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.

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.

Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials.

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research.

Evaluation of the analgesic efficacy and psychoactive effects of AZD1940, a novel peripherally acting cannabinoid agonist, in human capsaicin-induced pain and hyperalgesia.

The aim of the present study was to investigate the effects of AZD1940, a novel peripherally acting cannabinoid CB(1) /CB(2) receptor agonist, on capsaicin-induced pain and hyperalgesia, as well as on biomarkers of cannabinoid central nervous system (CNS) effects. The present study was a randomized, double-blind, placebo-controlled, four-sequence, two-period, cross-over study in 44 male healthy volunteers aged 20-45 years. The effects of two single oral doses of AZD1940 (400 and 800 µg) were compared with placebo. Pain intensity after intradermal capsaicin injections in the forearm was assessed on a continuous visual analogue scale (VAS; 0-100 mm). Primary and secondary hyperalgesia induced by application of capsaicin cream on the calf were assessed by measuring heat pain thresholds and the area of mechanical allodynia, respectively. The CNS effects were assessed at baseline and up to 24 h after dosing using a visual analogue mood scales (VAMS) for feeling 'stimulated', 'high', 'anxious', 'sedated' or 'down'. AZD1940 did not significantly attenuate ongoing pain or primary or secondary hyperalgesia compared with placebo. Mild CNS effects for AZD1940were observed on the VAMS for 'high' and 'sedated'. Dose-dependent mild-to-moderate CNS-related and gastrointestinal adverse events were reported following treatment with AZD1940. No evidence of analgesic efficacy was found for a peripherally acting CB(1)/CB(2) receptor agonist in the human capsaicin pain model. The emergence of mild dose-dependent CNS effects suggests that the dose range predicted from preclinical data had been attained.

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.

Lack of effect of central nervous system-active doses of nabilone on capsaicin-induced pain and hyperalgesia.

The aim of the present study was to investigate the effects of nabilone on capsaicin-induced pain and hyperalgesia, as well as on biomarkers of cannabinoid central nervous system (CNS) effects. A randomized, double-blind, placebo-controlled, crossover study was conducted in 30 healthy male volunteers receiving single doses of nabilone (1, 2 or 3 mg). Pain intensity after intradermal capsaicin injections in the forearm was assessed by continuous visual analogue scale (0-100 mm). Capsaicin cream was applied to the calf to induce hyperalgesia. Primary hyperalgesia was assessed by measuring heat pain thresholds, whereas secondary hyperalgesia was assessed by measuring the area where light tactile stimulation was felt to be painful. Pain and hyperalgesia were measured at baseline and 2-3.5 h after dosing. The CNS effects were assessed at baseline and up to 24 h after dosing using visual analogue mood scales for feeling 'stimulated', 'anxious', 'sedated' and 'down'. Plasma samples for pharmacokinetic analysis were obtained up to 24 h after drug administration. Nabilone did not significantly attenuate either ongoing pain or primary or secondary hyperalgesia, whereas dose-dependent CNS effects were observed from 1.5 to 6 h after dosing, being maximal at 4-6 h. Plasma concentrations of nabilone and its metabolite carbinol were maximal 1-2 h after dosing. Adverse events (AE) were common on nabilone treatment. Four subjects withdrew due to pronounced CNS AE (anxiety, agitation, altered perception, impaired consciousness). Although nabilone had marked CNS effects, no analgesic or antihyperalgesic effects were observed.

Further validation of the THINC-it tool and extension of the normative data set in a study of n = 10.019 typical controls.

INTRODUCTION: We report further validation and normative data for the THINC-Integrated Tool (THINC-it), a measure of cognitive function designed for use with individuals living with Major Depressive Disorder, but which is finding use in further psychiatric and neurological diseases. THINC-it comprises four objective computerised cognitive tests based on traditional psychological paradigms and a version of the Perceived Deficits Questionnaire assessment. METHODS: Sample size of n = 10.019 typical control study participants were tested on one to two occasions to further validate the reliability of THINC-it. Temporal reliability was assessed across 120-180 days. RESULTS: Test-retest reliability correlations varied between r = 0.50 and 0.72 for the component measures and r = 0.75 (95% confidence intervals 0.74, 0.76) for the THINC-it composite score. Normative data categorised by Age, Sex and Years of Education were calculated and the effect on task performance was reported. DISCUSSION: Our analysis confirms previously reported levels of reliability and validates previously reported normative data values.

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.

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.

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.

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.

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.

Effects of sleep or food deprivation during civilian survival training on cognition, blood glucose and 3-OH-butyrate.

OBJECTIVES: The study was designed to compare effects of food deprivation (FD) and sleep deprivation (SD) on cognition during survival training. METHODS: In a cross-over design (n=12), the effects of FD (up to 66 hours followed by 500 kcal intake over 24 hours) and SD (up to 50 hours) on cognitive variables, blood glucose, and 3-OH-butyrate were studied. RESULTS: Food deprivation and SD impaired attention-dependent tasks. The FD impairment of simple reaction time was independent of blood glucose levels, which were normalized by a 500 kcal intake over 24 hours while the reaction time was not. Sleep deprivation and FD impaired maze-solving performance on all variables except rule breaks, which were significantly occurring after 50 hours of SD. Delayed word recall was impaired by SD for 50 hours. On the Balloon Analogue Risk Task, SD was associated with reduced risk-taking. In a gambling task, both SD for 50 hours and FD for 66 hours were associated with a tendency to make early choices when presented with consecutive choices, but the risk-taking was not affected. CONCLUSIONS: Sleep deprivation has multiple cognitive effects, including attention, memory, visual-spatial ability, and risk-taking. Food deprivation had no affect on risk-taking, while the other tasks were affected in a way similar to SD but were less pronounced. The FD effects on cognition did not appear to depend on blood sugar levels. The need to sleep should be prioritized in survival situations to avoid cognitive 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.

Increase of BDNF serum concentration in lithium treated patients with early Alzheimer's disease.

Preclinical and clinical studies gave evidence that lithium could be useful in the treatment of Alzheimer's disease (AD). In experimental investigations, lithium induces brain-derived neurotrophic factor (BDNF). Recent studies have found a decrease of BDNF in the serum and brains of AD patients with potentially consecutive lack of neurotrophic support. We assessed the influence of a lithium treatment on BDNF serum concentration in a subset of a greater sample recruited for a randomized, single-blinded, placebo-controlled, parallel-group multicenter 10-week study, investigating the efficacy of lithium treatment in AD patients. In AD patients treated with lithium, a significant increase of BDNF serum levels, and additionally a significant decrease of ADAS-Cog sum scores in comparison to placebo-treated patients, were found. Diminution of cognitive impairment was inversely correlated with lithium serum concentration. Upregulation of BDNF might be part of a neuroprotective effect of lithium in AD patients. The results of the present investigation encourage performing studies with longer treatment phases to observe potential positive long-term effects of lithium in AD patients.

Longitudinal stability of CSF biomarkers in Alzheimer's disease.

Biomarker levels in cerebrospinal fluid (CSF) may serve as surrogate markers for treatment efficacy in clinical trials of disease-modifying drugs against Alzheimer's disease (AD). A prerequisite, however, is that the marker is sufficiently stable over time in individual patients. Here, we tested the stability of the three established CSF biomarkers for AD, total tau (T-tau), tau phosphorylated at threonine 181 (P-tau(181)) and the 42 amino acid isoform of beta-amyloid (Abeta42), over 6 months in a cohort of AD patients on stable treatment with acetylcholinesterase (AChE) inhibitors. Fifty-three patients completed the study, 29 men and 24 women, mean age (+/-S.D.) 76.1+/-7.9 years. Mean levels of CSF biomarkers were very stable between baseline and endpoint, with coefficients of variation (CVs) of 4.4-6.1%. Intra-individual biomarker levels at baseline and endpoint were also highly correlated with Pearson r-values above 0.95 (p<0.0001), for all three markers. We conclude that T-tau, P-tau and Abeta42 concentrations in CSF are remarkably stable over a 6-month period in individual AD patients. This suggest that these biomarkers may have a potential to identify and monitor very minor biochemical changes induced by treatment, and thus support their possible usefulness as surrogate markers in clinical trials with drug candidates with disease-modifying potential, such as secretase inhibitors, Abeta immunotherapy and tau phosphorylation inhibitors.

Norms for healthy adults aged 18-87 years for the Cognitive Drug Research System: An automated set of tests of attention, information processing and memory for use in clinical trials.

The Cognitive Drug Research (CDR) System is a set of nine computerized tests of attention, information processing, working memory, executive control and episodic memory which was designed for repeated assessments in research projects. The CDR System has been used extensively in clinical trials involving healthy volunteers for over 30 years, and a database of 7751 individuals aged 18-87 years has been accumulated for pre-treatment data from these studies. This database has been analysed, and the relationships between the various scores with factors, including age, gender and years of full-time education, have been identified. These analyses are reported in this paper, along with tables of norms for the various key measures from the core tasks stratified by age and gender. These norms can be used for a variety of purposes, including the determination of eligibility for participation in clinical trials and the everyday relevance of research findings from the system. In addition, these norms provide valuable information on gender differences and the effects of normal ageing on major aspects of human cognitive function.