Femke Lamers

Purpose: Patients with cancer frequently experience a wide range of emotions in response to their diagnosis and treatment. Emotions are essentially adaptive, helping patients to cope with the disease. In a minority of patients, however, emotions may become maladaptive and warrant professional mental healthcare. In the field of psycho-oncology, the intensity of emotions is used to distinguish between adaptive and maladaptive emotions. Emotion dynamics (that is changes in emotions over time) may provide additional information on the adaptiveness of the emotions. The aim of this study was to investigate whether emotion dynamics (specifically inertia, variability, and instability) as well as emotion intensity are indicators of maladaptive emotions. Method: Patients with a solid malignancy (metastatic and non-metastatic disease; n = 132) were categorized as having adaptive or maladaptive emotions based on one of two reference standards: the psychiatric diagnostic assessment or the subjective need for professional mental healthcare. Patients reported on eight negative emotional symptoms for 14 days using Ecological Momentary Assessment on their mobile phone. Autocorrelation (inertia), standard deviation (variability), root mean square successive differences (instability), and emotional intensity were compared between patients with adaptive or maladaptive emotions using t -tests. Results: Variability and instability as well as intensity were higher in patients with maladaptive emotions, compared to patients with adaptive emotions. Little evidence was found that patients with maladaptive emotions experienced more emotional inertia. Conclusion: This is the first study to report that in patients with cancer, emotional instability and variability are indicators of maladaptive emotions, in addition to the intensity of emotions.

Ecological Momentary Assessments (EMA) of psychological constructs enables examination of between‐person differences and within‐person fluctuations across timescales: across days and within days. Research has mostly focused on between/within‐person levels and on affect dynamics, leaving potential within‐person timescale distinctions and self‐reported cognitive processes understudied. We applied multilevel variance decomposition to EMA items from the HowNutsAreTheDutch study (30 days; three daily beeps/assessments; N = 1299; t = 60,189; 84% female; mean age = 39; range = 16–86), partitioning their variance into: between‐person, within‐person/across days, within‐person/within‐day (structured by the beeps), and residual/unexplained within‐person/within‐day variance. We analysed positive affect (PA), negative affect (NA), and self‐reported cognitive processes across three domains: evaluative, perseverative, and concentration. Cognitive items showed higher average between‐person variance than NA and PA (61.8%, 53.1%, 34.5%, respectively). Within‐person/across days variance was moderate (7%–15.9%), while structured within‐person/within‐day variance was minimal (0.3%–2.8%). Residual variance was largest for PA, then NA, and smallest for cognition (averages of 47%, 33.4%, 26.9%, respectively). Total within‐person variance (across days, within day, and residual) was comparable to between‐person variance for most items, and exceeded it for PA, suggesting that psychological variables contain both state and trait components to varying degrees. Overall, results revealed heterogeneous variance profiles, which are dependent on construct type, level of observation, and timescale.

BACKGROUND Remote measurement technologies (RMTs) offer a potential solution for monitoring depression symptoms in situ while reducing reliance on self-report assessments. Sleep and physical activity measures derived from wearable devices may capture behavioural patterns relevant to depressions severity, but their incremental value for predicting current depression severity remains unclear. OBJECTIVE To evaluate the predictive value of wearable-derived sleep and physical activity measures for current depression severity, and to determine whether RMT-derived behavioural subtypes improve prediction compared with individual RMT features and baseline sociodemographic and clinical characteristics. METHODS Longitudinal data from the Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD) cohort were used to predict three-monthly depression severity, at three to 36-months of follow-up, measured using the Inventory of Depressive Symptomatology- Self Report. Elastic net and random forest models were compared across different combinations of baseline individual characteristics, individual sleep and physical activity RMT features, and RMT-derived behavioural subtypes. RMT features were derived from data collected during the seven days preceding each IDS-SR assessment. Behavioural subtypes were previously derived from these longitudinal RMT data using a Hidden Markov model, which estimated participants’ subtype membership probabilities at each timepoint. Prediction models were evaluated using nested group five-fold cross-validation and assessed using root mean square error, R2 and mean absolute error. RESULTS Models including baseline depression severity achieved R2 values of approximately 0.6, with RMSE of approximately 10 and MAE of 8. Adding individual RMT features of RMT-derived subtypes provided little improvement in predictive performance. Excluding baseline depression severity reduced R2 to approximately 0.1, while RMSE increased to approximately 15 and MAE to 12, demonstrating the substantial contribution of baseline depression severity to predictive performance. CONCLUSIONS Wearable-derived sleep and physical activity measures and their behavioural subtypes provided limited additional predictive value for current depression severity beyond baseline individual characteristics, particularly baseline depression severity. Although these measures did not substantially improve prediction in the present models, they may still capture complementary behavioural information relevant to understanding depression.

Background: Cancer is a major stressor leading to a wide range of emotions. Emotions, also negative emotions, are essentially adaptive, in the sense that they help patients cope with cancer. Emotions can also be maladaptive, if they are disproportionately severe or persistent, or interfere with functioning. Our overall goal was to explore whether disturbance of life goals provides a fruitful framework for developing indicators that can distinguish between adaptive and maladaptive emotions. Objective: To test the hypotheses that (1) goal disturbance and emotional symptoms are reciprocally and positively associated to each other; and (2) the association between goal disturbance and emotional symptoms is stronger in patients with maladaptive emotions as compared to patients with adaptive emotions. Methods: Patients with (non)metastasized disease who recently started systemic treatment (n = 132) filled out Ecological Momentary Assessments 5 times per day for 14 consecutive days in which emotional symptoms and activities related to life goals were documented. The importance of life goals in six domains was assessed with an interview (work/study, disease-related health, lifestyle health, social activities, religious, spiritual and intellectual activities, and leisure). Patients were grouped as having adaptive or maladaptive emotions based on a reference standard (a psychiatric diagnostic assessment, or the subjective need for professional mental healthcare). Linear mixed models were built in R, with either goal disturbance or negative emotional symptoms as dependent variable. Results: Goal disturbance for social activities and leisure activities was significantly associated with more emotional symptoms. Goal disturbance for disease-related health activities was significantly associated with less emotional symptoms. No significant association was found for other activities (with one exception). With negative emotions as outcome variable, goal disturbance was more strongly associated with more emotional symptoms in patients with maladaptive emotions than patients with adaptive emotions for social, leisure and lifestyle health activities. Conclusion: Further research is warranted to develop an indicator of maladaptive emotions in patients with cancer based on the extent of goal disturbance of social, leisure and lifestyle health activities.

Objectives: Associations between physical activity and affect (activity‐affect dynamics) vary among individuals for which reasons remain unclear. We examined whether such heterogeneity is explained by psychiatric status or sociodemographic, clinical, and ambulatory assessment characteristics. Methods: Two‐week ambulatory assessment data of 300 participants with current ( n = 79), subthreshold ( n = 67), and no ( n = 154) depressive and/or anxiety disorders or symptoms were obtained from the Netherlands Study of Depression and Anxiety. Positive and negative affect (PA/NA) were assessed with ecological momentary assessment (5xdaily) and physical activity using actigraphy. Group iterative multiple model estimation was used to model associations shared across the sample, psychiatric subgroups, and those specific to individuals. Results: No activity‐affect associations were shared across the sample (i.e., present in > 75% of all individuals) or psychiatric subgroups (i.e., present in > 51% of individuals within subgroups). Nevertheless, 45% of participants had at least one activity‐affect association, with considerable heterogeneity in their nature. The most frequent association was a positive contemporaneous association between physical activity and PA (present in 25% of the sample). Conclusions: These findings suggest large heterogeneity in activity‐affect dynamics among individuals and underscore the importance of considering the unique dynamics of the individual.

Background: Efforts to advance our understanding of depression have long been constrained by the disorder’s vast symptom heterogeneity and by the reliance on self-report, which offers only a partial view of phenotypic expression. Digital phenotyping provides an opportunity to address these core challenges by generating real-time, objective data on behavior and physiology, offering new perspectives on understanding depression phenotypes. Yet, prior efforts to identify such objectively derived subtypes have relied on predefined diagnostic labels or supervised models, limiting discovery to existing clinical categories. Objective: This study aimed to identify subtypes of depression based on objective sleep and activity data using an unsupervised learning method and to explore how participants transition between these subtypes over time. Methods: We analyzed longitudinal Fitbit data from 623 participants with recurrent depression enrolled in the Remote Assessment of Disease and Relapse in Major Depressive Disorder study. To identify our subtypes, we applied Gaussian mixture models and hidden Markov models, incorporating a thorough model selection approach that combined grouped cross-validation and seed selection to ensure robustness. Results: Three activity subtypes (high, light, and low activity) and 4 sleep subtypes (efficient early sleepers, efficient late sleepers, disrupted sleepers, and variable late sleepers) were consistently identified. These subtypes align with known associations between depression and behavioral patterns. Transition modeling revealed stability within individuals over follow-up, further suggesting the presence of behavioral phenotypes rather than momentary fluctuations. Conclusions: The results demonstrate that wearable-derived features can identify reproducible and clinically relevant behavioral subtypes of sleep and activity in individuals with major depressive disorder. These subtypes reflect known behavioral correlates of depression and may offer a data-driven framework for reducing phenotypic heterogeneity, improving research stratification, and supporting personalized patient monitoring. Further work is needed to validate these findings in independent cohorts and evaluate their potential use in reducing noise when using sleep or activity data to predict depression outcomes.

Background: Depression and anxiety are prevalent but commonly missed and misdiagnosed, an important concern because many patients do not experience spontaneous recovery, and the duration of untreated illness is associated with worse outcomes. Objective: This study aims to explore the potential of using smartphone-tracked behavioral markers to support diagnostics and improve recognition of these disorders. Methods: We used the dedicated Behapp digital phenotyping platform to passively track location and app usage in 217 individuals, comprising symptomatic (n=109; depression/anxiety diagnosis or symptoms) and asymptomatic individuals (n=108; no diagnosis/symptoms). After quantifying 46 behavioral markers (eg, % time at home), we applied a machine learning approach to (1) determine which markers are relevant for depression/anxiety recognition and (2) develop and evaluate diagnostic prediction models for doing so. Results: Our analysis identifies the total number of GPS-based trajectories as a potential marker of depression/anxiety, where individuals with fewer trajectories are more likely to be symptomatic. Models using this feature in combination with demographics or in isolation outperformed demographics-only models (area under the receiver operating characteristic curve _Mdn =0.60 vs 0.60 vs 0.51). Conclusions: Collectively, these findings indicate that smartphone-tracked behavioral markers have limited discriminant ability in our study but potential to support future depression/anxiety diagnostics.