PREDICTING THE INDIVIDUAL MOOD LEVEL BASED ON DIARY DATA

Understanding mood changes of individuals with depressive disorders is crucial in order to guide personalized therapeutic interventions. Based on diary data, in which clients of an online depression treatment report their activities as free text, we categorize these activities and predict the mood level of clients. We apply a bag-of-words text-mining approach for activity categorization and explore recurrent neuronal networks to support this task. Using the identified activities, we develop partial ordered logit models with varying levels of heterogeneity among clients to predict their mood. We estimate the parameters of these models by employing Markov Chain Monte Carlo techniques and compare the models regarding their predictive performance. Therefore, by combining text-mining and Bayesian estimation techniques, we apply a two-stage analysis approach in order to reveal relationships between various activity categories and the individual mood level. Our findings indicate that the mood level is influenced negatively when participants report about sickness or rumination. Social activities have a positive influence on the mood. By understanding the influences of daily activities on the individual mood level, we hope to improve the efficacy of online behavior therapy, provide support in the context of clinical decision-making, and contribute to the development of personalized interventions

Taking Context to Heart: Momentary Emotions, Menstrual Cycle, and Cardiac Autonomic Regulation

Background. Emotions have long been discussed in conjunction with the autonomic nervous system. Most research on heart-mood linkages does not consider sex differences or an evident underlying mechanism for sex differences- sex hormone levels. Further, most research is limited to cross-sectional and laboratory studies. The degree to which affect-autonomic associations manifest in everyday life may be different and may vary by sex and, for women, by menstrual cycle phase. This study employs ambulatory monitoring of cardiovascular measures (e.g., heart rate and heart rate variability; HRV) and concurrent emotions (e.g., sadness, stress, anxiety, anger, and happiness) in everyday life to better characterize affect-autonomic associations as a function of sex and menstrual cycle phase. Methods. Participants (N = 174) were monitored over a 5-day observation period (one 2-day and one 3-day session), using an ambulatory 24-hour electrocardiogram to monitor heart rate and ecological momentary assessment to record emotions every approximately 30 minutes. Women were monitored in both the early to mid-follicular and -luteal phases of their menstrual cycles and men in two comparably distanced sessions. Results. Multilevel models indicated that for both sexes, negative emotions and happiness were associated with elevated heart rate. Relative to men, women exhibited elevated heart rate and reduced HRV during reports of anger. For women, during the luteal phase, momentary reports of sadness, stress, and anxiety were found to predict increased heart rate and reduced HRV. Conclusion. These findings demonstrate the importance of considering sex and menstrual cycle phase in research investigating heart-mood linkages

Zooming into daily life : Within-person associations between physical activity and affect in young adults

Funding The first author was funded by the LEAD Graduate School & Research Network [GSC1028], a project of the Excellence Initiative of the German federal and state governments. Acknowledgements We thank Laura Grube, Leona Hellwig, Parvin Nemati, and Sarah Schmid for their study assistance and all the individuals who participated and made this research feasible.Peer reviewedPostprin

Stress in nurses : stress-related affect and its determinants examined over the nursing day

Peer reviewedPostprin

Self-determination theory and diminished functioning: the role of interpersonal control and psychological need thwarting

Drawing from self-determination theory (Ryan & Deci, 2002), three studies explored the social-environmental conditions that satisfy versus thwart psychological needs and, in turn, impact psychological functioning and well/ill-being. In cross-sectional Studies 1 and 2, structural equation modeling analyses supported latent factor models in which need satisfaction was predicted by athletes’ perceptions of autonomy support and need thwarting was better predicted by perceived coach control. Athletes’ perceptions of need satisfaction predicted positive outcomes associated with sport participation (vitality and positive affect) whereas perceptions of need thwarting more consistently predicted maladaptive outcomes (disordered eating, burnout, depression, negative affect, and physical symptoms). In addition, athletes’ perceptions of psychological need thwarting were significantly associated with perturbed physiological arousal (elevated levels of secretory immunoglobulin A) prior to training. The final study involved the completion of a diary and supported the relations observed in the cross-sectional studies at a daily level. These findings have important implications for the operationalization and measurement of interpersonal styles and psychological needs

Tracking daily fatigue fluctuations in multiple sclerosis : ecological momentary assessment provides unique insights

The preparation of this manuscript was supported by a UK Economic and Social Research Council (ESRC) PhD studentship (ES/1026266/1) awarded to DP. The study was funded by the Psychology Unit at the University of Southampton. The authors declare that they have no conflict of interest. The authors thank all participants of this study. Open access via Springer Compact Agreement.Peer reviewedPublisher PD

Cognitive, behavioral, and autonomic correlates of mind wandering and perseverative cognition in major depression

Autonomic dysregulation has been hypothesized to play a role in the relationships between psychopathology and cardiovascular risk. An important transdiagnostic factor that has been associated with autonomic dysfunction is perseverative cognition (PC), mainly present in Major Depressive Disorder (MDD) in the form of rumination. As the ability to adaptively let our mind wander without ruminating is critical to mental health, this study aimed to examine the autonomic concomitants of functional vs. dysfunctional intrusive thoughts in MDD. Ambulatory heart rate (HR) and variability (HRV) of 18 MDD subjects and 18 healthy controls were recorded for 24 h. Approximately every 30 min during waking hours subjects reported their ongoing thoughts and moods using electronic diaries. Random regression models were performed. Compared to controls, MDD subjects were more often caught during episodes of PC. In both groups, PC required more effort to be inhibited and interfered more with ongoing activities compared to mind wandering (MW) (ps < 0.0001). This cognitive rigidity was mirrored by autonomic inflexibility, as PC was characterized by lower HRV (p < 0.0001) compared to MW. A worse mood was reported by MDD patients compared to controls, independently of their ongoing cognitive process. Controls, however, showed the highest mood worsening during PC compared to being on task and MW. HRV during rumination correlated with self-reported somatic symptoms on the same day and several dispositional traits. MDD subjects showed lower HRV during sleep, which correlated with hopelessness rumination. Results show that PC is associated with autonomic dysfunctions in both healthy and MDD subjects. Understanding when spontaneous thought is adaptive and when it is not may clarify its role in the etiology of mood disorders, shedding light on the still unexplained association between psychopathology, chronic stress, and risk for health

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts