A total of 1205 older adults were selected in Weifang City, China, in August 2021: 613 (50.9%) MOA and 592 (49.1%) LOA. Up to November 30, 2020, the total household population of Weifang City was 9.39 million, of which 2.04 million, accounting for 21.7% of the total population, were aged 60 years and above, and the migrant population in the city was 2.38 million [29].

Multistage cluster random sampling was used to select participants. In the first stage, 4 districts were selected as primary sampling units (PSUs) based on the economic development and geographical location of Weifang City. In the second stage, in total, 4 subdistricts from the PSUs were selected as secondary sampling units (SSUs). In the third stage, in total, 4 communities were selected from the SSUs. The population aged 60 years or above in the selected communities in Weifang City constituted the entire sample. The inclusion criteria for participants were (1) age 60 years and above and (2) clear awareness and cognition. The MOA’s household registration was beyond Weifang City, while the LOA’s household registration was in Weifang City.

After completing training on the research background, questionnaire content, and social survey techniques, a total of 25 university students were assigned as interviewers for this study. Approximately 20-minute face-to-face interviews were conducted with each participant. Initially, 1208 older adults were selected and interviewed. However, 3 (0.2%) participants were excluded as they answered their questionnaires incorrectly or incompletely. Ultimately, a total of 1205 (99.8%) participants were included in the sample.

Descriptive statistics was used to clarify the sociodemographic characteristics of the older adults. The migrant-local difference between the sociodemographic characteristics among older adults was investigated using the chi-square test, and the migrant-local difference between social support, sleep disturbance, and loneliness was explored using the t test. Statistical significance was defined as P<.05. All statistical analyses were performed using SPSS version 24.0 (SPSS Inc). Structural equation modeling (SEM) was performed to investigate the impact of the migrant-local difference on the relationship between social support, sleep disturbance, and loneliness among older adults in Weifang City. The best-fit model was estimated using maximum likelihood estimation. The model fitness indices goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA) were used; hypothetical models are considered well fitted to the data when GFI>0.90, AGFI>0.90, CFI>0.90, and RMSEA<0.05 [41]. SEM was conducted using AMOS 24.0 (IBM Corp).

The survey and data were obtained with written informed consent from all participants. The research program of the study was reviewed and approved by the Institutional Review Board (IRB) of Public Health and Preventive Medicine in Shandong University (#20180225) and was in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Table 1 demonstrates the sociodemographic characteristics of the participants. We included 1205 older adults in the analysis, of which 613 (50.9%) were MOA and 592 (49.1%) were LOA. The MOA were mostly female (n=448, 73.1%); more than half (n=342, 55.8%) were 60-65 years old; most of them were married (n=539, 87.9%); 346 (56.4%) had primary school education or less; 461 (75.2%) were occupied in agricultural, forestry, and animal husbandry and fishery industries; 217 (35.4%) belonged to the Q1 (the poorest quartile) monthly household income group; 351 (57.3%) had no chronic disease; 335 (54.6%) had physical pain; and 195 (31.8%) reported a good health status. The LOA were also mostly women (n=437, 73.8%); 192 (32.4%) were 60-65 years old; 432 (73%) were married; 287 (48.5%) had primary school education or less; 400 (67.6%) worked in occupations other than agricultural, forestry, and animal husbandry and fishery industries; 219 (37%) belonged to the Q4 (the richest quartile) monthly household income group; 355 (60%) had chronic disease; 351 (59.3%) had physical pain; and 169 (28.5%) reported a good health status. The differences between the MOA and the LOA were statistically significant for age (P<.001), marital status (P<.001), education level (P<.02), job before retirement (P<.001), monthly household income (P<.001), chronic disease (P<.001), and self-reported health status (P<.001).

Table 2 shows the characteristics of social support, sleep quality, and loneliness among the MOA and LOA. The mean total score of loneliness was 8.58 (SD 3.03) for the MOA and 8.00 (SD 2.79) for the LOA. Specifically, the mean scores of individual items were as follows: often feel a lack of friends, 1.53 (SD 0.79) for the MOA and 1.36 (SD 0.71) for the LOA; often feel no one can be trusted, 1.47 (SD 0.73) for the MOA and 1.37 (SD 0.71) for the LOA; often feel left out, 1.37 (SD 0.62) for the MOA and 1.28 (SD 0.55) for the LOA; often feel separated from others, 1.37 (SD 0.67) for the MOA and 1.30 (SD 0.64) for the LOA; often feel shy, 1.36 (SD 0.64) for the MOA and 1.25 (SD 0.52) for the LOA; and often feel surrounded by people but not cared for, 1.49 (SD 0.72) for the MOA and 1.43 (SD 0.71) for the LOA. Statistically significant differences were found for the total score (t₁₂₀₃=–3.442, P=.01), often feel a lack of friends (t₁₂₀₃=–3.704, P<.001), often feel no one can be trusted (t₁₂₀₃=–2.326, P=.02), often feel left out (t₁₂₀₃=–2.672, P=.008), and often feel shy (t₁₂₀₃=–3.365, P=.001) between the MOA and the LOA.

The mean total score of sleep disturbance was 4.29 (SD 3.57) for the MOA and 4.58 (SD 3.66) for the LOA. Specifically, the mean scores of individual items were as follows: subjective sleep quality, 0.88 (SD 0.83) for the MOA and 0.97 (SD 0.86) for the LOA; sleep latency, 1.11 (SD 1.21) for the MOA and 1.12 (SD 1.19) for the LOA; sleep continuity, 0.54 (SD 0.84) for the MOA and 0.68 (SD 0.97) for the LOA; habitual sleep efficiency, 0.16 (SD 0.50) for the MOA and 0.18 (SD 0.56) for the LOA; sleep disorder, 0.97 (SD 0.56) for the MOA and 0.97 (SD 0.57) for the LOA; use of sleep medicine, 0.13 (SD 0.55) for the MOA and 0.18 (SD 0.62) for the LOA; and daytime dysfunction, 0.52 (SD 0.78) for the MOA and 0.48 (SD 0.77) for the LOA. Statistically significant differences were found for subjective sleep quality (t₁₂₀₃=1.968, P=.05) and sleep continuity (t₁₂₀₃=2.822, P=.005) between the MOA and the LOA.

The mean total score of social support was 38.89 (SD 6.63) for the MOA and 39.51 (SD 6.86) for the LOA. Specifically, the mean scores of individual items were as follows: objective social support, 8.47 (SD 1.64) for the MOA and 7.95 (SD 2.30) for the LOA; subjective social support, 23.47 (SD 4.79) for the MOA and 24.43 (SD 4.40) for the LOA; and social support use, 6.94 (SD 2.26) for the MOA and 7.14 (SD 2.44) for the LOA. Statistical differences were found for objective social support (t₁₂₀₃=–4.545, P<.001) and subjective social support (t₁₂₀₃=3.608, P<.001) between the MOA and the LOA.

The relationship between social support, sleep disturbance, and loneliness is illustrated in Table 4 and in Figures 1 and 2 among the MOA and LOA, respectively.

The mean score of loneliness among the MOA (mean 8.58, SD 3.03) and the LOA (mean 8.00, SD 2.79) indicated a fairly low level of loneliness in both groups in this research, also lower than the mean score of loneliness reported in a previous study among the rural empty-nest older adults in China (mean 16.19, SD 3.90) [44]. Moreover, the MOA showed a mildly higher loneliness than the LOA in this study, which is similar to a previous study that showed that the mean score of loneliness is higher among older Turkish German adults than their native-born German counterparts [10].

Using SEM, this study investigated the migrant-local difference in the association between social support, sleep disturbance, and loneliness among MOA and LOA. First, the results illustrated that social support affected loneliness among both MOA and LOA, although the effect was stronger in the MOA than in the LOA. Second, sleep disturbance exerted a direct positive effect on loneliness among both MOA and LOA, although the effect was stronger in the LOA than in the MOA. Third, social support affected sleep disturbance negatively and directly among both MOA and LOA, although the effect was stronger in the MOA than in the LOA.

A negative relationship between social support and loneliness was found among both MOA and LOA, which is consistent with previous research [45,46]. The association between social support and loneliness can be explained by the main effect and buffer effect models [47-49]. The main effect model holds that social support has a universal gain effect and that any increase in social support is bound to result in an increase in health status (eg, a lower level of loneliness), regardless of an individual’s social support level. In the buffer effect model, social support is only associated with physical and mental health in stressful situations, which cushions the negative effects of stressful events on physical and mental health and maintains or improves an individual’s physical and mental health [50]. Through these 2 models, social support can increase positive emotional experiences, control negative emotional experiences, and finally reduce loneliness.

Sleep disturbance was found could exert a positive effect on loneliness among both MOA and LOA, which is similar to a previous study that showed that individuals with poor sleep quality report higher levels of loneliness [51]. A study among adolescents also showed that loneliness is significantly positively associated with sleep disturbance [52]. The positive association between sleep disturbance and loneliness may be due to the result of sleep disturbance–related metabolic, neural, and hormonal processes [51]; moreover, sleep disturbance may result in exhaustion in older adults, which further reduces their communication with others, leading to loneliness.

A negative association between social support and sleep disturbance was found among both MOA and LOA, which is similar to previous studies that have reported that higher social support would generally predict less sleep disturbance [53-55]. Previous studies have also shown that sleep disturbance is significantly associated with social support among Chinese medical staff [56], as well as young Chinese rural residents [57] in China. Moreover, a meta-analysis showed a robust association between social support and favorable sleep outcomes [58]; that is, higher social support would result in less sleep disruption and less sleep disturbance.

Regarding the migrant-local difference, a negative effect of social support on loneliness was found among both MOA and LOA, although the effect was higher in the MOA than in the LOA. The difference between these 2 groups may be because the MOA migrated to a new place and faced many issues with adaptation, such as acculturation stress [59,60], a low sense of psychological identity [61], and less use of outpatient mental health care services [62]; all these factors would increase their loneliness. In addition, after moving to the city where their children lived, the support network of the MOA in their hometowns broke but a new urban support network was not yet formed; consequently, the MOA faced a situation in which they lacked social support [63]. Thus, compared with the LOA, the MOA were more lonely with less social support, so the effect of social support on loneliness was higher among the MOA than the LOA.

Regarding the migrant-local difference, a direct positive effect of sleep disturbance on loneliness was found among both MOA and LOA, although the effect was higher in the LOA than in the MOA. This difference between the 2 groups may be because the LOA in this study were older and more of them were single/divorced/separated/widowed compared to the MOA, and these 2 factors (age and marital status) have been found to be negatively associated with sleep quality in previous studies [64,65]; thus, compared with MOA, LOA would be likely to have more sleep disturbance. A previous study illustrated that insomnia is not only a symptom of mood disorders but also a risk factor for mental disorder development [66]. Therefore, compared with MOA, LOA have more severe sleep disturbance; furthermore, severe sleep disturbance exerts more effect on loneliness among LOA.

Considering the migrant-local difference, an association was found between social support and sleep disturbance among both MOA and LOA, but it was slightly stronger among the MOA than the LOA. This may be because compared with the LOA, the MOA faced reduced social support [67], more social alienation [68], and worse social integration [69]. Among the MOA, after migration to a new city, because of support network reconstruction and conservative awareness, it is difficult to adapt to the local community in a short time [70]. All these factors make MOA more dependent on social support, and further, social support has a stronger effect on sleep disturbance among MOA compared with LOA.

Due to the migrant-local difference, different supportive measures need to be targeted for MOA and LOA. First, since the negative relationship between social support and loneliness in this study was stronger in the MOA, family members should provide more social support for older adults, especially for MOA, and provide them with more knowledge about the importance and prevention of loneliness, while providing social support. Second, the community should design more programs and create activities for MOA, which could increase the interpersonal communication between the MOA, as well as between the MOA and the LOA, by enhancing their peer social support, thus decreasing sleep disturbance and loneliness among both groups, especially the MOA. Third, since the positive relationship between sleep disturbance and loneliness in this study was stronger among the LOA, it is suggested that the community pay more attention to LOA (eg, no loud noises at night), while the family members should create a better sleeping environment (eg, adequate sleeping temperature and well-ventilated bedroom). Fourth, community health care providers should provide more mental health monitoring for both MOA and LOA, especially for older adults with sleep disturbance. Fifth, the findings of this study could attract more Chinese scholars to focus on the level of loneliness among MOA and LOA, especially sociocultural and physical health status contributors, and conduct comparative research between different areas among MOA and LOA in the future.

There are several limitations of this study. First, we used cross-sectional data, which could not be well explored for causal relationships. Second, socioeconomic status plays a fundamental role in the study of population health, which was not explored in this study; we will examine this more in future studies. Third, some other factors that may also affect loneliness among MOA and LOA (eg, stress and depression) were not included in the study, which will be considered in future studies. Fourth, many other factors (eg, medication usage) might influence the sleep quality in older adults, and information related to health status is also a potential confounder of the effect of the quality of sleep, so future studies should examine whether other mediators can explain the internal mechanism underlying the relationship between sleep disturbance and social support.

This study illustrated the migrant-local difference in the association between social support, sleep quality, and loneliness between MOA and LOA in Weifang City, China. The results showed that (1) social support exerts a negative effect on loneliness among both MOA and LOA, although the effect is higher in MOA; (2) loneliness is significantly and positively associated with sleep disturbance among both MOA and LOA, although the effect is higher in LOA; and (3) there is a negative association between social support and sleep disturbance among both MOA and LOA, yet it is slightly higher in the MOA. This study may provide helpful information on how to decrease and prevent loneliness among older adults by enhancing their social support and decreasing their sleep disturbance.