PsyCog: A computerised mini battery for assessing cognition in psychosis.

Despite the functional impact of cognitive deficit in people with psychosis, objective cognitive assessment is not typically part of routine clinical care. This is partly due to the length of traditional assessments and the need for a highly trained administrator. Brief, automated computerised assessments could help to address this issue. We present data from an evaluation of PsyCog, a computerised, non-verbal, mini battery of cognitive tests. Healthy Control (HC) (N = 135), Clinical High Risk (CHR) (N = 233), and First Episode Psychosis (FEP) (N = 301) participants from a multi-centre prospective study were assessed at baseline, 6 months, and 12 months. PsyCog was used to assess cognitive performance at baseline and at up to two follow-up timepoints. Mean total testing time was 35.95 min (SD = 2.87). Relative to HCs, effect sizes of performance impairments were medium to large in FEP patients (composite score G = 1.21, subtest range = 0.52-0.88) and small to medium in CHR patients (composite score G = 0.59, subtest range = 0.18-0.49). Site effects were minimal, and test-retest reliability of the PsyCog composite was good (ICC = 0.82-0.89), though some practice effects and differences in data completion between groups were found. The present implementation of PsyCog shows it to be a useful tool for assessing cognitive function in people with psychosis. Computerised cognitive assessments have the potential to facilitate the evaluation of cognition in psychosis in both research and in clinical care, though caution should still be taken in terms of implementation and study design.

The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.

Regulatory Qualification of a Cross-Disease Digital Measure: Benefits and Challenges from the Perspective of IMI Consortium IDEA-FAST.

Background: Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality, and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA. Summary: The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations, and the need for validation in both heterogeneous and homogeneous populations. Key Messages: EMA highlighted the challenges teams may encounter when developing a cross-disease measure, though benefits potentially include reduced resources for the technology developer and health authority, faster access to innovation across different therapeutic fields, and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.

Prediction of mental effort derived from an automated vocal biomarker using machine learning in a large-scale remote sample.

Introduction: Biomarkers of mental effort may help to identify subtle cognitive impairments in the absence of task performance deficits. Here, we aim to detect mental effort on a verbal task, using automated voice analysis and machine learning. Methods: Audio data from the digit span backwards task were recorded and scored with automated speech recognition using the online platform NeuroVocalixTM, yielding usable data from 2,764 healthy adults (1,022 male, 1,742 female; mean age 31.4 years). Acoustic features were aggregated across each trial and normalized within each subject. Cognitive load was dichotomized for each trial by categorizing trials at >0.6 of each participants' maximum span as "high load." Data were divided into training (60%), test (20%), and validate (20%) datasets, each containing different participants. Training and test data were used in model building and hyper-parameter tuning. Five classification models (Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting) were trained to predict cognitive load ("high" vs. "low") based on acoustic features. Analyses were limited to correct responses. The model was evaluated using the validation dataset, across all span lengths and within the subset of trials with a four-digit span. Classifier discriminant power was examined with Receiver Operating Curve (ROC) analysis. Results: Participants reached a mean span of 6.34 out of 8 items (SD = 1.38). The Gradient Boosting classifier provided the best performing model on test data (AUC = 0.98) and showed excellent discriminant power for cognitive load on the validation dataset, across all span lengths (AUC = 0.99), and for four-digit only utterances (AUC = 0.95). Discussion: A sensitive biomarker of mental effort can be derived from vocal acoustic features in remotely administered verbal cognitive tests. The use-case of this biomarker for improving sensitivity of cognitive tests to subtle pathology now needs to be examined.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Mobile App to Enhance Patient Activation and Patient-Provider Communication in Major Depressive Disorder Management: Collaborative, Randomized Controlled Pilot Study.

BACKGROUND: Enhanced patient-provider engagement can improve patient health outcomes in chronic conditions, including major depressive disorder (MDD). OBJECTIVE: We evaluated the impact of a digitally enabled care mobile app, Pathway, designed to improve MDD patient-provider engagement. Patients used a mobile interface to assess treatment progress and share this information with primary care providers (PCPs). METHODS: In this 52-week, real-world effectiveness and feasibility study conducted in primary care clinics, 40 patients with MDD who were recently prescribed antidepressant monotherapy were randomized to use a mobile app with usual care (20/40, 50%) or usual care alone (20/40, 50%). Patients in the app arm engaged with the app daily for 18 weeks; a report was generated at 6-week intervals and shared with the PCPs to facilitate shared treatment decision-making discussions. The patients discontinued the app at week 18 and were followed through year 1. Coprimary outcome measures, assessed via research visits, included change from baseline in the 13-item Patient Activation Measure (PAM-13) and 7-item Patient-Provider Engagement Scale scores at week 18. Additional outcome measures included depression severity (9-item Patient Health Questionnaire [PHQ-9]) and cognitive symptoms (5-item Perceived Deficits Questionnaire-Depression). RESULTS: All 37 patients (app arm: n=18, 49%; usual care arm: n=19, 51%) who completed the 18-week follow-up period (n=31, 84% female, mean age 36, SD 11.3 years) had moderate to moderately severe depression. Improvements in PAM-13 and PHQ-9 scores were observed in both arms. Increases in PAM-13 scores from baseline to 18 weeks were numerically greater in the app arm than in the usual care arm (mean 10.5, SD 13.2 vs mean 8.8, SD 9.4; P=.65). At 52 weeks, differences in PAM-13 scores from baseline demonstrated significantly greater improvements in the app arm than in the usual care arm (mean 20.2, SD 17.7 vs mean 1.6, SD 14.2; P=.04). Compared with baseline, PHQ-9 scores decreased in both the app arm and the usual care arm at 18 weeks (mean 7.8, SD 7.2 vs mean 7.0, SD 6.5; P=.73) and 52 weeks (mean 9.5, SD 4.0 vs mean 4.7, SD 6.0; P=.07). Improvements in 7-item Patient-Provider Engagement Scale and WHO-5 scores were observed in both arms at 18 weeks and were sustained through 52 weeks in the app arm. Improvements in WHO-5 scores at 52 weeks were significantly greater in the app arm than in the usual care arm (41.5 vs 20.0; P=.02). CONCLUSIONS: Patients with MDD will engage with a mobile app designed to track treatment and disease progression. PCPs will use the data generated as part of their assessment to inform clinical care. The study results suggest that an app-enabled clinical care pathway may enhance patient activation and benefit MDD management. TRIAL REGISTRATION: ClinicalTrials.gov NCT03242213; https://clinicaltrials.gov/ct2/show/NCT03242213.

Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

BACKGROUND: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. OBJECTIVE: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. METHODS: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies' ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. RESULTS: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. CONCLUSIONS: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35442.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Correction: An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study.

[This corrects the article DOI: 10.2196/32165.].

Test-retest reliability on the Cambridge Neuropsychological Test Automated Battery: Comment on Karlsen et al. (2020).

Test-retest reliability is essential to the development and validation of psychometric tools. Here we respond to the article by Karlsen et al. (Applied Neuropsychology: Adult, 2020), reporting test-retest reliability on the Cambridge Neuropsychological Test Automated Battery (CANTAB), with results that are in keeping with prior research on CANTAB and the broader cognitive assessment literature. However, after adopting a high threshold for adequate test-retest reliability, the authors report inadequate reliability for many measures. In this commentary we provide examples of stable, trait-like constructs which we would expect to remain highly consistent across longer time periods, and contrast these with measures which show acute within-subject change in response to contextual or psychological factors. Measures characterized by greater true within-subject variability typically have lower test-retest reliability, requiring adequate powering in research examining group differences and longitudinal change. However, these measures remain sensitive to important clinical and functional outcomes. Setting arbitrarily elevated test-retest reliability thresholds for test adoption in cognitive research limits the pool of available tools and precludes the adoption of many well-established tests showing consistent contextual, diagnostic, and treatment sensitivity. Overall, test-retest reliability must be balanced with other theoretical and practical considerations in study design, including test relevance and sensitivity.

An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study.

BACKGROUND: Several app-based studies share similar characteristics of a light touch approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active study tasks while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies, reporting low retention and adherence. OBJECTIVE: This study aims to describe an alternative to a light touch digital health study that involved a participant-centric design including high friction app-based assessments, semicontinuous passive data from wearable sensors, and a digital engagement strategy centered on providing knowledge and support to participants. METHODS: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study included US frontline health care workers followed between May and November 2020. The study comprised 3 main components: (1) active and passive assessments of stress and symptoms from a smartphone app, (2) objective measured assessments of acute stress from wearable sensors, and (3) a participant codriven engagement strategy that centered on providing knowledge and support to participants. The daily participant time commitment was an average of 10 to 15 minutes. Retention and adherence are described both quantitatively and qualitatively. RESULTS: A total of 365 participants enrolled and started the study, and 81.0% (n=297) of them completed the study for a total study duration of 4 months. Average wearable sensor use was 90.6% days of total study duration. App-based daily, weekly, and every other week surveys were completed on average 69.18%, 68.37%, and 72.86% of the time, respectively. CONCLUSIONS: This study found evidence for the feasibility and acceptability of a participant-centric digital health study approach that involved building trust with participants and providing support through regular phone check-ins. In addition to high retention and adherence, the collection of large volumes of objective measured data alongside contextual self-reported subjective data was able to be collected, which is often missing from light touch digital health studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04713111; https://clinicaltrials.gov/ct2/show/NCT04713111.

Neuropsychological performance in young adults with cannabis use disorder.

BACKGROUND AND AIMS: Cannabis is a commonly used recreational drug in young adults. The worldwide prevalence in 18- to 25-year-olds is approximately 35%. Significant differences in cognitive performance have been reported previously for groups of cannabis users. However, the groups are often heterogeneous in terms of cannabis use. Here, we study daily cannabis users with a confirmed diagnosis of cannabis use disorder (CUD) to examine cognitive performance on measures of memory, executive function and risky decision-making. METHODS: Forty young adult daily cannabis users with diagnosed CUD and 20 healthy controls matched for sex and premorbid intelligence quotient (IQ) were included. The neuropsychological battery implemented was designed to measure multiple modes of memory (visual, episodic and working memory), risky decision-making and other domains of executive function using subtests from the Cambridge Neuropsychological Test Automated Battery (CANTAB). RESULTS: Our results showed that young adult daily cannabis users with CUD perform significantly poorer on tasks of visual and episodic memory compared with healthy controls. In addition, executive functioning was associated with the age of onset. CONCLUSIONS: Further research is required to determine whether worse performance in cognition results in cannabis use or is a consequence of cannabis use. Chronic heavy cannabis use during a critical period of brain development may have a particularly negative impact on cognition. Research into the persistence of cognitive differences and how they relate to functional outcomes such as academic/career performance is required.

Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings.

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools.

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression.

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

Comparing Web-Based and Lab-Based Cognitive Assessment Using the Cambridge Neuropsychological Test Automated Battery: A Within-Subjects Counterbalanced Study.

BACKGROUND: Computerized assessments are already used to derive accurate and reliable measures of cognitive function. Web-based cognitive assessment could improve the accessibility and flexibility of research and clinical assessment, widen participation, and promote research recruitment while simultaneously reducing costs. However, differences in context may influence task performance. OBJECTIVE: This study aims to determine the comparability of an unsupervised, web-based administration of the Cambridge Neuropsychological Test Automated Battery (CANTAB) against a typical in-person lab-based assessment, using a within-subjects counterbalanced design. The study aims to test (1) reliability, quantifying the relationship between measurements across settings using correlational approaches; (2) equivalence, the extent to which test results in different settings produce similar overall results; and (3) agreement, by quantifying acceptable limits to bias and differences between measurement environments. METHODS: A total of 51 healthy adults (32 women and 19 men; mean age 36.8, SD 15.6 years) completed 2 testing sessions, which were completed on average 1 week apart (SD 4.5 days). Assessments included equivalent tests of emotion recognition (emotion recognition task [ERT]), visual recognition (pattern recognition memory [PRM]), episodic memory (paired associate learning [PAL]), working memory and spatial planning (spatial working memory [SWM] and one touch stockings of Cambridge), and sustained attention (rapid visual information processing [RVP]). Participants were randomly allocated to one of the two groups, either assessed in-person in the laboratory first (n=33) or with unsupervised web-based assessments on their personal computing systems first (n=18). Performance indices (errors, correct trials, and response sensitivity) and median reaction times were extracted. Intraclass and bivariate correlations examined intersetting reliability, linear mixed models and Bayesian paired sample t tests tested for equivalence, and Bland-Altman plots examined agreement. RESULTS: Intraclass correlation (ICC) coefficients ranged from ρ=0.23-0.67, with high correlations in 3 performance indices (from PAL, SWM, and RVP tasks; ρ≥0.60). High ICC values were also seen for reaction time measures from 2 tasks (PRM and ERT tasks; ρ≥0.60). However, reaction times were slower during web-based assessments, which undermined both equivalence and agreement for reaction time measures. Performance indices did not differ between assessment settings and generally showed satisfactory agreement. CONCLUSIONS: Our findings support the comparability of CANTAB performance indices (errors, correct trials, and response sensitivity) in unsupervised, web-based assessments with in-person and laboratory tests. Reaction times are not as easily translatable from in-person to web-based testing, likely due to variations in computer hardware. The results underline the importance of examining more than one index to ascertain comparability, as high correlations can present in the context of systematic differences, which are a product of differences between measurement environments. Further work is now needed to examine web-based assessments in clinical populations and in larger samples to improve sensitivity for detecting subtler differences between test settings.

A prospective five-year cohort study of undiagnosed sleep apnea in patients undergoing coronary artery bypass graft surgery.

BACKGROUND: We aimed to study prospectively the nature and effect of sleep apnea-hypopnea syndrome (SAHS) in patients undergoing coronary artery bypass graft (CABG) surgery over five years of follow-up. METHODS: Patients undergoing CABG surgery (N.=145) were assessed longitudinally (baseline, 1 year, and 5 years post-surgery) using the 'STOP-BANG' screen of sleep apnea risk. Additionally, all patients had a preoperative multiple-channel sleep-study, providing acceptable data for an obstructive and central apnea, and desaturation index in 97 patients. RESULTS: Preoperatively, over half (63%) of patients obtained an apnea-hypopnea index score (combining apnea types) in the moderate-severe range for SAHS, and STOP-BANG threshold score (>3/8) was reached by most (95%) patients. Despite some improvement in 'STOP symptoms' at 1-year follow-up, most patients (98%) remained at risk of SAHS at 5 years post-surgery. There was an underlying and chronic relationship between STOP-BANG score and cardiac symptoms at both baseline and 5-year follow-up. Additionally, SAHS variables were associated with greater incidence of acute postoperative events, and generally with increased length of stay on the intensive care unit. CONCLUSIONS: We confirm that SAHS is common in CABG-surgery patients, presenting additional clinical challenges and cost implications. The underlying pathophysiology is complex, including upper airway obstruction and cardiorespiratory changes of heart failure. In patients presenting for CABG-surgery, we show chronic susceptibility to SAHS, likely associated with traditional risk factors e.g. obesity but perhaps also with gradual decline in heart function itself. Superimposed on this, there is potential for exacerbated risk of morbidity at the time of CABG surgery itself.

Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study.

BACKGROUND: Cognitive symptoms are common in major depressive disorder and may help to identify patients who need treatment or who are not experiencing adequate treatment response. Digital tools providing real-time data assessing cognitive function could help support patient treatment and remediation of cognitive and mood symptoms. OBJECTIVE: The aim of this study was to examine feasibility and validity of a wearable high-frequency cognitive and mood assessment app over 6 weeks, corresponding to when antidepressant pharmacotherapy begins to show efficacy. METHODS: A total of 30 patients (aged 19-63 years; 19 women) with mild-to-moderate depression participated in the study. The new Cognition Kit app was delivered via the Apple Watch, providing a high-resolution touch screen display for task presentation and logging responses. Cognition was assessed by the n-back task up to 3 times daily and depressed mood by 3 short questions once daily. Adherence was defined as participants completing at least 1 assessment daily. Selected tests sensitive to depression from the Cambridge Neuropsychological Test Automated Battery and validated questionnaires of depression symptom severity were administered on 3 occasions (weeks 1, 3, and 6). Exploratory analyses examined the relationship between mood and cognitive measures acquired in low- and high-frequency assessment. RESULTS: Adherence was excellent for mood and cognitive assessments (95% and 96%, respectively), did not deteriorate over time, and was not influenced by depression symptom severity or cognitive function at study onset. Analyses examining the relationship between high-frequency cognitive and mood assessment and validated measures showed good correspondence. Daily mood assessments correlated moderately with validated depression questionnaires (r=0.45-0.69 for total daily mood score), and daily cognitive assessments correlated moderately with validated cognitive tests sensitive to depression (r=0.37-0.50 for mean n-back). CONCLUSIONS: This study supports the feasibility and validity of high-frequency assessment of cognition and mood using wearable devices over an extended period in patients with major depressive disorder.

Normative data from linear and nonlinear quantile regression in CANTAB: Cognition in mid-to-late life in an epidemiological sample.

INTRODUCTION: Normative cognitive data can help to distinguish pathological decline from normal aging. This study presents normative data from the Cambridge Neuropsychological Test Automated Battery, using linear regression and nonlinear quantile regression approaches. METHODS: Heinz Nixdorf Recall study participants completed Cambridge Neuropsychological Test Automated Battery tests: paired-associate learning, spatial working memory, and reaction time. Data were available for 1349-1529 healthy adults aged 57-84 years. Linear and nonlinear quantile regression analyses examined age-related changes, adjusting for sex and education. Quantile regression differentiated seven performance bands (percentiles: 97.7, 93.3, 84.1, 50, 15.9, 6.7, and 2.3). RESULTS: Normative data show age-related cognitive decline across all tests, but with quantile regression revealing heterogeneous trajectories of cognitive aging, particularly for the test of episodic memory function (paired-associate learning). DISCUSSION: This study presents normative data from Cambridge Neuropsychological Test Automated Battery in mid-to-late life. Quantile regression can model heterogeneity in age-related cognitive trajectories as seen in the paired-associate learning episodic memory measure.

Dissociable effects of acute SSRI (escitalopram) on executive, learning and emotional functions in healthy humans.

Serotonin is implicated in multiple executive functions including goal-directed learning, cognitive flexibility, response inhibition and emotional regulation. These functions are impaired in several psychiatric disorders, such as depression and obsessive-compulsive disorder. We tested the cognitive effects of the selective serotonin reuptake inhibitor escitalopram, using an acute and clinically relevant dose (20 mg), in 66 healthy male and female volunteers in a double-blind, placebo-controlled study. Participants performed a cognitive test battery including a probabilistic and reversal learning task, the CANTAB intra-dimensional/extra-dimensional shift test of cognitive flexibility, a response inhibition task with interleaved stop-signal and No-Go trials and tasks measuring emotional processing. We showed that acute escitalopram administration impaired learning and cognitive flexibility, but improved the ability to inhibit responses in stop-signal trials while leaving unaffected acute emotional processing. Our findings suggest a dissociation of effects of acute escitalopram on cognitive functions, possibly mediated by differential modulation of brain serotonin levels in distinct functional neural circuits.