Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (ß=-93.61, P<.001), increased sleep variability (ß=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: ß=0.55, P=.001; sleep offset: ß=1.12, P<.001; M10 onset: ß=0.73, P=.003; HR acrophase: ß=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (ß of PHQ-8 × spring = -31.51, P=.002) and summer (ß of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (ß of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

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.

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.

Affective cognition in eating disorders: a systematic review and meta-analysis of the performance on the "Reading the Mind in the Eyes" Test.

BACKGROUND: The Reading the Mind in the Eyes Test (RMET) is listed in the National Institute of Mental Health's Research Domain Criteria as a tool apt to measure the understanding of others' mental states. People diagnosed with anorexia nervosa (AN) showed poorer performances on the RMET than healthy controls. Less data are available concerning other eating disorders. METHODS: Systematic review of four major databases from inception to July 15, 2021 following the PRISMA guidelines. Meta-analysis of cross-sectional observational studies comparing the scores of the RMET between patients with eating disorders and age- and-gender matched control groups. RESULTS: Out of 21 studies, we retrieved 29 independent samples of patients diagnosed with an eating disorder. Patients with active AN (n = 580) showed worse performances on the RMET than controls (n = 1019). Year of publication accounted for 61% of the (substantial: I2 = 81%) heterogeneity in the meta-analysis. Earlier studies were more likely to find worse performances on the RMET of patients with active AN than later studies. Patients with bulimia nervosa (n = 185) performed poorly as compared to controls (n = 249), but the results were not statistically significant on the random-effect model. Obese patients with binge-eating disorder (n = 54) did not differ on the RMET from obese controls (n = 52). Patients with eating disorder not otherwise specified (n = 57) showed minimal differences compared to controls (n = 96). Study quality was good in six studies only. CONCLUSIONS: Patients with eating disorders do not suffer from an impaired understanding of others' mental states, except for a still-to-be-identified subgroup of patients with active AN. LEVEL OF EVIDENCE: I, systematic review and meta-analysis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder.

The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming popular, given their potential for providing rich data on symptom change and indicators of future state in recurrent conditions such as major depressive disorder (MDD). Understanding recruitment into RMT research is fundamental for improving historically small sample sizes, reducing loss of statistical power, and ultimately producing results worthy of clinical implementation. There is a need for the standardisation of best practices for successful recruitment into RMT research. The current paper reviews lessons learned from recruitment into the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study, a large-scale, multi-site prospective cohort study using RMT to explore the clinical course of people with depression across the UK, the Netherlands, and Spain. More specifically, the paper reflects on key experiences from the UK site and consolidates these into four key recruitment strategies, alongside a review of barriers to recruitment. Finally, the strategies and barriers outlined are combined into a model of lessons learned. This work provides a foundation for future RMT study design, recruitment and evaluation.