Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

BACKGROUND

Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples.

METHODS

The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics.

RESULTS

From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings.

LIMITATIONS

Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets.

CONCLUSION

This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.

Multilingual markers of depression in remotely collected speech samples: A preliminary analysis

BACKGROUND

Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data.

METHODS

We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features.

RESULTS

Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses.

LIMITATIONS

Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features.

CONCLUSIONS

Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.

The association between persistent cognitive difficulties and depression and functional outcomes in people with major depressive disorder

BACKGROUND

Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes.

METHODS

This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent major depressive disorder (MDD; N = 623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every 3 months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.

RESULTS

508 individuals (81.5%) provided data (mean age: 46.6, s.d.: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (<25% of timepoints), those with high persistence (>75% of timepoints) reported significantly higher levels of depression (B = 5.17, s.e. = 2.21, p = 0.019) and functional impairment (B = 4.82, s.e. = 1.79, p = 0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.

CONCLUSIONS

We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

BACKGROUND

Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features.

OBJECTIVE

We aimed to address these 3 challenges to inform future work in stratified analyses.

METHODS

Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.

RESULTS

We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.

CONCLUSIONS

This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study

The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression's levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the "new normality", while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them.

Subclinical psychotic symptoms in Indian adults: Application of the Community Assessment of Psychic Experiences (CAPE)

BACKGROUND

The study investigated the psychometric properties of the Community, Assessment of Psychic Experiences (CAPE-42), a self-report instrument in Indians.

METHOD

CAPE-42 was translated in Hindi and tested on 312 Indian adults recruited online and through paper-pencil assessment. Confirmatory factor analysis (CFA) was employed to establish the factor structure of the positive, negative and depressive dimensions of CAPE-42: the bifactor model was tested to evaluate whether items converge into a major single factor defining psychotic-proneness in individuals. Latent class analysis (LCA) was conducted to identify subgroups with a different endorsement of subclinical psychotic symptoms.

, RESULTS

CAPE-Hindi showed good reliability (Cronbach's alpha>0.80). CFA confirmed, a good fit for the bifactor model, factor loading was acceptable for all items in the general factor (Omega-h =0.83) and explained the primary variance of the subscales. Residual variance was explained by the positive, negative and depressive factors (Omega H =0.33, 0.04 and 0.12, respectively). LCA identified three classes traceable, to the three dimensions; a low endorsement group (n = 155; 50 %); a less consistent, group with endorsement on positive and depressive items (n = 117; 38 %), and a high, endorsement group (n = 40;13 %).

CONCLUSION

Hindi CAPE-42 showed good reliability and factorial validity.

Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.

Understanding the Subjective Experience of Long-term Remote Measurement Technology Use for Symptom Tracking in People With Depression: Multisite Longitudinal Qualitative Analysis

BACKGROUND

Remote measurement technologies (RMTs) have the potential to revolutionize major depressive disorder (MDD) disease management by offering the ability to assess, monitor, and predict symptom changes. However, the promise of RMT data depends heavily on sustained user engagement over extended periods. In this paper, we report a longitudinal qualitative study of the subjective experience of people with MDD engaging with RMTs to provide insight into system usability and user experience and to provide the basis for future promotion of RMT use in research and clinical practice.

OBJECTIVE

We aimed to understand the subjective experience of long-term engagement with RMTs using qualitative data collected in a longitudinal study of RMTs for monitoring MDD. The objectives were to explore the key themes associated with long-term RMT use and to identify recommendations for future system engagement.

METHODS

In this multisite, longitudinal qualitative research study, 124 semistructured interviews were conducted with 99 participants across the United Kingdom, Spain, and the Netherlands at 3-month, 12-month, and 24-month time points during a study exploring RMT use (the Remote Assessment of Disease and Relapse-Major Depressive Disorder study). Data were analyzed using thematic analysis, and interviews were audio recorded, transcribed, and coded in the native language, with the resulting quotes translated into English.

RESULTS

There were 5 main themes regarding the subjective experience of long-term RMT use: research-related factors, the utility of RMTs for self-management, technology-related factors, clinical factors, and system amendments and additions.

CONCLUSIONS

The subjective experience of long-term RMT use can be considered from 2 main perspectives: experiential factors (how participants construct their experience of engaging with RMTs) and system-related factors (direct engagement with the technologies). A set of recommendations based on these strands are proposed for both future research and the real-world implementation of RMTs into clinical practice. Future exploration of experiential engagement with RMTs will be key to the successful use of RMTs in clinical care.

Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis

BACKGROUND

Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS).

METHODS

Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender.

RESULTS

Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods.

CONCLUSIONS

Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.

Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis

BACKGROUND

Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored.

OBJECTIVE

The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings.

METHODS

We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features.

RESULTS

Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R²=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R²=0.06).

CONCLUSIONS

This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder

BACKGROUND

Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.

METHODS

The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.

RESULTS

A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.

LIMITATIONS

Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.

CONCLUSIONS

These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study

Introduction

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.

Objectives

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

Methods

RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.

Results

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.

Conclusions

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.

Disclosure

No significant relationships.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

BACKGROUND

The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored.

OBJECTIVE

We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.

METHODS

Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.

RESULTS

This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility.

CONCLUSIONS

Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

BACKGROUND

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data.

METHODS

Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months.

RESULTS

Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types.

CONCLUSIONS

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

BACKGROUND

Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.

OBJECTIVE

The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.

METHODS

We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.

RESULTS

Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day).

CONCLUSIONS

Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Measurement invariance between online and paper-and-pencil formats of the Launay-Slade Hallucinations scale-extended (LSHS-E) in the Chilean population: Invariance between LSHS-E formats

Research on the multidimensionality of hallucination-like experiences (HLEs) can contribute to the study of psychotic risk. The Launay-Slade Hallucinations Scale-Extended (LSHS-E) is one of the most widely used tools for research in HLEs, but the correspondence of its paper and online formats has not been established yet. Therefore, we studied the factorial structure and measurement invariance between online and paper-and-pencil versions of LSHS-E in a Chilean population. Two thousand eighty-six completed the online version, and 578 students completed the original paper-and-pencil version. After matching by sex, age, civil status, alcohol and cannabis consumption, and psychiatric treatment received, we selected 543 students from each group. We conducted a confirmatory factor analysis of a four-factor model and a hierarchical model that included a general predisposition to hallucination, explaining the strong relationship between the different types of hallucinations. Both models showed a good fit to the data and were invariant between paper-and-pencil and online versions. Also, the LSHS-E has good reliability in both online and paper-and-pencil formats. This study shows that the online LSHS-E possesses psychometric properties equivalent to the paper-and-pencil version. It should be considered a valuable tool for research of psychosis determinants in the COVID-19 era.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12144-021-02497-7.

Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology

BACKGROUND

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown - pre-, during and post - in adults with a recent history of MDD across multiple centres.

METHODS

This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration.

RESULTS

In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (- 12.16; 95% CI - 18.39 to - 5.92; p <  0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p <  0.001, respectively) as compared to those who were not.

CONCLUSIONS

We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid - 19 pandemic.

Subclinical depression and anxiety impact verbal memory functioning differently in men and women -an fMRI study

BACKGROUND

Depressive symptoms are known to affect memory efficiency in various populations. More specifically, several studies conducted in patients suffering from schizophrenia have indicated that memory efficiency is affected by depressed mood in female patients and by anxiety in male patients. We investigated, using neuroimaging techniques, whether similar gender-specific associations with subclinical depression and anxiety could be observed in a non-clinical sample.

METHOD

Forty-five healthy Spanish-speaking individuals (23 females) were administered a verbal memory task. Lists of high- and low-frequency words were presented. Immediate free recall was requested after the learning of each list, and a yes/no recognition task was completed during the acquisition of the fMRI data.

RESULTS

Regression analyses revealed that higher depression scores in women, and higher anxiety scores in men, were associated with poorer recall. In women, higher depression scores were further associated with decreased cerebral activity in the right temporoparietal junction, left inferior occipitotemporal gyrus, bilateral thalamus, and left anterior cingulate during correct recognition of target words. In men, anxiety scores were not associated with any cerebral activity.

CONCLUSIONS

Subclinical depression in women appears to affect memory efficiency by impacting cerebral regions specifically recruited for the cognitive demands of the task, as well as cerebral regions more generally involved in arousal, decision-making, and emotional regulation. Anxiety in men might impact the encoding memory processes. The results, although preliminary, suggest that gender differences may need to be taken into account when developing strategies for the cognitive and pharmacological remediation of memory impairment.

Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

BACKGROUND

Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.

OBJECTIVE

This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).

METHODS

The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.

RESULTS

A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547).

CONCLUSIONS

Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

Background

Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings.

Objective

The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8).

Methods

Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature.

Results

We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires.

Conclusions

We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

Validation of the Launay-Slade Hallucination Scale among Indian Healthy Adults

Psychotic Like Experiences (PLEs) have been reported in several cultures. The 16 item Launay-Slade Hallucination Scale-Extended (LSHS-E) measures hallucination like experiences (HLEs) in the general population. This study investigated the psychometric properties and the factor structure of LSHS-E Hindi among healthy adults of Delhi. LSHS-E was translated from English to Hindi and then back to English. It was administered as a paper pencil questionnaire to 182 adults from the general population. Reliability of LSHS-E Hindi was measured using Cronbach's alpha and factor structure was established using confirmatory factor analysis (CFA). It was tested against the Community Assessment of Psychic Experiences (CAPE-42) for convergent and divergent validity. Latent Class Analysis (LCA) was performed to identify subgroups with different endorsement of HLEs. Among 182, 18 participants reporting mental and neurological disorders were excluded. LSHS-E Hindi had good reliability (0.85; 95% CI: 0.82 to 0.88). CFA of Hindi LSHS-E revealed the a priori four-factor solution to be best, namely: 'intrusive thoughts', 'vivid daydreams', 'multisensory HLEs', 'auditory and visual HLEs'. LSHS-E Hindi showed stronger correlation with positive domain of CAPE than with negative and depression domains. LCA revealed three classes: low, intermediate and high endorsement of HLEs. Participants with highest endorsement of HLEs were less educated and had highest endorsement on all CAPE dimensions. LSHS-E Hindi has good psychometric properties and can be used to study HLEs in Indians. The four-factor structure model depicts the multidimensionality of HLEs, with 'intrusive thoughts' being the most commonly reported HLE in the sample. LCA supports the continuum hypothesis of HLEs.

Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

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.

Photoplethysmographic Waveform Analysis for Autonomic Reactivity Assessment in Depression

OBJECTIVE

In the present study, a photoplethysmographic (PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder (MDD) and healthy control (HC) subjects is presented.

METHODS

PPG recordings of 40 MDD and 40 HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate (HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity.

RESULTS

Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave ( A₁₃) in both HC and MDD group. However, autonomic reactivity indices related to  A₁₃ reached higher values in HC than in MDD subjects (Cohen's [Formula: see text]), implying that the stress response in depressed patients is reduced. A statistically significant ( ) negative correlation ( r=-0.5) between depression severity scores and A₁₃ was found.

CONCLUSION

A decreased autonomic reactivity is associated with higher degree of depression.

SIGNIFICANCE

Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression.

Comparison of the touch-screen and traditional versions of the Corsi block-tapping test in patients with psychosis and healthy controls

BACKGROUND

Working memory (WM) refers to the capacity system for temporary storage and processing of information, which is known to depend on the integrity of the prefrontal cortex. Impairment in working memory is a core cognitive deficit among individuals with psychotic disorders. The Corsi block-tapping test is a widely-used instrument to assess visuospatial working memory. The traditional version is composed of 9 square blocks positioned on a physical board. In recent years, the number of digital instruments has increased significantly; several advantages might derive from the use of a digital version of the Corsi test.

METHODS

This study aimed to compare the digital and traditional versions of the Corsi test in 45 patients with psychotic disorders and 45 healthy controls. Both groups completed a neuropsychological assessment involving attention and working memory divided into the two conditions.

RESULTS

Results were consistent between the traditional and digital versions of the Corsi test. The digital version, as well as the traditional version, can discriminate between patients with psychosis and healthy controls. Overall, patients performed worse with respect to the healthy comparison group. The traditional Corsi test was positively related to intelligence and verbal working memory, probably due to a more significant effort to execute the test.

CONCLUSIONS

The digital Corsi might be used to enhance clinical practice diagnosis and treatment.The digital version can be administered in a natural environment in real-time. Further, it is easy to administer while ensuring a standard procedure.

Retrospective analysis of patients with locally advanced or metastatic gastric cancer in Argentina

Aim

To assess patient and disease characteristics, treatment patterns and associated costs in patients with locally advanced or metastatic gastric cancer in Argentina, in the public and private sectors.

Methods

A historic cohort of patients who had received first-line chemotherapy treatment (platinum analog and/or a fluoropyrimidine) and were followed-up for at least three months after the last administration of a first-line cytotoxic agent were eligible. Case-report forms were prepared based on medical records from four Argentinian hospitals. Estimates of treatment costs were also calculated using the unit costs of the participating hospitals.

Results

Of 101 patients, more than three quarters (79.2%) were male, 41.6% were diagnosed with metastatic stage IV disease (mean age, 57.7years), and 27.7 % had a smoking history. Before locally advanced or metastatic gastric cancer diagnosis, 42.4% of the patients had received total gastrectomy. Ninety-seven percent of the patients received a doublet or triplet therapy, of which epirubicin in combination with oxaliplatin and capecitabine was the most common treatment (38%), followed by capecitabine plus oxaliplatin (29%). Around 36% of the patients responded to first-line treatment (complete and partial response). Out of the 76.2% of the patients who followed a second-line treatment, 37.7% were still administered a platinum analog and/or fluoropyrimidine. During the reported follow-up period, 50% of the patients progressed, and 32.8% had stable disease. The best supportive care consisted mostly of outpatient visits after last-line therapy (16.8%), palliative radiotherapy (16.8%), and surgery (30.7%). We observed significant differences between public and private hospital costs.

Conclusions

Understanding treatment patterns in patients with locally advanced or metastatic gastric cancer may help address unmet medical needs for better patient management and improvement of their clinical outcome in Argentina.

Retrospective analysis of patients with advanced or metastatic gastric cancer in Colombia

Aims: To assess patient and disease characteristics, treatment patterns, and associated costs in patients with advanced or metastatic gastric cancer (A/MGC) in Colombia, in both the public and private hospitals. Materials and methods: A total of 145 patients who had received first-line chemotherapy treatment (platinum analog and/or a fluoropyrimidine) and were followed for at least 3 months after the last administration of a first-line cytotoxic agent were eligible for inclusion. Case-report forms were elaborated based on the patients' medical records from three Colombian hospitals. Estimates of treatment costs were calculated using unit costs from the participating hospitals. Results: Of the 145 patients, more than half (64.83%) were male, 79.56% were diagnosed with metastatic stage IV disease (mean age = 58.14 years). Prior to MGC diagnosis, 31.71% of the patients being operated on received a total gastrectomy; 66.9% of the patients received a doublet therapy, of which 5-fluorouracil (5-FU) in combination with cisplatin was the standard treatment (14%), followed by combination with leucovorin (12%). Only around 10% of the patients responded to first-line treatment. Out of 41.38% of the patients who received a second-line treatment, 71.67% were still administered a platinum analog and/or fluoropyrimidine. During the follow-up period, 52% of the patients progressed and 20% achieved stable disease. Best supportive care mostly consisted of outpatient visits after last line-therapy (72.41%), palliative radiotherapy (18.6%), and surgery (37.2%). Limitations and conclusions: Gastric cancer is one of the main causes of cancer-related death in Colombia, as most of the patients are diagnosed at an advanced stage, when prognosis is poor. Treatment patterns are highly heterogeneous. Second-line treatments were mostly initiated with paclitaxel, capecitabine, irinotecan, or cisplatin.

Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: A qualitative analysis

BACKGROUND

Innovative uses of mobile health (mHealth) technology for real-time measurement and management of epilepsy may improve the care provided to patients. For instance, seizure detection and quantifying related problems will have an impact on quality of life and improve clinical management for people experiencing frequent and uncontrolled seizures. Engaging patients with mHealth technology is essential, but little is known about patient perspectives on their acceptability. The aim of this study was to conduct an in-depth qualitative analysis of what people with uncontrolled epilepsy think could be the potential uses of mHealth technology and to identify early potential barriers and facilitators to engagement in three European countries.

METHOD

Twenty people currently experiencing epileptic seizures took part in five focus groups held across the UK, Italy, and Spain. Participants all completed written consent and a demographic questionnaire prior to the focus group commencing, and each group discussion lasted 60-120 min. A coding frame, developed from a systematic review of the previous literature, was used to structure a thematic analysis. We extracted themes and subthemes from the discussions, focusing first on possible uses of mHealth and then the barriers and facilitators to engagement.

RESULTS

Participants were interested in mHealth technology as a clinical detection tool, e.g., to aid communication about seizure occurrence with their doctors. Other suggested uses included being able to predict or prevent seizures, and to improve self-management. Key facilitators to engagement were the ability to raise awareness, plan activities better, and improve safety. Key barriers were the potential for increased stigma and anxiety. Using familiar and customizable products could be important moderators of engagement.

CONCLUSION

People with uncontrolled epilepsy think that there is a scope for mHealth technology to be useful in healthcare as a detection or prediction tool. The costs will be compared with the benefits when it comes to engagement, and ongoing work with patients and other stakeholders is needed to design practical resources.

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

BACKGROUND

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.

METHODS

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).

DISCUSSION

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.

A Cross-National Investigation of Hallucination-Like Experiences in 10 Countries: The E-CLECTIC Study

Hallucination-like experiences (HLEs) are typically defined as sensory perceptions in the absence of external stimuli. Multidimensional tools, able to assess different facets of HLEs, are helpful for a better characterization of hallucination proneness and to investigate the cross-national variation in the frequencies of HLEs. The current study set out to establish the validity, factor structure, and measurement invariance of the Launay-Slade Hallucinations Scale-Extended (LSHS-E), a tool to assess HLEs. A total of 4419 respondents from 10 countries were enrolled. Network analyses between the LSHS-E and the 3 dimensions of the Community Assessment of Psychic Experiences (CAPE) were performed to assess convergent and divergent validity of the LSHS-E. Confirmatory factor analysis was used to test its measurement invariance. The best fit was a 4-factor model, which proved invariant by country and clinical status, indicating cross-national stability of the hallucination-proneness construct. Among the different components of hallucination-proneness, auditory-visual HLEs had the strongest association with the positive dimension of the CAPE, compared with the depression and negative dimensions. Participants who reported a diagnosis of a mental disorder scored higher on the 4 LSHS-E factors. Small effect size differences by country were found in the scores of the 4 LSHS-E factors even after taking into account the role of socio-demographic and clinical variables. Due to its good psychometric properties, the LSHS-E is a strong candidate tool for large investigations of HLEs.

Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis

BACKGROUND

Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts.

OBJECTIVE

This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries.

METHODS

Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes.

RESULTS

Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 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 was noted across all countries.

CONCLUSIONS

Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.

Depression, auditory-verbal hallucinations, and delusions in patients with schizophrenia: Different patterns of association with prefrontal gray and white matter volume

Structural brain abnormalities, including decreased gray matter (GM) and white matter (WM) volume, have been observed in patients with schizophrenia. These decrements were found to be associated with positive and negative symptoms, but affective symptoms (depression and anxiety) were poorly explored. We hypothesized that abnormalities in GM and WM volume might also be related to affective symptoms. GM and WM volumes were calculated from high-resolution T1 structural images acquired from 24 patients with schizophrenia and 26 healthy controls, and the associations of positive, negative, and affective symptoms with the brain volumes that showed significant reduction in patients were investigated. Patients demonstrated GM volume reductions in the bilateral prefrontal cortex, and WM volume reductions in the right frontal and left corpus callosum. Prefrontal cortex volume was significantly and inversely associated with both auditory-verbal hallucinations and depression severity. WM volume alterations, in contrast, were related to alogia, anhedonia, and delusions. The combined impact of auditory-verbal hallucinations and depression on similar sub-regions of the prefrontal cortex suggests that depression is involved in hearing voices. Further, this adverse impact of depression on prefrontal GM volume may underlie the impairment demonstrated by these patients in cognitive tasks that rely on executive processes.

Measurement invariance of the Spanish Launay-Slade Hallucinations Scale-Extended version between putatively healthy controls and people diagnosed with a mental disorder

OBJECTIVES

The current study aimed at evaluating the reliability, convergent and divergent validity, and factor structure of the Spanish Launay-Slade Hallucinations Scale-Extended version (LSHS-E) in people with mental disorders and healthy controls.

METHODS

Four hundred and twenty-two individuals completed the Spanish LSHS-E and the Spanish Community Assessment of Psychic Experiences. The convergent and divergent validity of the LSHS-E was assessed with the three dimensions of the Community Assessment of Psychic Experiences (positive, negative, and depressive dimensions) in healthy controls and people with a mental disorder. Factor structure of the LSHS-E was assessed using confirmatory factor analysis and measurement invariance.

RESULTS

The LSHS-E had a good reliability in healthy controls and people with a mental disorder (Cronbach's = 0.83 and 0.91, respectively). The LSHS-E was more strongly associated with positive psychotic-like experiences than with depressive and negative symptoms. Four factors were found: (a) "intrusive thoughts"; (b) "vivid daydreams"; (c) "multisensory hallucination-like experiences"; and (d) "auditory-visual hallucination-like experiences" that were invariant between the group of healthy controls and people with a mental disorder.

CONCLUSION

The Spanish version of the LSHS-E possesses adequate psychometric properties, and the confirmatory factor analysis findings provide further support for the multidimensionality of proneness to hallucination in clinical and nonclinical samples.

Impact of pain and remission in the functioning of patients with depression in Mainland China, Taiwan, and Hong Kong

INTRODUCTION

The present study analyzes functioning during the course of treatment of a major depressive disorder in Mainland China, Taiwan, and Hong Kong.

METHODS

Data in this post hoc analysis were taken from a 24-week prospective, observational study in 12 countries worldwide. Of these, 422 patients were included from Mainland China (N = 205; 48.6%), Taiwan (N = 199; 47.2%), and Hong Kong (N = 18; 4.2%). Functioning was measured with the Sheehan Disability Scale, pain with the Somatic Symptom Inventory, and depression severity with the Quick Inventory of Depressive Symptomatology Self Report 16. Patients were classified as having no pain, persistent pain, or remitted pain. A mixed model with repeated measures was fitted to analyze the relationship between pain and functioning, adjusting for severity and other factors.

RESULTS

At baseline, 40% of the patients had painful physical symptoms. At 24 weeks, 6% of the patients had persistent pain. Sixty percent of the patients achieved remission. Patients with pain had a higher severity of depression score and lower functioning (P < .05) at baseline. At 24 weeks, patients with persistent pain had lower functioning (P < .05). The regression model found that clinical remission was associated with higher functioning at endpoint and that patients with persistent pain had lower functioning at endpoint when compared with the no-pain group.

CONCLUSIONS

Patients presenting with pain had lower functioning at baseline. At 24 weeks, pain persistence was associated with significantly lower functioning as measured by the Sheehan Disability Scale. Clinical remission was associated with better functional outcomes. The course of pain was related to achieving remission.

Neuropsychological correlates of schizotypy: a systematic review and meta-analysis of cross-sectional studies

INTRODUCTION

Cognitive deficits can precede the onset of psychotic episodes and predict the onset of the illness in individuals with schizotypy traits. In some studies, high levels of schizotypy were associated with impairments in memory, attention, executive functions, and verbal fluency. This review provides a more comprehensive understanding of cognitive impairments related to schizoytpy.

METHODS

 A systematic review of "schizotypy and neuropsychological measures" was conducted, and it retrieved 67 studies. All papers with case-control design showing means and standard deviations from neuropsychological measures were included in a meta-analysis (n = 40). A comparison between our finding and another metaanalysis with patients with schizophrenia-spectrum disorders [Fatouros-Bergman, H., Cervenka, S., Flyckt, L., Edman, G., & Farde, L. (2014). Meta-analysis of cognitive performance in drugnaive patients with schizophrenia. Schizophrenia Research. doi: 10.1016/j.schres.2014.06.034 ] was performed to study the similarities on the MATRICS domains between the two disorders.

RESULTS

We found evidence of worse functioning of verbal and visual-spatial working memory, and of language in people with schizotypy or with schizotypal traits. Working memory deficit is present in both schizotypy and schizophrenia with larger effect sizes compared to other domains.

CONCLUSIONS

 Working memory deficit might be a cognitive marker of the risk of psychosis. Interventions targeting cognitive deficits early may be crucial to the prevention of psychosis.

Global brain asymmetry is increased in schizophrenia and related to avolition

OBJECTIVE

Schizophrenia may be the result of a failure of the normal lateralization process of the brain. However, whole-brain asymmetry has not been assessed up to date. Here, we propose a novel measure of global brain asymmetry based on the Dice coefficient to quantify similarity between brain hemispheres.

METHOD

Global gray and white matter asymmetry was calculated from high-resolution T1 structural images acquired from 24 patients with schizophrenia and 26 healthy controls, age- and sex-matched. Some of the analyses were replicated in a much larger sample (n = 759) obtained from open-access online databases.

RESULTS

Patients with schizophrenia had more global gray matter asymmetry than controls. Additionally, increased gray matter asymmetry was associated with avolition, whereas the inverse relationship was found for anxiety at a trend level. These analyses were replicated in a larger sample and confirmed previous results.

CONCLUSION

Our findings suggest that global gray matter asymmetry is related to the concept of developmental stability and is a useful indicator of perturbations during neurodevelopment.

The role of set-shifting in auditory verbal hallucinations

BACKGROUND

Auditory verbal hallucinations (AVHs) are a cardinal characteristic of psychosis. Recent research on the neuropsychological mechanism of AVHs has focused on source monitoring failure, but a few studies have suggested the involvement of attention, working memory, processing speed, verbal learning, memory, and executive functions. In this study we examined the neuropsychological profile of patients with AVHs, assuming that the mechanism underlying this symptom could be a dysfunction of specific cognitive domains.

METHODS

A large neuropsychological battery including set-shifting, working memory, processing speed, attention, fluency, verbal learning and memory, and executive functions was administered to 90 patients with psychotic disorders and 44 healthy controls. The group of patients was divided into two groups: 46 patients with AVHs in the current episode and 44 who denied auditory hallucinations or other modalities in the current episode. AVHs were assessed with the Psychotic Symptom Rating Scales (PSYRATS); the Launay-Slade Hallucination Scale was used to measure long-term propensity to auditory verbal hallucination-like experiences (HLEs) in the sample.

RESULTS

Patients showed poorer performances on all neuropsychological measures compared to the healthy controls' group. In the original dataset without missing data (n=58), patients with AVHs (n=29) presented poorer set shifting and verbal learning, higher levels of visual attention, and marginally significant poorer semantic fluency compared to patients without AVHs (n=29). In the logistic model on the multiple imputed dataset (n=90, 100 imputed datasets), lower capacity of set shifting and semantic fluency distinguished patients with AVHs from those without them.

CONCLUSIONS

Patients experiencing persistent AVHs might fail to shift their attention away from the voices; poorer semantic fluency could be a secondary deficit of set-shifting failure.

Visual Imagery and False Memory for Pictures: A Functional Magnetic Resonance Imaging Study in Healthy Participants

Background

Visual mental imagery might be critical in the ability to discriminate imagined from perceived pictures. Our aim was to investigate the neural bases of this specific type of reality-monitoring process in individuals with high visual imagery abilities.

Methods

A reality-monitoring task was administered to twenty-six healthy participants using functional magnetic resonance imaging. During the encoding phase, 45 words designating common items, and 45 pictures of other common items, were presented in random order. During the recall phase, participants were required to remember whether a picture of the item had been presented, or only a word. Two subgroups of participants with a propensity for high vs. low visual imagery were contrasted.

Results

Activation of the amygdala, left inferior occipital gyrus, insula, and precuneus were observed when high visual imagers encoded words later remembered as pictures. At the recall phase, these same participants activated the middle frontal gyrus and inferior and superior parietal lobes when erroneously remembering pictures.

Conclusions

The formation of visual mental images might activate visual brain areas as well as structures involved in emotional processing. High visual imagers demonstrate increased activation of a fronto-parietal source-monitoring network that enables distinction between imagined and perceived pictures.

Deficits in metaphor but not in idiomatic processing are related to verbal hallucinations in patients with psychosis

There is scant evidence that the verbal cognitive deficits observed in patients with psychosis are related to auditory verbal hallucinations. The understanding of metaphors and idiomatic expressions was investigated in a cohort of 90 patients with active psychosis, and in 44 healthy controls. The Psychotic Symptom Rating Scales (PSYRATS: verbal hallucinations subscale) was used to measure the current verbal hallucinations episode; a subscore of the Launay-Slade Hallucination Scale was used to measure long-term propensity to auditory verbal hallucination-like experiences (HLEs) in the sample. The concurrent influence of education, IQ, and cognitive functioning in memory, attention, fluency, and processing speed on metaphor and idioms processing was investigated. Patients performed worse than healthy controls on all neuropsychological measures. Metaphor, but not idioms processing was poorer in patients with verbal hallucinations (n=46) when compared to patients without verbal hallucinations in the current episode (n=44). By taking into account confounding variables, the ability to produce explanations of metaphors was related to scores on the verbal HLEs in the whole sample of patients. Metaphor-comprehension deficit was related to the occurrence of auditory verbal hallucinations in patients with psychosis, suggesting that abnormal pragmatic inferential abilities have an impact on the mechanisms that cause hallucinatory experiences.

Bifactor structure of the schizotypal personality questionnaire (SPQ)

The schizotypal personality questionnaire (SPQ) is used to characterize schizotypy, a complex construct helpful for the investigation of schizophrenia-related psychopathology and putative endophenotypes. The SPQ factor structure at item level has been rarely replicated and no study had tested a bifactor model of the SPQ so far. The unidimensional, the correlated, the second-order and the bifactor models of the SPQ were tested to evaluate whether the items converge into a major single factor defining the schizotypy-proneness of the participants, to be used for grouping purpose. Parallel principal component analysis (PCA) and confirmatory factor analysis (CFA) were used to determine the optimal number of factors and components in a cross-sectional, survey design involving 649 college students (males: 47%). The first-order, nine-subscale model was confirmed by CFA in the whole sample. The best evidence from parallel PCA in the training set was in favor of a two-factor model; the bifactor implementation of this model showed good fit in the subsequent CFA. Two main dimensions of positive and negative symptoms underlie schizotypy in non-clinical samples, entailing specific risk of psychosis. On a measurement level, the study provided support for the use of the total scores of the SPQ to characterize schizotypy.

The independence of schizotypy from affective temperaments--a combined confirmatory factor analysis of SPQ and the short TEMPS-A

Sparse evidence of a co-aggregation of the risk of schizophrenia and bipolar disorder provides support for a shared but nonspecific genetic etiology of bipolar disorder and schizophrenia. Temperaments are conceptualized as trait sub-syndromic conditions of major pathologies. This study set out to test the hypothesis of a continuum between schizotypy and affective temperaments versus the alternative hypothesis of their independence based on a cross-sectional, survey design involving 649 (males: 47%) college students. The short 39-item TEMPS-A and the SPQ were used as measures of the affective temperaments and of schizotypy, respectively. Confirmatory factor analyses were applied to a unidimensional model, to a standard correlate traits model, to second-order representations of a common latent structure, and to a bifactor model. Confirmatory bifactor modeling provided evidence against a complete independence of the dimensions subsumed by the affective and the schizotypal traits. The best solution distinguished between two sub-domains grouping positive symptoms and negative symptoms as measured by the SPQ subscales, and a sub-domain related to the affective temperaments as measured by the TEMPS-A. Limitations due to the use of subscales from two different tools should be taken into account.

Auditory verbal hallucinations in persons with and without a need for care

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.

Symptom dimensions of the psychotic symptom rating scales in psychosis: a multisite study

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.

Prevalence and dimensionality of hallucination-like experiences in young adults

BACKGROUND

The study of hallucination-like experiences (HLEs) in non-clinical populations is increasingly used to corroborate etiological models of psychosis. This method capitalizes on the absence of confounding factors that typically affect the study of hallucinations in clinical subjects.

AIM

To estimate the prevalence of HLEs in young adults; validate the mutidimensionality and explore the correlates of latent HLEs clusters.

METHODS

Cross-sectional survey design. The extended 16-item Launay-Slade Hallucination Scale (LSHS-E) and the 12-item General Health Questionnaire (GHQ-12) were administered to 649 Italian college students (males: 47%). Confirmatory factorial analysis was used to test multidimensionality of the LSHS-E. Hierarchical nested, progressively constrained models were used to assess configural, metric and scalar invariance of the LSHS-E. Latent class analysis was used to test the existence of different profiles of responding across the identified hallucination-proneness dimensions.

RESULTS

Factor analysis showed that the four-factor model had the best fit. Factors were invariant across demographic variables and levels of psychological distress. Three latent classes were found: a large class with no HLEs (70% of participants), a multisensory HLEs class (18.8%), and a high hallucination-proneness class (11%). Among those reporting high levels of HLEs, approximately half reported scores indicative of considerable psychological distress.

CONCLUSIONS

Although HLEs have a relatively high prevalence in the general population, the majority of those experiences happen in isolation and are not associated to psychological distress. Approximately half of those individuals experiencing high levels of HLEs report significant psychological distress. This may be indicative of general risk for mental health conditions rather than specific risk for psychosis.

Oxytocin and autism: a systematic review of randomized controlled trials

OBJECTIVE

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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.

Preoccupation and distress are relevant dimensions in delusional beliefs

BACKGROUND AND PURPOSE

A large number of subjective experiences and beliefs with some degree of affinity with psychotic symptoms can be found in the general population. However, the appraisal of these psychotic-like experiences in terms of associated distress, raised preoccupation, and the conviction with which the experience is held can be more discriminative in distinguishing people in need for care from those who simply hold unusual or uncommon beliefs because of cultural reasons.

METHOD

In this study, 81 patients with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, diagnosis of schizophrenia or an affective disorder with psychotic features were compared on the Peters et al Delusions Inventory (PDI) to 210 people from the same local area, who had never received a formal diagnosis of a mental disorder.

RESULTS

Patients scored higher than controls on the PDI total score and on its distress, preoccupation, and conviction subscales. A stepwise logistic regression model showed PDI-preoccupation (odds ratio, 2.46; 95% confidence interval, 1.52-3.98) and, marginally, PDI-distress (odds ratio = 1.58; 95% confidence interval, 0.93-2.58) adding discriminative power to PDI total score in distinguishing patients from controls.

CONCLUSIONS

The evaluation of the severity of delusion-like experiences and beliefs is important in discriminating patients diagnosed with psychosis from people who are not in need of care.

The psychometric discriminative properties of the Peters et al Delusions Inventory: a receiver operating characteristic curve analysis

OBJECTIVE

To establish sensitivity, specificity, and discriminative validity of the Peters et al Delusions Inventory (PDI), a recently released self-compiled questionnaire aimed at measuring psychosis proneness in the general population.

METHOD

Eighty-one patients diagnosed with a mental disorder with psychotic features (schizophrenia, schizoaffective disorder, bipolar disorder, major depression) and 210 putatively healthy individuals were invited to complete the PDI and the brief version of the General Health Questionnaire (GHQ-12). The receiver operating characteristic analysis has been used to extract the best threshold to discriminate between cases, those with a psychotic disorder, and noncases, those without a psychotic disorder.

RESULTS

The best PDI threshold in discriminating between cases and noncases was 8 (sensitivity, 74%; specificity, 79%). The corresponding figures of sensitivity and specificity for a GHQ-12 cutoff of 6 were 63% and 83%, respectively. The value of the area under the receiver operating characteristic curve was found to be higher for the PDI (0.815) than for the GHQ-12 (0.770), although at a barely detectable, statistically significant difference (Z = 1.45, P = .073).

CONCLUSION

The expected high negative predictive value of the PDI (96%) in the general population suggests it will be a valuable tool in future research on psychosis proneness.