International Journal of Methods in Psychiatric Research (Mar 2024)

Ecological monitoring of emotional intensity, variability, and instability in individuals with schizophrenia spectrum disorders: Results of a multicentre study

  • Cristina Zarbo,
  • Manuel Zamparini,
  • Alessandra Patrono,
  • Cosima Calini,
  • Philip D. Harvey,
  • Letizia Casiraghi,
  • Massimo Clerici,
  • Matteo Malvezzi,
  • Matteo Rocchetti,
  • Fabrizio Starace,
  • Giovanni deGirolamo,
  • DiAPAson Collaborators

DOI
https://doi.org/10.1002/mpr.1992
Journal volume & issue
Vol. 33, no. 1
pp. n/a – n/a

Abstract

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Abstract Background Evaluating emotional experiences in the life of people with Schizophrenia Spectrum Disorder (SSD) is fundamental for developing interventions aimed at promoting well‐being in specific times and contexts. However, little is known about emotional variability in this population. In DiAPAson project, we evaluated between‐ and within‐person differences in emotional intensity, variability, and instability between people with SSD and healthy controls, and the association with psychiatric severity and levels of functioning. Methods 102 individuals diagnosed with SSD (57 residential patients, 46 outpatients) and 112 healthy controls were thoroughly evaluated. Daily emotions were prospectively assessed with Experience Sampling Method eight times a day for a week. Statistical analyses included ANOVA, correlations, and generalized linear models. Results Participants with SSD, and especially residential patients, had a higher intensity of negative emotions when compared to controls. Moreover, all people with SSD reported a greater between‐person‐variability of both positive and negative emotions and greater intra‐variability of negative emotions than healthy controls. In addition, the emotion variability in people with SSD does not follow a linear or quadratic trend but is more “chaotic” if compared to controls. Conclusions Adequate assessments of positive and negative emotional experiences and their time course in people with SSD can assist mental health professionals with well‐being assessment, implementing targeted interventions through the identification of patterns, triggers, and potential predictors of emotional states.

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