A review of bodily dysfunction in depression: Empirical findings, theoretical perspectives, and implications for treatment.

Abstract

Emotional disturbances are well-recognized features of depression and contemporary psychotherapeutic programs offer a variety of treatment strategies directly targeting these disturbances (e.g., cognitive restructuring, decentering, acceptance). In addition to emotional disturbances, evidence increasingly points to depression also being characterized by profound bodily dysfunction. The goal of the present article is to evaluate the potential of bodily dysfunction as a treatment target in interventions aimed at alleviating emotional problems in depression. We begin by delineating two of the major yet diverging theoretical predictions about the role of the body in and for emotions. We then review evidence of bodily dysfunction in depression with a focus on inflammation, endocrine functioning, the motoric system (i.e., posture and movement), physiological reactivity, and interoception. In our review, we (a) present cross-sectional research investigating differences between healthy and clinical groups, (b) describe experimental evidence, looking at acute and long-term effects of manipulation of bodily parameters on symptoms of depression, and (c) explore longitudinal evidence on how bodily dysfunction may or may not normalize upon successful pharmacological or psychological treatment of depression. We conclude by discussing how bodily dysfunction may be shaping and constraining the emotional lives of depressed individuals and outlining how treatment aimed at alleviating emotional problems in depression may benefit from targeting bodily dysfunction. Public Health Significance StatementThe present study reviews empirical evidence of bodily dysfunction in depression. The findings suggest a variety of bodily alterations in depression that may contribute to emotional disturbances. Treatment targeting the body may be valuable in alleviating emotional problems in depression.

Data mining techniques in psychotherapy: applications for studying therapeutic alliance

“…ML techniques may contribute to the understanding of the role of TA as an in-session emerging and dynamic interpersonal process, by analyzing the underlying neurophysiological substrate at a dyadic level (client and therapist). Although biological variables such as HR and EDA are more diffuse constructs (not reliably observable) than others analyzed in ML studies in psychotherapy 31 , they might be reliable signals of in-session dynamics as they are related to empathy, safety, engagement, compassion, and emotional co-regulation, i.e., variables associated with the therapeutic alliance 35 , 36 . Therefore the objective of this exploratory study was to leverage data mining techniques to analyse and uncover meaningful patterns in a psychotherapy dataset including therapeutic alliance data.…”