Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

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 smartphone-based test for the assessment of attention deficits in delirium: A case-control diagnostic test accuracy study in older hospitalised patients.

BACKGROUND: Delirium is a common and serious acute neuropsychiatric syndrome which is often missed in routine clinical care. Inattention is the core cognitive feature. Diagnostic test accuracy (including cut-points) of a smartphone Delirium App (DelApp) for assessing attention deficits was assessed in older hospital inpatients. METHODS: This was a case-control study of hospitalised patients aged ≥65 years with delirium (with or without pre-existing cognitive impairment), who were compared to patients with dementia without delirium, and patients without cognitive impairment. Reference standard delirium assessment, which included a neuropsychological test battery, was based on Diagnostic and Statistical Manual of Mental Disorders-5 criteria. A separate blinded assessor administered the DelApp arousal assessment (score 0-4) and attention task (0-6) yielding an overall score of 0 to 10 (lower scores indicate poorer performance). Analyses included receiver operating characteristic curves and sensitivity and specificity. Optimal cut-points for delirium detection were determined using Youden's index. RESULTS: A total of 187 patients were recruited, mean age 83.8 (range 67-98) years, 152 (81%) women; n = 61 with delirium; n = 61 with dementia without delirium; and n = 65 without cognitive impairment. Patients with delirium performed poorly on the DelApp (median score = 4/10; inter-quartile range 3.0, 5.5) compared to patients with dementia (9.0; 5.5, 10.0) and those without cognitive impairment (10.0; 10.0, 10.0). Area under the curve for detecting delirium was 0.89 (95% Confidence Interval 0.84, 0.94). At an optimal cut-point of ≤8, sensitivity was 91.7% (84.7%, 98.7%) and specificity 74.2% (66.5%, 81.9%) for discriminating delirium from the other groups. Specificity was 68.3% (56.6%, 80.1%) for discriminating delirium from dementia (cut-point ≤6). CONCLUSION: Patients with delirium (with or without pre-existing cognitive impairment) perform poorly on the DelApp compared to patients with dementia and those without cognitive impairment. A cut-point of ≤8/10 is suggested as having optimal sensitivity and specificity. The DelApp is a promising tool for assessment of attention deficits associated with delirium in older hospitalised adults, many of whom have prior cognitive impairment, and should be further validated in representative patient cohorts.

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.

Diagnostic test accuracy of a novel smartphone application for the assessment of attention deficits in delirium in older hospitalised patients: a prospective cohort study protocol.

BACKGROUND: Delirium is a common and serious clinical syndrome which is often missed in routine clinical care. The core cognitive feature is inattention. We developed a novel bedside neuropsychological test for assessing inattention in delirium implemented on a smartphone platform (DelApp). We aim to evaluate the diagnostic performance of the DelApp in a representative cohort of older hospitalised patients. METHODS: This is a prospective study of older non-scheduled hospitalised patients (target n = 500, age ≥ 65), recruited from elderly care and acute orthopaedic wards. Exclusion criteria are: non-English speakers; severe vision or hearing impairment; photosensitive epilepsy. A structured reference standard delirium assessment based on DSM-5 criteria will be used, which includes a cognitive test battery administered by a trained assessor (Orientation-Memory-Concentration Test, Abbreviated Mental Test-10, Delirium Rating Severity Scale-Revised-98, digit span, months and days backwards, Vigilance A' test) and assessment of arousal (Observational Scale of Level of Arousal, Richmond Agitation Sedation Scale). Prior change in cognition will be documented using the Informant Questionnaire on Cognitive Decline in the Elderly. Patients will be categorized as delirium (with/without dementia), possible delirium, dementia, no cognitive impairment, or undetermined. A separate assessor (blinded to diagnosis and assessments) will administer the DelApp index test within 3 h of the reference standard assessment. The DelApp comprises assessment of arousal (score 0-4) and sustained attention (score 0-6), yielding a total score between 0 and 10 (higher score = better performance). Outcomes (length of stay, mortality and discharge location) will be collected at 12 weeks. We will evaluate a priori cutpoints derived from a previous case-control study. Measures of the accuracy of DelApp will include sensitivity, specificity, positive and negative predictive values, and area under the ROC curve. We plan repeat assessments on up to 4 occasions in a purposive subsample of 30 patients (15 delirium, 15 no delirium) to examine changes over time. DISCUSSION: This study evaluates the diagnostic test accuracy of a novel smartphone test for delirium in a representative cohort of older hospitalised patients, including those with dementia. DelApp has the potential to be a convenient, objective method of improving delirium assessment for older people in acute care. TRIAL REGISTRATION: Clinical trials.gov, NCT02590796 . Registered on 29 Oct 2015. Protocol version 5, dated 25 July 2016.

Early intervention in Alzheimer's disease: a health economic study of the effects of diagnostic timing.

BACKGROUND: Intervention and treatment in Alzheimer's disease dementia (AD-dementia) can be cost effective but the majority of patients are not diagnosed in a timely manner. Technology is now available that can enable the earlier detection of cognitive loss associated with incipient dementia, offering the potential for earlier intervention in the UK health care system. This study aimed to determine to what extent the timing of an intervention affects its cost-effectiveness. METHODS: Using published data describing cognitive decline in the years prior to an AD diagnosis, we modelled the effects on healthcare costs and quality-adjusted life years of hypothetical symptomatic and disease-modifying interventions. Early and standard interventions were assumed to have equal clinical effects, but the early intervention could be applied up to eight years prior to standard diagnosis. RESULTS: A symptomatic treatment which immediately improved cognition by one MMSE point and reduced in efficacy over three years, would produce a maximum net benefit when applied at the earliest timepoint considered, i.e. eight years prior to standard diagnosis. In this scenario, the net benefit was reduced by around 17% for every year that intervention was delayed. In contrast, for a disease-modifying intervention which halted cognitive decline for one year, economic benefits would peak when treatment effects were applied two years prior to standard diagnosis. In these models, the maximum net benefit of the disease modifying intervention was fifteen times larger than that of the symptomatic treatment. CONCLUSION: Timeliness of intervention is likely to have an important impact on the cost-effectiveness of both current and future treatments. Healthcare policy should aim to optimise the timing of AD-dementia diagnosis, which is likely to necessitate detecting and treating patients several years prior to current clinical practice.