Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Moderating Role of Future Time Perspective

Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Moderating Role of Future Time Perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Moderating Role of Future Time Perspective Yu-Rim Lee, Jong-Sun Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6451753/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Depressive disorders are characterized by persistent negative emotional states and cognitive impairments that significantly impact quality of life. Loneliness is a key predictor of depression and interacts with future time perspective(FTP), a subjective perception of remaining time in life. While FTP influences emotional and behavioral motivations, its role in moderating the loneliness-depression relationship is underexplored. This study used ecological momentary assessment(EMA) to investigate these relationships in daily life. Method Participants completed a 14-day EMA protocol, responding four times daily via smartphone SMS. Emotional states were measured using a visual analogue scale (VAS) for 14 negative affects and one positive affect. multilevel modelling (MLM) examined within- and between-individual effects and FTP's moderating role. Results Participants (aged 19–50 years) completed 3,183 EMA observations with an adherence rate of 96.3%. The final dataset consisted of 3,304 data points from 56 assessments (level 1) and 59 participants (level 2), with an average response rate of 96.48%. Loneliness significantly predicted depression at the within-individual level, with substantial between-individual variation in this relationship. In addition, FTP moderated the effect of loneliness on depression, suggesting its potential buffering role. Conclusions This study demonstrates the utility of EMA in capturing real-time emotional dynamics and highlights FTP's protective role against loneliness-induced depression. The findings support the use of digital tools for real-time monitoring and interventions, as well as strategies like future-oriented group training (FOGT) and social support programs to mitigate depression's impact. These approaches offer practical solutions to improve mental health care. Depression Ecological momentary assessment Loneliness Futrue time perspective Multilevel modeling MLM EMA Digital phenotype Figures Figure 1 Background Depressive disorders are mental health conditions characterized by persistent negative emotional states, including sadness, emptiness, or hopelessness, along with a pronounced loss of interest or pleasure in daily activities( 1 ). These emotional symptoms, moreover, are frequently accompanied by physical and cognitive impairments, such as insomnia, weight loss, and difficulties with thinking and attention, which collectively have a profound impact on an individual’s quality of life( 2 ). In particular, depressive disorders are strongly associated with suicidal ideation and suicide attempts ( 3 ), as a meta-analysis reported that the suicide rate among individuals with depressive disorders is approximately 1.6 times higher than that of the general population( 2 ). Furthermore, the effects of depressive symptoms extend beyond individual health and well-being, resulting in significant socio-economic burdens, including reduced productivity, diminished consumption opportunities, and increased social care costs( 4 , 5 ). Moreover, depressive disorders are closely associated with an individual’s emotional experiences, characterized by heightened negative affect and diminished positive affect. Negative affect encompasses a range of negative emotional experiences, including loneliness, sadness, helplessness, guilt, and anger, all of which are recognized as strong predictors of depression( 6 , 7 ). In addition, individuals experiencing depression or at high risk of depression tend to process information about the self, the world and the future with a negative bias. According to Beck's theory of depression, people with depression often report having "no future" and tend to perceive the future as a singular, hopeless state ( 8 ). Furthermore, individuals with high depressive tendencies experienced limitations in imagining the future as long and expansive, even when their future time perspective (FTP) was experimentally manipulated, and demonstrated a persistent attentional bias towards negative information ( 9 ). Building on this theoretical foundation, a number of studies have examined the relationship between FTP and depression. FTP was first introduced in Socioemotional Selectivity Theory (SST). It refers to an individual's subjective perception of the time remaining until the end of their life ( 10 ). The perception of limited time left, regardless of age, plays an important role in shaping goal setting, preferences, activity choices and emotional experiences, and is identified as a factor distinct from personality traits( 11 , 12 ). Individuals who perceive their future as extended selectively focus on goals and plans and strive for future optimisation through pursuits such as knowledge acquisition and self-development( 13 , 14 ). Conversely, individuals who perceive their future as limited prioritise meaningful activities and emotional experiences in the present over future-oriented goals such as self-improvement and knowledge acquisition ( 13 , 14 ). However, these emotional motivations do not lead to improved emotion regulation skills or subjective well-being, and individuals with a limited FTP consistently exhibit highly negative and maladaptive emotional functioning, regardless of age( 15 ). Also, individuals with a limited FTP tend to experience more pronounced negative affect and depressive symptoms( 16 ). In contrast, individuals with an extended FTP show higher levels of purpose in life, positive affect, and life satisfaction, which serve as significant protective factors against depression( 17 , 18 ). In addition, among the negative affect variables, loneliness is another factor that is closely related to a FTP.Moreover , loneliness has been identified as a significant risk factor, linked to an increased likelihood of depression and suicide ( 19 – 21 ). Loneliness is defined not only as social isolation, but also as a discrepancy between desired and actual social relationships ( 22 ) and is closely related to the perception of being socially unacceptable to others ( 23 ). This perception may activate cognitive mechanisms that promote rumination on negative past experiences( 24 ), thereby reinforcing a limited FTP.Building on these findings, previous studies have explored the relationships between loneliness, depression and FTP, demonstrating strong associations between these variables( 25 , 26 ). However, research investigating these relationships remains limited, highlighting the growing importance of exploring this area further. Also, FTP is known to influence the emotional and behavioural motivations of individuals of all ages. To date, research on FTP has focused primarily on older adults or middle-aged individuals who are beginning to experience the effects of ageing( 25 , 27 ). While these studies have provided valuable insights into the relationships between FTP, emotional functioning and well-being, there remain significant gaps in understanding the role of FTP in broader age groups. Moreover, most prior studies exploring the relationship between depression and emotional variables have employed cross-sectional designs. Such approaches are inherently limited in capturing the dynamic nature of emotional experiences and are often reliant on retrospective self-reports, which are vulnerable to recall bias( 28 ). Additionally, these studies are frequently conducted in controlled laboratory settings, which may fail to accurately reflect the emotional states experienced by individuals in their natural daily environments. To address these limitations, the present study employs ecological momentary assessment (EMA), a methodology designed to collect real-time data on self-reported symptoms and behaviours as they occur in participants' everyday contexts. In this study, EMA was implemented using smartphone-based Short Message Service (SMS) prompts, allowing for the immediate recording of emotional and behavioural responses. This approach reduces recall bias and captures natural fluctuations in emotional variables, providing a more ecologically valid understanding of the relationship between depressive mood and emotional factors. EMA is particularly well suited to investigating how these associations unfold in daily life and whether findings from traditional studies can be replicated in real-world settings. Accordingly, the primary aim of this study is to identify the affective variables that influence depression in daily life and to examine whether FTP is a protective factor in the relationship between depression and loneliness. Methods Participants Participants were recruited through social media, online communities, and other related platforms from August 6 to August 11, 2021, targeting adults aged 19 to 50 years. Information about the study was provided to individuals who volunteered, and a total of 64 participants signed the consent form. As a result, after excluding 5 participants who withdrew during the study, the final sample consisted of 59 participants. Sociodemographic of the participants was collected through an online survey link, which included gender, age, education level and marital status. Sociodemographic information of the participants is presented in Table 1 . This study was approved by the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003). Table 1 Sociodemographic characteristics of participants (N = 59) Sociodemographic data Age (Mean, Standard deviation) 33.98 (8.15) Biological Sex Male, % (n) 18.6% ( 11 ) Female, % (n) 81.4% ( 48 ) Marital Status Married, % (n) 54.2% ( 32 ) Single, % (n) 45.8% ( 27 ) Educational Level High School Graduate, % (n) 20.3% ( 12 ) College Graduate (Bachelor's), % (n) 64.4% ( 38 ) College Graduate (Master's), % (n) 11.9% ( 7 ) College Graduate (Doctorate), % (n) 3.4% ( 2 ) Procedure Participants were first told about the study and signed an informed consent form. A self-report questionnaire was administered at the beginning and end of the study, using the same scale on both occasions. Then, during the 14-day study period, participants responded to EMA four times a day (morning − 09:00, lunch − 12:00, dinner − 17:00, and night − 21:00) via an online survey link sent via SMS. A reminder message was sent if participants did not respond within 30 minutes. On the first day, the self-report questionnaire was delivered with the morning EMA as a pre-survey and participants were instructed to complete the self-report questionnaire first. Similarly, on the last night, the self-report questionnaire was delivered with the final EMA as a post-survey, and participants completed the self-report questionnaire last. Measure Self-report questionnaire At baseline, demographic information such as gender, age, marital status and highest level of education was collected. The Future Time Perspective (FTP) ( 29 ) scale was also used to measure the participant's subjective view of future time. It consists of 10 items and is scored on a 7-point Likert scale (1: not at all to 7: very much). Higher scores indicate that the individual has an expanded view of the future. Ecological Momentary Assessment Participants reported their emotional state four times a day (morning – 09 :00, lunch − 12:00, dinner − 17:00 and night − 21:00) via an online link provided by SMS. Emotional state was assessed using a visual analogue scale (VAS). The VAS is a tool that rates the intensity of an emotion or symptom on a continuum from 0 (not at all) to 10 (very strong) and is useful for quantifying participants' subjective state( 30 , 31 ). VAS are widely used in a variety of research and clinical settings because their simple and intuitive design minimises the burden on participants. Because of these characteristics, VAS are often used in EMA studies that require repeated responses to the same questionnaire at short intervals. In this study, we used VAS to measure the intensity of 14 negative affect and 1 positive affect: sadness, anger, worry, anxiety, irritation, lethargy, alienation, fear, shame, burnout, loneliness, depression, guilt, terror and happiness. Using these methods, it explores the types of depression and the negative feelings associated with them that the participants experience on a day-to-day basis. Data Analysis The study was based on a total of 3304 data points, including 56 assessments at level 1 and 59 participants at level 2. Participants had an average response rate of 96.48% (minimum 66.07%, maximum 100%), resulting in 3188 valid observations. Given the nested structure of the data, analysis was conducted using multilevel modelling (MLM). It has been reported that an adequate sample size for multilevel modelling is generally when there are at least five observations within an individual at level 1 and a sample size of 50 or more at level 2 to produce reliable results( 32 , 33 ). In particular, if there are no missing data at level 1, a sample size of 50 or more at level 2 can ensure reliable estimates( 34 ). In this study, momentary depression was specified as the outcome variable and other emotional states assessed by the EMA were examined as potential predictors at Level 1. At level 2, FTP was included as a higher-level predictor to examine its moderating effect on the relationship between momentary depression and loneliness. Data pre-processing In the case of this study, the average number of within-individual observations at level 1 was 56, and the total number of data at level 2 was 59, which met the conditions for producing sufficiently reliable results through multilevel model analysis. In addition, we applied multiple imputation using the mice package of the R software to compensate for missing data, to ensure the reliability of the estimates. All statistical analyses were conducted using R version 4.4.1 and SPSS Statistics version 26. Descriptive statistics and frequency analyses for demographic variables were performed with SPSS Statistics version 26. Mixed-effects model analyses were conducted using the lmerTest package ( 35 ), an extension of the lme4 package ( 36 ), in RStudio version 2024.04.2 + 764. Prior to the main analyses, person-mean centering (PMC) was applied to the within-person affective variables (predictors) collected via EMA. This approach involves calculating the deviation of each individual’s momentary affect score from their own mean for that variable. In other words, the affective variables were centred on each individual's mean, so that the values reflect the extent to which a given moment deviates from that person's typical level of affect. This procedure removes between-person differences in mean levels of affect, allowing for a clearer examination of within-person variation( 37 ). Using this approach, we examined within-person associations between momentary affect and level 1 depression. In addition, we tested for cross-level interaction effects by examining whether individual differences in the strength of the within-person associations between affect and depression were accounted for by a level 2 variable. Data analysis plan This study used MLM to examine the multidimensional influences of within-person and between-person factors on depression. The analysis was carried out in four steps as follows (a) Null model testing (Model 0): A null model was tested to assess whether the data were suitable for multilevel analysis to examine between-individual effects on depression. The intraclass correlation coefficient (ICC) was used as an index of the proportion of variance in depression attributable to within-individual and between-individual levels. (b) Examination of affective variables (Model 1): We examined multicollinearity between affective variables collected through EMA and included these variables as independent predictors to analyse their effects on depression at the within- individual (level 1) level. Through this process, we exploratively identified affective variables that significantly influenced depression. (c) Level 1 relationship between loneliness and depression: To analyse the relationship between loneliness and depression at level 1, a random intercept model (Model 2) and a random intercept and slope model (Model 3) were constructed. Model fit was compared to determine whether the inclusion of a random slope significantly improved the explanatory power of the model. (d) Level 2 analysis (Model 4): Level 2 (between- individual) analysis explored factors that influence the relationship between loneliness and depression. This step aimed to identify higher level variables that explain how the relationship between loneliness and depression varies across individuals. Results Null model testing (Model 0) First, the ICC for depression collected via EMA was calculated to assess whether there was sufficient variability at both the within-person and between-person levels. The result showed an ICC of 0.641, indicating that 64.1% of the variability in depression is explained by between individual differences, while 36.9% is explained by within individual variability. This shows that the between-person differences in depression are substantial, justifying the use of multilevel modelling in the analysis. Examination of affective variables (Model 1) To assess potential multicollinearity between the EMA affective variables, correlation analysis and variance inflation factor (VIF) calculations were performed. As shown in the correlation matrix (Fig. 1 ), all bivariate correlations were less than or equal to 0.5 and all VIF values were well below the commonly accepted cut-off of 5 (Table 2 ), indicating that multicollinearity was not a concern in the current analysis. Table 2 Variance inflation factor of affective variables Variance inflation factor (VIF) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 2.16 2.13 2.14 2.40 1.93 1.64 2.05 2.28 1.45 1.52 1.84 1.74 1.92 1.17 Note. 1 = sadness, 2 = anger, 3 = worry, 4 = fear, 5 = irritation, 6 = lethargy, 7 = alienation, 8 = anxiety, 9 = shame, 10 = burnout, 11 = loneliness, 12 = guilt 13 = terror, 14 = happiness The affective variables that had a significant impact on depression at level 1 were then examined (Table 3 ). The results showed that sadness, irritation, lethargy, anxiety, burnout, loneliness, guilt, terror, and happiness were significant predictors of depression. Other variables such as anger, worry, fear, alienation, and shame had no significant effect. Among the significant predictors identified, loneliness was further analyzed due to its unique role in the onset and maintenance of depression. Loneliness has consistently been recognized as a critical risk factor contributing to both the development and persistence of depression ( 38 ). In particular, loneliness has been shown to exacerbate depressive symptoms by facilitating maladaptive cognitive processes, such as increased rumination ( 39 ). Given the strong theoretical and empirical associations between loneliness and depression, a follow-up analysis was conducted in the first stage to examine their within-individual association in more depth. Subsequently, FTP was introduced as a Level 2 variable to investigate its moderating role in this relationship. Table 3 Multilevel model analysis of level-1 affective variables predicting depression Model 1 Random effects Variance Standard Deviation(SD) Id (intercept) 3.4548 1.8587 Residual 0.8967 0.9469 Fixed effect Estimate (SE) 95% CI p-value intercept 2.301 (0.24) [1.826, 2.777] < 0.001 *** Sadness 0.192 (0.02) [0.153, 0.232] < 0.001 *** Anger -0.019 (0.02) [-0.053, 0.015] 0.273 Worry − 0.01 (0.02) [-0.041, 0.019] 0.48 Fear -0.008 (0.02) [-0.046, 0.029] 0.654 Irritation 0.11 (0.01) [0.084, 0.137] < 0.001 *** Lethargy 0.117 (0.01) [0.093, 0.141] < 0.001 *** Alienation -0.01 (0.02) [-0.054, 0.033] 0.639 Anxiety 0.128 (0.02) [0.093, 0.162] < 0.001 *** Shame 0.012 (0.02) [-0.030, 0.054] 0.577 Burnout 0.061 (0.01) [0.040, 0.082] < 0.001 *** Loneliness 0.191 (0.02) [0.153, 0.229] < 0.001 *** Guilt 0.173 (0.02) [0.135, 0.211] < 0.001 *** Terror 0.119 (0.02) [0.076, 0.163] < 0.001 *** Happiness -0.063 (0.01) [-0.084, -0.041] < 0.001 *** Note. SE = Standard error. CI = Confidence interval. *p < .05; **p < .01; ***p < .001. Level 1 relationship between loneliness and depression (Model 2, Model 3) Next, we examined the within-individual effects of loneliness on depression and explored whether there were between-individual differences in this relationship. First, we analyzed the within-individual relationship between loneliness and depression using two models: Model 2, which included only a random intercept, and Model 3, which included both a random intercept and a random slope, as shown in Table 4 . We then compared the fit of these models to determine whether the inclusion of a random slope and the assumption of interindividual differences significantly improved the model fit(Table 5 ). Table 4 Multilevel model estimates for the relationship between loneliness and depression Model 2: Random Intercept Random effects Variance Stendard Deviation(SD) Id (intercept) 3.445 1.856 Residual 1.429 1.195 Fixed effect Estimate (SE) 95% CI p-value (Intercept) 2.301 (0.24) [1.826, 2.777] < 0.001 *** Loneliness 0.6 (0.02) [0.565, 0.635] < 0.001 *** Model 3: Random Intercept and Slope Random effects Variance Stendard Deviation(SD) Id (intercept) 3.448 1.8568 Loneliness 0.096 0.3096 Residual 1.298 1.1391 Fixed effect Estimate (SE) 95% CI p-value Intercept 2.301 (0.24) [1.826, 2.777] < 0.001 *** Loneliness 0.496 (0.06) [0.386, 0.606] < 0.001 *** Note. SE = Standard error. CI = Confidence interval *p < .05; **p < .01; ***p < .001. A comparison of the fit between Model 2 and Model 3 indicates that Model 3 provides a better fit, as evidenced by its lower AIC (10612) and BIC (10648) values compared to Model 2 (AIC = 10851, BIC = 10875). This improvement highlights that including the random slope for the relationship between loneliness and depression significantly enhances the model fit (χ² = 243.35, df = 2, p < 0.001). The results from Model 3 indicate that a one-unit increase in loneliness is associated with an average increase of 0.0.496 in depression (p < 0.001). Furthermore, the significantly better fit of the model with the random slope suggests substantial between-individual variation in the positive association between loneliness and depression. Table 5 Compare the model fit of model 1 and model 2 AIC BIC X 2 (df) p-value Model 2 10851 10875 243.35 ( 2 ) < 0.001 *** Model 3 10612 10648 Note. AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion, *p < .05; **p < .01; ***p < .001. Level 2 analysis (Model 4) Finally, Model 4 was analyzed to explore the role of future time perspective in the relationship between loneliness and depression(Table 6 ). Table 6 Level-2 moderation analysis of FTP on loneliness and depression" Model 4 Random effects Variance Stendard Deviation(SD) Id (intercept) 3.2173 1.7937 Loneliness 0.0687 0.2621 Residual 1.2992 1.1398 Fixed effect Estimate (SE) 95% CI p-value Intercept 5.775 (1.55) [2.732, 8.818] < 0.001 *** Loneliness -0.429 (0.32) [-1.054, 0.197] 0.187 FTP -0.759 (0.34) [-1.417, -0.102] 0.027* Loneliness*FTP 0.202 (0.07) [0.065, 0.339] 0.006** Note. SE = Standard error. CI = Confidence interval. *p < .05; **p < .01; ***p < .001. The results of Model 4 revealed a significant interaction between loneliness and FTP in predicting depression (Estimate = 0.202, p = .006). This interaction suggests that a more expansive future time perspective may attenuate the negative effect of loneliness on depressive symptoms. Discussion This study utilised EMA to investigate emotional factors influencing depression. It also examined the relationship between depression and loneliness and FTP moderated this relationship. The results showed that among negative affects, sadness, irritability, lethargy, anxiety, burnout, loneliness, guilt and fear significantly exacerbated depression. In contrast, happiness was the positive affects that significantly alleviated depression. These findings are consistent with previous research highlighting the strong link between negative affects and the severity of depression symptoms. However, this study is unique in that it uses EMA to identify specific emotional factors associated with depression in daily life. This approach provides a nuanced understanding of how moment-to-moment emotional experiences contribute to fluctuations in depression. This study found that loneliness significantly predicted depression at the within-individual levels. In addition, FTP was found to moderate this relationship. Level-2 analyses indicated that a more extended FTP moderated the within-person relationship between loneliness and depression, such that the negative association was attenuated. These findings are consistent with previous research identifying loneliness as a risk factor that exacerbates depressive symptoms and increases the likelihood of developing clinical depression( 40 ). However, unlike previous studies that focused primarily on older adult populations, this study included a broader age range, including people from their 20s to 50s( 21 , 41 – 43 ). This highlights the ability of research to generalise the relationship between loneliness and depression across different stages of the life course. Furthermore, while loneliness is closely related to depression, it is recognised as a theoretically and statistically distinct construct( 44 ). From an emotion regulation perspective, loneliness interferes with the use of adaptive cognitive strategies and induces maladaptive cognitive strategies such as self-blame and rumination ( 45 ). Specifically, loneliness activates the social monitoring system (SMS), which heightens sensitivity to negative social cues while impairing the ability to reframe social situations adaptively ( 46 ). As a result, lonely individuals exhibit excessive attention to social rejection cues and interpret ambiguous social interactions more negatively, which reinforces negative self-perceptions and increases the likelihood of self-focused rumination ( 47 ). Rumination in particular plays a central role in the relationship between loneliness and depression. As noted by Zawadzki, Graham ( 24 ), loneliness predominantly triggers self-focused rumination, which is characterised by repetitive thoughts about personal shortcomings, failed relationships and social inadequacies. This past-oriented rumination not only reinforces negative self-perceptions, but also intensifies feelings of regret and self-blame ( 48 ). Such patterns suppress the use of active and adaptive coping strategies and activate depression-related schemas that promote a pessimistic view of interpersonal relationships( 49 , 50 ). Consequently, this dynamic perpetuates an emotion-cognition cycle that exacerbates loneliness, increases rumination, and worsens depression( 51 , 52 ). In this context, it is theoretically plausible that FTP interacts with loneliness to influence depressive symptoms, aligning with the findings of previous studies that have examined the relationships among these three variables( 25 , 27 ). Furthermore, considering previous research that validated a full mediation model in which loneliness affects depressive symptoms through rumination( 52 ), and findings suggesting that individuals with a limited future time perspective focus more on the past than on the present or future( 15 ), extended FTP may act as a protective factor that mitigates the negative impact of loneliness on depression. Specifically, it is expected that an extensive perspective on the future will suppress the activation of rumination induced by loneliness, thereby reducing the negative impact of loneliness on depression. Based on the findings of this study, the following intervention strategies and therapeutic implications can be applied in clinical practice. First, the use of EMA in this study demonstrated the potential to predict fluctuations in depression by measuring emotional states in real time. Building on this, a system could be developed where individuals use smartphones or wearable devices to continuously record their emotional changes, allowing clinicians to detect early warning signs and intervene promptly. In addition, real-time emotional monitoring data can be used to provide personalised feedback tailored to each individual's emotional patterns. For example, interventions such as mindfulness training or recommendations to see a clinician could be suggested when negative affects such as loneliness and depression increase. Such digital systems would allow mental health clinicians to monitor patients' conditions remotely, overcoming the limitations of traditional face-to-face consultations and greatly improving the accessibility of services. Furthermore, as loneliness is identified in this study as a significant risk factor for depression, strategies to increase social support are crucial. To this end, it is important to help individuals develop new social relationships or rebuild existing ones through community-based programmes. Initiatives such as social skills training and the activation of local community networks could be implemented to promote social connectedness. In addition, the findings suggest that an extended FTP serves as a protective factor that moderates the negative relationship between loneliness and depression severity. Thus, therapeutic approaches aimed at promoting goal-directed and future-oriented thinking and behaviour are essential. In particular, future-oriented group training(FOGT) may be particularly beneficial ( 53 ). This training has been shown to significantly reduce suicidal ideation and depression, with effects lasting at least three months after the intervention ( 54 ). By integrating these approaches, it is hoped that the effectiveness of mental health interventions can be enhanced, providing innovative solutions to the challenges associated with traditional therapeutic methods. First, the study was conducted within the specific cultural and social context of South Korea, with a relatively small sample size (n = 59). Although the sample size was sufficient to produce statistically significant results, it limits the generalisability of the findings to populations with different cultural backgrounds or demographic characteristics. Given that loneliness, FTP and depression are sensitive to cultural and environmental factors, future research should include more diverse cultural contexts and larger sample sizes to increase the generalizability of the findings. Second, although the study used an EMA design to examine affective variability and temporal associations between the three variables, it did not explicitly examine causal relationships or temporal precedence. To address this limitation, future research could use statistical methods such as dynamic structural equation modelling (DSEM) to account for temporal dynamics and clarify the causal mechanisms underlying the observed relationships. Finally, the study did not control for additional psychological or external factors that might influence the relationships between depression, loneliness and FTP.For example, personality traits (e.g. neuroticism), levels of social support and stress could potentially affect the interactions between these variables. Future research that takes these factors into account in its design may provide more comprehensive and robust results. Conclusion This study examined the relationships between depression, loneliness and FTP using EMA. The results showed that negative emotions, including sadness, irritability and loneliness, significantly exacerbated depression, while happiness acted as a protective factor. Loneliness was a significant predictor of depression at both within- and between-individual levels, and FTP moderated this relationship. Specifically, individuals with extended FTP experienced a reduced negative impact of loneliness on depression. These findings highlight the importance of real-time affective monitoring and future-oriented thinking in the management of depression. The use of EMA and digital tools enables personalised, real-time interventions, improving accessibility and facilitating early detection of depressive symptoms. In addition, strategies such as FOGT and enhancing social support can effectively mitigate the impact of loneliness on depression, offering innovative approaches to mental health care. Abbreviations EMA Ecological Momentary Assessment VAS Visual Analogue Scale MLM Multilevel Modelling FOGT Future-Oriented Group Training SST Socioemotional Selectivity Theory SMS Short Message Service PMC Person-Mean Centering ICC Intraclass Correlation Coefficient VIF Variance Inflation Factor AIC Akaike Information Criterion BIC Bayesian Information Criterion SMS Social Monitoring System DSEM Dynamic Structural Equation Modelling Declarations Ethics approval and consent to participate Approval was obtained from the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003). All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All participants provided written informed consent prior to participation. Consent for publication Not applicable Availability of data and materials All data generated or analysed during this study are included in supplementary information files. Competing interests The authors declare that they have no competing interests Funding This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2074932). Authors' contributions JL conceived and designed the work, acquired the data, drafted the manuscript, and substantively revised it. YL analyzed and interpreted the data and also contributed to drafting the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable References APA. Diagnostic and statistical manual of mental disorders: DSM-5: American psychiatric association Washington, DC; 2013. Kendler KS. The phenomenology of major depression and the representativeness and nature of DSM criteria. American Journal of Psychiatry. 2016;173(8):771-80. 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Psychiatry. 2017;80(2):104-17. Holwerda TJ, Van Tilburg TG, Deeg DJ, Schutter N, Van R, Dekker J, et al. Impact of loneliness and depression on mortality: results from the Longitudinal Ageing Study Amsterdam. The British Journal of Psychiatry. 2016;209(2):127-34. van Beljouw IM, van Exel E, de Jong Gierveld J, Comijs HC, Heerings M, Stek ML, et al. “Being all alone makes me sad”: loneliness in older adults with depressive symptoms. International psychogeriatrics. 2014;26(9):1541-51. Cacioppo JT, Cacioppo S. Loneliness in the modern age: An evolutionary theory of loneliness (ETL). Advances in experimental social psychology. 58: Elsevier; 2018. p. 127-97. Erzen E, Çikrikci Ö. The effect of loneliness on depression: A meta-analysis. International Journal of Social Psychiatry. 2018;64(5):427-35. Zawadzki MJ, Graham JE, Gerin W. Rumination and anxiety mediate the effect of loneliness on depressed mood and sleep quality in college students. Health Psychology. 2013;32(2):212. Bergman YS, Segel-Karpas D. Future time perspective, loneliness, and depressive symptoms among middle-aged adults: A mediation model. Journal of affective disorders. 2018;241:173-5. Nowakowska I. Lonely and thinking about the past: The role of time perspectives, Big Five traits and perceived social support in loneliness of young adults during COVID-19 social distancing. Current Issues in Personality Psychology. 2020;8(3):175-84. Dutt AJ, Wahl H-W. Future time perspective and general self-efficacy mediate the association between awareness of age-related losses and depressive symptoms. European Journal of Ageing. 2019;16:227-36. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1-32. Carstensen L. Future time perspective scale. Unpublished manuscript, Stanford University. 1996. Abend R, Dan O, Maoz K, Raz S, Bar-Haim Y. Reliability, validity and sensitivity of a computerized visual analog scale measuring state anxiety. Journal of behavior therapy and experimental psychiatry. 2014;45(4):447-53. Huang Z, Kohler IV, Kämpfen F. A single-item visual analogue scale (VAS) measure for assessing depression among college students. Community mental health journal. 2020;56(2):355-67. Maas CJ, Hox JJ. Sufficient sample sizes for multilevel modeling. Methodology. 2005;1(3):86-92. Kerkhoff D, Nussbeck FW. The influence of sample size on parameter estimates in three-level random-effects models. Frontiers in psychology. 2019;10:1067. Du H, Wang L. The impact of the number of dyads on estimation of dyadic data analysis using multilevel modeling. Methodology. 2016. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests in linear mixed effects models. Journal of statistical software. 2017;82(13). Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. Journal of statistical software. 2015;67:1-48. Neubauer AB, Voelkle MC, Voss A, Mertens UK. Estimating reliability of within-person couplings in a multilevel framework. Journal of Personality Assessment. 2020. Kraav S-L, Lehto SM, Junttila N, Ruusunen A, Kauhanen J, Hantunen S, et al. Depression and loneliness may have a direct connection without mediating factors. Nordic journal of psychiatry. 2021;75(7):553-7. Luttenbacher I, Breukel JS, Adamson MM. The mediating role of rumination in the relationship between loneliness and depression in university students during the COVID-19 pandemic. COVID. 2021;1(2):447-57. Van As BAL, Imbimbo E, Franceschi A, Menesini E, Nocentini A. The longitudinal association between loneliness and depressive symptoms in the elderly: a systematic review. International Psychogeriatrics. 2022;34(7):657-69. Achterbergh L, Pitman A, Birken M, Pearce E, Sno H, Johnson S. The experience of loneliness among young people with depression: a qualitative meta-synthesis of the literature. BMC psychiatry. 2020;20:1-23. Aylaz R, Aktürk Ü, Erci B, Öztürk H, Aslan H. Relationship between depression and loneliness in elderly and examination of influential factors. Archives of gerontology and geriatrics. 2012;55(3):548-54. Cacioppo JT, Hughes ME, Waite LJ, Hawkley LC, Thisted RA. Loneliness as a specific risk factor for depressive symptoms: cross-sectional and longitudinal analyses. Psychology and aging. 2006;21(1):140. Hawkley LC, Masi CM, Berry JD, Cacioppo JT. Loneliness is a unique predictor of age-related differences in systolic blood pressure. Psychology and aging. 2006;21(1):152. Preece DA, Goldenberg A, Becerra R, Boyes M, Hasking P, Gross JJ. Loneliness and emotion regulation. Personality and Individual Differences. 2021;180:110974. Gardner WL, Pickett CL, Jefferis V, Knowles M. On the outside looking in: Loneliness and social monitoring. Personality and Social Psychology Bulletin. 2005;31(11):1549-60. Qualter P, Vanhalst J, Harris R, Van Roekel E, Lodder G, Bangee M, et al. Loneliness across the life span. Perspectives on psychological science. 2015;10(2):250-64. Yun RC, Fardghassemi S, Joffe H. Thinking too much: How young people experience rumination in the context of loneliness. Journal of Community & Applied Social Psychology. 2023;33(1):102-22. Lyubomirsky S, Caldwell ND, Nolen-Hoeksema S. Effects of ruminative and distracting responses to depressed mood on retrieval of autobiographical memories. Journal of personality and social psychology. 1998;75(1):166. Lyubomirsky S, Nolen-Hoeksema S. Effects of self-focused rumination on negative thinking and interpersonal problem solving. Journal of personality and social psychology. 1995;69(1):176. Spasojević J, Alloy LB. Rumination as a common mechanism relating depressive risk factors to depression. Emotion. 2001;1(1):25. Luo J, Wong NM, Zhang R, Wu J, Shao R, Chan CC, et al. A network analysis of rumination on loneliness and the relationship with depression. Nature Mental Health. 2024:1-12. van Beek W, Kerkhof A, Beekman A. Future oriented group training for suicidal patients: a randomized clinical trial. BMC psychiatry. 2009;9:1-7. De Jaegere E, Stas P, Van Heeringen K, Dumon E, van Landschoot R, Portzky G. Future‐Oriented Group Training for suicidal individuals: A randomized controlled trial. Suicide and Life‐Threatening Behavior. 2023;53(2):270-81. Additional Declarations No competing interests reported. Supplementary Files supplementaryfile.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6451753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471730382,"identity":"a113c5cf-8178-474d-a39f-6c67162eb586","order_by":0,"name":"Yu-Rim Lee","email":"","orcid":"","institution":"Kangwon National University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Rim","middleName":"","lastName":"Lee","suffix":""},{"id":471730383,"identity":"54c18beb-5ce1-47e2-a16f-f3fc5059a096","order_by":1,"name":"Jong-Sun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACxgYILcPAwHwYWSKBoBYeBga2ZOK0wABQC48xcVqY23sPv/zZdoeHn73ns8HPHbWJ/dINjB9+MKTl43RYz7k0a962ZzySPWc3J/aeOZ44c84BZskehhzLBlxaZuSYGTO2HeYxuJG7+QBv27HcDTcSGKQZGCoMcNoC1GL4E6jF/v6bxwf/QrQw/yagxfgBL8gWCR7mZN62GpAWNqAtObi19JwxY+Y5d5hH4kyasbFs24H6mTMS2yx7DNJwajFs7zH++KPssBx/++HHkm/b6oz5JZIP3/hRkYxbSwMDmwQSH5QCQNGLUwMDgzwwaj4g8etwKx0Fo2AUjIIRCwCD1lkuFNWYmgAAAABJRU5ErkJggg==","orcid":"","institution":"Kangwon National University","correspondingAuthor":true,"prefix":"","firstName":"Jong-Sun","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-04-15 06:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6451753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6451753/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84869058,"identity":"4e783c40-a312-47f2-aeac-34a4a972fd0c","added_by":"auto","created_at":"2025-06-18 08:46:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation patterns of affective variables\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6451753/v1/84b85e3e0db873f96db5c156.png"},{"id":102964623,"identity":"0cf772ea-d974-43e5-b0db-207ff29ad26e","added_by":"auto","created_at":"2026-02-19 04:23:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6451753/v1/df4ef162-099f-4613-95d8-66ee87d30eac.pdf"},{"id":84870496,"identity":"e7894bcd-e29c-4699-9246-a7c2b766ac50","added_by":"auto","created_at":"2025-06-18 08:54:43","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":552531,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.csv","url":"https://assets-eu.researchsquare.com/files/rs-6451753/v1/c2b0bcec096a40f5e43d6a4c.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Moderating Role of Future Time Perspective","fulltext":[{"header":"Background","content":"\u003cp\u003eDepressive disorders are mental health conditions characterized by persistent negative emotional states, including sadness, emptiness, or hopelessness, along with a pronounced loss of interest or pleasure in daily activities(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These emotional symptoms, moreover, are frequently accompanied by physical and cognitive impairments, such as insomnia, weight loss, and difficulties with thinking and attention, which collectively have a profound impact on an individual\u0026rsquo;s quality of life(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In particular, depressive disorders are strongly associated with suicidal ideation and suicide attempts (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), as a meta-analysis reported that the suicide rate among individuals with depressive disorders is approximately 1.6 times higher than that of the general population(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Furthermore, the effects of depressive symptoms extend beyond individual health and well-being, resulting in significant socio-economic burdens, including reduced productivity, diminished consumption opportunities, and increased social care costs(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, depressive disorders are closely associated with an individual\u0026rsquo;s emotional experiences, characterized by heightened negative affect and diminished positive affect. Negative affect encompasses a range of negative emotional experiences, including loneliness, sadness, helplessness, guilt, and anger, all of which are recognized as strong predictors of depression(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, individuals experiencing depression or at high risk of depression tend to process information about the self, the world and the future with a negative bias. According to Beck's theory of depression, people with depression often report having \"no future\" and tend to perceive the future as a singular, hopeless state (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, individuals with high depressive tendencies experienced limitations in imagining the future as long and expansive, even when their future time perspective (FTP) was experimentally manipulated, and demonstrated a persistent attentional bias towards negative information (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Building on this theoretical foundation, a number of studies have examined the relationship between FTP and depression. FTP was first introduced in Socioemotional Selectivity Theory (SST). It refers to an individual's subjective perception of the time remaining until the end of their life (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The perception of limited time left, regardless of age, plays an important role in shaping goal setting, preferences, activity choices and emotional experiences, and is identified as a factor distinct from personality traits(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Individuals who perceive their future as extended selectively focus on goals and plans and strive for future optimisation through pursuits such as knowledge acquisition and self-development(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Conversely, individuals who perceive their future as limited prioritise meaningful activities and emotional experiences in the present over future-oriented goals such as self-improvement and knowledge acquisition (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, these emotional motivations do not lead to improved emotion regulation skills or subjective well-being, and individuals with a limited FTP consistently exhibit highly negative and maladaptive emotional functioning, regardless of age(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Also, individuals with a limited FTP tend to experience more pronounced negative affect and depressive symptoms(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In contrast, individuals with an extended FTP show higher levels of purpose in life, positive affect, and life satisfaction, which serve as significant protective factors against depression(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, among the negative affect variables, loneliness is another factor that is closely related to a \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP.Moreover\u003c/span\u003e\u003cspan address=\"http://FTP.Moreover\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, loneliness has been identified as a significant risk factor, linked to an increased likelihood of depression and suicide (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Loneliness is defined not only as social isolation, but also as a discrepancy between desired and actual social relationships (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and is closely related to the perception of being socially unacceptable to others (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This perception may activate cognitive mechanisms that promote rumination on negative past experiences(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), thereby reinforcing a limited \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP.Building\u003c/span\u003e\u003cspan address=\"http://FTP.Building\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e on these findings, previous studies have explored the relationships between loneliness, depression and FTP, demonstrating strong associations between these variables(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, research investigating these relationships remains limited, highlighting the growing importance of exploring this area further. Also, FTP is known to influence the emotional and behavioural motivations of individuals of all ages. To date, research on FTP has focused primarily on older adults or middle-aged individuals who are beginning to experience the effects of ageing(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). While these studies have provided valuable insights into the relationships between FTP, emotional functioning and well-being, there remain significant gaps in understanding the role of FTP in broader age groups. Moreover, most prior studies exploring the relationship between depression and emotional variables have employed cross-sectional designs. Such approaches are inherently limited in capturing the dynamic nature of emotional experiences and are often reliant on retrospective self-reports, which are vulnerable to recall bias(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Additionally, these studies are frequently conducted in controlled laboratory settings, which may fail to accurately reflect the emotional states experienced by individuals in their natural daily environments. To address these limitations, the present study employs ecological momentary assessment (EMA), a methodology designed to collect real-time data on self-reported symptoms and behaviours as they occur in participants' everyday contexts. In this study, EMA was implemented using smartphone-based Short Message Service (SMS) prompts, allowing for the immediate recording of emotional and behavioural responses. This approach reduces recall bias and captures natural fluctuations in emotional variables, providing a more ecologically valid understanding of the relationship between depressive mood and emotional factors. EMA is particularly well suited to investigating how these associations unfold in daily life and whether findings from traditional studies can be replicated in real-world settings. Accordingly, the primary aim of this study is to identify the affective variables that influence depression in daily life and to examine whether FTP is a protective factor in the relationship between depression and loneliness.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were recruited through social media, online communities, and other related platforms from August 6 to August 11, 2021, targeting adults aged 19 to 50 years. Information about the study was provided to individuals who volunteered, and a total of 64 participants signed the consent form. As a result, after excluding 5 participants who withdrew during the study, the final sample consisted of 59 participants. Sociodemographic of the participants was collected through an online survey link, which included gender, age, education level and marital status. Sociodemographic information of the participants is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study was approved by the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Mean, Standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.98 (8.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.4% (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.2% (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.8% (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Graduate, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.3% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate (Bachelor's), % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4% (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate (Master's), % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9% (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate (Doctorate), % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e Participants were first told about the study and signed an informed consent form. A self-report questionnaire was administered at the beginning and end of the study, using the same scale on both occasions. Then, during the 14-day study period, participants responded to EMA four times a day (morning \u0026minus;\u0026thinsp;09:00, lunch \u0026minus;\u0026thinsp;12:00, dinner \u0026minus;\u0026thinsp;17:00, and night \u0026minus;\u0026thinsp;21:00) via an online survey link sent via SMS. A reminder message was sent if participants did not respond within 30 minutes. On the first day, the self-report questionnaire was delivered with the morning EMA as a pre-survey and participants were instructed to complete the self-report questionnaire first. Similarly, on the last night, the self-report questionnaire was delivered with the final EMA as a post-survey, and participants completed the self-report questionnaire last.\u003c/p\u003e\n\u003ch3\u003eMeasure\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSelf-report questionnaire\u003c/h2\u003e \u003cp\u003eAt baseline, demographic information such as gender, age, marital status and highest level of education was collected. The Future Time Perspective (FTP) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) scale was also used to measure the participant's subjective view of future time. It consists of 10 items and is scored on a 7-point Likert scale (1: not at all to 7: very much). Higher scores indicate that the individual has an expanded view of the future.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEcological Momentary Assessment\u003c/h3\u003e\n\u003cp\u003e Participants reported their emotional state four times a day (morning \u0026ndash; 09 :00, lunch \u0026minus;\u0026thinsp;12:00, dinner \u0026minus;\u0026thinsp;17:00 and night \u0026minus;\u0026thinsp;21:00) via an online link provided by SMS. Emotional state was assessed using a visual analogue scale (VAS). The VAS is a tool that rates the intensity of an emotion or symptom on a continuum from 0 (not at all) to 10 (very strong) and is useful for quantifying participants' subjective state(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). VAS are widely used in a variety of research and clinical settings because their simple and intuitive design minimises the burden on participants. Because of these characteristics, VAS are often used in EMA studies that require repeated responses to the same questionnaire at short intervals. In this study, we used VAS to measure the intensity of 14 negative affect and 1 positive affect: sadness, anger, worry, anxiety, irritation, lethargy, alienation, fear, shame, burnout, loneliness, depression, guilt, terror and happiness. Using these methods, it explores the types of depression and the negative feelings associated with them that the participants experience on a day-to-day basis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe study was based on a total of 3304 data points, including 56 assessments at level 1 and 59 participants at level 2. Participants had an average response rate of 96.48% (minimum 66.07%, maximum 100%), resulting in 3188 valid observations. Given the nested structure of the data, analysis was conducted using multilevel modelling (MLM). It has been reported that an adequate sample size for multilevel modelling is generally when there are at least five observations within an individual at level 1 and a sample size of 50 or more at level 2 to produce reliable results(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In particular, if there are no missing data at level 1, a sample size of 50 or more at level 2 can ensure reliable estimates(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In this study, momentary depression was specified as the outcome variable and other emotional states assessed by the EMA were examined as potential predictors at Level 1. At level 2, FTP was included as a higher-level predictor to examine its moderating effect on the relationship between momentary depression and loneliness.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData pre-processing\u003c/h3\u003e\n\u003cp\u003eIn the case of this study, the average number of within-individual observations at level 1 was 56, and the total number of data at level 2 was 59, which met the conditions for producing sufficiently reliable results through multilevel model analysis. In addition, we applied multiple imputation using the mice package of the R software to compensate for missing data, to ensure the reliability of the estimates. All statistical analyses were conducted using R version 4.4.1 and SPSS Statistics version 26. Descriptive statistics and frequency analyses for demographic variables were performed with SPSS Statistics version 26. Mixed-effects model analyses were conducted using the lmerTest package (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), an extension of the lme4 package (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), in RStudio version 2024.04.2\u0026thinsp;+\u0026thinsp;764.\u003c/p\u003e \u003cp\u003ePrior to the main analyses, person-mean centering (PMC) was applied to the within-person affective variables (predictors) collected via EMA. This approach involves calculating the deviation of each individual\u0026rsquo;s momentary affect score from their own mean for that variable. In other words, the affective variables were centred on each individual's mean, so that the values reflect the extent to which a given moment deviates from that person's typical level of affect. This procedure removes between-person differences in mean levels of affect, allowing for a clearer examination of within-person variation(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Using this approach, we examined within-person associations between momentary affect and level 1 depression. In addition, we tested for cross-level interaction effects by examining whether individual differences in the strength of the within-person associations between affect and depression were accounted for by a level 2 variable.\u003c/p\u003e\n\u003ch3\u003eData analysis plan\u003c/h3\u003e\n\u003cp\u003eThis study used MLM to examine the multidimensional influences of within-person and between-person factors on depression. The analysis was carried out in four steps as follows\u003c/p\u003e \u003cp\u003e(a) Null model testing (Model 0): A null model was tested to assess whether the data were suitable for multilevel analysis to examine between-individual effects on depression. The intraclass correlation coefficient (ICC) was used as an index of the proportion of variance in depression attributable to within-individual and between-individual levels.\u003c/p\u003e\u003cp\u003e(b) Examination of affective variables (Model 1): We examined multicollinearity between affective variables collected through EMA and included these variables as independent predictors to analyse their effects on depression at the within- individual (level 1) level. Through this process, we exploratively identified affective variables that significantly influenced depression.\u003c/p\u003e \u003cp\u003e(c) Level 1 relationship between loneliness and depression: To analyse the relationship between loneliness and depression at level 1, a random intercept model (Model 2) and a random intercept and slope model (Model 3) were constructed. Model fit was compared to determine whether the inclusion of a random slope significantly improved the explanatory power of the model.\u003c/p\u003e \u003cp\u003e(d) Level 2 analysis (Model 4): Level 2 (between- individual) analysis explored factors that influence the relationship between loneliness and depression. This step aimed to identify higher level variables that explain how the relationship between loneliness and depression varies across individuals.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNull model testing (Model 0)\u003c/h2\u003e \u003cp\u003eFirst, the ICC for depression collected via EMA was calculated to assess whether there was sufficient variability at both the within-person and between-person levels. The result showed an ICC of 0.641, indicating that 64.1% of the variability in depression is explained by between individual differences, while 36.9% is explained by within individual variability. This shows that the between-person differences in depression are substantial, justifying the use of multilevel modelling in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExamination of affective variables (Model 1)\u003c/h2\u003e \u003cp\u003eTo assess potential multicollinearity between the EMA affective variables, correlation analysis and variance inflation factor (VIF) calculations were performed. As shown in the correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), all bivariate correlations were less than or equal to 0.5 and all VIF values were well below the commonly accepted cut-off of 5 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that multicollinearity was not a concern in the current analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance inflation factor of affective variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eVariance inflation factor (VIF)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003eNote.\u003c/em\u003e 1\u0026thinsp;=\u0026thinsp;sadness, 2\u0026thinsp;=\u0026thinsp;anger, 3\u0026thinsp;=\u0026thinsp;worry, 4\u0026thinsp;=\u0026thinsp;fear, 5\u0026thinsp;=\u0026thinsp;irritation, 6\u0026thinsp;=\u0026thinsp;lethargy, 7\u0026thinsp;=\u0026thinsp;alienation, 8\u0026thinsp;=\u0026thinsp;anxiety, 9\u0026thinsp;=\u0026thinsp;shame, 10\u0026thinsp;=\u0026thinsp;burnout, 11\u0026thinsp;=\u0026thinsp;loneliness, 12\u0026thinsp;=\u0026thinsp;guilt 13\u0026thinsp;=\u0026thinsp;terror, 14\u0026thinsp;=\u0026thinsp;happiness\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe affective variables that had a significant impact on depression at level 1 were then examined (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that sadness, irritation, lethargy, anxiety, burnout, loneliness, guilt, terror, and happiness were significant predictors of depression. Other variables such as anger, worry, fear, alienation, and shame had no significant effect. Among the significant predictors identified, loneliness was further analyzed due to its unique role in the onset and maintenance of depression. Loneliness has consistently been recognized as a critical risk factor contributing to both the development and persistence of depression (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In particular, loneliness has been shown to exacerbate depressive symptoms by facilitating maladaptive cognitive processes, such as increased rumination (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Given the strong theoretical and empirical associations between loneliness and depression, a follow-up analysis was conducted in the first stage to examine their within-individual association in more depth. Subsequently, FTP was introduced as a Level 2 variable to investigate its moderating role in this relationship.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel model analysis of level-1 affective variables predicting depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStandard Deviation(SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eId (intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.4548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.8587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.8967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.9469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEstimate (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.301 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[1.826, 2.777]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSadness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.192 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.153, 0.232]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.019 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.053, 0.015]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.01 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.041, 0.019]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.008 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.046, 0.029]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrritation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.11 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.084, 0.137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLethargy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.117 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.093, 0.141]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlienation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.01 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.054, 0.033]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.128 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.093, 0.162]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.012 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.030, 0.054]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.061 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.040, 0.082]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.191 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.153, 0.229]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.173 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.135, 0.211]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerror\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.119 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.076, 0.163]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHappiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.063 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-0.084, -0.041]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. SE\u0026thinsp;=\u0026thinsp;Standard error. CI\u0026thinsp;=\u0026thinsp;Confidence interval. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLevel 1 relationship between loneliness and depression (Model 2, Model 3)\u003c/h2\u003e \u003cp\u003eNext, we examined the within-individual effects of loneliness on depression and explored whether there were between-individual differences in this relationship. First, we analyzed the within-individual relationship between loneliness and depression using two models: Model 2, which included only a random intercept, and Model 3, which included both a random intercept and a random slope, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We then compared the fit of these models to determine whether the inclusion of a random slope and the assumption of interindividual differences significantly improved the model fit(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel model estimates for the relationship between loneliness and depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 2: Random Intercept\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStendard Deviation(SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eId (intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEstimate (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.301 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[1.826, 2.777]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.6 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.565, 0.635]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3: Random Intercept and Slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStendard Deviation(SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eId (intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.8568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.3096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.1391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEstimate (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.301 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[1.826, 2.777]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.496 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.386, 0.606]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. SE\u0026thinsp;=\u0026thinsp;Standard error. CI\u0026thinsp;=\u0026thinsp;Confidence interval *p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA comparison of the fit between Model 2 and Model 3 indicates that Model 3 provides a better fit, as evidenced by its lower AIC (10612) and BIC (10648) values compared to Model 2 (AIC\u0026thinsp;=\u0026thinsp;10851, BIC\u0026thinsp;=\u0026thinsp;10875). This improvement highlights that including the random slope for the relationship between loneliness and depression significantly enhances the model fit (χ\u0026sup2; = 243.35, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results from Model 3 indicate that a one-unit increase in loneliness is associated with an average increase of 0.0.496 in depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the significantly better fit of the model with the random slope suggests substantial between-individual variation in the positive association between loneliness and depression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompare the model fit of model 1 and model 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e243.35 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion, BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion, *p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLevel 2 analysis (Model 4)\u003c/h2\u003e \u003cp\u003eFinally, Model 4 was analyzed to explore the role of future time perspective in the relationship between loneliness and depression(Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLevel-2 moderation analysis of FTP on loneliness and depression\"\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStendard Deviation(SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eId (intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.2173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.7937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.2621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.2992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.1398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEstimate (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5.775 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[2.732, 8.818]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.429 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-1.054, 0.197]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.759 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[-1.417, -0.102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness*FTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.202 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e[0.065, 0.339]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. SE\u0026thinsp;=\u0026thinsp;Standard error. CI\u0026thinsp;=\u0026thinsp;Confidence interval. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of Model 4 revealed a significant interaction between loneliness and FTP in predicting depression (Estimate\u0026thinsp;=\u0026thinsp;0.202, p\u0026thinsp;=\u0026thinsp;.006). This interaction suggests that a more expansive future time perspective may attenuate the negative effect of loneliness on depressive symptoms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilised EMA to investigate emotional factors influencing depression. It also examined the relationship between depression and loneliness and FTP moderated this relationship. The results showed that among negative affects, sadness, irritability, lethargy, anxiety, burnout, loneliness, guilt and fear significantly exacerbated depression. In contrast, happiness was the positive affects that significantly alleviated depression. These findings are consistent with previous research highlighting the strong link between negative affects and the severity of depression symptoms. However, this study is unique in that it uses EMA to identify specific emotional factors associated with depression in daily life. This approach provides a nuanced understanding of how moment-to-moment emotional experiences contribute to fluctuations in depression.\u003c/p\u003e \u003cp\u003eThis study found that loneliness significantly predicted depression at the within-individual levels. In addition, FTP was found to moderate this relationship. Level-2 analyses indicated that a more extended FTP moderated the within-person relationship between loneliness and depression, such that the negative association was attenuated. These findings are consistent with previous research identifying loneliness as a risk factor that exacerbates depressive symptoms and increases the likelihood of developing clinical depression(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, unlike previous studies that focused primarily on older adult populations, this study included a broader age range, including people from their 20s to 50s(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This highlights the ability of research to generalise the relationship between loneliness and depression across different stages of the life course. Furthermore, while loneliness is closely related to depression, it is recognised as a theoretically and statistically distinct construct(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). From an emotion regulation perspective, loneliness interferes with the use of adaptive cognitive strategies and induces maladaptive cognitive strategies such as self-blame and rumination (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Specifically, loneliness activates the social monitoring system (SMS), which heightens sensitivity to negative social cues while impairing the ability to reframe social situations adaptively (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). As a result, lonely individuals exhibit excessive attention to social rejection cues and interpret ambiguous social interactions more negatively, which reinforces negative self-perceptions and increases the likelihood of self-focused rumination (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Rumination in particular plays a central role in the relationship between loneliness and depression. As noted by Zawadzki, Graham (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), loneliness predominantly triggers self-focused rumination, which is characterised by repetitive thoughts about personal shortcomings, failed relationships and social inadequacies. This past-oriented rumination not only reinforces negative self-perceptions, but also intensifies feelings of regret and self-blame (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Such patterns suppress the use of active and adaptive coping strategies and activate depression-related schemas that promote a pessimistic view of interpersonal relationships(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Consequently, this dynamic perpetuates an emotion-cognition cycle that exacerbates loneliness, increases rumination, and worsens depression(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, it is theoretically plausible that FTP interacts with loneliness to influence depressive symptoms, aligning with the findings of previous studies that have examined the relationships among these three variables(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Furthermore, considering previous research that validated a full mediation model in which loneliness affects depressive symptoms through rumination(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and findings suggesting that individuals with a limited future time perspective focus more on the past than on the present or future(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), extended FTP may act as a protective factor that mitigates the negative impact of loneliness on depression. Specifically, it is expected that an extensive perspective on the future will suppress the activation of rumination induced by loneliness, thereby reducing the negative impact of loneliness on depression.\u003c/p\u003e \u003cp\u003eBased on the findings of this study, the following intervention strategies and therapeutic implications can be applied in clinical practice. First, the use of EMA in this study demonstrated the potential to predict fluctuations in depression by measuring emotional states in real time. Building on this, a system could be developed where individuals use smartphones or wearable devices to continuously record their emotional changes, allowing clinicians to detect early warning signs and intervene promptly. In addition, real-time emotional monitoring data can be used to provide personalised feedback tailored to each individual's emotional patterns. For example, interventions such as mindfulness training or recommendations to see a clinician could be suggested when negative affects such as loneliness and depression increase. Such digital systems would allow mental health clinicians to monitor patients' conditions remotely, overcoming the limitations of traditional face-to-face consultations and greatly improving the accessibility of services.\u003c/p\u003e \u003cp\u003eFurthermore, as loneliness is identified in this study as a significant risk factor for depression, strategies to increase social support are crucial. To this end, it is important to help individuals develop new social relationships or rebuild existing ones through community-based programmes. Initiatives such as social skills training and the activation of local community networks could be implemented to promote social connectedness. In addition, the findings suggest that an extended FTP serves as a protective factor that moderates the negative relationship between loneliness and depression severity. Thus, therapeutic approaches aimed at promoting goal-directed and future-oriented thinking and behaviour are essential. In particular, future-oriented group training(FOGT) may be particularly beneficial (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This training has been shown to significantly reduce suicidal ideation and depression, with effects lasting at least three months after the intervention (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). By integrating these approaches, it is hoped that the effectiveness of mental health interventions can be enhanced, providing innovative solutions to the challenges associated with traditional therapeutic methods.\u003c/p\u003e \u003cp\u003eFirst, the study was conducted within the specific cultural and social context of South Korea, with a relatively small sample size (n\u0026thinsp;=\u0026thinsp;59). Although the sample size was sufficient to produce statistically significant results, it limits the generalisability of the findings to populations with different cultural backgrounds or demographic characteristics. Given that loneliness, FTP and depression are sensitive to cultural and environmental factors, future research should include more diverse cultural contexts and larger sample sizes to increase the generalizability of the findings. Second, although the study used an EMA design to examine affective variability and temporal associations between the three variables, it did not explicitly examine causal relationships or temporal precedence. To address this limitation, future research could use statistical methods such as dynamic structural equation modelling (DSEM) to account for temporal dynamics and clarify the causal mechanisms underlying the observed relationships. Finally, the study did not control for additional psychological or external factors that might influence the relationships between depression, loneliness and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP.For\u003c/span\u003e\u003cspan address=\"http://FTP.For\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e example, personality traits (e.g. neuroticism), levels of social support and stress could potentially affect the interactions between these variables. Future research that takes these factors into account in its design may provide more comprehensive and robust results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the relationships between depression, loneliness and FTP using EMA. The results showed that negative emotions, including sadness, irritability and loneliness, significantly exacerbated depression, while happiness acted as a protective factor. Loneliness was a significant predictor of depression at both within- and between-individual levels, and FTP moderated this relationship. Specifically, individuals with extended FTP experienced a reduced negative impact of loneliness on depression. These findings highlight the importance of real-time affective monitoring and future-oriented thinking in the management of depression. The use of EMA and digital tools enables personalised, real-time interventions, improving accessibility and facilitating early detection of depressive symptoms. In addition, strategies such as FOGT and enhancing social support can effectively mitigate the impact of loneliness on depression, offering innovative approaches to mental health care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEMA\u0026nbsp; \u0026nbsp;Ecological Momentary Assessment\u003cbr\u003eVAS\u0026nbsp; \u0026nbsp; \u0026nbsp;Visual Analogue Scale\u003cbr\u003eMLM\u0026nbsp; \u0026nbsp;Multilevel Modelling\u003cbr\u003eFOGT\u0026nbsp;Future-Oriented Group Training\u003cbr\u003eSST\u0026nbsp; \u0026nbsp; \u0026nbsp;Socioemotional Selectivity Theory\u003cbr\u003eSMS\u0026nbsp; \u0026nbsp;\u0026nbsp;Short Message Service\u003cbr\u003ePMC\u0026nbsp; \u0026nbsp;\u0026nbsp;Person-Mean Centering\u003cbr\u003eICC\u0026nbsp; \u0026nbsp; \u0026nbsp;Intraclass Correlation Coefficient\u003cbr\u003eVIF\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Variance Inflation Factor\u003cbr\u003eAIC\u0026nbsp; \u0026nbsp; \u0026nbsp;Akaike Information Criterion\u003cbr\u003eBIC\u0026nbsp; \u0026nbsp; \u0026nbsp;Bayesian Information Criterion\u003cbr\u003eSMS\u0026nbsp; \u0026nbsp;\u0026nbsp;Social Monitoring System\u003cbr\u003eDSEM Dynamic Structural Equation Modelling\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003). All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2074932).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJL conceived and designed the work, acquired the data, drafted the manuscript, and substantively revised it. YL analyzed and interpreted the data and also contributed to drafting the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAPA. Diagnostic and statistical manual of mental disorders: DSM-5: American psychiatric association Washington, DC; 2013.\u003c/li\u003e\n\u003cli\u003eKendler KS. The phenomenology of major depression and the representativeness and nature of DSM criteria. American Journal of Psychiatry. 2016;173(8):771-80.\u003c/li\u003e\n\u003cli\u003eMelhem NM, Porta G, Oquendo MA, Zelazny J, Keilp JG, Iyengar S, et al. Severity and variability of depression symptoms predicting suicide attempt in high-risk individuals. JAMA psychiatry. 2019;76(6):603-13.\u003c/li\u003e\n\u003cli\u003eChisholm D, Sweeny K, Sheehan P, Rasmussen B, Smit F, Cuijpers P, et al. 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Effects of self-focused rumination on negative thinking and interpersonal problem solving. Journal of personality and social psychology. 1995;69(1):176.\u003c/li\u003e\n\u003cli\u003eSpasojević J, Alloy LB. Rumination as a common mechanism relating depressive risk factors to depression. Emotion. 2001;1(1):25.\u003c/li\u003e\n\u003cli\u003eLuo J, Wong NM, Zhang R, Wu J, Shao R, Chan CC, et al. A network analysis of rumination on loneliness and the relationship with depression. Nature Mental Health. 2024:1-12.\u003c/li\u003e\n\u003cli\u003evan Beek W, Kerkhof A, Beekman A. Future oriented group training for suicidal patients: a randomized clinical trial. BMC psychiatry. 2009;9:1-7.\u003c/li\u003e\n\u003cli\u003eDe Jaegere E, Stas P, Van Heeringen K, Dumon E, van Landschoot R, Portzky G. Future‐Oriented Group Training for suicidal individuals: A randomized controlled trial. Suicide and Life‐Threatening Behavior. 2023;53(2):270-81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depression, Ecological momentary assessment, Loneliness, Futrue time perspective, Multilevel modeling, MLM, EMA, Digital phenotype","lastPublishedDoi":"10.21203/rs.3.rs-6451753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6451753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepressive disorders are characterized by persistent negative emotional states and cognitive impairments that significantly impact quality of life. Loneliness is a key predictor of depression and interacts with future time perspective(FTP), a subjective perception of remaining time in life. While FTP influences emotional and behavioral motivations, its role in moderating the loneliness-depression relationship is underexplored. This study used ecological momentary assessment(EMA) to investigate these relationships in daily life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed a 14-day EMA protocol, responding four times daily via smartphone SMS. Emotional states were measured using a visual analogue scale (VAS) for 14 negative affects and one positive affect. multilevel modelling (MLM) examined within- and between-individual effects and FTP's moderating role.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants (aged 19–50 years) completed 3,183 EMA observations with an adherence rate of 96.3%. The final dataset consisted of 3,304 data points from 56 assessments (level 1) and 59 participants (level 2), with an average response rate of 96.48%. Loneliness significantly predicted depression at the within-individual level, with substantial between-individual variation in this relationship. In addition, FTP moderated the effect of loneliness on depression, suggesting its potential buffering role.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates the utility of EMA in capturing real-time emotional dynamics and highlights FTP's protective role against loneliness-induced depression. The findings support the use of digital tools for real-time monitoring and interventions, as well as strategies like future-oriented group training (FOGT) and social support programs to mitigate depression's impact. These approaches offer practical solutions to improve mental health care.\u003c/p\u003e","manuscriptTitle":"Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Moderating Role of Future Time Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 08:46:38","doi":"10.21203/rs.3.rs-6451753/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3bb1edf-cd31-4737-a7b8-ff279f259a40","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T23:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 08:46:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6451753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6451753","identity":"rs-6451753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}