Disentangling the Relationships Between the Clinical Symptoms of Schizophrenia Spectrum Disorders and Theory of Mind: A Meta-analysis

BACKGROUND AND HYPOTHESIS

Previous studies have suggested links between clinical symptoms and theory of mind (ToM) impairments in schizophrenia spectrum disorders (SSD), but it remains unclear whether some symptoms are more strongly linked to ToM than others.

STUDY DESIGN

A meta-analysis (Prospero; CRD42021259723) was conducted to quantify and compare the strength of the associations between ToM and the clinical symptoms of SSD (Positive, Negative, Cognitive/Disorganization, Depression/Anxiety, Excitability/Hostility). Studies (N = 130, 137 samples) including people with SSD and reporting a correlation between clinical symptoms and ToM were retrieved from Pubmed, PsycNet, Embase, Cochrane Library, Science Direct, Proquest, WorldCat, and Open Gray. Correlations for each dimension and each symptom were entered into a random-effect model using a Fisher's r-to-z transformation and were compared using focused-tests. Publication bias was assessed with the Rosenthal failsafe and by inspecting the funnel plot and the standardized residual histogram.

STUDY RESULTS

The Cognitive/Disorganization (Zr = 0.28) and Negative (Zr = 0.24) dimensions revealed a small to moderate association with ToM, which was significantly stronger than the other dimensions. Within the Cognitive/Disorganization dimension, Difficulty in abstract thinking (Zr = 0.36) and Conceptual disorganization (Zr = 0.39) showed the strongest associations with ToM. The association with the Positive dimension (Zr = 0.16) was small and significantly stronger than the relationship with Depression/Anxiety (Zr = 0.09). Stronger associations were observed between ToM and clinical symptoms in younger patients, those with an earlier age at onset of illness and for tasks assessing a combination of different mental states.

CONCLUSIONS

The relationships between Cognitive/Disorganization, Negative symptoms, and ToM should be considered in treating individuals with SSD.

Le phénotypage digital pour une pratique clinique en santé mentale mieux informée

Objectifs Cette revue trouve sa motivation dans l’observation que la prise de décision clinique en santé mentale est limitée par la nature des mesures typiquement obtenues lors de l’entretien clinique et la difficulté des cliniciens à produire des prédictions justes sur les états mentaux futurs des patients. L’objectif est de présenter un survol représentatif du potentiel du phénotypage digital couplé à l’apprentissage automatique pour répondre à cette limitation, tout en en soulignant les faiblesses actuelles.

Méthode Au travers d’une revue narrative de la littérature non systématique, nous identifions les avancées technologiques qui permettent de quantifier, instant après instant et dans le milieu de vie naturel, le phénotype humain au moyen du téléphone intelligent dans diverses populations psychiatriques. Des travaux pertinents sont également sélectionnés afin de déterminer l’utilité et les limitations de l’apprentissage automatique pour guider les prédictions et la prise de décision clinique. Finalement, la littérature est explorée pour évaluer les barrières actuelles à l’adoption de tels outils.

Résultats Bien qu’émergeant d’un champ de recherche récent, de très nombreux travaux soulignent déjà la valeur des mesures extraites des senseurs du téléphone intelligent pour caractériser le phénotype humain dans les sphères comportementale, cognitive, émotionnelle et sociale, toutes étant affectées par les troubles mentaux. L’apprentissage automatique permet d’utiles et justes prédictions cliniques basées sur ces mesures, mais souffre d’un manque d’interprétabilité qui freinera son emploi prochain dans la pratique clinique. Du reste, plusieurs barrières identifiées tant du côté du patient que du clinicien freinent actuellement l’adoption de ce type d’outils de suivi et d’aide à la décision clinique.

Conclusion Le phénotypage digital couplé à l’apprentissage automatique apparaît fort prometteur pour améliorer la pratique clinique en santé mentale. La jeunesse de ces nouveaux outils technologiques requiert cependant un nécessaire processus de maturation qui devra être encadré par les différents acteurs concernés pour que ces promesses puissent être pleinement réalisées.

[Improved Mental Health Clinical Practice Informed by Digital Phenotyping]

Objectives This review is motivated by the observation that clinical decision-making in mental health is limited by the nature of the measures obtained in conventional clinical interviews and the difficulty for clinicians to make accurate predictions about their patients' future mental states. Our objective is to offer a representative overview of the potential of digital phenotyping coupled with machine learning to address this limitation, while highlighting its own current weaknesses. Methods Through a non-systematic narrative review of the literature, we identify the technological developments that make it possible to quantify, moment by moment and in ecologically valid settings, the human phenotype in various psychiatric populations using the smartphone. Relevant work is also selected in order to determine the usefulness and limitations of machine learning to guide predictions and clinical decision-making. Finally, the literature is explored to assess current barriers to the adoption of such tools. Results Although emerging from a recent field of research, a large body of work already highlights the value of measurements extracted from smartphone sensors in characterizing the human phenotype in behavioral, cognitive, emotional and social spheres that are all impacted by mental disorders. Machine learning permits useful and accurate clinical predictions based on such measures, but suffers from a lack of interpretability that will hamper its use in clinical practice in the near future. Moreover, several barriers identified both on the patient and clinician sides currently hamper the adoption of this type of monitoring and clinical decision support tools. Conclusion Digital phenotyping coupled with machine learning shows great promise for improving clinical practice in mental health. However, the youth of these new technological tools requires a necessary maturation process to be guided by the various concerned actors so that these promises can be fully realized.