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

Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Contextual 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Assessment Study of the Contextual Role of Future Time Perspective Yu-Rim Lee, Jong-Sun Lee This is a preprint; it has not been peer reviewed by a journal.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-9317791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Although loneliness is a core interpersonal risk factor for depression, its unique contribution—independent of other co-occurring affects—remains insufficiently explored in daily life. Furthermore, according to Socioemotional Selectivity Theory, the psychological impact of social disconnection may vary depending on an individual’s Future Time Perspective (FTP), yet the moderating role of FTP in the loneliness–depression link lacks empirical evidence. Objective Using Ecological Momentary Assessment (EMA), this study aimed to disentangle the unique association between momentary loneliness and depression while controlling for 13 concurrent affects, and to examine whether FTP moderates this relationship in real-world contexts. Method Fifty-nine adults completed a 14-day EMA protocol, responding four times per day via smartphone. At each prompt, participants rated 14 negative affects and one positive affect on 0–9 visual analogue scale (VAS) scales; depression was analyzed as the outcome variable, with the remaining affects entered as predictors. Using MLM, the study examined which affects were associated with depression at the within-individual level and tested whether FTP, assessed once at baseline with a validated scale, moderated the association between loneliness and depression at the between-individual level. Results Multilevel analysis demonstrated that momentary loneliness showed a robust within-person association with depression, remaining significant even after accounting for 13 other concurrent affective states. Model comparisons confirmed substantial between-person variability in the loneliness–depression relationship, providing a statistical basis for moderation. A significant cross-level interaction revealed that FTP moderated this association. Specifically, simple slope and Johnson-Neyman analyses indicated that while a more expansive FTP was associated with lower depressive levels under conditions of low momentary loneliness, it was also linked to heightened emotional reactivity; the link between loneliness and depression was significantly more pronounced for individuals with an expansive FTP compared to those with a limited FTP. Conclusions This study suggest that depression in daily life reflects dynamic emotional processes shaped by the interplay of multiple negative and positive affects. Multilevel analyses further revealed that FTP moderated the association between loneliness and depression, such that individuals with a more expansive FTP linked to stronger positive associations between momentary loneliness and depressive affect. These findings extend socioemotional selectivity theory by suggesting that FTP may function as a context-dependent framework, conferring emotional benefits under certain conditions while amplifying vulnerability in contexts of social disconnection. Depression Ecological momentary assessment Loneliness Future time perspective Multilevel modeling MLM EMA Digital phenotyping Figures Figure 1 Figure 2 Figure 3 Introduction Depression is one of the most common psychiatric disorders worldwide, characterized by persistent sadness, emptiness, hopelessness, and a marked loss of interest or pleasure in daily activities ( 1 ). These affective symptoms co-occur with somatic and cognitive problems such as insomnia, weight changes, and cognitive impairment, and are closely associated with suicidal ideation and attempts ( 2 , 3 ). Moreover, beyond the individual dimension, depression causes a considerable socioeconomic burden, including decreased productivity and increased social care costs ( 4 , 5 ). The Tripartite Model proposed by Clark and Watson ( 6 ) provides a foundational theoretical framework for understanding the affective architecture of depression. This model posits that the core of depression is characterized by a specific combination of low positive affect (PA)—manifesting as anhedonia and lack of vitality—and high negative affect (NA), which reflects generalized distress. While this framework has been instrumental in distinguishing depression from anxiety (which is characterized primarily by high NA), focusing solely on broad affective dimensions may obscure the specific, qualitative nature of the depressive experience ( 7 ). Therefore, to attain a more granular understanding of depressive pathology, it is essential to identify the unique contributions of discrete emotions that exist beneath these broad affective categories. Within this broad NA dimension, depression manifests through a multifaceted affective profile that contributes to its clinical heterogeneity. First, sadness and guilt represent the core internalizing affects of depression. Sadness serves as the most representative clinical indicator for assessing depressive severity (Mouchet-Mages & Baylé, 2008), while guilt reflects a cognitive-affective profile where negative self-schemas co-occur with emotional distress( 8 ). Second, the affective landscape of depression is defined not only by the presence of NA but also by the deficit of PA. Happiness, as a primary experience of PA, does not merely reflect the absence of anhedonia but functions as a critical protective factor associated with emotional resilience ( 9 ). Furthermore, depression frequently encompasses externalizing and high-arousal states that signify physiological and psychological dysregulation. Anger and irritability are representative affects related to hostility; when maladaptively internalized or expressed, these states are linked to heightened psychosocial dysfunction and impaired impulse control ( 10 , 11 ). Similarly, threat-related affects such as anxiety, worry, fear, and terror represent a distinct cluster characterized by high arousal. While anxiety and worry show pervasive comorbidity with depression ( 12 ), states of fear and terror—particularly when linked to trauma or perceived insecurity—are associated with the persistence and intensification of depressive distress ( 13 – 15 ). However, characterizing depression solely through intrapersonal affective dimensions (i.e., PA and NA) is insufficient. Depression is not merely an internal disorder of mood regulation; it is inextricably linked to the social context, often manifesting through symptoms of social withdrawal, rejection sensitivity, and a perceived breakdown of interpersonal bonds ( 16 , 17 ). Consequently, to fully capture the phenomenology of depression, it is necessary to extend the Tripartite framework by examining specific interpersonal affects that may function distinctly from generalized distress. Among these interpersonal constructs, loneliness represents a critical dimension. Loneliness is defined not only as social isolation, but also as a discrepancy between desired and actual social relationships ( 18 ) and is closely related to the perception of being socially unacceptable to others( 19 ). The potent association between loneliness and depression is fundamentally rooted in the evolutionary theory of loneliness, which posits that social disconnection functions as a “social signal” of threat to survival—analogous to physical pain ( 20 ). This perceived isolation triggers a state of hypervigilance and negative cognitive biases, which in turn co-occur with core depressive symptoms such as sadness and low self-worth ( 21 , 22 ). While these theoretical links are robust, empirical investigations have focused disproportionately on older adult populations( 21 , 23 ). However, contemporary research underscores that loneliness has emerged as a pervasive public health issue—a 'modern epidemic'—affecting individuals across the entire lifespan, with alarmingly high prevalence observed among young and middle-aged adults( 24 , 25 ). Given that the experience of social disconnection is a fundamental human distress that transcends specific life stages, there is a critical need to clarify the universal mechanisms through which loneliness associates with depression in more diverse demographics( 26 , 27 ). Furthermore, to determine the unique contribution of loneliness, it must be examined within a comprehensive affective profile that accounts for other co-occurring negative emotions—an area that remains insufficiently explored in real-time daily life research. The psychological impact of loneliness is not uniform across all individuals. The extent to which social disconnection translates into depressive distress is likely filtered through stable cognitive frameworks that dictate an individual’s social motivations and goals. The present study focuses on Future Time Perspective (FTP) as a key moderator of this relationship. Originating from Socioemotional Selectivity Theory (SST), FTP refers to the subjective perception of time remaining in life, ranging on a continuum from expansive to limited ( 28 ). This temporal horizon is not merely a situational appraisal but a stable lens that shapes goal prioritization ( 29 ). Individuals with an expansive FTP typically prioritize knowledge acquisition and long-term network building. In contrast, those with a limited FTP prioritize present-oriented goals, such as emotional meaningfulness and the optimization of intimate social bonds ( 30 , 31 ). FTP is particularly relevant to the context of loneliness because SST is fundamentally a theory of social motivation. While previous research has examined FTP and affective well-being in isolation ( 32 ), the interactive dynamics between FTP and loneliness remain under-explored ( 33 , 34 ). We propose that FTP acts as a moderator because it determines the "subjective value" assigned to social experiences. For individuals with an expansive FTP, who view the future as open-ended, loneliness may be perceived as a significant impediment to their long-term social trajectory, potentially intensifying the association between social disconnection and depressive distress. Conversely, a limited FTP, which focuses resources on immediate emotional regulation, may alter sensitivity to social deficits. By conceptualizing FTP as a pre-existing cognitive framework, this study seeks to clarify why the same level of loneliness results in varying degrees of depressive symptoms. Moreover, most prior studies on these variables have relied on cross-sectional designs or retrospective self-reports, which are vulnerable to recall bias and fail to capture the dynamic nature of emotional experiences in natural environments ( 35 ). To address these theoretical gaps and methodological limitations, the present study employs Ecological Momentary Assessment (EMA). This methodology allows for the real-time capture of the co-occurrence between loneliness, depression, and other affective states in daily life, providing high ecological validity. The primary aims of this study are threefold: ( 1 ) to identify the specific affective profile associated with depression in daily life, disentangling the unique contribution of loneliness from other concurrent affects; ( 2 ) to examine the robust association between loneliness and depression within everyday contexts; and ( 3 ) to test whether FTP moderates the link between loneliness and depression. Methods Participants Participants were recruited through social media, online communities, and other related platforms from August 6 to August 25, 2021, targeting adults aged 19 to 50 years. Eligibility for participation was restricted to individuals within this age range, and no additional inclusion or exclusion criteria were applied. Before participation, individuals were provided with detailed information about the study’s purpose, procedures, confidentiality, and their rights as participants. Given that the study was conducted entirely online, informed consent was obtained electronically. Participants were presented with an online consent form and asked to indicate their agreement by selecting either “I agree to participate” or “I do not agree to participate.” Only those who actively selected “I agree to participate” were able to proceed with the survey. A total of 64 participants provided informed consent and completed the study. 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 sex, age, education level and marital status. This study was approved by the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003). Procedure Participants were first provided with information about the study and provided electronic informed consent prior to participation. On the morning of the first day, they completed demographic questions and the FTP questionnaire via an online survey link. Beginning on the same day, participants engaged in EMA for 14 consecutive days. Each day, they received survey links via short message service (SMS) four times (09:00, 12:00, 17:00, and 21:00) and were asked to respond to the EMA prompts at each time point. If participants did not respond within 30 minutes, a reminder message was sent to encourage completion. Measure Future Time Perspective (FTP; Moderator Variable) The FTP scale was also used to measure the participant's subjective view of future time (36). It consists of 10 items and is scored on a 7-point Likert scale (1: not at all to 7: very much). A higher FTP score reflects a broader future orientation, meaning that individuals perceive more opportunities, goals, and possibilities ahead rather than feeling that time is limited. Prior to the 14-day EMA period, participants completed the FTP scale once at baseline through an online survey. For analysis, FTP was calculated as each participant’s mean score across the 10 items, providing a single index of future orientation per person. This continuous score was entered as a Level-2 moderator variable in the MLM. Ecological Momentary Assessment Participants reported their emotional states four times per day (morning – 09:00, lunch – 12:00, dinner – 17:00, and night – 21:00) via an online link sent through SMS. All momentary states were assessed using a Visual Analogue Scale (VAS), an intuitive self-report instrument with low response burden (37, 38). Participants indicated the intensity of each emotion on a scale from 0 ("not at all") to 9 ("very much"). The primary outcome was momentary depression, assessed with the item, “How depressed do you feel right now?” Predictors included 13 negative affects (sadness, anger, worry, anxiety, irritation, lethargy, alienation, fear, shame, burnout, loneliness, guilt, and terror) and one positive affect (happiness), all phrased in the same format as the depression item. Data were collected in a long format with multiple observations per participant. While the outcome was analyzed using raw scores, predictors were person-mean centered to isolate within-person variance (see Data Pre-processing for details). 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. Given the nested structure of the data, analysis was conducted using 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(39, 40). 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(41). Although EMA data can be conceptualized as a three-level structure (assessments nested within days, nested within persons), we employed a two-level model for the following reasons. According to Nezlek (42), unless specific theoretical meaning is assigned to individual days (e.g., treating days as fixed effects), there is often no strong conceptual basis for organizing observations by days across participants. Since our research focused on momentary psychological fluctuations rather than day-specific effects, we treated the 14-day observations as a continuous stream of intra-individual variance. Furthermore, a two-level approach was preferred to maintain model parsimony, as simpler models often provide more stable and precise estimates by avoiding the excessive estimation of random error terms and complex covariances required in three-level models. 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 All statistical analyses were conducted using R version 4.4.1 and SPSS Statistics version 26. Demographic characteristics were analyzed using SPSS, while multilevel modeling (MLM) was performed using the lmerTest package in R(43). 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. This method involves predicting and imputing the missing values of each variable using regression models based on the observed values of other variables, and iteratively updating these predictions over multiple iterations. In the present study, predictive mean matching (PMM) was applied for continuous variables, with a single imputation (m = 1) performed. The imputation procedure was run for up to 50 iterations to ensure convergence, and a fixed random seed was set to ensure reproducibility. Prior to the main analyses, person-mean centering (PMC) was applied to the within-individual 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 centered 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-individual variation(44). Using this approach, we examined within-individual 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-individual 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-individual and between-individual factors on depression. The analysis was carried out in five 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 (45). (b) Baseline model of loneliness (Model 1): To establish a baseline, we first examined the within-individual (Level 1) relationship between momentary loneliness and depression. A random intercept model was used to estimate the fundamental association before considering other variables. (c) Unique effects of loneliness (Model 2): To verify whether loneliness has a unique influence on depression beyond other affective states, we first assessed the correlations and multicollinearity among the 14 affective variables collected via EMA. After confirming that there were no significant multicollinearity issues, we conducted a multivariable analysis (Model 2) including these variables as covariates. This step served to empirically justify loneliness as the primary predictor for subsequent interaction analysis by demonstrating its robust and independent association with depression even after controlling for a broad range of concurrent emotions. (d) Evaluation of random slope and model comparison (Model 3): Next, to assess individual differences in the loneliness-depression relationship, we compared a random intercept model (Model 1: loneliness only) with a random intercept and slope model (Model 3: loneliness only). Likelihood Ratio Test (LRT) and information criteria (AIC, BIC) were used to confirm if allowing the slope of loneliness to vary across individuals significantly improved model fit. (e) Moderating Effect of Future Time Perspective (Model 4): Finally, Model 4 was constructed by adding the interaction between loneliness (Level 1) and FTP (Level 2) to the random slope model. In this level-2 analysis, FTP was treated as a person-level predictor to examine how the loneliness-depression relationship varies as a function of individual differences in future orientation. To ensure the robustness of our findings, we included age, sex, and the person-specific mean of loneliness (Loneliness_m) as covariates in our multilevel models. Loneliness_m was calculated by averaging each participant’s momentary loneliness scores to account for stable between-individual differences. To further clarify the nature of this moderation, a simple slope analysis was conducted to evaluate the association between loneliness and depression at different levels of the moderator (e.g., − 1 SD, Mean, and + 1 SD). Additionally, the Johnson-Neyman (J-N) technique (Hayes & Rockwood, 2017) was employed to identify the specific region of significance—the precise range of FTP scores where the moderating effect on the loneliness-depression relationship is statistically significant(46). Model adequacy was additionally assessed by calculating the coefficient of determination (R²) for each model estimated in the analysis. Following the approach of Nakagawa and Schielzeth (47), both marginal R², representing the variance explained by fixed effects only, and conditional R², representing the variance explained by the combination of fixed and random effects, were reported. These indices were computed using the performance package in R. Results The sociodemographic characteristics of the participants are presented in Table 1 . Table 1 Sociodemographic characteristics of participants (N = 59) Demographics Category Number Percentage (%) Age Mean (SD) 33.66 (8.08) - Biological Sex Male 11 18.6% Female 48 81.4% Marital Status Married 32 54.2% Single 27 45.8% Educational Level High School Graduate 12 20.3% College Graduate (Bachelor's) 38 64.4% College Graduate (Master's) 7 11.9% College Graduate (Doctorate) 2 3.4% Note. Sociodemographic characteristics were self-reported at baseline. Educational level was assessed as the highest degree completed (high school, bachelor’s, master’s, or doctorate). 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-individual and between-individual levels. The result showed an ICC of 0.642, indicating that 64.2% of the variability in depression is explained by between individual differences, while 35.8% is explained by within individual variability. This shows that the between-individual differences in depression are substantial, justifying the use of multilevel modelling in the analysis. Consistent with this, the null model showed a conditional R² of .642 (marginal R² = .000), indicating that most of the variance was attributable to between-individual differences in depression rather than within-individual fluctuations. Baseline model of loneliness (Model 1) In the baseline random intercept model (Model 1), momentary loneliness was significantly associated with depression (Estimate = 0.600, p < .001; see Table 2 ). In this model, the fixed effect of loneliness accounted for 9% of the variance in depression (marginal R² = .090), with a total variance of 73.3% explained when including random effects (conditional R² = .733). These results indicate that higher levels of momentary loneliness were accompanied by higher reported levels of depression, specifically with a 0.496-unit difference in depression per unit of loneliness. Table 2 Baseline Model for the Relationship Between Momentary Loneliness and Depression (Model 1) Random effects Variance Standard Deviation (SD) Id (intercept) 3.445 1.856 Residual 1.429 1.195 Fixed effect Estimate (SE) 95% CI \(\:\beta\:\) p-value (Intercept) 2.301 (0.243) [1.826, 2.777] 0.000 < 0.001 *** Loneliness 0.600 (0.018) [0.565, 0.635] 0.302 < 0.001 *** Model 1 : Marginal R² = .090, Conditional R² = .733› Note Estimate = unstandardized regression coefficient; β = standardized regression coefficient. SE = Standard error of Estimate. CI = 95% confidence interval for B. Marginal R² represents the variance explained by fixed effects; Conditional R² represents the variance explained by both fixed and random effects. * p < .05; **p < .01; ***p < .001. Unique effects of loneliness (Model 2) 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 0.55 and all VIF values were well below the commonly accepted cut-off of 5 (Table 3 ), indicating that multicollinearity was not a concern in the current analysis. Table 3 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 associations between depression and the 14 affective variables were examined at the within-individual level (Table 4 ). After confirming that there were no significant multicollinearity issues among the variables, Model 2 was estimated by including all affective states as covariates to identify the unique contribution of loneliness. The results showed that sadness, irritation, lethargy, anxiety, burnout, loneliness, guilt, terror, and happiness were significantly associated with depression, while no such associations were found for anger, worry, fear, alienation, and shame. Specifically, loneliness maintained a significant association with depression even after accounting for the other concurrent emotions (Estimate = 0.191, p < .001). Model fit indices indicated that the fixed effects accounted for 18.8% of the variance in depression (marginal R² = .188), and the full model including random effects explained 83.3% of the variance (conditional R² = .833). These findings provide an empirical basis for a follow-up analysis focusing on the within-individual relationship between loneliness and depression. Table 4 Association between Loneliness and Depression Controlling for Other Affective States (Model 2) Random effects Variance Standard Deviation (SD) Id (intercept) 3.455 1.859 Residual 0.897 0.947 Fixed effect Estimate (SE) 95% CI \(\:\beta\:\) p-value intercept 2.301 (0.243) [1.826, 2.777] 0.000 < 0.001 *** Sadness 0.193 (0.020) [0.153, 0.232] 0.099 < 0.001 *** Anger -0.019 (0.017) [-0.053, 0.015] -0.011 0.273 Worry -0.011 (0.015) [-0.041, 0.019] -0.007 0.480 Fear -0.009 (0.019) [-0.046, 0.029] -0.005 0.654 Irritation 0.110 (0.014) [0.084, 0.137] 0.081 < 0.001 *** Lethargy 0.117 (0.012) [0.093, 0.141] 0.089 < 0.001 *** Alienation -0.010 (0.022) [-0.054, 0.033] -0.005 0.639 Anxiety 0.128 (0.018) [0.093, 0.162] 0.078 < 0.001 *** Shame 0.012 (0.022) [-0.030, 0.054] 0.005 0.577 Burnout 0.061 (0.011) [0.040, 0.082] 0.050 < 0.001 *** Loneliness 0.191 (0.019) [0.153, 0.229] 0.096 < 0.001 *** Guilt 0.173 (0.019) [0.135, 0.211] 0.084 < 0.001 *** Terror 0.119 (0.022) [0.076, 0.163] 0.053 < 0.001 *** Happiness -0.063 (0.011) [-0.084, -0.041] -0.044 < 0.001 *** Model 2 : Marginal R² = .188, Conditional R² = .833 Note Estimate = unstandardized regression coefficient; β = standardized regression coefficient. SE = Standard error of Estimate. CI = 95% confidence interval for B. Marginal R² represents the variance explained by fixed effects; Conditional R² represents the variance explained by both fixed and random effects. * p < .05; **p < .01; ***p < .001. Evaluation of random slope and model comparison(Model 1, Model 3) Next, we examined the within-individual association between loneliness and depression and explored whether there were significant between-individual differences in this relationship. Specifically, the relationship was analyzed by comparing two models: Model 1, which included only a random intercept, and Model 3, which included both a random intercept and a random slope for loneliness (Table 6 ). This step was conducted to determine whether the magnitude of the loneliness-depression association varies significantly across individuals, thereby providing a statistical basis for investigating potential moderators at the between-individual level. Momentary loneliness was significantly associated with depression in both the baseline random intercept model (Model 1; Estimate = 0.600, p < .001) and the random intercept and slope model (Model 3; Estimate = 0.496, p < .001). The parameter estimates for Model 3 are summarized in Table 5 . In Model 3, the fixed effect of loneliness accounted for 6.3% of the variance in depression (marginal R² = .063), while the total variance explained by both fixed and random effects was 75.1% (conditional R² = .751). Table 5 Random Intercept and Slope Model (Model 3) for the Association Between Loneliness and Depression Random effects Variance Standard Deviation (SD) Id (intercept) 3.448 1.857 Loneliness 0.096 0.310 Residual 1.298 1.139 Fixed effect Estimate (SE) 95% CI \(\:\beta\:\) p-value Intercept 2.301 (0.243) [1.826, 2.777] 0.000 < 0.001 *** Loneliness 0.496 (0.056) [0.386, 0.606] 0.250 < 0.001 *** Model 3 : Marginal R² = .063, Conditional R² = .751 Note Estimate = unstandardized regression coefficient; β = standardized regression coefficient. SE = Standard error of Estimate. CI = 95% confidence interval for B. Marginal R² represents the variance explained by fixed effects; Conditional R² represents the variance explained by both fixed and random effects. * p < .05; **p < .01; ***p < .001. Table 6 Compare the model fit of model 1 and model 3 AIC BIC X 2 (df) p-value Model 1 10851 10875 243.35 ( 2 ) < 0.001 *** Model 3 10612 10648 To determine whether the inclusion of a random slope significantly improved the model fit, we compared the fit indices of Model 1 and Model 3 (Table 6 ). Model 3 provided a superior fit, as evidenced by its lower AIC (10612) and BIC (10648) values compared to Model 1 (AIC = 10851, BIC = 10875). The Likelihood Ratio Test further confirmed that allowing the slope of loneliness to vary across individuals significantly enhanced the model fit (χ² = 243.35, df = 2, p < 0.001). These results indicate that while higher levels of loneliness are generally associated with higher reported levels of depression, the magnitude of this association varies substantially across individuals. Moderating Effect of Future Time Perspective (Model 4): Finally, Model 4 was analyzed to examine whether Future Time Perspective (FTP) moderates the association between loneliness and depression (Table 7 ). The results revealed a significant cross-level interaction between loneliness and FTP(Estimate = 0.102, p = .027). after adjusting for the covariates of age, sex, and between-individual levels of loneliness (Loneliness_m). This interaction suggests that a more expansive future time perspective may exacerbate the negative effect of momentary loneliness on depression. Among the covariates, the person-specific mean of loneliness (Loneliness_m) was a strong predictor of depressive symptoms (Estimate = 0.915, p < 0.001), whereas age (Estimate =-0.002, p = 0.856) and sex (Estimate = 0.274, p = 0.312) did not show significant associations with depression in this model. The final model explained 61.0% of the variance via fixed effects (marginal R² = .610) and 75.1% when including random effects (conditional R² = .751). suggesting that considering FTP as a higher-level moderator improved the explanatory power of the model. These findings indicate that the moderating role of FTP remains robust even when stable individual differences and demographic factors are rigorously controlled, thereby enhancing the explanatory power and validity of the proposed model. Table 7 Multilevel Moderation of Future Time Perspective on the Loneliness–Depression Association (Model 4) Random effects Variance Standard Deviation (SD) Id (intercept) 0.624 0.790 Loneliness 0.079 0.281 Residual 1.298 1.139 Fixed effect Estimate (SE) 95% CI \(\:\beta\:\) p-value Intercept 1.221 (0.803) [-0.352, 2.795] -0.097 0.134 Main Predictors Loneliness 0.006 (0.217) [-0.420, 0.431] 0.251 0.980 FTP -0.193 (0.104) [-0.396, 0.011] -0.089 0.069 Interaction Loneliness*FTP 0.102 (0.044) [0.015, 0.189] 0.055 0.027 * Covariates Loneliness_m 0.915 (0.063) [0.791, 1.040] 0.695 < 0.001 *** Age -0.002 (0.013) [-0.028, 0.023] -0.008 0.856 Sex(Female) 0.274 (0.269) [-0.252, 0.801] 0.119 0.312 Model 4 : Marginal R² = .610, Conditional R² = .751 Note Estimate = unstandardized regression coefficient; β = standardized regression coefficient. SE = Standard error of Estimate. CI = 95% confidence interval for B. ‘Loneliness was person-mean centered to capture within-individual variation, while ‘Loneliness_m’ represents the between-individual mean level of loneliness for each individual. Age was included as a continuous covariate. Sex was dummy-coded (0 = Male, 1 = Female). * p < .05; **p < .01; ***p < .001. As shown in Fig. 2 , at lower levels of loneliness, individuals with higher FTP reported lower depression. However, as loneliness increased, the positive association between loneliness and depression became steeper among those with higher FTP.To statistically validate these observed trends, a simple slope analysis was conducted across different levels of FTP. As presented in Table 8 , the simple slope analysis revealed that the positive association between momentary loneliness and depression was significant at all levels of FTP:low (-1 SD; Estimate = 0.39, p < .001), mean (Estimate = 0.50, p < .001), and high (+ 1 SD; Estimate = 0.61, p < .001). While all slopes were statistically significant, the magnitude of the relationship between loneliness and depression increased as the level of FTP rose. The Johnson-Neyman technique was employed to identify the exact range of FTP where the moderation effect remained significant (Fig. 3 A). The results indicated that the moderating effect of FTP was statistically significant when FTP scores were above 2.28, covering the majority of the observed range [1.00, 6.40]. The interaction plot (Fig. 3 B) provides a visual representation of the nature of this moderation. A distinct protective effect of high FTP was observed, particularly during moments of lower loneliness. Specifically, Specifically, at lower levels of momentary loneliness, individuals with high FTP (+ 1 SD) reported significantly lower levels of predicted depression compared to those with low FTP (-1 SD). This suggests that while FTP is associated with a stronger reactivity to loneliness, its primary benefit in daily life manifests as maintained emotional stability during periods of relatively low social distress. Table 8 Simple Slope Analysis for the Interaction of Momentary Loneliness and FTP on Depression Condition (FTP) Score Estimate SE p-value -1 SD (Low) 3.77 0.39 0.07 < 0.001 *** Mean 4.83 0.50 0.05 < 0.001 *** + 1 SD (High) 5.90 0.61 0.07 < 0.001 *** Note. Estimate= Unstandardized coefficient; SE = Standard Error. The standardized coefficient ( \(\:\beta\:\) ) for the interaction term (Momentary Loneliness X FTP) was 0.055, 95% CI [0.008, 0.101]. * p < .05; **p < .01; ***p < .001. Discussion This study employed Ecological Momentary Assessment (EMA) to investigate the complex interplay between various affective states and depression in daily life. By utilizing a multilevel modeling approach, we first established a baseline association between loneliness and depression (Model 1), and subsequently demonstrated that this relationship remained significant even when accounting for 13 other concurrent affective states (Model 2). These findings suggest that loneliness shares a unique association with depression, beyond the effects of other concurrent affective states. Furthermore, our analysis revealed significant individual differences in the strength of the loneliness–depression link (Model 3), which were found to be moderated by an individual’s FTP (Model 4). The findings of the multivariable analysis (Model 2) indicated that various affective clusters were concurrently associated with depression in daily life, supporting a wealth of prior research. Specifically, Sadness and guilt represent core affective clusters of depression, and their importance has been consistently emphasized in prior research ( 8 , 48 , 49 ). While sadness is often discussed in relation to depressive episodes following loss experiences ( 50 ), the present findings show that sadness repeatedly experienced in everyday contexts is also closely linked with reported depression. Particularly, within the framework of Beck’s cognitive triad, the results suggest that guilt shares a significant association with depressive symptoms, potentially reflecting a cognitive-affective profile where self-blame and negative self-schemas co-occur with depression (Kiang et al., 2017). Beyond these core affects, irritability, anxiety, and terror represent a distinct cluster of affective states characterized by heightened arousal and shared vulnerabilities in cognitive control. Prior studies have identified irritability as a core clinical feature of depression across developmental contexts and, when chronically persistent, as a salient risk factor ( 1 , 51 , 52 ). The present findings reinforce this by demonstrating that irritability experienced in everyday contexts is significantly associated with depression, underscoring its relevance at the daily level. Similarly, while anxiety has been consistently reported to show high diagnostic comorbidity with depression ( 53 , 54 ), the current results indicate that this link extends beyond clinical diagnosis to a consistent co-occurrence in daily life. Furthermore, although terror has been distinguished from anxiety in prior research by its acute and intense threat perception ( 55 ), the present study suggests that terror may not simply be a variant of anxiety but may represent a distinct affective dimension that shares a unique association with depressive distress in everyday contexts. In addition to these emotional states, lethargy and burnout show significant co-occurrence with depression, which can be understood in relation to anhedonia and chronic stress. Anhedonia is characterized by reduced motivation to pursue rewards and a diminished capacity to experience pleasure, and has been linked to dysfunction in the dopaminergic reward circuitry ( 56 ). Burnout, in turn, has been associated with dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis and inflammatory responses, which often involve fatigue and social withdrawal that overlap with the somatic features of depression ( 57 ). The present findings demonstrate that lethargy and burnout are closely intertwined with depressive symptoms, suggesting that these states overlap with core clinical features of depression such as fatigue, decreased energy, and reduced activity ( 58 , 59 ). These results underscore the importance of addressing both reward processing and stress regulation within the multifaceted profile of depression. In addition to these internal motivational and somatic states, the role of social disconnection—specifically loneliness—showed a distinct association within the multifaceted profile of depression. Loneliness can be understood within the framework of social pain theory, which posits that the brain interprets social exclusion as a primary threat to survival, processing it through neural circuits similar to those engaged in physical pain ( 60 ). These shared neural mechanisms suggest that the experience of being socially disconnected may involve psychological processes that are intertwined with depressive distress, potentially reflecting shared pathways of emotional dysregulation ( 61 ). The present study supports this theoretical account by demonstrating that loneliness maintains a significant and independent association with depression, even when diverse affective states are concurrently accounted for. In contrast to these distress-related states, happiness was negatively associated with momentary depression. While positive affect has been often conceptualized as a psychological resources, this association should be interpreted cautiously within the present EMA framework, reflecting current emotional dynamics rather than a direct buffering process. While prior research often frames an expansive FTP as a protective psychological resource, the present findings suggest that its role may be better understood as a context-sensitive cognitive framework associated with the degree of interconnectedness between loneliness and depressive symptoms. Specifically, at lower levels of momentary loneliness, individuals with a more expansive FTP co-occurred with lower reported levels of depression, which aligns with the view highlighting the adaptive value of an open-ended future under relatively stable emotional conditions ( 31 , 62 ). However, the observed interaction pattern revealed that the positive association between loneliness and depression was significantly more pronounced among individuals with a more expansive FTP, indicating heightened sensitivity to social disconnection. This pattern aligns with the hypothesis stressing differential susceptibility( 63 ), which propose that certain individual characteristics reflect heightened sensitivity to contextual influences rather than uniform protection. From this viewpoint, a more expansive FTP may confer emotional benefits in socially stable contexts while simultaneously amplifying vulnerability under conditions of social stress. Similarly, within a vulnerability stress framework ( 64 – 66 ), FTP may function as a latent cognitive vulnerability that becomes salient when individual experiences momentary loneliness, leading to transient social disconnection to broader concerns about future relational outcomes and life trajectories. In contrast, the relatively weakened link between loneliness and depression among those with a more limited FTP can be interpreted through Socioemotional Selectivity Theory. As noted by Lang and Carstensen ( 67 ), a limited time perspective is often associated with a prioritization of emotion-regulation goals and present-focused socially meaningful experience over future-oriented information seeking. This shift in motivational focus may buffer the immediate emotional impact of social disconnection, resulting in a weaker concurrent association between momentary loneliness and depressive distress. Taken together, these findings highlight the context-dependent nature of FTP, illustrating that a temporal outlook does not operate uniformly as a protective factor, but rather modulates sensitivity to social-emotional experiences in daily life. The present findings highlight the importance of affect-specific profiling in interventions for depression. Regular monitoring of how emotions such as sadness, irritability, lethargy, anxiety, fear, loneliness, guilt, and happiness fluctuate within individuals may allow for tailored, modular interventions that target those affects most closely linked with depression. For example, when irritability and heightened arousal are pronounced, attention-shifting training or stimulus control may be appropriate; when lethargy and anhedonia are central, behavioral activation and reinforcement scheduling may be effective; when guilt and self-criticism predominate, self-compassion training or cognitive restructuring may be beneficial; and when loneliness is salient, interpersonal cognitive restructuring or social skills training may be particularly useful. Beyond symptom reduction, interventions that aim to enhance positive affect are also crucial. Gratitude exercises, mindfulness-based programs, and other strategies that strengthen positive affect may help individuals cultivate meaning and satisfaction in daily life, thereby enhancing emotional resilience. Importantly, the present study emphasizes that interventions targeting FTP should not assume that expansion is invariably protective. Thus, interventions addressing FTP should emphasize flexibility rather than a uniform pursuit of expansion, helping individuals adaptively shift between future-, present-, and past-oriented perspectives depending on their situational and affective contexts. This study has several limitations. First, it was conducted with a relatively small sample drawn from Korea, which limits the generalizability of the findings. Because affective experiences and time perspectives are strongly shaped by cultural values and social structures, future research should employ large-scale longitudinal designs across diverse cultures and age groups to examine both the universality and cultural specificity of these associations. Second, although the EMA method captured affective variability in real-time, the current study relied on concurrent (within-moment) associations rather than lagged analyses. This decision was primarily based on our research objective to capture the immediate phenomenological experience and the 'co-occurring' affective profile of depression as it manifests in daily life. Given that the interplay between social perception (e.g., loneliness) and affective distress often manifests instantaneously, a concurrent model was deemed appropriate for identifying these multifaceted associations. However, this approach is limited in establishing causal precedence and addressing the issue of directionality. As noted in prior literature, the association between loneliness and depression may be reciprocal ( 68 , 69 ). It is plausible that individuals with higher levels of momentary depression may experience or report their affective states differently than those with lower levels, rather than these affective states solely functioning as precursors to depression. Consequently, it is not possible from the present analyses to determine which state occurred first or caused the other. Future studies should employ lagged modeling—such as dynamic structural equation modeling (DSEM) or multilevel vector autoregression (VAR)—to more precisely determine the temporal ordering and potential bidirectional influences between these variables. Third, although this study controlled for demographic variables such as sex and age, as well as individual mean levels of loneliness at the between-person level, it did not sufficiently incorporate other key individual difference variables. Beyond basic factors like neuroticism and perceived social support, variables such as perceived stress, coping styles, and rumination tendencies were not accounted for in the current models. Given that these factors often function as significant moderators or mediators in the complex associations between FTP, loneliness, and depressive symptoms, their absence may limit a more granular understanding of the results. Future research should therefore integrate these dispositional characteristics to provide a more comprehensive perspective on the multifaceted nature of depression. Finally, the analyses relied exclusively on self-reported affective data without incorporating behavioral or physiological indicators. However, the developmental course of depression is closely related not only to subjective affective experiences but also to behavioral avoidance, physiological arousal, and neurobiological processes. Future studies should thus adopt multimodal assessment frameworks that integrate affective, behavioral, and physiological dimensions to enhance the ecological validity of research on depression. Conclusion In conclusion, this study used EMA and multilevel modeling to investigate the affective dynamics associated with depression and the moderating role of FTP.The findings suggest that depression is not the associated with a single affective state but rather is characterized by a complex profile of multiple affects, including both negative affects such as sadness, irritability, lethargy, anxiety, fear, loneliness, and guilt, and the protective influence of positive affect such as happiness. Moreover, FTP appeared to moderate the within-individual association between loneliness and depression, such that an expansive FTP was linked with stronger associations, whereas a more limited FTP was associated with a weakened link. These results are broadly consistent with socioemotional selectivity theory, while also contributing new evidence that FTP may act as either a protective or vulnerability factor depending on contextual conditions. Together, the study highlights the importance of considering both affective profiles and subjective time perspectives in understanding the multifaceted nature of depression. Although further research is needed to address the cultural, methodological, and measurement limitations of the present design, the findings underscore the potential of tailored, affect-sensitive, and time-perspective–informed interventions to enhance clinical practice. List of abbreviations EMA Ecological Momentary Assessment VAS Visual Analogue Scale MLM Multilevel Modelling FTP Future Time Perspective 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 DSEM Dynamic Structural Equation Modelling HPA Hypothalamic Pituitary Adrenal NA Negative Affect PA Positive Affect 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 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) and National Research Foundation of Korea (NRF-2022R1F1A1074783). Author Contribution JL conceived and designed the study, acquired the data, drafted the manuscript, and revised it critically for important intellectual content. YL analyzed and interpreted the data and contributed to drafting the manuscript. All authors read and approved the final manuscript. Acknowledgements This article is based on the first author's Master's thesis. Data Availability The datasets generated and/or analyzed during the current study are included in the supplementary information files. References APA. 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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-9317791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625162839,"identity":"1e0df17d-13e4-4cfb-a7a7-5952be69ceed","order_by":0,"name":"Yu-Rim Lee","email":"","orcid":"","institution":"Kangwon National University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Rim","middleName":"","lastName":"Lee","suffix":""},{"id":625162841,"identity":"393cb2e4-ec31-42c9-ac4a-b024feb60daa","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":"2026-04-04 05:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9317791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9317791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107355997,"identity":"59d83cf1-b2fe-4575-96d2-6f42f95d0158","added_by":"auto","created_at":"2026-04-20 16:55:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation patterns of affective variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. All coefficients represent Spearman’s ρ correlations computed using pairwise deletion. All correlations were statistically significant at p \u0026lt; .001. The color scale ranges from –1 (blue, negative correlation) to +1 (red, positive correlation).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9317791/v1/518899a31a7e04b2479164f6.png"},{"id":107355951,"identity":"19907b55-47db-4379-8593-6c7f18d45bbe","added_by":"auto","created_at":"2026-04-20 16:55:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe moderating effect of FTP on the relationship between loneliness and depression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. The y-axis represents predicted depression scores, and the x-axis represents momentary loneliness (person-mean centered). Lines indicate predicted values at low (–1 SD), mean, and high (+1 SD) levels of FTP. All other covariates (age, sex, and between-individual loneliness) were held constant at their mean levels or reference category for the predictions. Negative values on the y-axis reflect model-based estimates derived from the centering process and do not represent observed raw scores.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9317791/v1/8439be32c8a13fd0ffe71bfb.png"},{"id":107355953,"identity":"92f4fd8f-9db6-43e7-8b8c-67eed367f99f","added_by":"auto","created_at":"2026-04-20 16:55:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJohnson-Neyman and interaction plots for the moderator Future Time Perspective (FTP) on the relationship between momentary loneliness and depression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003e(A) Johnson-Neyman plot: Identifies the region of significance for the moderator (FTP). The moderation effect is statistically significant when FTP scores are above 2.28 (at α= .05), covering the majority of the observed range [1.00, 6.40]. (B) Interaction plot: Illustrates the relationship between momentary loneliness and predicted depression at different FTP levels (±1 SD). As loneliness decreases (moving to the left on the x-axis), individuals with high FTP (+1 SD) exhibit a pronounced protective effect, reporting significantly lower levels of depression compared to those with low FTP (-1 SD).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9317791/v1/10cd36ab6ebb3f296bf5d0fb.png"},{"id":107356019,"identity":"7ac1d959-d7ee-46a5-b9ed-86036a67d7a4","added_by":"auto","created_at":"2026-04-20 16:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":984152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9317791/v1/09400bfe-f63f-4d26-8406-44eb74973568.pdf"},{"id":107355954,"identity":"ebf30fd9-9992-4f33-a75b-3e3b203b7be2","added_by":"auto","created_at":"2026-04-20 16:55:42","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":601050,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.csv","url":"https://assets-eu.researchsquare.com/files/rs-9317791/v1/a0f124929bb62e4dbe2b52f1.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Contextual Role of Future Time Perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is one of the most common psychiatric disorders worldwide, characterized by persistent sadness, emptiness, hopelessness, and a marked loss of interest or pleasure in daily activities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These affective symptoms co-occur with somatic and cognitive problems such as insomnia, weight changes, and cognitive impairment, and are closely associated with suicidal ideation and attempts (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Moreover, beyond the individual dimension, depression causes a considerable socioeconomic burden, including decreased productivity and increased social care costs (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Tripartite Model proposed by Clark and Watson (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) provides a foundational theoretical framework for understanding the affective architecture of depression. This model posits that the core of depression is characterized by a specific combination of low positive affect (PA)\u0026mdash;manifesting as anhedonia and lack of vitality\u0026mdash;and high negative affect (NA), which reflects generalized distress. While this framework has been instrumental in distinguishing depression from anxiety (which is characterized primarily by high NA), focusing solely on broad affective dimensions may obscure the specific, qualitative nature of the depressive experience (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, to attain a more granular understanding of depressive pathology, it is essential to identify the unique contributions of discrete emotions that exist beneath these broad affective categories.\u003c/p\u003e \u003cp\u003eWithin this broad NA dimension, depression manifests through a multifaceted affective profile that contributes to its clinical heterogeneity. First, sadness and guilt represent the core internalizing affects of depression. Sadness serves as the most representative clinical indicator for assessing depressive severity (Mouchet-Mages \u0026amp; Bayl\u0026eacute;, 2008), while guilt reflects a cognitive-affective profile where negative self-schemas co-occur with emotional distress(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Second, the affective landscape of depression is defined not only by the presence of NA but also by the deficit of PA. Happiness, as a primary experience of PA, does not merely reflect the absence of anhedonia but functions as a critical protective factor associated with emotional resilience (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Furthermore, depression frequently encompasses externalizing and high-arousal states that signify physiological and psychological dysregulation. Anger and irritability are representative affects related to hostility; when maladaptively internalized or expressed, these states are linked to heightened psychosocial dysfunction and impaired impulse control (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, threat-related affects such as anxiety, worry, fear, and terror represent a distinct cluster characterized by high arousal. While anxiety and worry show pervasive comorbidity with depression (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), states of fear and terror\u0026mdash;particularly when linked to trauma or perceived insecurity\u0026mdash;are associated with the persistence and intensification of depressive distress (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, characterizing depression solely through intrapersonal affective dimensions (i.e., PA and NA) is insufficient. Depression is not merely an internal disorder of mood regulation; it is inextricably linked to the social context, often manifesting through symptoms of social withdrawal, rejection sensitivity, and a perceived breakdown of interpersonal bonds (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Consequently, to fully capture the phenomenology of depression, it is necessary to extend the Tripartite framework by examining specific interpersonal affects that may function distinctly from generalized distress. Among these interpersonal constructs, loneliness represents a critical dimension. Loneliness is defined not only as social isolation, but also as a discrepancy between desired and actual social relationships (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and is closely related to the perception of being socially unacceptable to others(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The potent association between loneliness and depression is fundamentally rooted in the evolutionary theory of loneliness, which posits that social disconnection functions as a \u0026ldquo;social signal\u0026rdquo; of threat to survival\u0026mdash;analogous to physical pain (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This perceived isolation triggers a state of hypervigilance and negative cognitive biases, which in turn co-occur with core depressive symptoms such as sadness and low self-worth (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). While these theoretical links are robust, empirical investigations have focused disproportionately on older adult populations(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, contemporary research underscores that loneliness has emerged as a pervasive public health issue\u0026mdash;a 'modern epidemic'\u0026mdash;affecting individuals across the entire lifespan, with alarmingly high prevalence observed among young and middle-aged adults(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Given that the experience of social disconnection is a fundamental human distress that transcends specific life stages, there is a critical need to clarify the universal mechanisms through which loneliness associates with depression in more diverse demographics(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Furthermore, to determine the unique contribution of loneliness, it must be examined within a comprehensive affective profile that accounts for other co-occurring negative emotions\u0026mdash;an area that remains insufficiently explored in real-time daily life research.\u003c/p\u003e \u003cp\u003eThe psychological impact of loneliness is not uniform across all individuals. The extent to which social disconnection translates into depressive distress is likely filtered through stable cognitive frameworks that dictate an individual\u0026rsquo;s social motivations and goals. The present study focuses on Future Time Perspective (FTP) as a key moderator of this relationship. Originating from Socioemotional Selectivity Theory (SST), FTP refers to the subjective perception of time remaining in life, ranging on a continuum from expansive to limited (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This temporal horizon is not merely a situational appraisal but a stable lens that shapes goal prioritization (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Individuals with an expansive FTP typically prioritize knowledge acquisition and long-term network building. In contrast, those with a limited FTP prioritize present-oriented goals, such as emotional meaningfulness and the optimization of intimate social bonds (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). FTP is particularly relevant to the context of loneliness because SST is fundamentally a theory of social motivation. While previous research has examined FTP and affective well-being in isolation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), the interactive dynamics between FTP and loneliness remain under-explored (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We propose that FTP acts as a moderator because it determines the \"subjective value\" assigned to social experiences. For individuals with an expansive FTP, who view the future as open-ended, loneliness may be perceived as a significant impediment to their long-term social trajectory, potentially intensifying the association between social disconnection and depressive distress. Conversely, a limited FTP, which focuses resources on immediate emotional regulation, may alter sensitivity to social deficits. By conceptualizing FTP as a pre-existing cognitive framework, this study seeks to clarify why the same level of loneliness results in varying degrees of depressive symptoms. Moreover, most prior studies on these variables have relied on cross-sectional designs or retrospective self-reports, which are vulnerable to recall bias and fail to capture the dynamic nature of emotional experiences in natural environments (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). To address these theoretical gaps and methodological limitations, the present study employs Ecological Momentary Assessment (EMA). This methodology allows for the real-time capture of the co-occurrence between loneliness, depression, and other affective states in daily life, providing high ecological validity.\u003c/p\u003e \u003cp\u003eThe primary aims of this study are threefold: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to identify the specific affective profile associated with depression in daily life, disentangling the unique contribution of loneliness from other concurrent affects; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to examine the robust association between loneliness and depression within everyday contexts; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to test whether FTP moderates the link between loneliness and depression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eParticipants were recruited through social media, online communities, and other related platforms from August 6 to August 25, 2021, targeting adults aged 19 to 50 years. Eligibility for participation was restricted to individuals within this age range, and no additional inclusion or exclusion criteria were applied. Before participation, individuals were provided with detailed information about the study\u0026rsquo;s purpose, procedures, confidentiality, and their rights as participants. Given that the study was conducted entirely online, informed consent was obtained electronically. Participants were presented with an online consent form and asked to indicate their agreement by selecting either \u0026ldquo;I agree to participate\u0026rdquo; or \u0026ldquo;I do not agree to participate.\u0026rdquo; Only those who actively selected \u0026ldquo;I agree to participate\u0026rdquo; were able to proceed with the survey. A total of 64 participants provided informed consent and completed the study. 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 sex, age, education level and marital status. This study was approved by the Institutional Review Board of Kangwon National University (KWNUIRB-2019-12-006-003).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants were first provided with information about the study and provided electronic informed consent prior to participation. On the morning of the first day, they completed demographic questions and the FTP questionnaire via an online survey link. Beginning on the same day, participants engaged in EMA for 14 consecutive days. Each day, they received survey links via short message service (SMS) four times (09:00, 12:00, 17:00, and 21:00) and were asked to respond to the EMA prompts at each time point. If participants did not respond within 30 minutes, a reminder message was sent to encourage completion.\u003c/p\u003e\n\u003ch3\u003eMeasure\u003c/h3\u003e\n\u003cdiv\u003e\n\u003ch2\u003eFuture Time Perspective (FTP; Moderator Variable)\u003c/h2\u003e\n\u003cp\u003eThe FTP scale was also used to measure the participant's subjective view of future time (36). It consists of 10 items and is scored on a 7-point Likert scale (1: not at all to 7: very much). A higher FTP score reflects a broader future orientation, meaning that individuals perceive more opportunities, goals, and possibilities ahead rather than feeling that time is limited. Prior to the 14-day EMA period, participants completed the FTP scale once at baseline through an online survey. For analysis, FTP was calculated as each participant\u0026rsquo;s mean score across the 10 items, providing a single index of future orientation per person. This continuous score was entered as a Level-2 moderator variable in the MLM.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEcological Momentary Assessment\u003c/h3\u003e\n\u003cp\u003eParticipants reported their emotional states four times per day (morning \u0026ndash; 09:00, lunch \u0026ndash; 12:00, dinner \u0026ndash; 17:00, and night \u0026ndash; 21:00) via an online link sent through SMS. All momentary states were assessed using a Visual Analogue Scale (VAS), an intuitive self-report instrument with low response burden (37, 38). Participants indicated the intensity of each emotion on a scale from 0 (\"not at all\") to 9 (\"very much\").\u003c/p\u003e\n\u003cp\u003eThe primary outcome was momentary depression, assessed with the item, \u0026ldquo;How depressed do you feel right now?\u0026rdquo; Predictors included 13 negative affects (sadness, anger, worry, anxiety, irritation, lethargy, alienation, fear, shame, burnout, loneliness, guilt, and terror) and one positive affect (happiness), all phrased in the same format as the depression item.\u003c/p\u003e\n\u003cp\u003eData were collected in a long format with multiple observations per participant. While the outcome was analyzed using raw scores, predictors were person-mean centered to isolate within-person variance (see Data Pre-processing for details).\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eThe study was based on a total of 3304 data points, including 56 assessments at level 1 and 59 participants at level 2. Given the nested structure of the data, analysis was conducted using 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(39, 40). 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(41).\u003c/p\u003e\n\u003cp\u003eAlthough EMA data can be conceptualized as a three-level structure (assessments nested within days, nested within persons), we employed a two-level model for the following reasons. According to Nezlek (42), unless specific theoretical meaning is assigned to individual days (e.g., treating days as fixed effects), there is often no strong conceptual basis for organizing observations by days across participants. Since our research focused on momentary psychological fluctuations rather than day-specific effects, we treated the 14-day observations as a continuous stream of intra-individual variance. Furthermore, a two-level approach was preferred to maintain model parsimony, as simpler models often provide more stable and precise estimates by avoiding the excessive estimation of random error terms and complex covariances required in three-level models.\u003c/p\u003e\n\u003cp\u003eIn 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\n\u003c/div\u003e\n\u003ch3\u003eData pre-processing\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using R version 4.4.1 and SPSS Statistics version 26. Demographic characteristics were analyzed using SPSS, while multilevel modeling (MLM) was performed using the lmerTest package in R(43). 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. This method involves predicting and imputing the missing values of each variable using regression models based on the observed values of other variables, and iteratively updating these predictions over multiple iterations. In the present study, predictive mean matching (PMM) was applied for continuous variables, with a single imputation (m\u0026thinsp;=\u0026thinsp;1) performed. The imputation procedure was run for up to 50 iterations to ensure convergence, and a fixed random seed was set to ensure reproducibility.\u003c/p\u003e\n\u003cp\u003ePrior to the main analyses, person-mean centering (PMC) was applied to the within-individual 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 centered 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-individual variation(44). Using this approach, we examined within-individual 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-individual 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-individual and between-individual factors on depression. The analysis was carried out in five steps as follows\u003c/p\u003e\n\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 (45).\u003c/p\u003e\n\u003cp\u003e(b) Baseline model of loneliness (Model 1): To establish a baseline, we first examined the within-individual (Level 1) relationship between momentary loneliness and depression. A random intercept model was used to estimate the fundamental association before considering other variables.\u003c/p\u003e\n\u003cp\u003e(c) Unique effects of loneliness (Model 2): To verify whether loneliness has a unique influence on depression beyond other affective states, we first assessed the correlations and multicollinearity among the 14 affective variables collected via EMA. After confirming that there were no significant multicollinearity issues, we conducted a multivariable analysis (Model 2) including these variables as covariates. This step served to empirically justify loneliness as the primary predictor for subsequent interaction analysis by demonstrating its robust and independent association with depression even after controlling for a broad range of concurrent emotions.\u003c/p\u003e\n\u003cp\u003e(d) Evaluation of random slope and model comparison (Model 3): Next, to assess individual differences in the loneliness-depression relationship, we compared a random intercept model (Model 1: loneliness only) with a random intercept and slope model (Model 3: loneliness only). Likelihood Ratio Test (LRT) and information criteria (AIC, BIC) were used to confirm if allowing the slope of loneliness to vary across individuals significantly improved model fit.\u003c/p\u003e\n\u003cp\u003e(e) Moderating Effect of Future Time Perspective (Model 4): Finally, Model 4 was constructed by adding the interaction between loneliness (Level 1) and FTP (Level 2) to the random slope model. In this level-2 analysis, FTP was treated as a person-level predictor to examine how the loneliness-depression relationship varies as a function of individual differences in future orientation. To ensure the robustness of our findings, we included age, sex, and the person-specific mean of loneliness (Loneliness_m) as covariates in our multilevel models. Loneliness_m was calculated by averaging each participant\u0026rsquo;s momentary loneliness scores to account for stable between-individual differences. To further clarify the nature of this moderation, a simple slope analysis was conducted to evaluate the association between loneliness and depression at different levels of the moderator (e.g., \u0026minus;\u0026thinsp;1 SD, Mean, and +\u0026thinsp;1 SD). Additionally, the Johnson-Neyman (J-N) technique (Hayes \u0026amp; Rockwood, 2017) was employed to identify the specific region of significance\u0026mdash;the precise range of FTP scores where the moderating effect on the loneliness-depression relationship is statistically significant(46).\u003c/p\u003e\n\u003cp\u003eModel adequacy was additionally assessed by calculating the coefficient of determination (R\u0026sup2;) for each model estimated in the analysis. Following the approach of Nakagawa and Schielzeth (47), both marginal R\u0026sup2;, representing the variance explained by fixed effects only, and conditional R\u0026sup2;, representing the variance explained by the combination of fixed and random effects, were reported. These indices were computed using the performance package in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe sociodemographic characteristics of the participants are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e33.66 (8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiological Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHigh School Graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCollege Graduate (Bachelor\u0026apos;s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCollege Graduate (Master\u0026apos;s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCollege Graduate (Doctorate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. Sociodemographic characteristics were self-reported at baseline. Educational level was assessed as the highest degree completed (high school, bachelor\u0026rsquo;s, master\u0026rsquo;s, or doctorate).\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eNull model testing (Model 0)\u003c/h2\u003e\n \u003cp\u003eFirst, the ICC for depression collected via EMA was calculated to assess whether there was sufficient variability at both the within-individual and between-individual levels. The result showed an ICC of 0.642, indicating that 64.2% of the variability in depression is explained by between individual differences, while 35.8% is explained by within individual variability. This shows that the between-individual differences in depression are substantial, justifying the use of multilevel modelling in the analysis. Consistent with this, the null model showed a conditional R\u0026sup2; of .642 (marginal R\u0026sup2; = .000), indicating that most of the variance was attributable to between-individual differences in depression rather than within-individual fluctuations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline model of loneliness (Model 1)\u003c/h2\u003e\n \u003cp\u003eIn the baseline random intercept model (Model 1), momentary loneliness was significantly associated with depression (Estimate\u0026thinsp;=\u0026thinsp;0.600, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In this model, the fixed effect of loneliness accounted for 9% of the variance in depression (marginal R\u0026sup2; = .090), with a total variance of 73.3% explained when including random effects (conditional R\u0026sup2; = .733). These results indicate that higher levels of momentary loneliness were accompanied by higher reported levels of depression, specifically with a 0.496-unit difference in depression per unit of loneliness.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Model for the Relationship Between Momentary Loneliness and Depression (Model 1)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eId (intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEstimate (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.301 (0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.826, 2.777]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.600 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.565, 0.635]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e: Marginal R\u0026sup2; = .090, Conditional R\u0026sup2; = .733\u0026rsaquo;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote\u0026nbsp;Estimate\u0026thinsp;=\u0026thinsp;unstandardized regression coefficient; \u0026beta;\u0026thinsp;=\u0026thinsp;standardized regression coefficient. SE\u0026thinsp;=\u0026thinsp;Standard error of Estimate. CI\u0026thinsp;=\u0026thinsp;95% confidence interval for B. Marginal R\u0026sup2; represents the variance explained by fixed effects; Conditional R\u0026sup2; represents the variance explained by both fixed and random effects. * p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eUnique effects of loneliness (Model 2)\u003c/h2\u003e\n \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. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), all bivariate correlations were less than or equal to 0.55 and all VIF values were well below the commonly accepted cut-off of 5 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that multicollinearity was not a concern in the current analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariance inflation factor of affective variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"14\" align=\"left\"\u003e\n \u003cp\u003eVariance inflation factor (VIF)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cp\u003eNote. 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/p\u003e\n \u003cp\u003eThe associations between depression and the 14 affective variables were examined at the within-individual level (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). After confirming that there were no significant multicollinearity issues among the variables, Model 2 was estimated by including all affective states as covariates to identify the unique contribution of loneliness. The results showed that sadness, irritation, lethargy, anxiety, burnout, loneliness, guilt, terror, and happiness were significantly associated with depression, while no such associations were found for anger, worry, fear, alienation, and shame. Specifically, loneliness maintained a significant association with depression even after accounting for the other concurrent emotions (Estimate\u0026thinsp;=\u0026thinsp;0.191, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Model fit indices indicated that the fixed effects accounted for 18.8% of the variance in depression (marginal R\u0026sup2; = .188), and the full model including random effects explained 83.3% of the variance (conditional R\u0026sup2; = .833). These findings provide an empirical basis for a follow-up analysis focusing on the within-individual relationship between loneliness and depression.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between Loneliness and Depression Controlling for Other Affective States (Model 2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eId (intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEstimate (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.301 (0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.826, 2.777]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.193 (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.153, 0.232]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.019 (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.053, 0.015]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.011 (0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.041, 0.019]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.009 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.046, 0.029]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrritation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.110 (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.084, 0.137]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLethargy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.117 (0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.093, 0.141]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlienation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.010 (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.054, 0.033]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.128 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.093, 0.162]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.012 (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.030, 0.054]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.061 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.040, 0.082]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.191 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.153, 0.229]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuilt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.173 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.135, 0.211]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerror\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.119 (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.076, 0.163]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.063 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.084, -0.041]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e: Marginal R\u0026sup2; = .188, Conditional R\u0026sup2; = .833\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote\u0026nbsp;Estimate\u0026thinsp;=\u0026thinsp;unstandardized regression coefficient; \u0026beta;\u0026thinsp;=\u0026thinsp;standardized regression coefficient. SE\u0026thinsp;=\u0026thinsp;Standard error of Estimate. CI\u0026thinsp;=\u0026thinsp;95% confidence interval for B. Marginal R\u0026sup2; represents the variance explained by fixed effects; Conditional R\u0026sup2; represents the variance explained by both fixed and random effects. * p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eEvaluation of random slope and model comparison(Model 1, Model 3)\u003c/h2\u003e\n \u003cp\u003eNext, we examined the within-individual association between loneliness and depression and explored whether there were significant between-individual differences in this relationship. Specifically, the relationship was analyzed by comparing two models: Model 1, which included only a random intercept, and Model 3, which included both a random intercept and a random slope for loneliness (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This step was conducted to determine whether the magnitude of the loneliness-depression association varies significantly across individuals, thereby providing a statistical basis for investigating potential moderators at the between-individual level.\u003c/p\u003e\n \u003cp\u003eMomentary loneliness was significantly associated with depression in both the baseline random intercept model (Model 1; Estimate\u0026thinsp;=\u0026thinsp;0.600, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and the random intercept and slope model (Model 3; Estimate\u0026thinsp;=\u0026thinsp;0.496, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The parameter estimates for Model 3 are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. In Model 3, the fixed effect of loneliness accounted for 6.3% of the variance in depression (marginal R\u0026sup2; = .063), while the total variance explained by both fixed and random effects was 75.1% (conditional R\u0026sup2; = .751).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRandom Intercept and Slope Model (Model 3) for the Association Between Loneliness and Depression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eId (intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEstimate (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.301 (0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.826, 2.777]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.496 (0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.386, 0.606]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e: Marginal R\u0026sup2; = .063, Conditional R\u0026sup2; = .751\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote\u0026nbsp;Estimate\u0026thinsp;=\u0026thinsp;unstandardized regression coefficient; \u0026beta;\u0026thinsp;=\u0026thinsp;standardized regression coefficient. SE\u0026thinsp;=\u0026thinsp;Standard error of Estimate. CI\u0026thinsp;=\u0026thinsp;95% confidence interval for B. Marginal R\u0026sup2; represents the variance explained by fixed effects; Conditional R\u0026sup2; represents the variance explained by both fixed and random effects. * p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCompare the model fit of model 1 and model 3\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e243.35 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo determine whether the inclusion of a random slope significantly improved the model fit, we compared the fit indices of Model 1 and Model 3 (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Model 3 provided a superior fit, as evidenced by its lower AIC (10612) and BIC (10648) values compared to Model 1 (AIC\u0026thinsp;=\u0026thinsp;10851, BIC\u0026thinsp;=\u0026thinsp;10875). The Likelihood Ratio Test further confirmed that allowing the slope of loneliness to vary across individuals significantly enhanced the model fit (\u0026chi;\u0026sup2; = 243.35, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate that while higher levels of loneliness are generally associated with higher reported levels of depression, the magnitude of this association varies substantially across individuals.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eModerating Effect of Future Time Perspective (Model 4):\u003c/h2\u003e\n \u003cp\u003eFinally, Model 4 was analyzed to examine whether Future Time Perspective (FTP) moderates the association between loneliness and depression (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The results revealed a significant cross-level interaction between loneliness and FTP(Estimate\u0026thinsp;=\u0026thinsp;0.102, p = .027). after adjusting for the covariates of age, sex, and between-individual levels of loneliness (Loneliness_m). This interaction suggests that a more expansive future time perspective may exacerbate the negative effect of momentary loneliness on depression. Among the covariates, the person-specific mean of loneliness (Loneliness_m) was a strong predictor of depressive symptoms (Estimate\u0026thinsp;=\u0026thinsp;0.915, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas age (Estimate =-0.002, p\u0026thinsp;=\u0026thinsp;0.856) and sex (Estimate\u0026thinsp;=\u0026thinsp;0.274, p\u0026thinsp;=\u0026thinsp;0.312) did not show significant associations with depression in this model. The final model explained 61.0% of the variance via fixed effects (marginal R\u0026sup2; = .610) and 75.1% when including random effects (conditional R\u0026sup2; = .751). suggesting that considering FTP as a higher-level moderator improved the explanatory power of the model. These findings indicate that the moderating role of FTP remains robust even when stable individual differences and demographic factors are rigorously controlled, thereby enhancing the explanatory power and validity of the proposed model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultilevel Moderation of Future Time Perspective on the Loneliness\u0026ndash;Depression Association (Model 4)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eId (intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEstimate (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.221 (0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[-0.352, 2.795]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Predictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.006 (0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[-0.420, 0.431]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.193 (0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[-0.396, 0.011]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness*FTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.102 (0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[0.015, 0.189]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.915 (0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[0.791, 1.040]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.002 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[-0.028, 0.023]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex(Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.274 (0.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[-0.252, 0.801]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e: Marginal R\u0026sup2; = .610, Conditional R\u0026sup2; = .751\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote\u0026nbsp;Estimate\u0026thinsp;=\u0026thinsp;unstandardized regression coefficient; \u0026beta;\u0026thinsp;=\u0026thinsp;standardized regression coefficient. SE\u0026thinsp;=\u0026thinsp;Standard error of Estimate. CI\u0026thinsp;=\u0026thinsp;95% confidence interval for B. \u0026lsquo;Loneliness was person-mean centered to capture within-individual variation, while \u0026lsquo;Loneliness_m\u0026rsquo; represents the between-individual mean level of loneliness for each individual. Age was included as a continuous covariate. Sex was dummy-coded (0\u0026thinsp;=\u0026thinsp;Male, 1\u0026thinsp;=\u0026thinsp;Female). * p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, at lower levels of loneliness, individuals with higher FTP reported lower depression. However, as loneliness increased, the positive association between loneliness and depression became steeper among those with higher \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP.To\u003c/span\u003e\u003c/span\u003e statistically validate these observed trends, a simple slope analysis was conducted across different levels of FTP.\u003c/p\u003e\n \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the simple slope analysis revealed that the positive association between momentary loneliness and depression was significant at all levels of \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP:low\u003c/span\u003e\u003c/span\u003e (-1 SD; Estimate\u0026thinsp;=\u0026thinsp;0.39, p \u0026lt; .001), mean (Estimate\u0026thinsp;=\u0026thinsp;0.50, p \u0026lt; .001), and high (+\u0026thinsp;1 SD; Estimate\u0026thinsp;=\u0026thinsp;0.61, p \u0026lt; .001). While all slopes were statistically significant, the magnitude of the relationship between loneliness and depression increased as the level of FTP rose.\u003c/p\u003e\n \u003cp\u003eThe Johnson-Neyman technique was employed to identify the exact range of FTP where the moderation effect remained significant (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). The results indicated that the moderating effect of FTP was statistically significant when FTP scores were above 2.28, covering the majority of the observed range [1.00, 6.40].\u003c/p\u003e\n \u003cp\u003eThe interaction plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) provides a visual representation of the nature of this moderation. A distinct protective effect of high FTP was observed, particularly during moments of lower loneliness. Specifically, Specifically, at lower levels of momentary loneliness, individuals with high FTP (+\u0026thinsp;1 SD) reported significantly lower levels of predicted depression compared to those with low FTP (-1 SD). This suggests that while FTP is associated with a stronger reactivity to loneliness, its primary benefit in daily life manifests as maintained emotional stability during periods of relatively low social distress.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSimple Slope Analysis for the Interaction of Momentary Loneliness and FTP on Depression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCondition (FTP)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1 SD (Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1 SD (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. Estimate= Unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;Standard Error. The standardized coefficient (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e) for the interaction term (Momentary Loneliness X FTP) was 0.055, 95% CI [0.008, 0.101]. * p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed Ecological Momentary Assessment (EMA) to investigate the complex interplay between various affective states and depression in daily life. By utilizing a multilevel modeling approach, we first established a baseline association between loneliness and depression (Model 1), and subsequently demonstrated that this relationship remained significant even when accounting for 13 other concurrent affective states (Model 2). These findings suggest that loneliness shares a unique association with depression, beyond the effects of other concurrent affective states. Furthermore, our analysis revealed significant individual differences in the strength of the loneliness\u0026ndash;depression link (Model 3), which were found to be moderated by an individual\u0026rsquo;s FTP (Model 4).\u003c/p\u003e \u003cp\u003eThe findings of the multivariable analysis (Model 2) indicated that various affective clusters were concurrently associated with depression in daily life, supporting a wealth of prior research. Specifically, Sadness and guilt represent core affective clusters of depression, and their importance has been consistently emphasized in prior research (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). While sadness is often discussed in relation to depressive episodes following loss experiences (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), the present findings show that sadness repeatedly experienced in everyday contexts is also closely linked with reported depression. Particularly, within the framework of Beck\u0026rsquo;s cognitive triad, the results suggest that guilt shares a significant association with depressive symptoms, potentially reflecting a cognitive-affective profile where self-blame and negative self-schemas co-occur with depression (Kiang et al., 2017). Beyond these core affects, irritability, anxiety, and terror represent a distinct cluster of affective states characterized by heightened arousal and shared vulnerabilities in cognitive control. Prior studies have identified irritability as a core clinical feature of depression across developmental contexts and, when chronically persistent, as a salient risk factor (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The present findings reinforce this by demonstrating that irritability experienced in everyday contexts is significantly associated with depression, underscoring its relevance at the daily level. Similarly, while anxiety has been consistently reported to show high diagnostic comorbidity with depression (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), the current results indicate that this link extends beyond clinical diagnosis to a consistent co-occurrence in daily life. Furthermore, although terror has been distinguished from anxiety in prior research by its acute and intense threat perception (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), the present study suggests that terror may not simply be a variant of anxiety but may represent a distinct affective dimension that shares a unique association with depressive distress in everyday contexts. In addition to these emotional states, lethargy and burnout show significant co-occurrence with depression, which can be understood in relation to anhedonia and chronic stress. Anhedonia is characterized by reduced motivation to pursue rewards and a diminished capacity to experience pleasure, and has been linked to dysfunction in the dopaminergic reward circuitry (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Burnout, in turn, has been associated with dysregulation of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis and inflammatory responses, which often involve fatigue and social withdrawal that overlap with the somatic features of depression (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The present findings demonstrate that lethargy and burnout are closely intertwined with depressive symptoms, suggesting that these states overlap with core clinical features of depression such as fatigue, decreased energy, and reduced activity (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). These results underscore the importance of addressing both reward processing and stress regulation within the multifaceted profile of depression. In addition to these internal motivational and somatic states, the role of social disconnection\u0026mdash;specifically loneliness\u0026mdash;showed a distinct association within the multifaceted profile of depression. Loneliness can be understood within the framework of social pain theory, which posits that the brain interprets social exclusion as a primary threat to survival, processing it through neural circuits similar to those engaged in physical pain (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). These shared neural mechanisms suggest that the experience of being socially disconnected may involve psychological processes that are intertwined with depressive distress, potentially reflecting shared pathways of emotional dysregulation (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The present study supports this theoretical account by demonstrating that loneliness maintains a significant and independent association with depression, even when diverse affective states are concurrently accounted for. In contrast to these distress-related states, happiness was negatively associated with momentary depression. While positive affect has been often conceptualized as a psychological resources, this association should be interpreted cautiously within the present EMA framework, reflecting current emotional dynamics rather than a direct buffering process.\u003c/p\u003e \u003cp\u003eWhile prior research often frames an expansive FTP as a protective psychological resource, the present findings suggest that its role may be better understood as a context-sensitive cognitive framework associated with the degree of interconnectedness between loneliness and depressive symptoms. Specifically, at lower levels of momentary loneliness, individuals with a more expansive FTP co-occurred with lower reported levels of depression, which aligns with the view highlighting the adaptive value of an open-ended future under relatively stable emotional conditions (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). However, the observed interaction pattern revealed that the positive association between loneliness and depression was significantly more pronounced among individuals with a more expansive FTP, indicating heightened sensitivity to social disconnection. This pattern aligns with the hypothesis stressing differential susceptibility(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), which propose that certain individual characteristics reflect heightened sensitivity to contextual influences rather than uniform protection. From this viewpoint, a more expansive FTP may confer emotional benefits in socially stable contexts while simultaneously amplifying vulnerability under conditions of social stress. Similarly, within a vulnerability stress framework (\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), FTP may function as a latent cognitive vulnerability that becomes salient when individual experiences momentary loneliness, leading to transient social disconnection to broader concerns about future relational outcomes and life trajectories.\u003c/p\u003e \u003cp\u003eIn contrast, the relatively weakened link between loneliness and depression among those with a more limited FTP can be interpreted through Socioemotional Selectivity Theory. As noted by Lang and Carstensen (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), a limited time perspective is often associated with a prioritization of emotion-regulation goals and present-focused socially meaningful experience over future-oriented information seeking. This shift in motivational focus may buffer the immediate emotional impact of social disconnection, resulting in a weaker concurrent association between momentary loneliness and depressive distress. Taken together, these findings highlight the context-dependent nature of FTP, illustrating that a temporal outlook does not operate uniformly as a protective factor, but rather modulates sensitivity to social-emotional experiences in daily life.\u003c/p\u003e \u003cp\u003eThe present findings highlight the importance of affect-specific profiling in interventions for depression. Regular monitoring of how emotions such as sadness, irritability, lethargy, anxiety, fear, loneliness, guilt, and happiness fluctuate within individuals may allow for tailored, modular interventions that target those affects most closely linked with depression. For example, when irritability and heightened arousal are pronounced, attention-shifting training or stimulus control may be appropriate; when lethargy and anhedonia are central, behavioral activation and reinforcement scheduling may be effective; when guilt and self-criticism predominate, self-compassion training or cognitive restructuring may be beneficial; and when loneliness is salient, interpersonal cognitive restructuring or social skills training may be particularly useful. Beyond symptom reduction, interventions that aim to enhance positive affect are also crucial. Gratitude exercises, mindfulness-based programs, and other strategies that strengthen positive affect may help individuals cultivate meaning and satisfaction in daily life, thereby enhancing emotional resilience. Importantly, the present study emphasizes that interventions targeting FTP should not assume that expansion is invariably protective. Thus, interventions addressing FTP should emphasize flexibility rather than a uniform pursuit of expansion, helping individuals adaptively shift between future-, present-, and past-oriented perspectives depending on their situational and affective contexts.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was conducted with a relatively small sample drawn from Korea, which limits the generalizability of the findings. Because affective experiences and time perspectives are strongly shaped by cultural values and social structures, future research should employ large-scale longitudinal designs across diverse cultures and age groups to examine both the universality and cultural specificity of these associations. Second, although the EMA method captured affective variability in real-time, the current study relied on concurrent (within-moment) associations rather than lagged analyses. This decision was primarily based on our research objective to capture the immediate phenomenological experience and the 'co-occurring' affective profile of depression as it manifests in daily life. Given that the interplay between social perception (e.g., loneliness) and affective distress often manifests instantaneously, a concurrent model was deemed appropriate for identifying these multifaceted associations. However, this approach is limited in establishing causal precedence and addressing the issue of directionality. As noted in prior literature, the association between loneliness and depression may be reciprocal (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). It is plausible that individuals with higher levels of momentary depression may experience or report their affective states differently than those with lower levels, rather than these affective states solely functioning as precursors to depression. Consequently, it is not possible from the present analyses to determine which state occurred first or caused the other. Future studies should employ lagged modeling\u0026mdash;such as dynamic structural equation modeling (DSEM) or multilevel vector autoregression (VAR)\u0026mdash;to more precisely determine the temporal ordering and potential bidirectional influences between these variables. Third, although this study controlled for demographic variables such as sex and age, as well as individual mean levels of loneliness at the between-person level, it did not sufficiently incorporate other key individual difference variables. Beyond basic factors like neuroticism and perceived social support, variables such as perceived stress, coping styles, and rumination tendencies were not accounted for in the current models. Given that these factors often function as significant moderators or mediators in the complex associations between FTP, loneliness, and depressive symptoms, their absence may limit a more granular understanding of the results. Future research should therefore integrate these dispositional characteristics to provide a more comprehensive perspective on the multifaceted nature of depression. Finally, the analyses relied exclusively on self-reported affective data without incorporating behavioral or physiological indicators. However, the developmental course of depression is closely related not only to subjective affective experiences but also to behavioral avoidance, physiological arousal, and neurobiological processes. Future studies should thus adopt multimodal assessment frameworks that integrate affective, behavioral, and physiological dimensions to enhance the ecological validity of research on depression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study used EMA and multilevel modeling to investigate the affective dynamics associated with depression and the moderating role of \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eFTP.The\u003c/span\u003e\u003cspan address=\"http://FTP.The\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e findings suggest that depression is not the associated with a single affective state but rather is characterized by a complex profile of multiple affects, including both negative affects such as sadness, irritability, lethargy, anxiety, fear, loneliness, and guilt, and the protective influence of positive affect such as happiness. Moreover, FTP appeared to moderate the within-individual association between loneliness and depression, such that an expansive FTP was linked with stronger associations, whereas a more limited FTP was associated with a weakened link. These results are broadly consistent with socioemotional selectivity theory, while also contributing new evidence that FTP may act as either a protective or vulnerability factor depending on contextual conditions. Together, the study highlights the importance of considering both affective profiles and subjective time perspectives in understanding the multifaceted nature of depression. Although further research is needed to address the cultural, methodological, and measurement limitations of the present design, the findings underscore the potential of tailored, affect-sensitive, and time-perspective\u0026ndash;informed interventions to enhance clinical practice.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eEMA Ecological Momentary Assessment\u003c/p\u003e\u003cp\u003eVAS Visual Analogue Scale\u003c/p\u003e\u003cp\u003eMLM Multilevel Modelling\u003c/p\u003e\u003cp\u003eFTP Future Time Perspective\u003c/p\u003e\u003cp\u003eSST Socioemotional Selectivity Theory\u003c/p\u003e\u003cp\u003eSMS Short Message Service\u003c/p\u003e\u003cp\u003ePMC Person-Mean Centering\u003c/p\u003e\u003cp\u003eICC Intraclass Correlation Coefficient\u003c/p\u003e\u003cp\u003eVIF Variance Inflation Factor\u003c/p\u003e\u003cp\u003eAIC Akaike Information Criterion\u003c/p\u003e\u003cp\u003eBIC Bayesian Information Criterion\u003c/p\u003e\u003cp\u003eDSEM Dynamic Structural Equation Modelling\u003c/p\u003e\u003cp\u003eHPA Hypothalamic Pituitary Adrenal\u003c/p\u003e\u003cp\u003eNA Negative Affect\u003c/p\u003e\u003cp\u003ePA Positive Affect\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e 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.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2074932) and National Research Foundation of Korea (NRF-2022R1F1A1074783).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJL conceived and designed the study, acquired the data, drafted the manuscript, and revised it critically for important intellectual content. YL analyzed and interpreted the data and contributed to drafting the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis article is based on the first author's Master's thesis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are included in the supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAPA. Diagnostic and statistical manual of mental disorders: DSM-5: American psychiatric association Washington, DC; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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. 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American Psychiatric Association Washington, DC; 2020. pp. 365\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrittlebank A, Regnard C. Terror or depression? A case report. Palliat Med. 1990;4(4):317\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreit S, Denier N, Mertse N, Walther S, Soravia LM, Federspiel A et al. The neurobiology of motivational anhedonia in patients with depression. Brain Imaging Behav. 2025:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei AA, Phang VWX, Lee YZ, Kow ASF, Tham CL, Ho Y-C, et al. Chronic Stress-Associated Depressive Disorders: The Impact of HPA Axis Dysregulation and Neuroinflammation on the Hippocampus\u0026mdash;A Mini Review. Int J Mol Sci. 2025;26(7):2940.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchonfeld IS, Bianchi R. Burnout and depression: two entities or one? 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Soc Sci Med. 2012;74(6):907\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression, Ecological momentary assessment, Loneliness, Future time perspective, Multilevel modeling, MLM, EMA, Digital phenotyping","lastPublishedDoi":"10.21203/rs.3.rs-9317791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9317791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough loneliness is a core interpersonal risk factor for depression, its unique contribution\u0026mdash;independent of other co-occurring affects\u0026mdash;remains insufficiently explored in daily life. Furthermore, according to Socioemotional Selectivity Theory, the psychological impact of social disconnection may vary depending on an individual\u0026rsquo;s Future Time Perspective (FTP), yet the moderating role of FTP in the loneliness\u0026ndash;depression link lacks empirical evidence.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eUsing Ecological Momentary Assessment (EMA), this study aimed to disentangle the unique association between momentary loneliness and depression while controlling for 13 concurrent affects, and to examine whether FTP moderates this relationship in real-world contexts.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eFifty-nine adults completed a 14-day EMA protocol, responding four times per day via smartphone. At each prompt, participants rated 14 negative affects and one positive affect on 0\u0026ndash;9 visual analogue scale (VAS) scales; depression was analyzed as the outcome variable, with the remaining affects entered as predictors. Using MLM, the study examined which affects were associated with depression at the within-individual level and tested whether FTP, assessed once at baseline with a validated scale, moderated the association between loneliness and depression at the between-individual level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultilevel analysis demonstrated that momentary loneliness showed a robust within-person association with depression, remaining significant even after accounting for 13 other concurrent affective states. Model comparisons confirmed substantial between-person variability in the loneliness\u0026ndash;depression relationship, providing a statistical basis for moderation. A significant cross-level interaction revealed that FTP moderated this association. Specifically, simple slope and Johnson-Neyman analyses indicated that while a more expansive FTP was associated with lower depressive levels under conditions of low momentary loneliness, it was also linked to heightened emotional reactivity; the link between loneliness and depression was significantly more pronounced for individuals with an expansive FTP compared to those with a limited FTP.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study suggest that depression in daily life reflects dynamic emotional processes shaped by the interplay of multiple negative and positive affects. Multilevel analyses further revealed that FTP moderated the association between loneliness and depression, such that individuals with a more expansive FTP linked to stronger positive associations between momentary loneliness and depressive affect. These findings extend socioemotional selectivity theory by suggesting that FTP may function as a context-dependent framework, conferring emotional benefits under certain conditions while amplifying vulnerability in contexts of social disconnection.\u003c/p\u003e","manuscriptTitle":"Loneliness and Depression in Daily Life: A Multilevel Ecological Momentary Assessment Study of the Contextual Role of Future Time Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 16:55:20","doi":"10.21203/rs.3.rs-9317791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"49184988118048202468688315940975860304","date":"2026-04-17T18:44:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T06:45:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T23:23:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T23:23:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-04-04T05:36:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3bb1edf-cd31-4737-a7b8-ff279f259a40","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:55:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 16:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9317791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9317791","identity":"rs-9317791","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}