The temporal order of emotional, cognitive, and behavioral gains in daily life during treatment of depression.

OBJECTIVE: Despite the importance for understanding mechanisms of change, little is known about the order of change in daily life emotions, cognitions, and behaviors during treatment of depression. This study examined the within-person temporal order of emotional, cognitive, and behavioral improvements using ecological momentary assessment data. METHOD: Thirty-two individuals with diagnosed depression completed ecological momentary assessment questions on emotions (sad mood, happy mood), behaviors (social interaction, number of activities), and cognitive variables (worrying, negative self-thoughts) 5 times a day during a 4-month period in which they underwent psychotherapy for depression. Nonparametric change-point analyses were used to determine the timing of gains (i.e., improvements in the mean of each variable) for each individual. We then established whether the first (i.e., earliest) gains in emotions preceded, followed, or occurred in the same week as cognitive and behavioral gains for each individual. RESULTS: Contrary to our hypotheses, first gains in behaviors did not precede first emotional gains (3 times, 8%) more often than they followed them (26 times, 70%). Cognitive gains often occurred in the same week as first emotional gains (43 times, 58%) and less often preceded (13 times, 18%) or followed emotional gains (18 times, 24%). CONCLUSION: The first improvements in behaviors did not tend to precede the first improvements in emotions likely because fewer behavioral gains were found. The finding that cognitive variables tend to improve around the same time as sad mood may explain why many studies failed to find that cognitive change predicts later change in depressive symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Monitoring emotional intensity and variability to forecast depression recurrence in real time in remitted adults.

OBJECTIVE: Recurrent depressive episodes are preceded by changing mean levels of repeatedly assessed emotions (e.g., feeling restless), which can be detected in real time using statistical process control (SPC). This study investigated whether monitoring changes in the standard deviation (SD) of emotions and negative thinking improves the early detection of recurrent depression. METHOD: Formerly depressed adults (N = 41) monitored their emotions five times a day for 4 consecutive months. During the study, 22 individuals experienced recurrent depression. We used SPC to detect warning signs (i.e., changing means and SDs) of four emotions (positive and negative affect with high or low arousal) and negative thinking. RESULTS: SD-based warning signs only preceded 23%-36% of recurrences, but almost never reflected a false alarm (0%-16%). Correspondingly, SD-based warnings had a high specificity (at the cost of sensitivity), while mean-based warnings had a higher sensitivity (but lower specificity). There was little overlap in mean- and SD-based warning signs. For the majority of emotions, monitoring for high SDs alongside monitoring changes in mean levels improved the detection of depression (p < .015) compared to when only monitoring for changing mean levels. CONCLUSIONS: Warning signs for depression manifest not only in changing mean levels of emotions and cognitions but also in increasing SDs. These warnings could eventually be used to detect not just who is at increased risk for depression but also when risk is rising. Further research is needed to evaluate the clinical utility of depression SPC. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individual-specific change points in circadian rest-activity rhythm and sleep in individuals tapering their antidepressant medication: an actigraphy study.

Group-level studies showed associations between depressive symptoms and circadian rhythm elements, though whether these associations replicate at the within-person level remains unclear. We investigated whether changes in circadian rhythm elements (namely, rest-activity rhythm, physical activity, and sleep) occur close to depressive symptom transitions and whether there are differences in the amount and direction of circadian rhythm changes in individuals with and without transitions. We used 4 months of actigraphy data from 34 remitted individuals tapering antidepressants (20 with and 14 without depressive symptom transitions) to assess circadian rhythm variables. Within-person kernel change point analyses were used to detect change points (CPs) and their timing in circadian rhythm variables. In 69% of individuals experiencing transitions, CPs were detected near the time of the transition. No-transition participants had an average of 0.64 CPs per individual, which could not be attributed to other known events, compared to those with transitions, who averaged 1 CP per individual. The direction of change varied between individuals, although some variables showed clear patterns in one direction. Results supported the hypothesis that CPs in circadian rhythm occurred more frequently close to transitions in depression. However, a larger sample is needed to understand which circadian rhythm variables change for whom, and more single-subject research to untangle the meaning of the large individual differences.

Day-to-day affect fluctuations in adults with childhood trauma history: a two-week ecological momentary assessment study.

BACKGROUND: Childhood trauma (CT) may increase vulnerability to psychopathology through affective dysregulation (greater variability, autocorrelation, and instability of emotional symptoms). However, CT associations with dynamic affect fluctuations while considering differences in mean affect levels across CT status have been understudied. METHODS: 346 adults (age = 49.25 ± 12.55, 67.0% female) from the Netherlands Study of Depression and Anxiety participated in ecological momentary assessment. Positive and negative affect (PA, NA) were measured five times per day for two weeks by electronic diaries. Retrospectively-reported CT included emotional neglect and emotional/physical/sexual abuse. Linear regressions determined associations between CT and affect fluctuations, controlling for age, sex, education, and mean affect levels. RESULTS: Compared to those without CT, individuals with CT reported significantly lower mean PA levels (Cohen's d = -0.620) and higher mean NA levels (d = 0.556) throughout the two weeks. CT was linked to significantly greater PA variability (d = 0.336), NA variability (d = 0.353), and NA autocorrelation (d = 0.308), with strongest effects for individuals reporting higher CT scores. However, these effects were entirely explained by differences in mean affect levels between the CT groups. Findings suggested consistency of results in adults with and without lifetime depressive/anxiety disorders and across CT types, with sexual abuse showing the smallest effects. CONCLUSIONS: Individuals with CT show greater affective dysregulation during the two-week monitoring of emotional symptoms, likely due to their consistently lower PA and higher NA levels. It is essential to consider mean affect level when interpreting the impact of CT on affect dynamics.

A Proof of Concept Study on Individual Trends in Suicidal Ideation: An Ecological Momentary Assessment Study of 5 Patients Over Three Months.

Background: Suicidal ideation (SI) is a significant and long-lasting mental health problem, with a third of individuals still experiencing SI after two years. To date, most Ecological Momentary Assessment (EMA) studies of SI have assessed its day-to-day course over one to four consecutive weeks and found no consistent trends in average SI severity over time. Aim: The current proof of concept study assessed daily fluctuations of SI over a time span of 3 to 6 months to explore whether individual trends in SI severity could be detected, and if so, if the trajectory of changes were gradual or sudden. The secondary aim was to explore whether changes in SI severity could be detected at an early stage. Method: Five adult outpatients with depression and SI used an EMA app on their smartphone in addition to their regular treatment for 3 to 6 months, where SI was assessed 3 times a day. To detect trends in SI for each patient, three models were tested: a null model, a gradual change model and a sudden change model. To detect changes in SI before a new plateau was reached, Early Warning Signals and Exponentially Weighted Moving Average control charts were used. Results: In each patient, average SI severity had a unique trajectory of sudden and/or gradual changes. Additionally, in some patients, increases in both sudden and gradual SI could be detected at an early stage. Conclusions: The study presents a first indication of unique individual trends in SI severity over a 3 to 6 months period. Though replication in a larger sample is needed to test how well results generalize, a first proof-of-concept is provided that both sudden and gradual changes in SI severity may be detectable at an early stage using the dynamics of time-series data.

Predicting recurrence of depression using cardiac complexity in individuals tapering antidepressants.

It is currently unknown whether the complexity and variability of cardiac dynamics predicts future depression and whether within-subject change herein precedes the recurrence of depression. We tested this in an innovative repeated single-subject study in individuals who had a history of depression and were tapering their antidepressants. In 50 individuals, electrocardiogram (ECG) derived Interbeat-interval (IBI) time-series data were collected for 5 min every morning and evening, for 4 months. Usable data were obtained from 14 participants who experienced a transition (i.e., a clinically significant increase in depressive symptoms) and 14 who did not. The mean, standard deviation, Higuchi dimension and multiscale entropy, calculated from IBIs, were examined for time trends. These quantifiers were also averaged over a baseline period and compared between the groups. No consistent trends were observed in any quantifier before increases in depressive symptoms within individuals. The entropy baseline levels significantly differed between the two groups (morning: P value < 0.001, Cohen's d = -2.185; evening: P value < 0.001, Cohen's d = -1.797) and predicted the recurrence of depressive symptoms, in the current sample. Moreover, higher mean IBIs and Higuchi dimensions were observed in individuals who experienced transitions. While we found little evidence to support the existence of within- individual warning signals in IBI time-series data preceding an upcoming depressive transition, our results indicate that individuals who taper antidepressants and showed lower entropy of cardiac dynamics exhibited a higher chance of recurrence of depression. Hence, entropy could be a potential digital phenotype for assessing the risk of recurrence of depression in the short term while tapering antidepressants.

Recurrence of depression can be foreseen by monitoring mental states with statistical process control.

Detecting early signs of recurrence of psychopathology is key for prevention and treatment. Personalized risk assessment is especially relevant for formerly depressed patients, for whom recurrence is common. We aimed to examine whether recurrence of depression can be accurately foreseen by applying Exponentially Weighted Moving Average (EWMA) statistical process control charts to Ecological Momentary Assessment (EMA) data. Participants were formerly depressed patients (n = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based EMA questionnaires a day for 4 months. EWMA control charts were used to prospectively detect structural mean shifts in high and low arousal negative affect (NA), high and low arousal positive affect (PA), and repetitive negative thinking in each individual. A significant increase in repetitive negative thinking (worry, negative thoughts about the self) was the most sensitive early sign of recurrence: this was detected in 18 out of 22 patients (82%) before recurrence and in 8 out of 19 patients (42%) who stayed in remission. A significant increase in NA high arousal (stress, irritation, restlessness) was the most specific early sign of recurrence: this was detected in 10 out of 22 patients (45%) before recurrence and in 2 out of 19 patients (11%) who stayed in remission. These mean changes were detected at least a month before recurrence in the majority of the participants. The outcomes were robust across EWMA parameter choices, but not when using fewer observations per day. The findings demonstrate the value of monitoring EMA data with EWMA charts for detecting prodromal symptoms of depression in real-time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Real-time monitoring of increases in restlessness to assess idiographic risk of recurrence of depressive symptoms.

BACKGROUND: This confirmatory study aimed to examine whether we can foresee recurrence of depressive symptoms using personalized modeling of rises in restlessness. METHODS: Participants were formerly depressed patients (N = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based Ecological Momentary Assessments (EMA) a day, for a period of 4 months, yielding a total of 21 180 observations. Statistical Process Control by means of Exponentially Weighted Moving Average (EWMA) control charts was used to detect rises in the EMA item 'I feel restless', for each individual separately. RESULTS: An increase in restlessness was detected in 68.3% of the participants with recurring depressive symptoms, and in 26.3% of those who stayed in remission (Fisher's exact test p = 0.01, sensitivity was 68.3%, specificity was 73.7%). In the participants with a recurrence and an increase in restlessness, this increase could be detected in the prodromal phase of depression in 93.3% of the cases and at least a month before the onset of the core symptoms of depression in 66.7% of the cases. CONCLUSIONS: Restlessness is a common prodromal symptom of depression. The sensitivity and specificity of the EWMA charts was at least as good as prognostic models based on cross-sectional patient characteristics. An advantage of the current idiographic method is that the EWMA charts provide real-time personalized insight in a within-person increase in early signs of depression, which is key to alert the right patient at the right time.

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications.

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

Transitions in depression: if, how, and when depressive symptoms return during and after discontinuing antidepressants.

PURPOSE: The aim of the current study is to provide insight into if, how, and when meaningful changes occur in individual patients who discontinue antidepressant medication. Agreement between macro-level quantitative symptom data, qualitative ratings, and micro-level Ecological Momentary Assessments is examined. METHODS: During and shortly after antidepressant discontinuation, depressive symptoms and 'feeling down' were measured in 56 participants, using the SCL-90 depression subscale weekly (macro-level) for 6 months, and 5 Ecological Momentary Assessments daily (micro-level) for 4 months (30.404 quantitative measurements in total). Qualitative information was also obtained, providing additional information to verify that changes were clinically meaningful. RESULTS: At the macro-level, an increase in depressive symptoms was found in 58.9% of participants that (a) was statistically reliable, (b) persisted for 3 weeks and/or required intervention, and (c) was clinically meaningful to patients. Of these increases, 30.3% happened suddenly, 42.4% gradually, and for 27.3% criteria were inconclusive. Quantitative and qualitative criteria showed a very high agreement (Cohen's κ = 0.85) regarding if a participant experienced a recurrence of depression, but a moderate agreement (Cohen's κ = 0.49) regarding how that change occurred. At the micro-level, 41.1% of participants experienced only sudden increases in depressed mood, 12.5% only gradual, 30.4% experienced both types of increase, and 16.1% neither. CONCLUSION: Meaningful change is common in patients discontinuing antidepressants, and there is substantial heterogeneity in how and when these changes occur. Depressive symptom change at the macro-level is not the same as depressive symptom change at the micro-level.

Detecting mean changes in experience sampling data in real time: A comparison of univariate and multivariate statistical process control methods.

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T², EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T² procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Early warning signals and critical transitions in psychopathology: challenges and recommendations.

Empirical evidence is mounting that monitoring momentary experiences for the presence of early warning signals (EWS) may allow for personalized predictions of meaningful symptom shifts in psychopathology. Studies aiming to detect EWS require intensive longitudinal measurement designs that center on individuals undergoing change. We recommend that researchers (1) define criteria for relevant symptom shifts a priori to allow specific hypothesis testing, (2) balance the observation period length and high-frequency measurements with participant burden by testing ambitious designs with pilot studies, and (3) choose variables that are meaningful to their patient group and facilitate replication by others. Thoroughly considered designs are necessary to assess the promise of EWS as a clinical tool to detect, prevent, or encourage impending symptom changes in psychopathology.

Early Warning Signals Based on Momentary Affect Dynamics can Expose Nearby Transitions in Depression: A Confirmatory Single-Subject Time-Series Study.

BACKGROUND: In complex systems early warning signals such as rising autocorrelation, variance and network connectivity are hypothesized to anticipate relevant shifts in a system. For direct evidence hereof in depression, designs are needed in which early warning signals and symptom transitions are prospectively assessed within an individual. Therefore, this study aimed to detect personalized early warning signals preceding the occurrence of a major symptom transition. METHODS: Six single-subject time-series studies were conducted, collecting frequent observations of momentary affective states during a time-period when participants were at increased risk of a symptom transition. Momentary affect states were reported three times a day over three to six months (95-183 days). Depressive symptoms were measured weekly using the Symptom CheckList-90. Presence of sudden symptom transitions was assessed using change point analysis. Early warning signals were analysed using moving window techniques. RESULTS: As change point analysis revealed a significant and sudden symptom transition in one participant in the studied period, early warning signals were examined in this person. Autocorrelation (r=0·51; p<2.2e-16), and variance (r=0·53; p<2.2e-16) in 'feeling down', and network connectivity (r=0·42; p<2.2e-16) significantly increased a month before this transition occurred. These early warnings also preceded the rise in absolute levels of 'feeling down' and the participant's personal indication of risk for transition. CONCLUSIONS: This study replicated the findings of a previous study and confirmed the presence of rising early warning signals a month before the symptom transition occurred. Results show the potential of early warning signals to improve personalized risk assessment in the field of psychiatry.