = revised UCLA Loneliness Scale.

Discussion

This study aimed to replicate the association between loneliness and poor subjective sleep quality and to test two potential models of the association between loneliness, social regularity, sleep quality and mood. The results showed a significant positive association between loneliness and subjective sleep quality. This relationship suggests that higher levels of perceived loneliness are associated with poorer subjective sleep quality. To the authors’ knowledge, this study provides the first independent replication of the association between subjective sleep quality and loneliness outside of North America and independent of the work conducted by Cacioppo and colleagues (Cacioppo et al., 2003; Cacioppo, Hawkley, Berntson et al., 2002). This finding adds to the growing evidence that there is an important link between these constructs and it supports the cross-cultural generalizability of the relationship between loneliness and subjective sleep quality.

The demonstration that loneliness and subjective sleep quality are linked has important clinical implications. While the direction of causality cannot be inferred from this study, patients who present with complaints of poor sleep quality may also be lonely, and given that loneliness itself is associated with a number of health problems, this finding is clinically important. Steps to address patients’ perceived loneliness may need to be considered as part of a comprehensive treatment response aimed at maximizing functioning and overall health.

Findings in relation to mood revealed robust relationships between the DASS-D scores and both loneliness (R-UCLA) and sleep quality (PSQI). These associations show that increased levels of depression are associated with increased loneliness and increased subjective sleep impairment. Clinically, these data suggest that mood should be further investigated in patients who present with a sleep quality complaint. Importantly, this study revealed that loneliness does not have utility as a construct independent of level of mood in regards to its relationship with sleep quality. This new finding suggests that even if loneliness is successfully targeted for the overall health benefits that this may confer on patients, the sleep complaints of those people who present with a combination of depression, loneliness and reports of poor sleep qualit, may persist. Such complaints necessitate independent specialist treatment.

The hypothesis that lifestyle regularity might mediate the relationship between subjective loneliness and subjective sleep quality was not supported by these data. In fact, no relationship was found between the SRM17 and either the R-UCLA or the PSQI. With the exception of loneliness, which has not been tested in this way previously, this finding contrasts with three previous reports of an association between the PSQI and the SRM17 (Carney et al., 2006; Monk et al., 2003; Zisberg et al., 2010). Accounting for this discrepancy is difficult. Zisberg’s study (Zisberg et al., 2010) had a much smaller sample (33 participants) of older adults, which could have included individuals with unrecognized pathology, and as such it is less directly comparable to this study. The age range of the present sample was similar to that reported in the other two previous studies (Carney et al., 2006; Monk et al., 2003), and this sample was of a similar size to that used by Monk et al. (2003). Carney and colleagues’ sample size was larger than the sample size in this study, but they used a two-group analysis of variance methodology that necessitates a larger sample (Carney et al., 2006). Correlations and regressions were used in this study with a sample size sufficient to detect a mediation effect. Given that this study had adequate power and was similar in other regards to the two previous studies with similar sample characteristics (Carney et al., 2006; Monk et al., 2003), this study raises questions about the generalizability and robustness of previously reported associations between daily routine and sleep quality. While the cross-sectional analyses conducted in this study could not assess the temporal relationships between loneliness, mood, sleep quality and social rhythm, and a study that does so arguably provides another way to test these relationships, the results of the mediation analyses reported here were not consistent with the expectations of a causal pathway (Kraemer et al., 2001).

Limitations

One limitation of this study may be the use of the R-UCLA to assess loneliness. Despite being described as the most commonly used method of assessing loneliness, some authors have commented that this measure does not operationalize loneliness as a multidimensional construct (Heinrich and Gullone, 2006). In their major review of the loneliness literature, Heinrich and Gullone also note a tendency towards over-reliance on self-report measures of loneliness, such as the R-UCLA. The present study has this limitation. Future studies could seek to refine the measurement of loneliness by including a multidimensional loneliness scale and a measure derived from informant report. A change of approach to the measurement of loneliness in future studies may provide a good test of the robustness of the association between this construct and subjective sleep quality that has now been demonstrated in three studies.

Conclusion

This study aids our understanding of the association between several factors, each of which are important to human health and well-being and commonly co-occur. This study sought to investigate a theoretically plausible link between these factors. These results have replicated the finding that loneliness and poor subjective sleep quality are strongly and positively associated. These findings suggest that the daily routine can be ruled out as the mechanism underlying this association. This study should advance the search for other factors that can explain the link between loneliness and complaints of poor sleep quality, and by so doing, this research has contributed to helping us discover the mechanisms that underlie these links.

Footnotes

Acknowledgements

Ethical clearance for this project was provided by the University of Queensland (project number: 07-PSYCH-4-97-JM). Funding for this project was provided by the School of Psychology, University of Queensland. The authors would like to thank the study volunteers.

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