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      Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

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          Abstract

          Objective

          We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices.

          Methods

          We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation.

          Results

          Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning.

          Limitations

          The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted.

          Conclusion

          The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

          Abstract

          Psychiatry; Biological psychiatry; Depression; Clinical research; Health informatics; Health technology; Diagnostics; Biomarkers; Machine learning; Wearable electronic devices; Heart rate; Sleep; Body temp

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          Most cited references 43

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          Wrist actigraphy.

          To record sleep, actigraph devices are worn on the wrist and record movements that can be used to estimate sleep parameters with specialized algorithms in computer software programs. With the recent establishment of a Current Procedural Terminology code for wrist actigraphy, this technology is being used increasingly in clinical settings as actigraphy has the advantage of providing objective information on sleep habits in the patient's natural sleep environment. Actigraphy has been well validated for the estimation of nighttime sleep parameters across age groups, but the validity of the estimation of sleep-onset latency and daytime sleeping is limited. Clinical guidelines and research suggest that wrist actigraphy is particularly useful in the documentation of sleep patterns prior to a multiple sleep latency test, in the evaluation of circadian rhythm sleep disorders, to evaluate treatment outcomes, and as an adjunct to home monitoring of sleep-disordered breathing. Actigraphy has also been well studied in the evaluation of sleep in the context of depression and dementia. Although actigraphy should not be viewed as a substitute for clinical interviews, sleep diaries, or overnight polysomnography when indicated, it can provide useful information about sleep in the natural sleep environment and/or when extended monitoring is clinically indicated.
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            Primary insomnia: a risk factor to develop depression?

            Chronic insomnia afflicts approximately 5-10% of the adult population in Western industrialized countries. Insomnia may be secondary, i.e. triggered and/or maintained by psychiatric/organic illnesses, the intake of prescribed/illicit drugs or alcohol, or by a combination of these factors. Insomnia can also occur as primary insomnia, caused by a psychophysiological hyperarousal process. In the present review a literature search was undertaken to identify longitudinal epidemiological studies which investigate the question whether primary insomnia at baseline predicts the development of depression at follow-up measurements. MEDLINE search for the medical subject headings insomnia and depression; identification of longitudinal epidemiological studies with at least two measurement points 1 year apart measuring insomnia and depression and indicating explicit criteria for both disorders. Eight relevant epidemiological studies were identified. Almost unambiguously insomnia at baseline significantly predicted an increased depression risk at follow-up 1-3 years later. As insomniac symptoms alone seem to be of predictive value for the development of depression in the succeeding years, it would be worthwhile to investigate if early adequate treatment is able to prevent psychiatric sequelae of primary insomnia.
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              The application of mHealth to mental health: opportunities and challenges.

              Advances in smartphones and wearable biosensors enable real-time psychological, behavioural, and physiological data to be gathered in increasingly precise and unobtrusive ways. Thus, moment-to-moment information about an individual's moods, cognitions, and activities can be collected, in addition to automated data about their whereabouts, behaviour, and physiological states. In this report, we discuss the potential of these new mobile digital technologies to transform mental health research and clinical practice. By drawing on results from the INSIGHT research project, we show how traditional boundaries between research and clinical practice are becoming increasingly blurred and how, in turn, this is leading to exciting new developments in the assessment and management of common mental disorders. Furthermore, we discuss the potential risks and key challenges associated with applying mobile technology to mental health.
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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                04 February 2020
                February 2020
                04 February 2020
                : 6
                : 2
                Affiliations
                [a ]Keio University School of Medicine, Tokyo, Japan
                [b ]Faculty of Science and Technology, Keio University, Kanagawa, Japan
                Author notes
                []Corresponding author. tkishimoto@ 123456keio.jp
                Article
                S2405-8440(20)30119-5 e03274
                10.1016/j.heliyon.2020.e03274
                7005437
                © 2020 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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