Baseline and Daily Predictors of Negative Affect Dynamics in Patients Diagnosed with Borderline Personality Disorder: Specific Effects on Daily Means, Instability, and Inertia

Baseline and Daily Predictors of Negative Affect Dynamics in Patients Diagnosed with Borderline Personality Disorder: Specific Effects on Daily Means, Instability, and Inertia | 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 Baseline and Daily Predictors of Negative Affect Dynamics in Patients Diagnosed with Borderline Personality Disorder: Specific Effects on Daily Means, Instability, and Inertia M. Constanza Vial, Antonella Davanzo, Eduardo Franco, Alex Behn This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7838183/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Negative affect is a core clinical feature of Borderline Personality Disorder (BPD). Identifying baseline characteristics and day-to-day processes that predict negative affect dynamics is key for improving treatment strategies and refining the conceptualization of the disorder. Objective This study examined how baseline characteristics (psychiatric history, depressive symptoms, BPD severity, personality functioning) and daily processes (interpersonal hypersensitivity, self-criticism, self-concept stability) relate to daily negative affect levels, instability, and inertia in 45 individuals with BPD, assessed via Ecological Momentary Assessment (EMA) Method Participants (n = 45) recruited from clinical services in Santiago, Chile completed baseline self-report measures (PHQ-9, ZAN-BPD, LPFS-BF), and demographics including age of onset and basic psychiatric history. Over 11 days, EMA prompts were delivered at varying intervals under three randomized schedules (25/day every 30 min, 13/day every hour, 5/day every three hours), assessing negative affect, interpersonal problems, self-concept stability, and self-criticism. Multilevel linear mixed models examined predictors of mean daily negative affect. Two additional mixed-effects models explored daily dynamics: an inertia model (predicting negative affect at one moment from the previous moment) and an instability model (mean squared successive difference, MSSD). Results Baseline trait-like variables did not predict mean daily negative affect. Daily averages of interpersonal hypersensitivity and self-criticism predicted daily negative affect at both within- and between-subject levels. Inertia analyses indicated that persistence of negative affect was predicted by self-criticism but not by interpersonal hypersensitivity. Conversely, instability of negative affect was predicted by interpersonal hypersensitivity but not by self-criticism. Conclusions Daily fluctuations in negative affect in BPD appear more strongly tied to situational stressors than to stable traits, highlighting the importance of context-sensitive assessment and intervention. Self-criticism is linked to persistence of negative affect, whereas interpersonal hypersensitivity is associated with its instability. Borderline Personality Disorder Ecological Momentary Assessment Negative Affect Interpersonal Hypersensitivity Self-criticism Self-concept Stability Background Borderline Personality Disorder and Research Relevance One of the central features of Borderline Personality Disorder (BPD) is the experience of intense negative affect dynamics, referring to overall levels of negative affect, negative affect instability such as rapid and intense fluctuations in emotions [ 1 ], and negative affect inertia or stickiness [ 2 ]. These features are central to BPD’s classification in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases, 11th Revision (ICD-11) [ 3 , 4 ]. BPD affects men and women equally and has a lifetime prevalence of 3%, exceeding bipolar disorder [ 5 ] and schizophrenia [ 6 ]. It also frequently coexists with depression (61%–83%) and anxiety (88%), often alongside trauma history [ 7 ]. BPD is highly debilitating, leading to marked impairments in occupational and social functioning—such as lower educational attainment, unstable relationships, and reduced life satisfaction [ 8 ]. It imposes a substantial burden on caregivers and healthcare systems due to high service use and hospitalization rates [ 8 ], accounting for 15–28% of outpatient cases, 20–60% of psychiatric admissions [ 7 , 9 ], and about 40% of hospitalizations for suicidality [ 10 ]. Rehospitalization rates are often twice those of other disorders, significantly increasing healthcare costs [ 11 ]. In Germany, BPD accounts for roughly 20% of psychiatric care costs (€4 billion annually) [ 12 ], and its total societal cost is estimated at €35,038 per patient per year [ 13 ]. Due to BPD’s high burden, several countries (e.g., UK, Australia, Netherlands, Spain) have developed national clinical treatment guidelines [ 14 ]. Despite its severity, recent findings challenge the view of BPD as a lifelong condition with a poor prognosis [ 15 ]; a meta-analysis of 11 studies suggests a 60% diagnostic remission rate [ 8 ]. Evidence-based guidelines recommend psychotherapy as the primary treatment, with medication reserved for managing comorbidities or crisis interventions [ 8 ]. Nonetheless, gaps remain in understanding BPD, underscoring the need for further research to improve treatment outcomes, reducing distress for individuals and caregivers, and alleviate the economic burden on healthcare systems [ 12 ]. Negative Affect in Borderline Personality Disorder Negative affect is a hallmark of BPD, characterized by instability and intensity, and is suggested as the driving force behind its severe clinical manifestations [ 16 – 18 ]. Patients with BPD present higher baseline levels of negative affect—defined as a general tendency to experience anxiety and dysphoric states—than healthy controls [ 19 ], often experiencing intense, abrupt and variable negative emotions that lead to significant distress [ 20 ]. The nature of negative affect in BPD differs from that observed in other affective disorders. Emotional sensitivity in BPD has primarily been linked to negative rather than positive mood states [ 21 ], distinguishing it from other affective disorders such as bipolar disorder [ 22 ]. Similarly, depressive symptoms in BPD often show limited response to antidepressants but may remit as BPD improves, suggesting they are more related to life dissatisfaction than to a distinct depressive disorder [ 7 ]. Patients with BPD also exhibit heightened reactivity to negative affect, with more intense emotions and slower recovery from distress [ 23 , 24 ], underscoring its central role in the disorder. Four main theoretical models emphasize the role of negative affect variability in BPD: Linehan’s biosocial theory links emotional dysregulation to interpersonal invalidation, leading to cognitive and behavioral difficulties [ 21 , 25 ]. The Psychodynamic Model [ 26 ], attributes emotional instability to identity diffusion and early aggressive drives [ 27 ]. The Mentalization Model focuses on impaired mentalization, which results in emotional dysregulation, impulsivity, and difficulties managing behavior [ 28 ]. The Interpersonal Hypersensity Model framework highlights interpersonal hypersensitivity as a primary factor underlying emotional reactivity in BPD [ 29 ]. Negative affect plays a central role in BPD’s heterogeneous clinical presentation. As Gunderson [ 7 ] noted, individuals share core dimensions but may exhibit distinct subtypes defined by predominant diagnostic features. This variability is reflected in symptom instability and dimensional intensity, with phenotypes characterized by impulsivity or by identity disturbance and chronic emptiness [ 30 , 31 ]. Such heterogeneity raises questions about whether BPD represents a unitary construct, overlapping symptoms, or distinct subtypes [ 32 ]. Further insight into negative affect may help refine these models and inform tailored interventions. Predictors of Negative Affect BPD Negative affect in BPD is influenced by several factors, including high comorbidity with depressive disorders, with 41% to 83% of individuals having a history of major depression and lifetime dysthymia prevalence ranging from 12% to 39% [ 1 ]. Nevertheless, while overlapping with depressive mechanisms, BPD’s affective instability is shaped by negative self-concept, dependency on others, and emotions such as anger, anxiety, and emptiness, distinguishing it from primary depressive disorders [ 33 – 35 ]. Affective intensity and regulation predict BPD traits even when controlling for depression [ 36 , 37 ], and BPD-specific interventions improve depressive symptoms where standard treatments do not [ 33 , 38 ]. Interpersonal dysfunction, another hallmark of BPD [ 39 , 40 ], is also associated with negative affective instability. BPD individuals show heightened emotional reactivity to social stressors and self-conscious emotions [ 41 ], with EMA studies confirming greater fluctuations in negative affect during social interactions [ 15 ] and heightened affective responses to daily events [ 42 ]. Heightened self-reported rejection sensitivity is common in BPD, which further amplifies negative affect dysregulation [ 15 ]. Altered social cognition in BPD leads to hypersensitivity to negative cues, difficulty recognizing positive signals, and misinterpretation of social inclusion [ 8 ]. Neural studies show distinct rejection processing [ 43 , 44 ] linked to abandonment fears [ 45 ]. Mentalization deficits further bias perceptions of rejection and hostility [ 46 ]. Disturbances in self and interpersonal functioning contribute to fluctuations in negative affect in BPD, leading to cognitive distortions and instability in goals, beliefs, and self-concept [ 7 , 47 ]. Self-esteem instability is closely linked to affective dysregulation [ 48 ] and manifests as incoherence across thoughts, feelings, and behaviors, distinguishing BPD from other disorders [ 8 ]. These disruptions are central to the ongoing debate between the DSM-5 categorical model and the AMPD, as the former focuses on symptom-based criteria and does not fully capture variations in self and interpersonal functioning. In contrast, the AMPD emphasizes dimensional severity through Criterion A (personality functioning). Criterion A shows greater predictive value for daily negative affect, supporting the AMPD’s validity [ 49 ], though further research is needed. Symptom severity in BPD, rather than mere presence, might also predict greater negative affect, with early severity in affect, impulsivity, and sociability forecasting BPD onset [ 50 , 51 ]. Studies have found that BPD symptoms at baseline (i.e., measured at the start of the study) predicted overall level and increasing fluctuations in negative affect [ 50 ] and that early symptom severity in areas such as affect, impulsivity, and sociability predicts the later onset of BPD [ 51 ]. Lastly, sociodemographic variables, such as younger age, lower education, and single status, may represent significant predictors daily negative affect in BPD, consistent with findings from a recent study by Gunderson et al. [ 7 ], which reported similar associations even after controlling for covariates. Current Gaps in Research Research on BPD has largely focused on its emotional features, which are central and persist compared to other symptoms [ 7 ], often causing significant impairment and distress. Non-suicidal self-injury (NSSI) and suicide attempts are common coping mechanisms for overwhelming negative affect [ 37 ]. Understanding how daily negative affect manifests and what predicts it could refine theoretical models, improve diagnosis, and guide treatment differentiation given BPD’s high comorbidity [ 7 ]. Additionally, determining whether predictors are consistent across patients could inform tailored psychotherapeutic interventions, while pharmacological treatment remains reserved for comorbidities or crises [ 8 ]. Investigating predictors of negative affect, particularly those linked to disturbances in self and interpersonal functioning, may help bridge gaps in BPD conceptualization and support dimensional perspectives of its etiology [ 49 ]. Understanding the mechanisms behind daily negative affect in BPD is another challenge, as individuals experience rapidly shifting emotions. Yet, most studies treat it as a static construct, overlooking its time-varying nature [ 24 ]. Retrospective or laboratory-based assessments often fail to capture these real-time dynamics, yielding mixed results due to their artificial and less personally relevant nature. EMA effectively tracks symptom fluctuations in daily life—including extreme shifts and environmental triggers—while minimizing recall bias, providing context-sensitive data that enhances validity, generalizability, and informs accurate diagnostics and therapeutic strategies [ 20 , 52 ]. Another underexplored aspect is the distinction between emotional inertia and instability. Emotional inertia refers to the tendency to maintain persistent negative affective states, whereas instability involves rapid and large fluctuations in mood. Studies [ 53 ] show that individuals with BPD exhibit both high emotional instability and greater inertia, suggesting that these dimensions may have different triggers and may require distinct therapeutic approaches. Understanding these patterns could help clinicians tailor interventions to patient-specific emotional dynamics; however, further research is needed to clarify how inertia and instability influence emotional regulation and daily functioning in BPD. Method Participants Forty-five adult participants, all aged 19 or older, diagnosed with BPD using the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II), were recruited from various specialized treatment facilities in Santiago, Chile. Of the total sample, 43 were women and 2 were men. Table 1 presents a detailed characterization of the sample, including socio-demographic and clinical variables. Table 1 Descriptive Baseline Analysis Variable n (%) M SD Range Sex Male: 2 (4.4%) Female: 43 (95.6%) – – – Previous Psychiatric Treatment Yes: 40 (88.9%) No: 5 (11.1%) Previous Psychiatric Hospitalization Yes: 33 (73.3%) No: 12 (26.7%) – – – Age of Onset (years) – 15.6 7.68 4–36 Depression Level (PHQ-9) – 14.7 5.87 3–27 Personality Functioning (LPFS-BF) – 32.5 7.31 26–47 Symptom Severity (ZAN-BPD) – 13.1 7.43 0–29 Self-Stability – 2.02 1.33 0–4 Self-Criticism – 1.34 1.17 0–4 Interpersonal Hypersensitivity – 0.92 0.84 0.02–3.07 Mean Negative Affect – 0.93 0.68 0.14–2.77 Note. Abbreviations: n (%) = number and percentage of participants; M = Mean; SD = Standard Deviation; PHQ-9 = Patient Health Questionnaire-9; LPFS-BF = Level of Personality Functioning Scale – Brief Form; ZAN-BPD = Zanarini Rating Scale for Borderline Personality Disorder. Continuous variables are presented as Mean ± SD and range (Min–Max). Procedure All procedures were approved by an accredited local Ethical Review Board. Participants provided written informed consent before enrollment, which emphasized confidentiality, voluntary participation, and a USD $ 15.00 compensation upon completion. Inclusion criteria was being 18 years or older and meeting DSM-IV criteria for BPD; exclusion criteria assessed through the MINI International Neuropsychiatric Interview (MINI) included high suicidal intent, current psychotic or manic episode, substance abuse or dependence. All participants had to own a smartphone with internet data services. BPD diagnosis was confirmed through a structured clinical interview conducted by trained psychologists (PhD, MSc, or MSc-in-training). Eligible participants registered in the Avicenna Research ( https://avicennaresearch.com/ ) mobile application and completed baseline self-reports including: PHQ-9, ZAN-BPD, LPFS-BF, and a brief demographic survey. The following day, they began an 11-day EMA protocol with prompts delivered at varying intervals under three randomized sequences of prompting conditions (25 prompts/day every 30 minutes, 13 prompts/day every hour, and 5 prompts/day every three hours). Prompts were pseud-randomized within 10-minute windows and expired after 15 minutes to ensure real-time responses. Baseline Measures Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD) [ 54 ]. ZAN-BPD was designed to assess the presence and severity of BPD symptoms. It assesses the four main areas of psychopathology in BPD through nine items: affectivity, cognition, impulsivity, and interpersonal relationships [ 54 ]. Each item is scored on a Likert scale from 0 to 4, resulting in a total score from 0 to 36 [ 54 ]. In general, the scale provides a total score reflecting BPD symptom severity in the assessed individual [ 54 ].The interview and self-report versions of the ZAN-BPD demonstrate high convergent validity (median = 0.70) [ 54 ] and good internal consistency (Cronbach’s alpha = 0.84) [ 54 ]. Levels of Personality Functioning Brief Form 2.0 (LPFS-BF 2.0) [ 55 ] LPFS-BF 2.0 is a brief self-report instrument that provides a rapid impression of the severity of a personality disorder. It consists of 12 items that are to be scored in a Likert scale format from 0 to 3 (0 = very false or often false; 1 = sometimes or somewhat false; 2 sometimes or somewhat true; 3 = very true or often true), with a maximum score of 36; a higher score indicates a higher presence and severity of a personality disorder [ 55 ]. The final LPFS-BF 2.0 produces a total personality functioning score and two subscales (Self and Interpersonal Functioning) to reflect the two broad categories of specific personality functioning outlined in the AMPD [ 56 ]. The LPFS-BF 2.0 has demonstrated satisfactory internal consistency, promising construct validity and sensitivity to change after three months of treatment [ 57 ]. The LPFS-BF 2.0 has been translated into Spanish and was used in a large cross-national study that included a Chilean sample [ 58 ]. Invariance testing across countries, including Chile, supports score comparability across languages and cultures [ 58 ]. Patient Health Questionnaire − 9 (PHQ-9) [ 59 ]. PHQ-9 is a nine item self-report scale, used for detecting mild, moderate, or severe depressive symptoms, and has proven to be an efficient diagnostic tool [ 59 ]. PHQ9 consists of 9 items that assess the presence of depressive symptoms (corresponding to DSM-IV criteria) experienced in the past 2 weeks. Each item has a corresponding severity index: 0 = "never", 1 = "some days", 2 = "more than half of the days", and 3 = "almost every day" [ 59 ]. Standard cut-off points have been proposed to classify severity levels: mild (5–9), moderate (10–14), moderately severe (15–19), and severe (20–27), with a score of ≥ 10 frequently used to identify clinically relevant cases [ 59 ]. The validated version in Chile was used in this study, which presents construct and predictive validity concurrent with the ICD-10 criteria for depression [ 60 ]. Socio-Demographic and Clinical History Survey To characterize our sample and later identify possible socio-demographic predictors for emotional variability and intensity in BPD, each participant completed a self-report questionnaire. It consisted of several multiple option or brief response questions, regarding the patient’s age, sex at birth, gender, occupation, educational level, nationality, ethnicity, habitational situation, medical/psychiatric/psychological treatment, psychiatric hospitalization antecedents and age of onset of mental health difficulties. Intensive Longitudinal Measures: Ecological Momentary Assessment Positive and Negative Affect Schedule – Short Version (PANAS) [ 61 ]. PANAS measures two dimensions of affective experience: positive and negative affect and specific facets of emotions. Psychometric properties of PANAS in Chile are known, as it has been validated in a Chilean sample [ 62 ]. The validated version of PANAS has adequate internal consistency, test re-test stability, factorial structure, and convergent validity. This abbreviated scale has yielded a high internal consistency, with a .92 alpha [ 63 ]. Following a procedure amply utilized in EMA for BPD [ 20 ], only 5 items of PANAS corresponding to negative emotion were utilized in this study 's EMA signals. This selection was made in line with the characteristic pattern of affective instability in BPD, as previously described, which is predominantly observed in negative affect rather than in positive affect. Participants were asked to rate the extent to which they felt five negative emotions—ashamed, hostile, nervous, scared, and upset—at that precise moment on a 5-point Likert scale. Only the highest score of each PANAS assessment was used to index peak affective intensity, an approach that focuses on affective peaks rather than mean levels and has precedent in EMA research [ 64 ]. Interpersonal Hypersensitivity: Items were selected from the Daily Stress Inventory (DSI) [ 65 ] using a version adapted for a Chilean sample [ 66 ]. This followed a procedure described by Smyth et al. [ 67 ] that has been replicated in different studies. Items from DSI, which is widely used and shows adequate psychometric properties [ 65 ], were extracted to create an interpersonal stress scale compatible with EMA’s intensive data collection program. Using an analogue scale, participants responded to items from the DSI, including: (1) I felt criticized and verbally attacked, (2) I felt misunderstood by others (interpersonal alienation). A third item was added, focusing on the experience of feeling rejected: (3) “Right now I feel rejected by others”. Self-Criticism Two items from the Beck Depression Inventory-II (BDI-II) [ 68 ] were used to capture momentary self-criticism. The first item (“Since the last time I responded, and including this moment, I feel disappointed in myself”) and the second item (“Since the last time I responded, and including this moment, I hate myself”) were adapted from the BDI-II. Self Concept Stability (SE-SCC) Following a procedure tested and described recently by Scala et al. [ 69 ], participants answered two items from the Self-Concept Clarity Scale (SCCS) [ 70 ], The first item (“Right now, I have a clear sense of who I am and what I am”) and the second item (“Since the last time I responded, and including this moment, I have clarity about where I want to go in life”) were adapted from the SCCS. All EMA items were rated on a visual analogue scale ranging from 0 (“very slightly or not at all”) to 4 (“extremely”). To capture repeated assessments of momentary self-esteem states, the items were modified with time-specific phrasing such as “since the last time I responded” or “right now”. Based on EMA dimensions, three composite variables were created to function as daily predictors of negative affect, each based on the average of a pair of items: Self-Concept Clarity (two items from the SCCS, namely self-concept clarity and self-direction), Self-Criticism (two items from the BDI-II: Self-disillusionment and Self-hate), and Interpersonal Hypersensitivity (using the average for the three interpersonal hypersensitivity items, namely feeling rejected, feeling criticized and feeling misunderstood). Statistical Analysis All EMA data was downloaded directly from Avicenna. The data was analyzed using the statistical environment R (version 4.0.4) [ 71 ]. Sample characteristics, including socio-demographic and clinical variables, were summarized using descriptive statistics (see Table 1 ). Multilevel linear mixed models [ 72 ] were used to model the effects of baseline predictors (age of onset, past psychiatric treatment, history of inpatient admissions, PHQ-9, LPFS-FB 2.0 and ZAN-BPD scores) and daily predictors (daily levels of interpersonal hypersensitivity, daily levels of self-stability, and daily levels of self-criticism) on daily averages of negative affect. Three consecutive models were fitted. The first model predicted mean daily negative affect from baseline predictors. The second model added the within-subject component of daily predictors using person-centered means, and the third model included between-subject components for daily predictors. Nested models were compared using likelihood-ratio tests and marginal/conditional R squared [ 73 ]. To examine temporal dynamics, two additional mixed-effects models were estimated. First, an inertia model [ 74 ] at the prompt level predicted negative affect at time t from affect at time t–1 (lag-1) and its interactions with momentary self-criticism and interpersonal hypersensitivity, testing whether these daily processes influenced the stickiness of negative mood during the day. Second, an instability model at the day level predicted within-day affective variability—quantified as the mean squared successive difference (MSSD) [ 75 ]—from daily averages of self-criticism and interpersonal hypersensitivity. Both models included the experimental condition (prompt-intensity group) as a categorical covariate to adjust for differences in sampling frequency across participants. Missing EMA prompts were treated as missing at random; no imputation was performed. Only the significant daily predictors from the multilevel model—momentary self-criticism and interpersonal hypersensitivity—were included in subsequent temporal dynamics models examining affective inertia and within-day instability (MSSD). Results Daily Negative Affect Levels: Baseline and Momentary Predictors Baseline predictors—including age of onset, previous psychiatric hospitalization, previous psychiatric treatment, depressive symptoms (PHQ-9), personality functioning (LPFS-BF), and BPD symptom severity (ZAN-BPD)—were not significantly associated with daily levels of negative affect. In contrast, daily predictors, specifically daily levels of self-criticism and interpersonal hypersensitivity, significantly predicted higher daily mean levels of negative affect at both the within- and between-person levels. In terms of predictive power, adding daily predictors significantly improved the prediction of daily negative affect beyond baseline trait-like covariates. Model fit increased from the baseline (R²m = 0.14, R²c = 0.66) to the within-person model (R²m = 0.25, R²c = 0.78; Δχ²(3) = 181.91, p < .001). Including both within- and between-person effects yielded the highest explanatory power (R²m = 0.59, R²c = 0.77; Δχ²(3) = 48.36, p = 0.18). AIC and BIC values also decreased consistently, confirming the incremental explanatory value of both within- and between-person effects. These results indicate that daily fluctuations in self-criticism and interpersonal hypersensitivity substantially increased explained variance in negative affect, highlighting that momentary shifts in these states meaningfully contribute to day-to-day emotional distress beyond stable trait-like individual differences. The marginal R² increased from 0.14 (baseline) to 0.59 in the final model that included within and between components in addition to baseline predictors, indicating that the inclusion of momentary and person-level daily predictors substantially enhanced the proportion of variance in negative affect explained by the fixed effects. The conditional R² remained high (~ 0.77–0.78), suggesting that a large share of total variability was also captured by random (person-level) components. At the within-person level, days characterized by higher-than-usual self-criticism (b = 0.24, p < .001) and interpersonal hypersensitivity (b = 0.28, p < .001) were associated with significantly higher daily levels of negative affect. Daily fluctuations in self-stability were not significantly related to the outcome (b = 0.01, p = .77). These findings indicate that day-to-day increases in self-critical and interpersonally hypersensitive states predict same-day elevations in negative affect, above and beyond each person’s typical levels of these variables. At the between-person level, participants who were generally higher in self-criticism (b = 0.16, p = .008) and interpersonal hypersensitivity (b = 0.56, p < .001) also exhibited higher average levels of daily negative affect. Between-person differences in self-stability were nonsignificant (p = .72). Results are presented in Table 2 . (Table 2 is provided at the end of the manuscript due to its length). Table 2 Daily Negative Affect Levels: Baseline and Momentary Predictors Predictor Estimate (β) SE t p 95% CI M0: Baseline Predictors Intercept 0.696 0.585 1.189 0.234 [− 0.451, 1.844] Age of Onset −0.016 0.013 −1.206 0.228 [− 0.041, 0.010] Previous Psychiatric Treatment 0.039 0.261 0.150 0.881 [− 0.472, 0.551] Previous Psychiatric Hospitalization −0.055 0.224 −0.245 0.807 [− 0.495, 0.385] Depression Level (PHQ-9) 0.019 0.027 0.694 0.487 [− 0.035, 0.073] Personality Functioning (LPFS-BF) −0.004 0.019 −0.205 0.837 [− 0.041, 0.033] Symptom Severity (ZAN-BPD) 0.026 0.020 1.315 0.189 [− 0.013, 0.065] M1: Baseline + Daily Predictors (Within-Person Effects) Intercept 0.707 0.585 1.208 0.227 [− 0.439, 1.853] Age of Onset −0.016 0.013 −1.230 0.219 [− 0.041, 0.009] Previous Psychiatric Treatment 0.047 0.261 0.181 0.856 [− 0.464, 0.558] Previous Psychiatric Hospitalization −0.055 0.224 −0.245 0.807 [− 0.495, 0.385] Depression Level (PHQ-9) 0.019 0.027 0.686 0.493 [− 0.035, 0.072] Personality Functioning (LPFS-BF) −0.004 0.019 −0.219 0.827 [− 0.041, 0.033] Symptom Severity (ZAN-BPD) 0.027 0.020 1.343 0.179 [− 0.012, 0.066] Self-Stability (within) 0.012 0.040 0.291 0.771 [− 0.067, 0.091] Self-Criticism (within) 0.238 0.037 6.390 < 0.001** [0.165, 0.311] Interpersonal Hypersensitivity (within) 0.284 0.035 8.238 < 0.001** [0.217, 0.352] M2: Baseline + Daily Predictors (Within- and Between-Person Components) Intercept −0.043 0.413 −0.105 0.917 [− 0.852, 0.765] Age of Onset (years) 0.004 0.008 0.465 0.642 [− 0.012, 0.020] Previous Psychiatric Treatment −0.061 0.159 −0.381 0.703 [− 0.373, 0.252] Previous Psychiatric Hospitalization −0.007 0.133 −0.051 0.959 [− 0.267, 0.253] Depression Level (PHQ-9) −0.005 0.016 −0.327 0.743 [− 0.037, 0.027] Personality Functioning (LPFS-BF) 0.008 0.012 0.643 0.520 [− 0.015, 0.030] Symptom Severity (ZAN-BPD) −0.001 0.012 −0.088 0.930 [− 0.025, 0.023] Self-Stability 0.012 0.040 0.287 0.774 [− 0.067, 0.091] Self-Criticism 0.238 0.037 6.412 < 0.001** [0.166, 0.311] Interpersonal Hypersensitivity 0.284 0.035 8.225 < 0.001** [0.216, 0.352] Self-Stability (between) 0.020 0.055 0.361 0.718 [− 0.088, 0.127] Self-Criticism (between) 0.164 0.062 2.635 0.008* [0.042, 0.286] Interpersonal Hypersensitivity (between) 0.555 0.081 6.872 < 0.001** [0.397, 0.714] Self-Stability (within) 0.012 0.040 0.287 0.774 [− 0.067, 0.091] Self-Criticism (within) 0.238 0.037 6.412 < 0.001** [0.166, 0.311] Interpersonal Hypersensitivity (within) 0.284 0.035 8.225 < 0.001** [0.216, 0.352] Note. Abbreviations: Estimate (β) = regression coefficient; SE = standard error; CI = confidence interval; t = t -value; p = p -value (statistical significance). Significance levels: p < 0.001(**), p < 0.05(*). Inertia Model: Momentary Predictors of Daily Negative Affect Persistence A multilevel inertia model revealed significant temporal dependencies in negative affect. Lagged negative affect (NAₜ₋₁) strongly predicted subsequent negative affect (b = 0.29, SE = 0.03, t = 11.10, p < .001), indicating substantial affective inertia. Higher momentary self-criticism (b = 0.10, SE = 0.02, t = 5.28, p < .001) and interpersonal hypersensitivity (b = 0.20, SE = 0.02, t = 8.85, p < .001) were also associated with higher concurrent negative affect. Crucially, the interaction between lagged negative affect and self-criticism (NAₜ₋₁ × self-criticism) was significant (b = 0.06, SE = 0.01, t = 4.95, p < .001), suggesting that when individuals were more self-critical, their negative affect carried over more strongly from one prompt to the next—that is, self-criticism increased affective stickiness. In contrast, the interaction between lagged negative affect and interpersonal hypersensitivity was not significant (p = .15), suggesting that although interpersonal hypersensitivity intensifies negative affect in the moment, it does not affect its temporal persistence. Results are presented in Table 3 . Table 3 Inertia Model: Momentary Predictors of Daily Negative Affect Persistence Predictor Estimate (β) SE t p Intercept 0.217 0.042 5.12 < .001** NA lag (t–1) 0.286 0.026 11.10 < .001** Daily Self-criticism 0.096 0.018 5.28 < .001** Daily Interpersonal Hypersensitivity 0.201 0.023 8.85 < .001** NA lag × Daily Self-Criticism 0.055 0.011 4.95 < .001** NA lag × Daily Interpersonal Hypersensitivity 0.018 0.013 1.45 .15 Note. Abbreviations: NA = Negative Affect; SE = Standard Error. Significance: **p < .001, *p < .05. Random intercept variance = 0.043; residual variance = 0.180. Instability Model - Momentary Predictors of Daily Negative Affect Volatility A linear mixed-effects model was estimated to examine whether daily levels of self-criticism and interpersonal hypersensitivity predicted day-to-day emotional instability, indexed by the mean squared successive difference (MSSD) in negative affect. Random intercepts were estimated for each participant (REML = 909.5), with a random intercept variance of 0.051 (SD = 0.23) and residual variance of 0.591 (SD = 0.77), based on 379 daily observations from 44 participants. Daily interpersonal hypersensitivity significantly predicted higher within-day instability in negative affect (b = 0.16, SE = 0.06, t = 2.40, p = .011), indicating that on days when participants felt more interpersonally sensitive or rejected, their negative affect fluctuated more sharply. In contrast, daily self-criticism was not significantly associated with affective instability (p = .40). The positive intercept reflects a baseline MSSD of approximately 0.24, indicating moderate average day-level affect variability even after accounting for predictors. Detailed parameter estimates are presented in Table 4 . Table 4 Instability Model - Momentary Predictors of Daily Negative Affect Volatility (MSSD) Predictor Estimate (β) SE t p Intercept 0.237 0.079 3.00 .003** Daily self-criticism −0.002 0.049 −0.05 .96 Daily interpersonal hypersensitivity 0.152 0.063 2.40 .011* Random intercept variance = 0.051; residual variance = 0.591. Significance: * p < .001, p < .05. Discussion Contrary to expectations, baseline predictors—including psychiatric history, depressive symptoms (PHQ-9), BPD severity (ZAN-BPD), and personality functioning (LPFS-BF)—did not significantly predict daily negative affect. Baseline measures, designed to capture stable, trait-like aspects of BPD, may not adequately reflect day-to-day fluctuations in negative affect, a key aspect of BD psychopathology and a frequent area of clinical concern. For instance, the LPFS-BF, aligned with the AMPD, emphasizes enduring impairments in self and interpersonal functioning, potentially missing daily variability of negative affect; as shown by Sinnaeve et al. [ 76 ]. Retrospective bias—common in BPD, where individuals may underestimate symptom frequency and overestimate emotional intensity [ 69 ] —may further limit the predictive value of baseline self-report measures. This highlights the importance of intensive longitudinal assessments to really capture daily or momentary processes. Using EMA allowed us to capture real-time fluctuations and identify the distinct contributions of self-criticism and interpersonal hypersensitivity to daily negative affect, which might not have been detectable with standard baseline measures. In contrast, momentary predictors, particularly self-criticism and interpersonal hypersensitivity, were consistently associated with elevated daily negative affect, both within-person (days with higher-than-usual states) and between-person (individuals with higher average levels). These results suggest that daily, dynamic processes may be more relevant than static traits for understanding emotional distress in BPD. Self-stability was not significant, possibly reflecting its trait-like nature. Given that only momentary self-criticism and interpersonal hypersensitivity were significant predictors of negative affect in the first models, subsequent analyses focused on these variables to examine temporal dynamics such as affective inertia and within-day instability. Self-criticism increased affective inertia, prolonging negative affect across time points, but did not affect within-day instability. Interpersonal hypersensitivity, in contrast, increased concurrent momentary negative affect and within-day negative affect instability. Self-criticism may explain why BPD individuals remain “stuck” in negative affect, whereas interpersonal hypersensitivity may drive rapid emotional shifts. Treatment Implications The findings of this study help clarify which factors contribute to difficulties in negative affect among individuals with BPD. Consistent with previous research [ 77 – 79 ], negative affect appears to be particularly sensitive to interpersonal stressors and disruptions in self-concept, such as self-criticism. Importantly, different variables influence negative affect through distinct pathways: self-criticism amplifies affective inertia, prolonging negative affect across time points, whereas interpersonal hypersensitivity drives rapid, short-term fluctuations, contributing to within-day emotional instability. Consequently, capturing individual differences within these domains is important. Some individuals may be more prone to sustained negative mood driven by heightened self-criticism, others may experience greater affective instability in response to interpersonal difficulties, and some may be equally impacted by both processes. Differentiating these profiles can inform more personalized treatment approaches tailored to each patient’s unique emotional vulnerabilities. Clinically, these findings suggest that interventions targeting self-criticism may help patients reduce the tendency to remain “stuck” in negative affect, whereas strategies focused on interpersonal sensitivity and emotion regulation may be more effective for managing rapid mood shifts. While all momentary variables showed significant correlations with negative affect, their differing impacts suggest that treatment should prioritize patient-specific triggers. Interventions may focus on enhancing emotional awareness and regulation related to interpersonal problems and self-criticism, helping patients develop more effective coping mechanisms and improve negative affect regulation. Established treatment models align with these distinctions: Dialectical Behavior Therapy (DBT) offers mindfulness, self-soothing, and distress tolerance strategies to reduce self-criticism and foster self-compassion [ 80 ]. As Linehan notes, emotional dysregulation can compromise self-awareness, leading to emptiness and identity disturbance [ 81 ], while Mentalization-Based Therapy (MBT) addresses interpersonal sensitivity by enhancing reflective functioning and promoting secure attachment patterns [ 29 ]. Present findings emphasize that treatment should focus on identifying which approaches most effectively help each patient improve in domains relevant to their predominant emotional patterns. Finally, incorporating momentary assessment methods into clinical practice could serve as a valuable therapeutic tool. EMA might not only help patients increase awareness of their emotional patterns and triggers in real time—supporting improved emotion regulation and interpersonal functioning—but also allow clinicians to identify temporal dynamics, such as affective inertia and within-day instability, that baseline measures cannot capture. This capacity to reveal the contribution of specific momentary states underscores EMA’s relevance for tailoring interventions to individual patterns of negative affect in BPD. Limitations in this Study First, our sample consisted of participants diagnosed with BPD who were already undergoing treatment. This implies that processes such as inertia or instability might be curtailed by the effect of treatments. Additionally, ongoing psychiatric or psychological treatment may have influenced baseline predictors, so these measures might not fully reflect participants’ unaltered patterns of affective change. It is not clear from this study if the negative affect dynamics identified are specific to BPD or transdiagnostic in nature. The predominance of female participants further limits generalizability, as results of this study may reflect dynamic processes in negative affect representative of women experiencing BPD. Replication studies including healthy controls, untreated BPD patients, male BPD participants, and individuals with other psychiatric disorders would help clarify these issues. Baseline measures, based on retrospective self-report and stable traits, may suffer from recall bias, limited specificity, and reduced variability. These factors, combined with potential low statistical power for between-subject comparisons, might explain why trait-like predictors failed to influence daily negative affect, although this finding has been reproduced in other studies and may be related to the fact that by design, measures that are constructed to capture trait like components of mental illness are not well suited to capture momentary or even daily fluctuations. Missing data in EMA assessments remains a concern. Participants might have missed prompts during moments of intense negative affect, potentially leading to underestimation of emotional variability and momentary predictors. Future research should implement strategies to reduce missing data or apply modeling techniques to account for it. Further Research Although this study identified significant temporal and predictive relationships between negative affect, momentary self-criticism, and interpersonal hypersensitivity, causality remains unclear. It is unknown whether interpersonal problems increase self-criticism, whether self-criticism precedes interpersonal problems, or if they mutually reinforce each other. Further research is needed to clarify these dynamics and determine which problems may resolve earlier in treatment, thereby guiding more effective clinical approaches. Individuals with BPD differ in sensitivity to emotional triggers, with some more reactive to interpersonal stressors and others to self-concept conflicts. Identifying these differences, particularly if they are stable over time, could point to the presence of distinct phenotypes within the disorder. Another important avenue for future research is to investigate the individual components that comprise the constructs of interpersonal hypersensitivity and self-criticism. Interpersonal hypersensitivity was measured using DSI items capturing feelings of criticism, misunderstanding, and rejection, while self-criticism was assessed with two BDI-II items on self-disappointment and self-hatred. Examining these components separately may clarify their distinct contributions to negative affect. Finally, exploring additional predictors, such as maladaptive personality traits or trauma-related triggers, and identifying interventions that best reduce reactivity to these stressors will be essential for developing more personalized and effective treatments. Abbreviations BPD – Borderline Personality Disorder DSM-5 – Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ICD-11 – International Classification of Diseases, 11th Revision AMPD – Alternative Model for Personality Disorders EMA – Ecological Momentary Assessment PHQ-9 – Patient Health Questionnaire-9 ZAN-BPD – Zanarini Rating Scale for Borderline Personality Disorder LPFS-BF – Level of Personality Functioning Scale – Brief Form NSSI – Non-Suicidal Self-Injury M – Mean SD – Standard Deviation Min – Minimum Max – Maximum SCID-II – Structured Clinical Interview for DSM-IV Axis II Personality Disorders MINI – Mini International Neuropsychiatric Interview LPFS-BF 2.0 – Level of Personality Functioning Scale – Brief Form 2.0 PANAS – Positive and Negative Affect Schedule DSI – Daily Stress Inventory BDI-II – Beck Depression Inventory-II SCCS – Self-Concept Clarity Scale SE-SCC – Self-Concept Stability MSSD – Mean Squared Successive Difference R²m – Marginal R Squared (proportion of variance explained by fixed effects) R²c – Conditional R Squared (proportion of variance explained by both fixed and random effects) AIC – Akaike Information Criterion BIC – Bayesian Information Criterion β – Estimate β SE – Standard Error CI – Confidence Interval N – Number of Measurements t – t-value p – p-value (significance) NAₜ₋₁ / NA lag – Negative Affect lag REML – Restricted Maximum Likelihood Declarations Ethics approval and consent to participate All procedures were approved by the Ethics Committee of the Pontifical Catholic University of Chile (Approval Code: 240730001 and 210331010). Consent for publication All participants provided written informed consent for publication of anonymized data. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests Statement The authors declare no competing interest related to this study. Funding Statement This study was supported by the Fondo Inicio VRI granted by Pontificia Universidad Católica de Chile and MIDAP. The funding source was not involved in the study design, data collection, analysis, interpretation, or writing of the manuscript. This project has been funded by the Foundation Botnar. The second author has been funded by the National Agency for Research and Development (ANID) / Scholarship Program / DOCTORADO BECAS CHILE/2019 - 21190831. This project has been supported by the Research Directorate of the Vice-Rectory for Research at the Pontifical Catholic University of Chile through the INICIO Grant 2020 and the ANID Millennium Science Initiative/Millennium Institute for Research on Depression and Personality-MIDAP ICS13_005. Authors' Contributions C.V.: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Visualization. A.D.: Methodology, Data curation, Investigation, Formal Analysis, Writing – review & editing, Project administration. E.F.: Validation, Formal analysis. A.B.: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Supervision, Project administration, Funding acquisition. All authors read and approved the final manuscript. Acknowledgements We would like to extend our sincere thanks to María Paz Kattan, Colomba Egenau, and Magdalena Daveggio, as well as to the Millennium Institute for Research in Depression and Personality (MIDAP) and the Fondo de Inicio UC of the Pontificia Universidad Católica de Chile, for their invaluable support and collaboration in the development of this paper. References Lieb K, Zanarini MC, Schmahl C, Linehan MM, Bohus M. Borderline personality disorder. The Lancet. 2004;364:453–61. https://doi.org/10.1016/S0140-6736(04)16770-6 Kuppens P, Allen NB, Sheeber LB. Emotional Inertia and Psychological Maladjustment. Psychol Sci [Internet]. 2010 [cited 2025 Oct 5];21:984–91. https://doi.org/10.1177/0956797610372634 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders [Internet]. 5th ed. Arlington, VA: American Psychiatric Association Publishing; 2013 [cited 2025 July 14]. https://www.psychiatry.org/psychiatrists/practice/dsm. 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J Behav Med [Internet]. Springer Science and Business Media LLC; 1987 [cited 2025 July 13];10:61–73. https://doi.org/10.1007/bf00845128 Encina Agurto YJ, Ávila Muñoz MV. Validación de una escala de estrés cotidiano en escolares chilenos. Rev Psicol [Internet]. Sistema de Bibliotecas PUCP; 2015 [cited 2025 July 13];33:363–85. https://doi.org/10.18800/psico.201502.005 Smyth JM, Wonderlich SA, Sliwinski MJ, Crosby RD, Engel SG, Mitchell JE, et al. Ecological momentary assessment of affect, stress, and binge‐purge behaviors: Day of week and time of day effects in the natural environment. Int J Eat Disord [Internet]. Wiley; 2009 [cited 2025 July 16];42:429–36. https://doi.org/10.1002/eat.20623 Beck AT, Steer RA, Brown GK. Beck Depression Inventory-II (BDI-II) [Internet]. San Antonio, TX: The Psychological Corporation; 1996. https://doi.org/10.1037/t00742-000 Scala JW, Levy KN, Johnson BN, Kivity Y, Ellison WD, Pincus AL, et al. The Role of Negative Affect and Self-Concept Clarity in Predicting Self-Injurious Urges in Borderline Personality Disorder Using Ecological Momentary Assessment. J Personal Disord [Internet]. Guilford Publications; 2018 [cited 2025 July 14];32:36–57. https://doi.org/10.1521/pedi.2018.32.supp.36 Campbell JD, Trapnell PD, Heine SJ, Lavallee LF, Lehman DR. Self-Concept Clarity: Measurement, Personality Correlates, and Cultural Boundaries. J Pers Soc Psychol. 1996;70:141–56. https://doi.org/10.1037/0022-3514.70.1.141 R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2021. https://www.R-project.org Curran PJ, Bauer DJ. The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change. Annu Rev Psychol [Internet]. 2011 [cited 2025 Oct 10];62:583–619. https://doi.org/10.1146/annurev.psych.093008.100356 Cahusac PMB. Likelihood Ratio Test and the Evidential Approach for 2 × 2 Tables. Entropy [Internet]. 2024 [cited 2025 Oct 10];26:375. https://doi.org/10.3390/e26050375 Abitante G, Cole DA, Bean C, Politte-Corn M, Liu Q, Dao A, et al. Temporal dynamics of positive and negative affect in adolescents: Associations with depressive disorders and risk. J Mood Anxiety Disord [Internet]. 2024 [cited 2025 Oct 10];7:100069. https://doi.org/10.1016/j.xjmad.2024.100069 Neumann J von, Kent RH, Bellinson HR, work(s): BIHR. The Mean Square Successive Difference. Ann Math Stat [Internet]. 1941;12:153–62. http://www.jstor.org/stable/2235765 Sinnaeve R, Vaessen T, Van Diest I, Myin‐Germeys I, Van Den Bosch LMC, Vrieze E, et al. Investigating the stress‐related fluctuations of level of personality functioning: A critical review and agenda for future research. Clin Psychol Psychother [Internet]. 2021 [cited 2025 Oct 2];28:1181–93. https://doi.org/10.1002/cpp.2566 Di Bartolomeo AA, Varma S, Fulham L, Fitzpatrick S. The moderating role of interpersonal problems on baseline emotional intensity and emotional reactivity in individuals with borderline personality disorder and healthy controls. J Exp Psychopathol [Internet]. SAGE Publications; 2022 [cited 2025 July 13];13. https://doi.org/10.1177/20438087221142481 Kim HK, Pears KC, Capaldi DM, Owen LD. Emotion dysregulation in the intergenerational transmission of romantic relationship conflict. J Fam Psychol [Internet]. 2009 [cited 2025 Oct 1];23:585–95. https://doi.org/10.1037/a0015935 De Meulemeester C, Lowyck B, Luyten P. The role of impairments in self–other distinction in borderline personality disorder: A narrative review of recent evidence. Neurosci Biobehav Rev [Internet]. 2021 [cited 2025 Oct 1];127:242–54. https://doi.org/10.1016/j.neubiorev.2021.04.022 Chaudhury SR, Galfalvy H, Biggs E, Choo T-H, Mann JJ, Stanley B. Affect in response to stressors and coping strategies: an ecological momentary assessment study of borderline personality disorder. Borderline Personal Disord Emot Dysregulation [Internet]. Springer Science and Business Media LLC; 2017 [cited 2025 July 13];4. https://doi.org/10.1186/s40479-017-0059-3 Linehan MM. DBT skills training manual: For the therapist. 1st ed. EDULP; 2020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Constanza Vial","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Constanza","lastName":"Vial","suffix":""},{"id":536952452,"identity":"ff5b0e35-e823-410e-b223-f868df1ca635","order_by":1,"name":"Antonella Davanzo","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Antonella","middleName":"","lastName":"Davanzo","suffix":""},{"id":536952453,"identity":"db3059c0-97d4-46d5-8123-cf4ff212cb3b","order_by":2,"name":"Eduardo Franco","email":"","orcid":"","institution":"Pontificia Universidad Católica del Perú","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Franco","suffix":""},{"id":536952454,"identity":"a6e66736-3212-4e92-9ec0-e5c4c8e0941d","order_by":3,"name":"Alex 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06:50:47","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225362,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7838183/v1/0a1fcb460342611ef7d30079.html"},{"id":96250994,"identity":"d84ec3de-6355-4c05-955a-b6776de23359","added_by":"auto","created_at":"2025-11-19 07:39:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1325370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7838183/v1/daf01dca-648d-4c58-b67c-78f0cd401efc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Baseline and Daily Predictors of Negative Affect Dynamics in Patients Diagnosed with Borderline Personality Disorder: Specific Effects on Daily Means, Instability, and Inertia","fulltext":[{"header":"Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eBorderline Personality Disorder and Research Relevance\u003c/h2\u003e\u003cp\u003eOne of the central features of Borderline Personality Disorder (BPD) is the experience of intense negative affect dynamics, referring to overall levels of negative affect, negative affect instability such as rapid and intense fluctuations in emotions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and negative affect inertia or \u003cem\u003estickiness\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These features are central to BPD\u0026rsquo;s classification in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases, 11th Revision (ICD-11) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. BPD affects men and women equally and has a lifetime prevalence of 3%, exceeding bipolar disorder [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and schizophrenia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It also frequently coexists with depression (61%\u0026ndash;83%) and anxiety (88%), often alongside trauma history [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBPD is highly debilitating, leading to marked impairments in occupational and social functioning\u0026mdash;such as lower educational attainment, unstable relationships, and reduced life satisfaction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It imposes a substantial burden on caregivers and healthcare systems due to high service use and hospitalization rates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], accounting for 15\u0026ndash;28% of outpatient cases, 20\u0026ndash;60% of psychiatric admissions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and about 40% of hospitalizations for suicidality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Rehospitalization rates are often twice those of other disorders, significantly increasing healthcare costs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Germany, BPD accounts for roughly 20% of psychiatric care costs (\u0026euro;4\u0026nbsp;billion annually) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and its total societal cost is estimated at \u0026euro;35,038 per patient per year [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to BPD\u0026rsquo;s high burden, several countries (e.g., UK, Australia, Netherlands, Spain) have developed national clinical treatment guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its severity, recent findings challenge the view of BPD as a lifelong condition with a poor prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; a meta-analysis of 11 studies suggests a 60% diagnostic remission rate [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Evidence-based guidelines recommend psychotherapy as the primary treatment, with medication reserved for managing comorbidities or crisis interventions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nonetheless, gaps remain in understanding BPD, underscoring the need for further research to improve treatment outcomes, reducing distress for individuals and caregivers, and alleviate the economic burden on healthcare systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eNegative Affect in Borderline Personality Disorder\u003c/h2\u003e\u003cp\u003eNegative affect is a hallmark of BPD, characterized by instability and intensity, and is suggested as the driving force behind its severe clinical manifestations [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Patients with BPD present higher baseline levels of negative affect\u0026mdash;defined as a general tendency to experience anxiety and dysphoric states\u0026mdash;than healthy controls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], often experiencing intense, abrupt and variable negative emotions that lead to significant distress [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe nature of negative affect in BPD differs from that observed in other affective disorders. Emotional sensitivity in BPD has primarily been linked to negative rather than positive mood states [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], distinguishing it from other affective disorders such as bipolar disorder [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, depressive symptoms in BPD often show limited response to antidepressants but may remit as BPD improves, suggesting they are more related to life dissatisfaction than to a distinct depressive disorder [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with BPD also exhibit heightened reactivity to negative affect, with more intense emotions and slower recovery from distress [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], underscoring its central role in the disorder.\u003c/p\u003e\u003cp\u003eFour main theoretical models emphasize the role of negative affect variability in BPD: Linehan\u0026rsquo;s biosocial theory links emotional dysregulation to interpersonal invalidation, leading to cognitive and behavioral difficulties [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Psychodynamic Model [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], attributes emotional instability to identity diffusion and early aggressive drives [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Mentalization Model focuses on impaired mentalization, which results in emotional dysregulation, impulsivity, and difficulties managing behavior [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Interpersonal Hypersensity Model framework highlights interpersonal hypersensitivity as a primary factor underlying emotional reactivity in BPD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNegative affect plays a central role in BPD\u0026rsquo;s heterogeneous clinical presentation. As Gunderson [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] noted, individuals share core dimensions but may exhibit distinct subtypes defined by predominant diagnostic features. This variability is reflected in symptom instability and dimensional intensity, with phenotypes characterized by impulsivity or by identity disturbance and chronic emptiness [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Such heterogeneity raises questions about whether BPD represents a unitary construct, overlapping symptoms, or distinct subtypes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Further insight into negative affect may help refine these models and inform tailored interventions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePredictors of Negative Affect BPD\u003c/h3\u003e\n\u003cp\u003eNegative affect in BPD is influenced by several factors, including high comorbidity with depressive disorders, with 41% to 83% of individuals having a history of major depression and lifetime dysthymia prevalence ranging from 12% to 39% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nevertheless, while overlapping with depressive mechanisms, BPD\u0026rsquo;s affective instability is shaped by negative self-concept, dependency on others, and emotions such as anger, anxiety, and emptiness, distinguishing it from primary depressive disorders [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Affective intensity and regulation predict BPD traits even when controlling for depression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and BPD-specific interventions improve depressive symptoms where standard treatments do not [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterpersonal dysfunction, another hallmark of BPD [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], is also associated with negative affective instability. BPD individuals show heightened emotional reactivity to social stressors and self-conscious emotions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], with EMA studies confirming greater fluctuations in negative affect during social interactions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and heightened affective responses to daily events [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Heightened self-reported rejection sensitivity is common in BPD, which further amplifies negative affect dysregulation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Altered social cognition in BPD leads to hypersensitivity to negative cues, difficulty recognizing positive signals, and misinterpretation of social inclusion [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Neural studies show distinct rejection processing [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] linked to abandonment fears [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Mentalization deficits further bias perceptions of rejection and hostility [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDisturbances in self and interpersonal functioning contribute to fluctuations in negative affect in BPD, leading to cognitive distortions and instability in goals, beliefs, and self-concept [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Self-esteem instability is closely linked to affective dysregulation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and manifests as incoherence across thoughts, feelings, and behaviors, distinguishing BPD from other disorders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These disruptions are central to the ongoing debate between the DSM-5 categorical model and the AMPD, as the former focuses on symptom-based criteria and does not fully capture variations in self and interpersonal functioning. In contrast, the AMPD emphasizes dimensional severity through Criterion A (personality functioning). Criterion A shows greater predictive value for daily negative affect, supporting the AMPD\u0026rsquo;s validity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], though further research is needed.\u003c/p\u003e\u003cp\u003eSymptom severity in BPD, rather than mere presence, might also predict greater negative affect, with early severity in affect, impulsivity, and sociability forecasting BPD onset [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Studies have found that BPD symptoms at baseline (i.e., measured at the start of the study) predicted overall level and increasing fluctuations in negative affect [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and that early symptom severity in areas such as affect, impulsivity, and sociability predicts the later onset of BPD [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, sociodemographic variables, such as younger age, lower education, and single status, may represent significant predictors daily negative affect in BPD, consistent with findings from a recent study by Gunderson et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which reported similar associations even after controlling for covariates.\u003c/p\u003e\n\u003ch3\u003eCurrent Gaps in Research\u003c/h3\u003e\n\u003cp\u003eResearch on BPD has largely focused on its emotional features, which are central and persist compared to other symptoms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], often causing significant impairment and distress. Non-suicidal self-injury (NSSI) and suicide attempts are common coping mechanisms for overwhelming negative affect [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Understanding how daily negative affect manifests and what predicts it could refine theoretical models, improve diagnosis, and guide treatment differentiation given BPD\u0026rsquo;s high comorbidity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, determining whether predictors are consistent across patients could inform tailored psychotherapeutic interventions, while pharmacological treatment remains reserved for comorbidities or crises [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Investigating predictors of negative affect, particularly those linked to disturbances in self and interpersonal functioning, may help bridge gaps in BPD conceptualization and support dimensional perspectives of its etiology [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding the mechanisms behind daily negative affect in BPD is another challenge, as individuals experience rapidly shifting emotions. Yet, most studies treat it as a static construct, overlooking its time-varying nature [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Retrospective or laboratory-based assessments often fail to capture these real-time dynamics, yielding mixed results due to their artificial and less personally relevant nature. EMA effectively tracks symptom fluctuations in daily life\u0026mdash;including extreme shifts and environmental triggers\u0026mdash;while minimizing recall bias, providing context-sensitive data that enhances validity, generalizability, and informs accurate diagnostics and therapeutic strategies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother underexplored aspect is the distinction between emotional inertia and instability. Emotional inertia refers to the tendency to maintain persistent negative affective states, whereas instability involves rapid and large fluctuations in mood. Studies [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] show that individuals with BPD exhibit both high emotional instability and greater inertia, suggesting that these dimensions may have different triggers and may require distinct therapeutic approaches. Understanding these patterns could help clinicians tailor interventions to patient-specific emotional dynamics; however, further research is needed to clarify how inertia and instability influence emotional regulation and daily functioning in BPD.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eForty-five adult participants, all aged 19 or older, diagnosed with BPD using the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II), were recruited from various specialized treatment facilities in Santiago, Chile. Of the total sample, 43 were women and 2 were men. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a detailed characterization of the sample, including socio-demographic and clinical variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive Baseline Analysis\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale: 2 (4.4%)\u003c/p\u003e\u003cp\u003eFemale: 43 (95.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes: 40 (88.9%)\u003c/p\u003e\u003cp\u003eNo: 5 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes: 33 (73.3%)\u003c/p\u003e\u003cp\u003eNo: 12 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Onset (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression Level (PHQ-9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u0026ndash;27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonality Functioning (LPFS-BF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u0026ndash;47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom Severity (ZAN-BPD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Criticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpersonal Hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u0026ndash;3.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Negative Affect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u0026ndash;2.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Abbreviations: n (%)\u0026thinsp;=\u0026thinsp;number and percentage of participants; M\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; PHQ-9\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-9; LPFS-BF\u0026thinsp;=\u0026thinsp;Level of Personality Functioning Scale \u0026ndash; Brief Form; ZAN-BPD\u0026thinsp;=\u0026thinsp;Zanarini Rating Scale for Borderline Personality Disorder. Continuous variables are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and range (Min\u0026ndash;Max).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e All procedures were approved by an accredited local Ethical Review Board. Participants provided written informed consent before enrollment, which emphasized confidentiality, voluntary participation, and a USD \u003cspan\u003e$\u003c/span\u003e15.00 compensation upon completion. Inclusion criteria was being 18 years or older and meeting DSM-IV criteria for BPD; exclusion criteria assessed through the \u003cem\u003eMINI International Neuropsychiatric Interview\u003c/em\u003e (MINI) included high suicidal intent, current psychotic or manic episode, substance abuse or dependence. All participants had to own a smartphone with internet data services.\u003c/p\u003e\u003cp\u003eBPD diagnosis was confirmed through a structured clinical interview conducted by trained psychologists (PhD, MSc, or MSc-in-training). Eligible participants registered in the \u003cem\u003eAvicenna Research (\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://avicennaresearch.com/\u003c/span\u003e\u003cspan address=\"https://avicennaresearch.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e mobile application and completed baseline self-reports including: PHQ-9, ZAN-BPD, LPFS-BF, and a brief demographic survey. The following day, they began an 11-day EMA protocol with prompts delivered at varying intervals under three randomized sequences of prompting conditions (25 prompts/day every 30 minutes, 13 prompts/day every hour, and 5 prompts/day every three hours). Prompts were pseud-randomized within 10-minute windows and expired after 15 minutes to ensure real-time responses.\u003c/p\u003e\n\u003ch3\u003eBaseline Measures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eZanarini Rating Scale for Borderline Personality Disorder\u003c/b\u003e \u003cem\u003e(ZAN-BPD)\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eZAN-BPD was designed to assess the presence and severity of BPD symptoms. It assesses the four main areas of psychopathology in BPD through nine items: affectivity, cognition, impulsivity, and interpersonal relationships [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Each item is scored on a Likert scale from 0 to 4, resulting in a total score from 0 to 36 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In general, the scale provides a total score reflecting BPD symptom severity in the assessed individual [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].The interview and self-report versions of the ZAN-BPD demonstrate high convergent validity (median\u0026thinsp;=\u0026thinsp;0.70) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and good internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.84) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eLevels of Personality Functioning Brief Form 2.0\u003c/b\u003e \u003cem\u003e(LPFS-BF 2.0)\u003c/em\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eLPFS-BF 2.0 is a brief self-report instrument that provides a rapid impression of the severity of a personality disorder. It consists of 12 items that are to be scored in a Likert scale format from 0 to 3 (0\u0026thinsp;=\u0026thinsp;very false or often false; 1\u0026thinsp;=\u0026thinsp;sometimes or somewhat false; 2 sometimes or somewhat true; 3\u0026thinsp;=\u0026thinsp;very true or often true), with a maximum score of 36; a higher score indicates a higher presence and severity of a personality disorder [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The final LPFS-BF 2.0 produces a total personality functioning score and two subscales (Self and Interpersonal Functioning) to reflect the two broad categories of specific personality functioning outlined in the AMPD [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The LPFS-BF 2.0 has demonstrated satisfactory internal consistency, promising construct validity and sensitivity to change after three months of treatment [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The LPFS-BF 2.0 has been translated into Spanish and was used in a large cross-national study that included a Chilean sample [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Invariance testing across countries, including Chile, supports score comparability across languages and cultures [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Health Questionnaire \u0026minus;\u0026thinsp;9 (PHQ-9)\u003c/b\u003e [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePHQ-9 is a nine item self-report scale, used for detecting mild, moderate, or severe depressive symptoms, and has proven to be an efficient diagnostic tool [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. PHQ9 consists of 9 items that assess the presence of depressive symptoms (corresponding to DSM-IV criteria) experienced in the past 2 weeks. Each item has a corresponding severity index: 0 = \"never\", 1 = \"some days\", 2 = \"more than half of the days\", and 3 = \"almost every day\" [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Standard cut-off points have been proposed to classify severity levels: mild (5\u0026ndash;9), moderate (10\u0026ndash;14), moderately severe (15\u0026ndash;19), and severe (20\u0026ndash;27), with a score of \u0026ge;\u0026thinsp;10 frequently used to identify clinically relevant cases [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The validated version in Chile was used in this study, which presents construct and predictive validity concurrent with the ICD-10 criteria for depression [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSocio-Demographic and Clinical History Survey\u003c/h2\u003e\u003cp\u003eTo characterize our sample and later identify possible socio-demographic predictors for emotional variability and intensity in BPD, each participant completed a self-report questionnaire. It consisted of several multiple option or brief response questions, regarding the patient\u0026rsquo;s age, sex at birth, gender, occupation, educational level, nationality, ethnicity, habitational situation, medical/psychiatric/psychological treatment, psychiatric hospitalization antecedents and age of onset of mental health difficulties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntensive Longitudinal Measures: Ecological Momentary Assessment\u003c/h2\u003e\u003cp\u003e\u003cb\u003ePositive and Negative Affect Schedule \u0026ndash; Short Version (PANAS)\u003c/b\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePANAS measures two dimensions of affective experience: positive and negative affect and specific facets of emotions. Psychometric properties of PANAS in Chile are known, as it has been validated in a Chilean sample [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The validated version of PANAS has adequate internal consistency, test re-test stability, factorial structure, and convergent validity. This abbreviated scale has yielded a high internal consistency, with a .92 alpha [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Following a procedure amply utilized in EMA for BPD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], only 5 items of PANAS corresponding to negative emotion were utilized in this study 's EMA signals. This selection was made in line with the characteristic pattern of affective instability in BPD, as previously described, which is predominantly observed in negative affect rather than in positive affect. Participants were asked to rate the extent to which they felt five negative emotions\u0026mdash;ashamed, hostile, nervous, scared, and upset\u0026mdash;at that precise moment on a 5-point Likert scale. Only the highest score of each PANAS assessment was used to index peak affective intensity, an approach that focuses on affective peaks rather than mean levels and has precedent in EMA research [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eInterpersonal Hypersensitivity:\u003c/h2\u003e\u003cp\u003eItems were selected from the Daily Stress Inventory (DSI) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] using a version adapted for a Chilean sample [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This followed a procedure described by Smyth et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] that has been replicated in different studies. Items from DSI, which is widely used and shows adequate psychometric properties [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], were extracted to create an interpersonal stress scale compatible with EMA\u0026rsquo;s intensive data collection program. Using an analogue scale, participants responded to items from the DSI, including: (1) I felt criticized and verbally attacked, (2) I felt misunderstood by others (interpersonal alienation). A third item was added, focusing on the experience of feeling rejected: (3) \u0026ldquo;Right now I feel rejected by others\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSelf-Criticism\u003c/h2\u003e\u003cp\u003eTwo items from the Beck Depression Inventory-II (BDI-II) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] were used to capture momentary self-criticism. The first item (\u0026ldquo;Since the last time I responded, and including this moment, I feel disappointed in myself\u0026rdquo;) and the second item (\u0026ldquo;Since the last time I responded, and including this moment, I hate myself\u0026rdquo;) were adapted from the BDI-II.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSelf Concept Stability (SE-SCC)\u003c/h2\u003e\u003cp\u003eFollowing a procedure tested and described recently by Scala et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], participants answered two items from the Self-Concept Clarity Scale (SCCS) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], The first item (\u0026ldquo;Right now, I have a clear sense of who I am and what I am\u0026rdquo;) and the second item (\u0026ldquo;Since the last time I responded, and including this moment, I have clarity about where I want to go in life\u0026rdquo;) were adapted from the SCCS.\u003c/p\u003e\u003cp\u003eAll EMA items were rated on a visual analogue scale ranging from 0 (\u0026ldquo;very slightly or not at all\u0026rdquo;) to 4 (\u0026ldquo;extremely\u0026rdquo;). To capture repeated assessments of momentary self-esteem states, the items were modified with time-specific phrasing such as \u0026ldquo;since the last time I responded\u0026rdquo; or \u0026ldquo;right now\u0026rdquo;. Based on EMA dimensions, three composite variables were created to function as daily predictors of negative affect, each based on the average of a pair of items: \u003cem\u003eSelf-Concept Clarity\u003c/em\u003e (two items from the SCCS, namely self-concept clarity and self-direction), \u003cem\u003eSelf-Criticism\u003c/em\u003e (two items from the BDI-II: Self-disillusionment and Self-hate), and \u003cem\u003eInterpersonal Hypersensitivity\u003c/em\u003e (using the average for the three interpersonal hypersensitivity items, namely feeling rejected, feeling criticized and feeling misunderstood).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll EMA data was downloaded directly from Avicenna. The data was analyzed using the statistical environment R (version 4.0.4) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Sample characteristics, including socio-demographic and clinical variables, were summarized using descriptive statistics (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multilevel linear mixed models [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] were used to model the effects of baseline predictors (age of onset, past psychiatric treatment, history of inpatient admissions, PHQ-9, LPFS-FB 2.0 and ZAN-BPD scores) and daily predictors (daily levels of interpersonal hypersensitivity, daily levels of self-stability, and daily levels of self-criticism) on daily averages of negative affect. Three consecutive models were fitted. The first model predicted mean daily negative affect from baseline predictors. The second model added the within-subject component of daily predictors using person-centered means, and the third model included between-subject components for daily predictors. Nested models were compared using likelihood-ratio tests and marginal/conditional R squared [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo examine temporal dynamics, two additional mixed-effects models were estimated. First, an inertia model [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] at the prompt level predicted negative affect at time \u003cem\u003et\u003c/em\u003e from affect at time \u003cem\u003et\u0026ndash;1\u003c/em\u003e (lag-1) and its interactions with momentary self-criticism and interpersonal hypersensitivity, testing whether these daily processes influenced the \u003cem\u003estickiness\u003c/em\u003e of negative mood during the day. Second, an instability model at the day level predicted within-day affective variability\u0026mdash;quantified as the \u003cem\u003emean squared successive difference\u003c/em\u003e (MSSD) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u0026mdash;from daily averages of self-criticism and interpersonal hypersensitivity. Both models included the experimental condition (prompt-intensity group) as a categorical covariate to adjust for differences in sampling frequency across participants. Missing EMA prompts were treated as missing at random; no imputation was performed. Only the significant daily predictors from the multilevel model\u0026mdash;momentary self-criticism and interpersonal hypersensitivity\u0026mdash;were included in subsequent temporal dynamics models examining affective inertia and within-day instability (MSSD).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDaily Negative Affect Levels: Baseline and Momentary Predictors\u003c/h2\u003e\u003cp\u003eBaseline predictors\u0026mdash;including age of onset, previous psychiatric hospitalization, previous psychiatric treatment, depressive symptoms (PHQ-9), personality functioning (LPFS-BF), and BPD symptom severity (ZAN-BPD)\u0026mdash;were not significantly associated with daily levels of negative affect. In contrast, daily predictors, specifically daily levels of self-criticism and interpersonal hypersensitivity, significantly predicted higher daily mean levels of negative affect at both the within- and between-person levels.\u003c/p\u003e\u003cp\u003eIn terms of predictive power, adding daily predictors significantly improved the prediction of daily negative affect beyond baseline trait-like covariates. Model fit increased from the baseline (R\u0026sup2;m\u0026thinsp;=\u0026thinsp;0.14, R\u0026sup2;c\u0026thinsp;=\u0026thinsp;0.66) to the within-person model (R\u0026sup2;m\u0026thinsp;=\u0026thinsp;0.25, R\u0026sup2;c\u0026thinsp;=\u0026thinsp;0.78; Δχ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;181.91, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Including both within- and between-person effects yielded the highest explanatory power (R\u0026sup2;m\u0026thinsp;=\u0026thinsp;0.59, R\u0026sup2;c\u0026thinsp;=\u0026thinsp;0.77; Δχ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;48.36, p\u0026thinsp;=\u0026thinsp;0.18). AIC and BIC values also decreased consistently, confirming the incremental explanatory value of both within- and between-person effects. These results indicate that daily fluctuations in self-criticism and interpersonal hypersensitivity substantially increased explained variance in negative affect, highlighting that momentary shifts in these states meaningfully contribute to day-to-day emotional distress beyond stable trait-like individual differences. The marginal R\u0026sup2; increased from 0.14 (baseline) to 0.59 in the final model that included within and between components in addition to baseline predictors, indicating that the inclusion of momentary and person-level daily predictors substantially enhanced the proportion of variance in negative affect explained by the fixed effects. The conditional R\u0026sup2; remained high (~\u0026thinsp;0.77\u0026ndash;0.78), suggesting that a large share of total variability was also captured by random (person-level) components.\u003c/p\u003e\u003cp\u003eAt the within-person level, days characterized by higher-than-usual self-criticism (b\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and interpersonal hypersensitivity (b\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were associated with significantly higher daily levels of negative affect. Daily fluctuations in self-stability were not significantly related to the outcome (b\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;.77). These findings indicate that day-to-day increases in self-critical and interpersonally hypersensitive states predict same-day elevations in negative affect, above and beyond each person\u0026rsquo;s typical levels of these variables.\u003c/p\u003e\u003cp\u003eAt the between-person level, participants who were generally higher in self-criticism (b\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;.008) and interpersonal hypersensitivity (b\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) also exhibited higher average levels of daily negative affect. Between-person differences in self-stability were nonsignificant (p\u0026thinsp;=\u0026thinsp;.72). Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is provided at the end of the manuscript due to its length).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDaily Negative Affect Levels: Baseline and Momentary Predictors\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM0: Baseline Predictors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.451, 1.844]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Onset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.041, 0.010]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.472, 0.551]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.495, 0.385]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression Level (PHQ-9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.035, 0.073]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonality Functioning (LPFS-BF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.041, 0.033]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom Severity (ZAN-BPD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.013, 0.065]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM1: Baseline\u0026thinsp;+\u0026thinsp;Daily Predictors (Within-Person Effects)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.439, 1.853]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Onset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.041, 0.009]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.464, 0.558]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.495, 0.385]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression Level (PHQ-9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.035, 0.072]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonality Functioning (LPFS-BF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.041, 0.033]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom Severity (ZAN-BPD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.012, 0.066]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Stability (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.067, 0.091]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Criticism (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.165, 0.311]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpersonal Hypersensitivity (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.217, 0.352]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM2: Baseline\u0026thinsp;+\u0026thinsp;Daily Predictors (Within- and Between-Person Components)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.852, 0.765]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Onset (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.012, 0.020]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.373, 0.252]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Psychiatric Hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.267, 0.253]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression Level (PHQ-9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.037, 0.027]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonality Functioning\u003c/p\u003e\u003cp\u003e(LPFS-BF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.015, 0.030]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom Severity (ZAN-BPD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.025, 0.023]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.067, 0.091]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Criticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.166, 0.311]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpersonal Hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.216, 0.352]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Stability (between)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.088, 0.127]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Criticism (between)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.042, 0.286]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpersonal Hypersensitivity (between)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.397, 0.714]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Stability (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;0.067, 0.091]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Criticism (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.166, 0.311]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpersonal Hypersensitivity (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.216, 0.352]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. Abbreviations: Estimate (β)\u0026thinsp;=\u0026thinsp;regression coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; CI\u0026thinsp;=\u0026thinsp;confidence interval; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003et\u003c/em\u003e-value; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ep\u003c/em\u003e-value (statistical significance). Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001(**), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05(*).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInertia Model: Momentary Predictors of Daily Negative Affect Persistence\u003c/h2\u003e\u003cp\u003eA multilevel inertia model revealed significant temporal dependencies in negative affect. Lagged negative affect (NAₜ₋₁) strongly predicted subsequent negative affect (b\u0026thinsp;=\u0026thinsp;0.29, SE\u0026thinsp;=\u0026thinsp;0.03, t\u0026thinsp;=\u0026thinsp;11.10, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating substantial affective inertia. Higher momentary self-criticism (b\u0026thinsp;=\u0026thinsp;0.10, SE\u0026thinsp;=\u0026thinsp;0.02, t\u0026thinsp;=\u0026thinsp;5.28, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and interpersonal hypersensitivity (b\u0026thinsp;=\u0026thinsp;0.20, SE\u0026thinsp;=\u0026thinsp;0.02, t\u0026thinsp;=\u0026thinsp;8.85, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were also associated with higher concurrent negative affect. Crucially, the interaction between lagged negative affect and self-criticism (NAₜ₋₁ \u0026times; self-criticism) was significant (b\u0026thinsp;=\u0026thinsp;0.06, SE\u0026thinsp;=\u0026thinsp;0.01, t\u0026thinsp;=\u0026thinsp;4.95, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that when individuals were more self-critical, their negative affect carried over more strongly from one prompt to the next\u0026mdash;that is, self-criticism increased affective stickiness. In contrast, the interaction between lagged negative affect and interpersonal hypersensitivity was not significant (p\u0026thinsp;=\u0026thinsp;.15), suggesting that although interpersonal hypersensitivity intensifies negative affect in the moment, it does not affect its temporal persistence. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eInertia Model: Momentary Predictors of Daily Negative Affect Persistence\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNA lag (t\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily Self-criticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily Interpersonal Hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNA lag \u0026times; Daily Self-Criticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNA lag \u0026times; Daily Interpersonal Hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Abbreviations: NA\u0026thinsp;=\u0026thinsp;Negative Affect; SE\u0026thinsp;=\u0026thinsp;Standard Error. Significance: **p\u0026thinsp;\u0026lt;\u0026thinsp;.001, *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. Random intercept variance\u0026thinsp;=\u0026thinsp;0.043; residual variance\u0026thinsp;=\u0026thinsp;0.180.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eInstability Model - Momentary Predictors of Daily Negative Affect Volatility\u003c/h2\u003e\u003cp\u003eA linear mixed-effects model was estimated to examine whether daily levels of self-criticism and interpersonal hypersensitivity predicted day-to-day emotional instability, indexed by the mean squared successive difference (MSSD) in negative affect. Random intercepts were estimated for each participant (REML\u0026thinsp;=\u0026thinsp;909.5), with a random intercept variance of 0.051 (SD\u0026thinsp;=\u0026thinsp;0.23) and residual variance of 0.591 (SD\u0026thinsp;=\u0026thinsp;0.77), based on 379 daily observations from 44 participants.\u003c/p\u003e\u003cp\u003eDaily interpersonal hypersensitivity significantly predicted higher within-day instability in negative affect (b\u0026thinsp;=\u0026thinsp;0.16, SE\u0026thinsp;=\u0026thinsp;0.06, t\u0026thinsp;=\u0026thinsp;2.40, p\u0026thinsp;=\u0026thinsp;.011), indicating that on days when participants felt more interpersonally sensitive or rejected, their negative affect fluctuated more sharply. In contrast, daily self-criticism was not significantly associated with affective instability (p\u0026thinsp;=\u0026thinsp;.40). The positive intercept reflects a baseline MSSD of approximately 0.24, indicating moderate average day-level affect variability even after accounting for predictors. Detailed parameter estimates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eInstability Model - Momentary Predictors of Daily Negative Affect Volatility (MSSD)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily self-criticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily interpersonal hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.011*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRandom intercept variance\u0026thinsp;=\u0026thinsp;0.051; residual variance\u0026thinsp;=\u0026thinsp;0.591. Significance: *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eContrary to expectations, baseline predictors\u0026mdash;including psychiatric history, depressive symptoms (PHQ-9), BPD severity (ZAN-BPD), and personality functioning (LPFS-BF)\u0026mdash;did not significantly predict daily negative affect. Baseline measures, designed to capture stable, trait-like aspects of BPD, may not adequately reflect day-to-day fluctuations in negative affect, a key aspect of BD psychopathology and a frequent area of clinical concern. For instance, the LPFS-BF, aligned with the AMPD, emphasizes enduring impairments in self and interpersonal functioning, potentially missing daily variability of negative affect; as shown by Sinnaeve et al. [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Retrospective bias\u0026mdash;common in BPD, where individuals may underestimate symptom frequency and overestimate emotional intensity [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] \u0026mdash;may further limit the predictive value of baseline self-report measures. This highlights the importance of intensive longitudinal assessments to really capture daily or momentary processes. Using EMA allowed us to capture real-time fluctuations and identify the distinct contributions of self-criticism and interpersonal hypersensitivity to daily negative affect, which might not have been detectable with standard baseline measures.\u003c/p\u003e\u003cp\u003eIn contrast, momentary predictors, particularly self-criticism and interpersonal hypersensitivity, were consistently associated with elevated daily negative affect, both within-person (days with higher-than-usual states) and between-person (individuals with higher average levels). These results suggest that daily, dynamic processes may be more relevant than static traits for understanding emotional distress in BPD. Self-stability was not significant, possibly reflecting its trait-like nature.\u003c/p\u003e\u003cp\u003eGiven that only momentary self-criticism and interpersonal hypersensitivity were significant predictors of negative affect in the first models, subsequent analyses focused on these variables to examine temporal dynamics such as affective inertia and within-day instability. Self-criticism increased affective inertia, prolonging negative affect across time points, but did not affect within-day instability. Interpersonal hypersensitivity, in contrast, increased concurrent momentary negative affect and within-day negative affect instability. Self-criticism may explain why BPD individuals remain \u0026ldquo;stuck\u0026rdquo; in negative affect, whereas interpersonal hypersensitivity may drive rapid emotional shifts.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eTreatment Implications\u003c/h2\u003e\u003cp\u003eThe findings of this study help clarify which factors contribute to difficulties in negative affect among individuals with BPD. Consistent with previous research [\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], negative affect appears to be particularly sensitive to interpersonal stressors and disruptions in self-concept, such as self-criticism. Importantly, different variables influence negative affect through distinct pathways: self-criticism amplifies affective inertia, prolonging negative affect across time points, whereas interpersonal hypersensitivity drives rapid, short-term fluctuations, contributing to within-day emotional instability.\u003c/p\u003e\u003cp\u003eConsequently, capturing individual differences within these domains is important. Some individuals may be more prone to sustained negative mood driven by heightened self-criticism, others may experience greater affective instability in response to interpersonal difficulties, and some may be equally impacted by both processes. Differentiating these profiles can inform more personalized treatment approaches tailored to each patient\u0026rsquo;s unique emotional vulnerabilities.\u003c/p\u003e\u003cp\u003eClinically, these findings suggest that interventions targeting self-criticism may help patients reduce the tendency to remain \u0026ldquo;stuck\u0026rdquo; in negative affect, whereas strategies focused on interpersonal sensitivity and emotion regulation may be more effective for managing rapid mood shifts. While all momentary variables showed significant correlations with negative affect, their differing impacts suggest that treatment should prioritize patient-specific triggers. Interventions may focus on enhancing emotional awareness and regulation related to interpersonal problems and self-criticism, helping patients develop more effective coping mechanisms and improve negative affect regulation.\u003c/p\u003e\u003cp\u003eEstablished treatment models align with these distinctions: Dialectical Behavior Therapy (DBT) offers mindfulness, self-soothing, and distress tolerance strategies to reduce self-criticism and foster self-compassion [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. As Linehan notes, emotional dysregulation can compromise self-awareness, leading to emptiness and identity disturbance [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], while Mentalization-Based Therapy (MBT) addresses interpersonal sensitivity by enhancing reflective functioning and promoting secure attachment patterns [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Present findings emphasize that treatment should focus on identifying which approaches most effectively help each patient improve in domains relevant to their predominant emotional patterns.\u003c/p\u003e\u003cp\u003eFinally, incorporating momentary assessment methods into clinical practice could serve as a valuable therapeutic tool. EMA might not only help patients increase awareness of their emotional patterns and triggers in real time\u0026mdash;supporting improved emotion regulation and interpersonal functioning\u0026mdash;but also allow clinicians to identify temporal dynamics, such as affective inertia and within-day instability, that baseline measures cannot capture. This capacity to reveal the contribution of specific momentary states underscores EMA\u0026rsquo;s relevance for tailoring interventions to individual patterns of negative affect in BPD.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eLimitations in this Study\u003c/h2\u003e\u003cp\u003e First, our sample consisted of participants diagnosed with BPD who were already undergoing treatment. This implies that processes such as inertia or instability might be curtailed by the effect of treatments. Additionally, ongoing psychiatric or psychological treatment may have influenced baseline predictors, so these measures might not fully reflect participants\u0026rsquo; unaltered patterns of affective change. It is not clear from this study if the negative affect dynamics identified are specific to BPD or transdiagnostic in nature. The predominance of female participants further limits generalizability, as results of this study may reflect dynamic processes in negative affect representative of women experiencing BPD. Replication studies including healthy controls, untreated BPD patients, male BPD participants, and individuals with other psychiatric disorders would help clarify these issues. Baseline measures, based on retrospective self-report and stable traits, may suffer from recall bias, limited specificity, and reduced variability. These factors, combined with potential low statistical power for between-subject comparisons, might explain why trait-like predictors failed to influence daily negative affect, although this finding has been reproduced in other studies and may be related to the fact that by design, measures that are constructed to capture trait like components of mental illness are not well suited to capture momentary or even daily fluctuations.\u003c/p\u003e\u003cp\u003eMissing data in EMA assessments remains a concern. Participants might have missed prompts during moments of intense negative affect, potentially leading to underestimation of emotional variability and momentary predictors. Future research should implement strategies to reduce missing data or apply modeling techniques to account for it.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFurther Research\u003c/h2\u003e\u003cp\u003eAlthough this study identified significant temporal and predictive relationships between negative affect, momentary self-criticism, and interpersonal hypersensitivity, causality remains unclear. It is unknown whether interpersonal problems increase self-criticism, whether self-criticism precedes interpersonal problems, or if they mutually reinforce each other. Further research is needed to clarify these dynamics and determine which problems may resolve earlier in treatment, thereby guiding more effective clinical approaches.\u003c/p\u003e\u003cp\u003eIndividuals with BPD differ in sensitivity to emotional triggers, with some more reactive to interpersonal stressors and others to self-concept conflicts. Identifying these differences, particularly if they are stable over time, could point to the presence of distinct phenotypes within the disorder. Another important avenue for future research is to investigate the individual components that comprise the constructs of interpersonal hypersensitivity and self-criticism. Interpersonal hypersensitivity was measured using DSI items capturing feelings of criticism, misunderstanding, and rejection, while self-criticism was assessed with two BDI-II items on self-disappointment and self-hatred. Examining these components separately may clarify their distinct contributions to negative affect.\u003c/p\u003e\u003cp\u003eFinally, exploring additional predictors, such as maladaptive personality traits or trauma-related triggers, and identifying interventions that best reduce reactivity to these stressors will be essential for developing more personalized and effective treatments.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBPD \u0026ndash; Borderline Personality Disorder\u003c/p\u003e\n\u003cp\u003eDSM-5 \u0026ndash; Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e\n\u003cp\u003eICD-11 \u0026ndash; International Classification of Diseases, 11th Revision\u003c/p\u003e\n\u003cp\u003eAMPD \u0026ndash; Alternative Model for Personality Disorders\u003c/p\u003e\n\u003cp\u003eEMA \u0026ndash; Ecological Momentary Assessment\u003c/p\u003e\n\u003cp\u003ePHQ-9 \u0026ndash; Patient Health Questionnaire-9\u003c/p\u003e\n\u003cp\u003eZAN-BPD \u0026ndash; Zanarini Rating Scale for Borderline Personality Disorder\u003c/p\u003e\n\u003cp\u003eLPFS-BF \u0026ndash; Level of Personality Functioning Scale \u0026ndash; Brief Form\u003c/p\u003e\n\u003cp\u003eNSSI \u0026ndash; Non-Suicidal Self-Injury\u003cbr\u003e\u0026nbsp;M \u0026ndash; Mean\u003c/p\u003e\n\u003cp\u003eSD \u0026ndash; Standard Deviation\u003c/p\u003e\n\u003cp\u003eMin \u0026ndash; Minimum\u003c/p\u003e\n\u003cp\u003eMax \u0026ndash; Maximum\u003c/p\u003e\n\u003cp\u003eSCID-II \u0026ndash; Structured Clinical Interview for DSM-IV Axis II Personality Disorders\u003c/p\u003e\n\u003cp\u003eMINI \u0026ndash; Mini International Neuropsychiatric Interview\u003c/p\u003e\n\u003cp\u003eLPFS-BF 2.0 \u0026ndash; Level of Personality Functioning Scale \u0026ndash; Brief Form 2.0\u003c/p\u003e\n\u003cp\u003ePANAS \u0026ndash; Positive and Negative Affect Schedule\u003c/p\u003e\n\u003cp\u003eDSI \u0026ndash; Daily Stress Inventory\u003c/p\u003e\n\u003cp\u003eBDI-II \u0026ndash; Beck Depression Inventory-II\u003c/p\u003e\n\u003cp\u003eSCCS \u0026ndash; Self-Concept Clarity Scale\u003c/p\u003e\n\u003cp\u003eSE-SCC \u0026ndash; Self-Concept Stability\u003c/p\u003e\n\u003cp\u003eMSSD \u0026ndash; Mean Squared Successive Difference\u003c/p\u003e\n\u003cp\u003eR\u0026sup2;m \u0026ndash; Marginal R Squared (proportion of variance explained by fixed effects)\u003c/p\u003e\n\u003cp\u003eR\u0026sup2;c \u0026ndash; Conditional R Squared (proportion of variance explained by both fixed and random effects)\u003c/p\u003e\n\u003cp\u003eAIC \u0026ndash; Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003eBIC \u0026ndash; Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003e\u0026beta; \u0026ndash; Estimate \u0026beta;\u003c/p\u003e\n\u003cp\u003eSE \u0026ndash; Standard Error\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eN \u0026ndash; Number of Measurements\u003c/p\u003e\n\u003cp\u003et \u0026ndash; t-value\u003c/p\u003e\n\u003cp\u003ep \u0026ndash; p-value (significance)\u003c/p\u003e\n\u003cp\u003eNAₜ₋₁ / NA lag \u0026ndash; Negative Affect lag\u003c/p\u003e\n\u003cp\u003eREML \u0026ndash; Restricted Maximum Likelihood\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAll procedures were approved by the Ethics Committee of the Pontifical Catholic University of Chile (Approval Code: 240730001 and 210331010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;All participants provided written informed consent for publication of anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests Statement\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eThe authors declare no competing interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding Statement\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cbr\u003eThis study was supported by the Fondo Inicio VRI granted by Pontificia Universidad Cat\u0026oacute;lica de Chile and MIDAP. The funding source was not involved in the study design, data collection, analysis, interpretation, or writing of the manuscript.\u003cbr\u003eThis project has been funded by the Foundation Botnar. The second author has been funded by the National Agency for Research and Development (ANID) / Scholarship Program / DOCTORADO BECAS CHILE/2019 - 21190831. This project has been supported by the Research Directorate of the Vice-Rectory for Research at the Pontifical Catholic University of Chile through the INICIO Grant 2020 and the ANID Millennium Science Initiative/Millennium Institute for Research on Depression and Personality-MIDAP ICS13_005.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; Contributions\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003eC.V.: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Visualization.\u003cbr\u003eA.D.: Methodology, Data curation, Investigation, Formal Analysis, Writing \u0026ndash; review \u0026amp; editing, Project administration.\u003cbr\u003eE.F.: Validation, Formal analysis.\u003cbr\u003e\u0026nbsp;A.B.: Conceptualization, Methodology, Validation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eWe would like to extend our sincere thanks to Mar\u0026iacute;a Paz Kattan, Colomba Egenau, and Magdalena Daveggio, as well as to the Millennium Institute for Research in Depression and Personality (MIDAP) and the Fondo de Inicio UC of the Pontificia Universidad Cat\u0026oacute;lica de Chile, for their invaluable support and collaboration in the development of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLieb K, Zanarini MC, Schmahl C, Linehan MM, Bohus M. 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Elsevier BV; 2021 [cited 2025 July 13];398:1528\u0026ndash;40. https://doi.org/10.1016/s0140-6736(21)00476-1 \u003c/li\u003e\n\u003cli\u003eEllison WD, Rosenstein LK, Morgan TA, Zimmerman M. Community and clinical epidemiology of borderline personality disorder. Psychiatr Clin North Am [Internet]. Elsevier BV; 2018 [cited 2025 July 14];41:561\u0026ndash;73. https://doi.org/10.1016/j.psc.2018.07.008 \u003c/li\u003e\n\u003cli\u003eGregory R, Sperry SD, Williamson D, Kuch-Cecconi R, Spink GLJ. High prevalence of borderline personality disorder among psychiatric inpatients admitted for suicidality. Psychodyn Psychiatry. 2021;49:285\u0026ndash;91. https://doi.org/10.1521/pedi_2021_35_508 \u003c/li\u003e\n\u003cli\u003eLewis KL, Fanaian M, Kotze B, Grenyer BFS. Mental health presentations to acute psychiatric services: 3-year study of prevalence and readmission risk for personality disorders compared with psychotic, affective, substance or other disorders. BJPsych Open [Internet]. 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DBT skills training manual: For the therapist. 1st ed. EDULP; 2020. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Borderline Personality Disorder, Ecological Momentary Assessment, Negative Affect, Interpersonal Hypersensitivity, Self-criticism, Self-concept Stability","lastPublishedDoi":"10.21203/rs.3.rs-7838183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7838183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNegative affect is a core clinical feature of Borderline Personality Disorder (BPD). Identifying baseline characteristics and day-to-day processes that predict negative affect dynamics is key for improving treatment strategies and refining the conceptualization of the disorder.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study examined how baseline characteristics (psychiatric history, depressive symptoms, BPD severity, personality functioning) and daily processes (interpersonal hypersensitivity, self-criticism, self-concept stability) relate to daily negative affect levels, instability, and inertia in 45 individuals with BPD, assessed via Ecological Momentary Assessment (EMA)\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003e Participants (n\u0026thinsp;=\u0026thinsp;45) recruited from clinical services in Santiago, Chile completed baseline self-report measures (PHQ-9, ZAN-BPD, LPFS-BF), and demographics including age of onset and basic psychiatric history. Over 11 days, EMA prompts were delivered at varying intervals under three randomized schedules (25/day every 30 min, 13/day every hour, 5/day every three hours), assessing negative affect, interpersonal problems, self-concept stability, and self-criticism. Multilevel linear mixed models examined predictors of mean daily negative affect. Two additional mixed-effects models explored daily dynamics: an inertia model (predicting negative affect at one moment from the previous moment) and an instability model (mean squared successive difference, MSSD).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBaseline trait-like variables did not predict mean daily negative affect. Daily averages of interpersonal hypersensitivity and self-criticism predicted daily negative affect at both within- and between-subject levels. Inertia analyses indicated that persistence of negative affect was predicted by self-criticism but not by interpersonal hypersensitivity. Conversely, instability of negative affect was predicted by interpersonal hypersensitivity but not by self-criticism.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDaily fluctuations in negative affect in BPD appear more strongly tied to situational stressors than to stable traits, highlighting the importance of context-sensitive assessment and intervention. Self-criticism is linked to persistence of negative affect, whereas interpersonal hypersensitivity is associated with its instability.\u003c/p\u003e","manuscriptTitle":"Baseline and Daily Predictors of Negative Affect Dynamics in Patients Diagnosed with Borderline Personality Disorder: Specific Effects on Daily Means, Instability, and Inertia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 17:21:03","doi":"10.21203/rs.3.rs-7838183/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba2be310-3a95-43ca-8b5e-f56bc08d4875","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T12:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 17:21:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7838183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7838183","identity":"rs-7838183","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}