{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T02:56:45Z","timestamp":1785207405716,"version":"3.55.0"},"reference-count":70,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (<jats:italic>n<\/jats:italic>= 287). As part of bCBT, patients were prompted to rate their current mood (on a 1\u201310 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Overall, four profiles were identified, which we labeled as: (1) \u201cvery negative and least variable mood\u201d (<jats:italic>n<\/jats:italic>= 14) (2) \u201cnegative and moderate variable mood\u201d (<jats:italic>n<\/jats:italic>= 204), (3) \u201cpositive and moderate variable mood\u201d (<jats:italic>n<\/jats:italic>= 41), and (4) \u201cnegative and highest variable mood\u201d (<jats:italic>n<\/jats:italic>= 28). The degree of emotional inertia was virtually identical across the profiles.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyt.2022.755809","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T06:13:33Z","timestamp":1647497613000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood"],"prefix":"10.3389","volume":"13","author":[{"given":"Claire R.","family":"van Genugten","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josien","family":"Schuurmans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriaan 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