Critical Review on the Mental Health and Welfare Act related to the Schizophrenia Spectrum Disorder

2020 ◽  
Vol 8 (2) ◽  
pp. 31-57
Author(s):  
Jung-Hoon Hwang,
2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S176-S176
Author(s):  
Ben Greer ◽  
Matteo Cella ◽  
Til Wykes

Abstract Background Most service users diagnosed with a schizophrenia-spectrum disorder are not aggressive, but this behaviour does occur in inpatient mental health services worldwide. Aggression is difficult to predict and is influenced by a combination of changeable psychological, psychophysiological, and behavioural factors. Current assessment methods are limited to observable behaviours, conducted relatively infrequently, and demonstrate poor predictive accuracy. Advances in active (experience sampling methodology) and passive (wearable psychophysiological sensors) remote monitoring enable monitoring of psychological, psychophysiological, and behavioural parameters in real-time. Monitoring real-time variability in these parameters could identify concerning changes earlier than is currently possible and enable support to be provided sooner. This study aimed to examine real-time variability psychological, psychophysiological, and behavioural factors among an inpatient sample, and relationship with behavioural incidents. Methods Service users (N=40) with a diagnosis of schizophrenia-spectrum disorder and/or antisocial personality disorder were recruited from a medium-secure inpatient forensic mental health service in the UK. Participants completed a blended active and passive remote monitoring study for seven consecutive days. Participants rated 20 psychological and behavioural items at random periods seven times per day, while wearing a passive remote monitoring device which simultaneously collected measurements of electrodermal activity, heart rate variability, and physical activity. Behavioural incidents occurring during the study were recorded from staff-completed behaviour rating scales, and participants’ electronic hospital records. Multi-level models were constructed to examine the role of psychophysiological, psychological, and behavioural factors in predicting behavioural incidents, controlling for covariates such as physical movement and medication. Results The findings demonstrate the within- and between-participant variability in psychological, psychophysiological, and behavioural parameters occurring in real-time, with high ecological validity. Multi-level modelling enabled the predictive ability of these changes in relation to behavioural incidents to be examined, in addition to the timeframe over which this predictive relationship exists. Discussion To our knowledge this is the first study to examine real-time change in psychological, psychophysiological, and behavioural parameters in relation to behavioural incidents. This blended active and passive remote monitoring approach can offer a temporally precise method of assessing change in these parameters, which participants regarded as acceptable. This novel method could assist in identifying concerning change in these parameters earlier and delivering timely support for service users experiencing difficulties, which could be explored in future research.


Psychosis ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 48-56 ◽  
Author(s):  
N. Mørkved ◽  
M. Endsjø ◽  
D. Winje ◽  
E. Johnsen ◽  
A. Dovran ◽  
...  

2021 ◽  
pp. 1-11
Author(s):  
J. N. de Boer ◽  
A. E. Voppel ◽  
S. G. Brederoo ◽  
H. G. Schnack ◽  
K. P. Truong ◽  
...  

Abstract Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.


Author(s):  
Omran Davarinejad ◽  
Tahereh Mohammadi Majd ◽  
Farzaneh Golmohammadi ◽  
Payam Mohammadi ◽  
Farnaz Radmehr ◽  
...  

Schizophrenia Spectrum Disorder (SSD) is a chronic psychiatric disorder with a modest treatment outcome. In addition, relapses are commonplace. Here, we sought to identify factors that predict relapse latency and frequency. To this end, we retrospectively analyzed data for individuals with SSD. Medical records of 401 individuals with SSD were analyzed (mean age: 25.51 years; 63.6% males) covering a five-year period. Univariate and multivariate Penalized Likelihood Models with Shared Log-Normal Frailty were used to determine the correlation between discharge time and relapse and to identify risk factors. A total of 683 relapses were observed in males, and 422 relapses in females. The Relapse Hazard Ratio (RHR) decreased with age (RHR = 0.99, CI: (0.98–0.998)) and with participants’ adherence to pharmacological treatment (HR = 0.71, CI: 0.58–0.86). In contrast, RHR increased with a history of suicide attempts (HR = 1.32, CI: 1.09–1.60), and a gradual compared to a sudden onset of disease (HR = 1.45, CI: 1.02–2.05). Gender was not predictive. Data indicate that preventive and therapeutic interventions may be particularly important for individuals who are younger at disease onset, have a history of suicide attempts, have experienced a gradual onset of disease, and have difficulties adhering to medication.


Author(s):  
Clara Sailer ◽  
Küster Jennifer ◽  
Stefan Borgwardt ◽  
Mirjam Christ-Crain

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