Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Psychological medicine
Characterizing executive dysfunctions in patients with schizo-obsessive comorbidity: comparing schizophrenia with obsessive-compulsive disorder
BACKGROUND: The term 'schizo-obsessive comorbidity (SOC)' is used to describe the presence of obsessive-compulsive symptoms or obsessive-compulsive disorder (OCD) in patients with schizophrenia (SOC). Recent studies have found overlapped executive dysfunctions in SCZ and OCD implicating shared pathophysiology. However, specific deficits in the components of executive function (EF) in patients with SOC remains unclear. METHODS: We recruited 37 patients with SOC, 68 patients with SCZ, 70 patients with OCD, and 59 healthy controls (HCs). All participants completed a battery of measures for EF components, namely initiation, sustained attention, online updating, switching, disinhibition, and planning. Apart from traditional group-mean analysis, we applied machine learning approaches to identify the unique patterns of EF among different clinical groups. RESULTS: The results showed that the three clinical groups could be distinguished from HCs. The feature importance analysis showed that, to classify clinical groups from HC, online updating was the core feature of SCZ patients, whereas disinhibition and online updating jointly determine classification between OCD patients and HC. In differentiating SOC from HC, online updating, planning, and disinhibition collectively served as key features. Machine learning algorithms classified SOC and OCD with acceptable performance but classified SOC and SCZ with lower performance. CONCLUSIONS: Deficits of EF are shared features among patients with SOC, SCZ, and OCD. However, the specific components of executive dysfunction in these clinical groups appeared distinct.
2 May 2026
Read appraisal →Psychological medicine
Treatment effect modifiers of virtual reality-based versus standard cognitive behavioral therapy for paranoia in schizophrenia spectrum disorders: an exploratory moderator analysis of clinical and demographic characteristics in the FaceYourFears trial
BACKGROUND: This exploratory study examined baseline characteristics modifying treatment effects on paranoia in individuals diagnosed with schizophrenia spectrum disorders following a 10-session virtual reality-based cognitive behavioral therapy for psychosis (VR-CBTp) or standard CBTp. METHODS: All participants in the FaceYourFears trial were included (n=254; CBTp n=128; VR-CBTp n=126). General linear and logistic regression models examined baseline variables associated with end-of-treatment paranoia. In covariate analyses, regression coefficients quantified associations across treatments. In moderation analyses, interaction terms (randomization x moderator) were tested, with corresponding regression coefficients estimated and assessed at the 25th (low), 50th (medium), and 75th (high) percentiles of continuous variables. RESULTS: Across treatments, higher baseline avolition, safety behavior, delusion severity, and cognitive biases were associated with end-of-treatment paranoia. Moderation analyses revealed interactions for avolition, delusion severity, and negative other-beliefs. Although avolition and delusion severity were associated with poorer outcomes overall, individuals with high avolition and those with moderate-to-high delusion severity improved more in VR-CBTp than CBTp, whereas participants with lower delusion severity showed better outcomes in CBTp. No demographic (age, gender, education, and occupation) or other clinical characteristics (diagnosis, paranoia, social anxiety, depression, anhedonia, total negative symptoms, functioning, core beliefs, or interpersonal trauma) were significantly associated with outcome. CONCLUSIONS: This exploratory study generates hypotheses for future research, including VR-CBTp's potential to engage individuals with high avolition. Given the modest effects and largely nonsignificant findings, both CBTp and VR-CBTp appear suitable for a wide range of individuals with paranoia, highlighting the importance of considering patient preferences.
2 May 2026
Read appraisal →Journal of medical Internet research
Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study
BACKGROUND: Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals' views, but very few studies have examined the perspectives of people with psychosis on this topic. OBJECTIVE: This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. METHODS: People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. RESULTS: Findings were described across 4 themes. First, accuracy was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a human-in-the-loop approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, trust, fears, and choice, noted the interplay between users' trust in the DRM system and their relationship with the clinical team. This theme described participants' fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants' views about the benefits of using a relapse prediction algorithm, including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. CONCLUSIONS: People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.
11 Apr 2026
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