Evidence map›Paper›PMID 32055728›Full record

ArticleHeliyon2020

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

Yuuki Tazawa, Kuo-Ching Liang, Michitaka Yoshimura, Momoko Kitazawa, Yuriko Kaise, Akihiro Takamiya, Aiko Kishi, Toshiro Horigome, Yasue Mitsukura, Masaru Mimura and 1 more

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In one paragraph

Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yuuki TazawaKeio University School of Medicine, Tokyo, Japan.
Kuo-Ching LiangKeio University School of Medicine, Tokyo, Japan.
Michitaka YoshimuraKeio University School of Medicine, Tokyo, Japan.
Momoko KitazawaKeio University School of Medicine, Tokyo, Japan.
Yuriko KaiseKeio University School of Medicine, Tokyo, Japan.
Akihiro TakamiyaKeio University School of Medicine, Tokyo, Japan.
Aiko KishiFaculty of Science and Technology, Keio University, Kanagawa, Japan.
Toshiro HorigomeKeio University School of Medicine, Tokyo, Japan.
Yasue MitsukuraFaculty of Science and Technology, Keio University, Kanagawa, Japan.
Masaru MimuraKeio University School of Medicine, Tokyo, Japan.
Taishiro KishimotoKeio University School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

methodsWe 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.

resultsForty-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.

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

Indexed as

Biological psychiatryBiomarkersBody tempClinical researchDepressionDiagnosticsHealth informaticsHealth technologyHeart rateMachine learningPsychiatrySleepWearable electronic devices

Identifiers

PMID32055728
PMCPMC7005437

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.