An examination of the quality of late-life depression websites on the Internet (Preprint)

BACKGROUND The Internet can increase the accessibility of mental health information and improve the mental health literacy of older adults. The quality of mental health information on the Internet can be inaccurate or biased, leading to misinformation OBJECTIVE This study’s objectives were to evaluate the quality, usability, and readability of websites providing information concerning depression in later life. METHODS Websites were identified through a Google search, and evaluated by assessing quality (DISCERN), usability (Patient Education Materials Assessment Tool; PEMAT) and readability (Simple Measure of Gobbledygook; SMOG). RESULTS The overall quality of late-life depression websites (N = 19) was moderate, usability was low, and readability was poor. No significant relationship was found between quality and readability of websites. CONCLUSIONS Websites can be improved by enhancing information quality, usability, and readability related to late-life depression. The use of high-quality websites may improve mental health literacy and shared treatment decision-making for older adults.

Characteristics of Dysregulated Proinflammatory Cytokines and Cognitive Dysfunction in Late-Life Depression and Amnestic Mild Cognitive Impairment

BackgroundLate-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are two different diseases associated with a high risk of developing Alzheimer’s disease (AD). Both diseases are accompanied by dysregulation of inflammation. However, the differences and similarities of peripheral inflammatory parameters in these two diseases are not well understood.MethodsWe used Luminex assays to measure 29 cytokines simultaneously in the plasma of two large cohorts of subjects at high risk for AD (23 LLD and 23 aMCI) and 23 normal controls (NCs) in the community. Demographics and lifestyle factors were also collected. Cognitive function was evaluated with the Chinese versions of the Montreal Cognitive Assessment (C-MoCA) and neuropsychological test battery (NTB).ResultsWe observed a remarkably increased level of IL-6 in the plasma and reduced levels of chemokines (CXCL11 and CCL13) in the LLD group compared with the aMCI group. The LLD group also showed lower levels of CXCL16 than the NC group. Furthermore, altered cytokine levels were associated with abnormal results in neuropsychological testing and Geriatric Depression Scale scores in both the LLD and aMCI groups. Notably, combinations of cytokines (IL-6 and CCL13) and two subitems of C-MoCA (orientation and short-term memory) generated the best area under the receiver operating characteristic curve (AUROC = 0.974).ConclusionA novel model based on proinflammatory cytokines and brief screening tests performs with fair accuracy in the discrimination between LLD and aMCI. These findings will give clues to provide new therapeutic targets for interventions or markers for two diseases with similar predementia syndromes.

An Aging Focused Unobtrusive and Privacy-Preserving Digital Behaviorome

Digital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC ≥ 0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.

EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression

Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.

Depression and Dementia: The Role of Cortisol and Vascular Brain Lesions. AGES-Reykjavik Study

Background: Late-life depression (LLD) is related to an increased risk of developing dementia; however, the biological mechanisms explaining this relationship remain unclear. Objective: To determine whether the relationship between LLD and dementia can be best explained by the glucocorticoid cascade or vascular hypothesis. Methods: Data are from 4,354 persons (mean age 76±5 years) without dementia at baseline from the AGES-Reykjavik Study. LLD was assessed with the MINI diagnostic interview (current and remitted major depressive disorder [MDD]) and the Geriatric Depression Scale-15. Morning and evening salivary cortisol were collected (glucocorticoid cascade hypothesis). White matter hyperintensities (WMH; vascular hypothesis) volume was assessed using 1.5T brain MRI. Using Cox proportional hazard models, we estimated the associations of LLD, cortisol levels, and WMH volume with incident all-cause dementia, AD, and non-AD dementia. Results: During 8.8±3.2 years of follow-up, 843 persons developed dementia, including 397 with AD. Current MDD was associated with an increased risk of developing all-cause dementia (HR = 2.17; 95% CI 1.66–2.67), with risks similar for AD and non-AD, while remitted MDD was not (HR = 1.02; 95% CI 0.55–1.49). Depressive symptoms were also associated with increased risk of dementia, in particular non-AD dementias. Higher levels of evening cortisol increased risk of dementia, but this was independent of MDD. WMH partially explained the relation between current MDD and dementia risk but remained increased (HR = 1.71; 95% CI 1.34–2.08). Conclusion: The current study highlights the importance of LLD in developing dementia. However, neither the glucocorticoid cascade nor the vascular hypotheses fully explained the relation between depression and dementia.

Evaluation of Platelet Glutamate Dehydrogenase Activity in Late-Life Depressions

The aim of the study is to evaluate the activity of platelet glutamate dehydrogenase (GDH) in late-life depression compared to the healthy control group and to reveal possible correlations with clinical data. Patients and methods: 42 elderly patients (60–86 years old) with depressive episodes of different nosological categories according to ICD-10 were examined: a single depressive episode (F32.0, F32.1), a depressive episode in recurrent depressive disorder (RDD — F33.0, F33.1) and a depressive episode in bipolar affective disorder (BD — F31.3). The activity of GDH and the severity of depression (using the Hamilton depressive scale, HAMD-17, and the Hamilton scale for assessing anxiety, HARS) were evaluated twice: before the starting the course of antidepressant therapy (day 0) and on the 28th day of the treatment course. Results: patients showed a significant decrease in the activity of GDH compared to the control group (p < 0.0008). Before the treatment, GDH activity was significantly reduced compared to the control in both RDD and BD (p < 0.002 and p < 0.004), whereas after the treatment, the decreased GDH activity was observed only in patients with BD (p < 0.002). When compared with the control group, male patients showed a significant decrease in GDH activity both before and after the treatment course (p < 0.017 and p < 0.027), whereas women patients showed the decrease only before the treatment (p < 0.014). Conclusion: the decreased platelet GDH activity in elderly depressions may indicate an impairment of glutamate metabolism. Gender differences were revealed in the reversal of GDH activity level after the therapy: in men, the level of GDH activity did not recover to control values after the treatment course. An elevation in the level of GDH to control values over a 28-day course of therapy occurred only in patients with RDD, but not in patients with BD.