Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

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Using sensor data from devices such as smart-watches or mobile phones is very popular in both computer science and medical research. Such movement data can predict certain health states or performance outcomes.However, in order to increase reliability and replication of the research it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "in-house". In this paper, we therefore present PSYKOSE, a publicly shared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia. Schizophrenia is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as symptoms of cognitive dysfunction and diminished motivation. In total, we have data from 22 patients with schizophrenia and 32 healthy control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The patients were assessed by medical experts from Haukeland University hospital. In addition to the data, we provide a baseline analysis and possible use-cases of the dataset.

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This study investigated the potential of recognising arousal in motor activity collected by wristworn accelerometers. We hypothesise that emotional arousal emerges from the generalised central nervous system which embeds affective states within motor activity. We formulate arousal detection as a statistical problem of separating two sets -motor activity under emotional arousal and motor activity without arousal. We propose a novel test regime based on machine learning assuming that the two sets can be distinguished if a machine learning classifier can separate the sets better than random guessing. To increase the statistical power of the testing regime, the performance of the classifiers is evaluated in a cross-validation framework, and to test if the classifiers perform better than random guessing, a repeated cross-validation corrected t-test is used. The classifiers were evaluated on the basis of accuracy and Matthew's correlation coefficient. The suggested procedures were further compared against a traditional multivariate paired Hotelling's T-squared test. The classifiers achieved an accuracy of about 60%, and according to the proposed t-test were significantly better than random guessing. The suggested test regime demonstrated higher statistical power than Hotelling's T-squared test, and we conclude that we can distinguish between motor activity under emotional arousal and without it.

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or movements collected via mobile phones to predict certain healthstates or for performance outcomes is a very popular topic in both computer science and medical research. To be able to perform reliable and reproducible research, it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or due to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "inhouse".In this paper, we, therefore, present PSYKOSE, a publiclyshared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia, which is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as introvert symptoms like cognitive dysfunction and diminished motivation.In total, we have data from 22 patients with schizophreniaand 32 control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The schizophrenic state was assessed by medical experts from Haukeland University hospital.In addition to the data, we provide a baseline analysis andpossible use-cases of the dataset

Background Antipsychotic treatment may improve clinical insight. However, previous studies have reported inconclusive findings on whether antipsychotics improve insight over and above the reduction in symptoms of psychosis. These studies assessed homogeneous samples in terms of stage of illness. Randomised studies investigating a mixed population of first- and multiepisode schizophrenia spectrum disorders might clarify this disagreement. Methods Our data were derived from a pragmatic, rater-blinded, semi-randomised trial that compared the effectiveness of amisulpride, aripiprazole and olanzapine. A sample of 144 patients with first- or multiepisode schizophrenia spectrum disorders underwent eight assessments during a 1-year follow-up. Clinical insight was assessed by item General 12 from the Positive and Negative Syndrome Scale (PANSS). We analysed latent growth curve models to test if the medications had a direct effect on insight that was over and above the reduction in total psychosis symptoms. Furthermore, we investigated whether there were differences between the study drugs in terms of insight. Results Based on allocation analysis, all three drugs were associated with a reduction in total psychosis symptoms in the initial phase (weeks 0–6). Amisulpride and olanzapine were associated with improved insight over and above what was related to the reduction in total psychosis symptoms in the long-term phase (weeks 6–52). However, these differential effects were lost when only including the participants that chose the first drug in the randomisation sequence. We found no differential effect on insight among those who were antipsychotic-naïve and those who were previously medicated with antipsychotics. Conclusions Our results suggest that antipsychotic treatment improves insight, but whether the effect on insight surpasses the effect of reduced total psychosis symptoms is more uncertain. Trial registration ClinicalTrials.gov Identifier: NCT01446328, 05.10.2011.