Identification of Geriatric Depression and Anxiety Using Activity Tracking Data and Minimal Geriatric Assessment Scales

Lee, Tae-Rim; Kim, Geon Ha; Choi, Mun-Taek

doi:10.3390/app12052488

Cited by 1 publication

(7 citation statements)

References 33 publications

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“…This study extends our previous work 11 by applying a DL approach to identify geriatric depression and anxiety using activity tracking and sleep data. A multi-label DL model is applied to handle comorbid depression and anxiety problems.…”

Section: Introductionsupporting

confidence: 59%

“…The dataset used in this study was obtained from a previous study 11 undertaken by Ewha Womans University Mokdong Hospital from May 2017 to May 2019. At that time, all participants provided written informed consent, and the research protocol was approved by the Institutional Review Board of Ewha Womans University Mokdong Hospital (EUMC 2016‐09‐042‐013).…”

Section: Sample Datamentioning

confidence: 99%

“…Additionally, they identified activity and sleep patterns using Acti-Graph, a relatively expensive solution. Lee et al 11 Coutts et al 13 analyzed health status using heart rate variability data collected from individuals wearing biobeam bands. Jakobsen et al 14 developed and compared both machine and DL depression classification models using actigraphy biosignal data.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, they identified activity and sleep patterns using ActiGraph, a relatively expensive solution. Lee et al 11 . developed a multi‐label classification model to aimed at detecting comorbid geriatric depression and anxiety.…”

Section: Introductionmentioning

confidence: 99%

“…, 1) the time-series inputs of the step counts and sleep stages are passed through deep networks, 2) the non-time-series inputs of minimal depression and anxiety assessment scores (K-SGDS 3, 8, 14 and K-GAI 5,11,20) are passed through a multi-layer perceptron (MLP),25 and 3) the reduced features are concatenated and processed by the multi-label classification layerusing diagnostic results provided by clinicians. The simple MLP part comprises two hidden layers and a dropout layer.…”

mentioning

confidence: 99%

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Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data

Lee,

Kim,

Choi

2024

Int J Geriat Psychiatry

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BackgroundGeriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self‐reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in‐depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time‐consuming and costly but also require systematic and cost‐effective approaches.ObjectiveThe main objective of this study was to investigate the feasibility of training an end‐to‐end deep learning (DL) model by directly inputting time‐series activity tracking and sleep data obtained from consumer‐grade wrist‐worn activity trackers to identify comorbid depression and anxiety.MethodsTo enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non‐time‐series data. The basic structure of the DL model was designed to process mixed‐input data and perform multi‐label‐based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short‐term memory (LSTM), were applied to process the time‐series data, and model selection was conducted by comparing the performances of the hyperparameters.ResultsThis study achieved significant results in the multi‐label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed‐input DL model based on activity tracking data. The comparison of hyper‐parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results.ConclusionsThis study can be considered as the first to develop a mixed‐input DL model based on activity tracking data for the multi‐label identification of late‐life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer‐grade wrist‐worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi‐label classification framework for identifying the complex symptoms of depression and anxiety.

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Section: Introductionsupporting

confidence: 59%

Section: Sample Datamentioning

confidence: 99%