Development and validation of the Chinese Geriatric Depression Risk calculator (CGD-risk): A screening tool to identify elderly Chinese with depression

Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4-8 hours before sleep onset

Wearable devices with sleep tracking functionalities can prompt behavioral changes to promote sleep, but proactively preventing poor sleep when it is likely to occur remains a challenge due to a lack of prediction models that can forecast sleep parameters prior to sleep onset. We developed models that forecast low sleep efficiency 4 and 8 hours prior to sleep onset using gradient boosting (CatBoost) and deep learning (Convolutional Neural Network Long Short-Term Memory, CNN-LSTM) algorithms trained exclusively on accelerometer data from 80,811 adults in the UK Biobank. Associations of various sleep and activity parameters with sleep efficiency were further examined. During repeated cross-validation, both CatBoost and CNN-LSTM exhibited excellent predictive performance (median AUCs > 0.90, median AUPRCs > 0.79). U-shaped relationships were observed between total activity within 4 and 8 hours of sleep onset and low sleep efficiency. Functional data analyses revealed higher activity 6 to 8 hours prior to sleep onset had negligible associations with sleep efficiency. Higher activity 4 to 6 hours prior had moderate beneficial associations, while higher activity within 4 hours had detrimental associations. Additional analyses showed that increased variability in sleep duration, efficiency, onset timing, and offset timing over the preceding four days was associated with lower sleep efficiency. Our study represents a first step towards wearable-based machine learning systems that proactively prevent poor sleep by demonstrating that sleep efficiency can be accurately forecasted prior to bedtime and by identifying pre-bed activity targets for subsequent intervention.

Key modifiable factors in urban-rural differences in depression among older adults in China: A comparative study between China and the United States

OBJECTIVES

Urban-rural differences in depression are evident among older adults in China but not in the United States. By comparing the two countries, this study aims to explore strategies for promoting regional equality in depression in China.

METHODS

Data from the China Health and Retirement Longitudinal Study (CHARLS) and the Health and Retirement Study (HRS) were utilized. Longitudinal data were used to describe urban-rural differences in depression prevalence among older adults in China (2011-2020) and the United States (2010-2020). Cross-sectional data from 2018 (CHARLS: 9840 participants; HRS: 10,381 participants) were used to identify key modifiable factors. A random forest algorithm was employed to determine the most important factors influencing depression, and comparisons between the two countries were made to identify modifiable factors. Multivariate logistic regression was used to analyze the relationship between these key modifiable factors and depression. A mediation model was applied to assess the mediating role of key modifiable factors in the relationship between residence and depression.

RESULTS

1) From 2011 to 2020, Urban-rural differences in depression prevalence among older adults were observed in China, but not in the United States. 2) In both China and the U.S., the top five factors ranked by importance were activities of daily living disability (ADLs), instrumental activities of daily living disability (IADLs), pain levels, self-reported health (SRH), and age. However, Urban-Rural Differences in ADLs, IADLs, and SRH were present in China but not in the United States. 3) ADLs, IADLs, and SRH collectively mediated 31.6 % (95 % CI: 0.268 - 0.360) of the relationship between residence and depression scores among older adults in China.

CONCLUSION

Urban-rural differences in physical health status (ADLs, IADLs, and self-reported health) among older adults in China are associated with Urban-Rural Differences in depression. The absence of such inequalities in the U.S. may offer insights for developing policies to promote regional equality in depression among older adults in China.

Moving Toward Meaningful Evaluations of Monitoring in e-Mental Health Based on the Case of a Web-Based Grief Service for Older Mourners: Mixed Methods Study

Background

Artificial intelligence (AI) tools hold much promise for mental health care by increasing the scalability and accessibility of care. However, current development and evaluation practices of AI tools limit their meaningfulness for health care contexts and therefore also the practical usefulness of such tools for professionals and clients alike.

Objective

The aim of this study is to demonstrate the evaluation of an AI monitoring tool that detects the need for more intensive care in a web-based grief intervention for older mourners who have lost their spouse, with the goal of moving toward meaningful evaluation of AI tools in e-mental health.

Methods

We leveraged the insights from three evaluation approaches: (1) the F1-score evaluated the tool's capacity to classify user monitoring parameters as either in need of more intensive support or recommendable to continue using the web-based grief intervention as is; (2) we used linear regression to assess the predictive value of users' monitoring parameters for clinical changes in grief, depression, and loneliness over the course of a 10-week intervention; and (3) we collected qualitative experience data from e-coaches (N=4) who incorporated the monitoring in their weekly email guidance during the 10-week intervention.

Results

Based on n=174 binary recommendation decisions, the F1-score of the monitoring tool was 0.91. Due to minimal change in depression and loneliness scores after the 10-week intervention, only 1 linear regression was conducted. The difference score in grief before and after the intervention was included as a dependent variable. Participants' (N=21) mean score on the self-report monitoring and the estimated slope of individually fitted growth curves and its standard error (ie, participants' response pattern to the monitoring questions) were used as predictors. Only the mean monitoring score exhibited predictive value for the observed change in grief (R2=1.19, SE 0.33; t16=3.58, P=.002). The e-coaches appreciated the monitoring tool as an opportunity to confirm their initial impression about intervention participants, personalize their email guidance, and detect when participants' mental health deteriorated during the intervention.

Conclusions

The monitoring tool evaluated in this paper identified a need for more intensive support reasonably well in a nonclinical sample of older mourners, had some predictive value for the change in grief symptoms during a 10-week intervention, and was appreciated as an additional source of mental health information by e-coaches who supported mourners during the intervention. Each evaluation approach in this paper came with its own set of limitations, including (1) skewed class distributions in prediction tasks based on real-life health data and (2) choosing meaningful statistical analyses based on clinical trial designs that are not targeted at evaluating AI tools. However, combining multiple evaluation methods facilitates drawing meaningful conclusions about the clinical value of AI monitoring tools for their intended mental health context.

Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study

Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Factors known to be associated with cognition that can be gathered from accelerometers, user interfaces, and other sensors within wearable devices could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation CatBoost, XGBoost, and Random Forest models performed best when predicting poor cognition based on tests measuring processing speed, working memory, and attention (median AUCs ≥0.82) compared to immediate and delayed recall (median AUCs ≥0.72) and categorical verbal fluency (median AUC ≥ 0.68). Activity and sleep parameters were also more strongly associated with poor cognition based on tests assessing processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that data collatable through wearable devices such as age, education, sleep parameters, activity summaries, and light exposure metrics could be used to differentiate between older adults with normal versus poor cognition. We further identified metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.

Effects of reminiscence therapy combined with memory specificity training (RT-MeST) on depressive symptoms in older adults: a randomized controlled trial protocol

BACKGROUND

Geriatric depression has become a serious public health problem, and reduced autobiographical memory and increased overgeneral memory, as the main cognitive markers of depression, are not only associated with current depressive symptoms but also associated with the onset and course of depression, which can lead to a range of harms. Economic and effective psychological interventions are urgently needed. The aim of this study is to confirm the effectiveness of reminiscence therapy combined with memory specificity training in improving autobiographical memory and depressive symptoms in older adults.

METHODS

In this multicentre, single-blind, three-arm parallel randomized controlled study, we aim to enrol 78 older adults aged 65 years or older with a score of ≥ 11 on the Geriatric Depression Scale, and participants will be randomly assigned to either a reminiscence therapy group, a reminiscence therapy with memory specificity training group or a usual care group. Assessments will be conducted at baseline (T0) as well as immediately post-intervention (T1) and 1 (T2), 3 (T3) and 6 (T4) months post-intervention. The primary outcome measure is self-reported depressive symptoms, measured using the GDS. Secondary outcome measures include measures of autobiographical memory, rumination, and social engagement.

DISCUSSION

We believe that the intervention will play a positive role in improving autobiographical memory and depressive symptoms in older adults. Poor autobiographical memory is a predictor of depression and a major cognitive marker, and improving autobiographical memory is of great significance in alleviating depressive symptoms in older people. If our program is effective, it will provide a convenient and feasible strategy for further promoting healthy ageing.

TRIAL REGISTRATION

ChiCTR2200065446.