Auditory verbal hallucinations (AVH) are complex experiences that occur in the context of various clinical disorders. AVH also occur in individuals from the general population who have no identifiable psychiatric or neurological diagnoses. This article reviews research on AVH in nonclinical individuals and provides a cross-disciplinary view of the clinical relevance of these experiences in defining the risk of mental illness and need for care. Prevalence rates of AVH vary according to measurement tool and indicate a continuum of experience in the general population. Cross-sectional comparisons of individuals with AVH with and without need for care reveal similarities in phenomenology and some underlying mechanisms but also highlight key differences in emotional valence of AVH, appraisals, and behavioral response. Longitudinal studies suggest that AVH are an antecedent of clinical disorders when combined with negative emotional states, specific cognitive difficulties and poor coping, plus family history of psychosis, and environmental exposures such as childhood adversity. However, their predictive value for specific psychiatric disorders is not entirely clear. The theoretical and clinical implications of the reviewed findings are discussed, together with directions for future research.

Background: In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. Objective: We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. Methods: We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. Results: We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. Conclusions: RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Mobile technology has the potential to provide more accurate, impactful data on the symptoms of depression, which could improve health management or even assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. While many barriers and facilitators are common across therapeutic areas and technology types, many may be culture and health context-specific. Objective: This study was designed to understand potential barriers and facilitators to engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. Methods: Participants with experience of depression took part in five focus groups held in three countries (2 in the UK, 2 in Spain, and 1 in Italy) investigating potential barriers and facilitators to the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. Results: Facilitators and barriers were organised as: (1) health-related factors; (2) user-related factors; and (3) technology-related factors. Fifty-eight subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience and ease of use were present across all countries. Conclusions: Similarities in the barriers and facilitators to use of mHealth technology have been found across Spain, Italy, and the UK. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

Little is known about the effectiveness of pharmacological interventions on autism spectrum disorder (ASD). This is a systematic review of the randomized controlled trials (RCTs) of oxytocin interventions in autism, made from January 1990 to September 2013. A search of computerized databases was supplemented by manual search in the bibliographies of key publications. The methodological quality of the studies included in the review was evaluated independently by two researchers, according to a set of formal criteria. Discrepancies in scoring were resolved through discussion. The review yielded seven RCTs, including 101 subjects with ASD (males=95) and 8 males with Fragile X syndrome. The main categories of target symptoms tested in the studies were repetitive behaviors, eye gaze, and emotion recognition. The studies had a medium to high risk of bias. Most studies had small samples (median=15). All the studies but one reported statistically significant between-group differences on at least one outcome variable. Most findings were characterized by medium effect size. Only one study had evidence that the improvement in emotion recognition was maintained after 6 weeks of treatment with intranasal oxytocin. Overall, oxytocin was well tolerated and side effects, when present, were generally rated as mild; however, restlessness, increased irritability, and increased energy occurred more often under oxytocin. RCTs of oxytocin interventions in autism yielded potentially promising findings in measures of emotion recognition and eye gaze, which are impaired early in the course of the ASD condition and might disrupt social skills learning in developing children. There is a need for larger, more methodologically rigorous RCTs in this area. Future studies should be better powered to estimate outcomes with medium to low effect size, and should try to enroll female participants, who were rarely considered in previous studies. Risk of bias should be minimized. Human long-term administration studies are necessary before clinical recommendations can be made.