Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we ...have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques.
The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity.
A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models.
Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01).
The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.
Emotional dysregulation (ED) is a core diagnostic symptom in borderline personality disorder (BPD) and an associated feature of attention-deficit/hyperactivity disorder (ADHD). We aimed to ...investigate differences in dynamical indices of ED in daily life in ADHD and BPD.
We used experience sampling method (ESM) and multilevel modelling to assess momentary changes in reports of affective symptoms, and retrospective questionnaire measures of ED in a sample of 98 adult females with ADHD, BPD, comorbid ADHD+BPD and healthy controls.
We found marked differences between the clinical groups and healthy controls. However, the ESM assessments did not show differences in the intensity of feeling angry and irritable, and the instability of feeling sad, irritable and angry, findings paralleled by data from retrospective questionnaires. The heightened intensity in negative emotions in the clinical groups compared to controls was only partially explained by bad events at the time of reporting negative emotions, suggesting both reactive and endogenous influences on ED in both ADHD and BPD.
This study supports the view that ED is a valuable trans-diagnostic aspect of psychopathology in both ADHD and BPD, with similar levels of intensity and instability. These findings suggest that the presence or severity of ED should not be used in clinical practice to distinguish between the two disorders.
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.
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).
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).
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.
•The study presents normative cognitive data from the Cambridge Neuropsychological Test Automated Battery in mid-to-late life.•Most tasks showed similar decline across performance bands with increasing age.•Quantile regression is sensitive for evaluating diverging trajectories with age.•Episodic memory showed accelerated decline in the average performance range.
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.
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.
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.
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.
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.
Background
Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on ...favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms.
Methods
We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features.
Results
For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study.
Objective: This study investigates excessive mind wandering (MW) in adult ADHD using a new scale: the Mind Excessively Wandering Scale (MEWS). Method: Data from two studies of adult ADHD was used in ...assessing the psychometric properties of the MEWS. Case-control differences in MW, the association with ADHD symptoms, and the contribution to functional impairment were investigated. Results: The MEWS functioned well as a brief measure of excessive MW in adult ADHD, showing good internal consistency (α > .9), and high sensitivity (.9) and specificity (.9) for the ADHD diagnosis, comparable with that of existing ADHD symptom rating scales. Elevated levels of MW were found in adults with ADHD, which contributed to impairment independently of core ADHD symptom dimensions. Conclusion: Findings suggest excessive MW is a common co-occurring feature of adult ADHD that has specific implications for the functional impairments experienced. The MEWS has potential utility as a screening tool in clinical practice to assist diagnostic assessment.
Abstract
Background
Changes in speech occur in early‐stage Alzheimer’s disease. A range of approaches have been used for eliciting and automatically analysing speech, but limited research has ...directly compared these methods.
Method
Participants from the AMYPRED‐UK (NCT04828122) and AMYPRED‐US (NCT04928976) studies completed four speech‐elicitation tasks: the Automatic Story Recall Task (ASRT), the Logical Memory Test (LMT), Semantic (Animals, Vegetables, Fruit) and Phonemic Verbal Fluency (F, A, S) Tasks. Responses were recorded and automatically transcribed. Analyses were completed with Novoic’s speech analysis software, using four key approaches: (1) feature extraction, (2) representations from large language models (LLM), (3) text‐similarity evaluation, and (4) autoscoring. Together, these evaluated audio, linguistic, and temporal speech domains. Outputs were entered into logistic regression models predicting Mild Cognitive Impairment (MCI)/mild AD and Amyloid positivity in MCI/mild AD. Area under the ROC curves (AUC) were evaluated via 5‐fold cross‐validation.
Result
165 older adults (including N = 74 MCI and 9 mild AD; of which N = 38 MCI/mild AD and amyloid beta positive) provided data for all tasks. Strength of model predictions varied by task, analytic approach and domain (Fig 1). AUCs predicting MCI/mild AD were generally higher for ASRTs compared to other tasks (Fig 1A). Text‐similarity approaches (G‐match: similarity of word embeddings between the source text and retelling, and V‐match: string‐based matching), produced the highest AUC for ASRTs (G‐match AUC = 0.87‐0.88), and the LMT (V‐match AUC = 0.83‐85). Autoscoring tasks produced the strongest predictor for the semantic fluency (AUC = 0.85), and also one of the strongest for phonemic fluency (AUC = 0.72). Changes in linguistic and temporal characteristics were more sensitive to MCI/mild AD, compared to audio‐only metrics. Amyloid in MCI/mild AD predictions were overall less strong (Fig 1B), with the most consistent pattern being better performance of text‐similarity approaches on story recall tasks (up to AUC = 0.77 for ASRTs, 0.73 for LMT).
Conclusion
Selection of task and analytic approach is important for developing more sensitive speech‐based testing. The best performing tasks (ASRT, Semantic Fluency) and metrics (text‐similarity metrics and autoscoring) have been incorporated in Novoic’s Storyteller automated remote speech‐based test.
Background
Changes in speech occur in early‐stage Alzheimer’s disease. A range of approaches have been used for eliciting and automatically analysing speech, but limited research has directly ...compared these methods.
Method
Participants from the AMYPRED‐UK (NCT04828122) and AMYPRED‐US (NCT04928976) studies completed four speech‐elicitation tasks: the Automatic Story Recall Task (ASRT), the Logical Memory Test (LMT), Semantic (Animals, Vegetables, Fruit) and Phonemic Verbal Fluency (F, A, S) Tasks. Responses were recorded and automatically transcribed. Analyses were completed with Novoic’s speech analysis software, using four key approaches: (1) feature extraction, (2) representations from large language models (LLM), (3) text‐similarity evaluation, and (4) autoscoring. Together, these evaluated audio, linguistic, and temporal speech domains. Outputs were entered into logistic regression models predicting Mild Cognitive Impairment (MCI)/mild AD and Amyloid positivity in MCI/mild AD. Area under the ROC curves (AUC) were evaluated via 5‐fold cross‐validation.
Result
165 older adults (including N = 74 MCI and 9 mild AD; of which N = 38 MCI/mild AD and amyloid beta positive) provided data for all tasks. Strength of model predictions varied by task, analytic approach and domain (Fig 1). AUCs predicting MCI/mild AD were generally higher for ASRTs compared to other tasks (Fig 1A). Text‐similarity approaches (G‐match: similarity of word embeddings between the source text and retelling, and V‐match: string‐based matching), produced the highest AUC for ASRTs (G‐match AUC = 0.87‐0.88), and the LMT (V‐match AUC = 0.83‐85). Autoscoring tasks produced the strongest predictor for the semantic fluency (AUC = 0.85), and also one of the strongest for phonemic fluency (AUC = 0.72). Changes in linguistic and temporal characteristics were more sensitive to MCI/mild AD, compared to audio‐only metrics. Amyloid in MCI/mild AD predictions were overall less strong (Fig 1B), with the most consistent pattern being better performance of text‐similarity approaches on story recall tasks (up to AUC = 0.77 for ASRTs, 0.73 for LMT).
Conclusion
Selection of task and analytic approach is important for developing more sensitive speech‐based testing. The best performing tasks (ASRT, Semantic Fluency) and metrics (text‐similarity metrics and autoscoring) have been incorporated in Novoic’s Storyteller automated remote speech‐based test.
Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating psychiatric disorder characterized by symptoms of inattention, impulsivity and motor restlessness. Consistently noted ...alongside these symptoms is mood instability in the form of irritability, volatility, swift changes in mood, hot temper and low frustration tolerance. The current diagnostic classification systems do not include mood instability as a core aspect of ADHD, but rather as an associated feature of the disorder. However, the literature suggests that overlapping cognitive deficits and neuroanatomical substrates may underlie both the classical ADHD symptoms and mood instability. Furthermore, common neurotherapeutic interventions in the form of stimulant medications or atomoxetine may help to alleviate both types of symptoms when they co-occur. This research suggests that mood instability and symptoms of ADHD may be interlinked and that mood instability may be better understood as a core feature of the ADHD syndrome.
•State of the art Ambulatory Assessment in ADHD research.•E-diary studies in children, adolescents and adults with ADHD.•Interventions for patients with ADHD provided by smartphones.
...Attention-deficit/hyperactive disorder (ADHD) is characterized by symptoms which are dynamic in nature: states of hyperactivity, inattention and impulsivity as core symptoms, and emotion dysregulation as associated feature. Although tremendous work has been done to investigate between-subject differences (how patients with ADHD differ from healthy controls or patients with other disorders), little is known about the relationship between symptoms with triggers and contexts, that may allow us to better understand their causes and consequences. Understanding the temporal associations between symptoms and environmental triggers in an ecologically valid manner may be the basis to developing just-in-time adaptive interventions. Fortunately, recent years have seen advances in methodology, hardware and innovative statistical approaches to study dynamic processes in daily life. In this narrative review, we provide a description of the methodology (ambulatory assessment), summarize the existing literature in ADHD, and discuss future prospects for these methods, namely mobile sensing to assess contextual information, real-time analyses and just-in-time adaptive interventions.
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