Evidence map›Paper›PMID 34103481›Full record

ArticleTranslational psychiatry2021

Personalized machine learning of depressed mood using wearables.

Rutvik V Shah, Gillian Grennan, Mariam Zafar-Khan, Fahad Alim, Sujit Dey, Dhakshin Ramanathan, Jyoti Mishra

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 7 pooled it
13.9field-weighted citation impact, top 1% of its field
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

61 citing papers in PubMed, 7 syntheses or guidelines pooled it, 110 citations in OpenAlex.

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1 more citing papers are in PubMed but not listed here.

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

7 authors at 1 institution in 1 country.

Rutvik V ShahDepartment of Psychiatry, University of California, San Diego, CA, USA.ORCID http://orcid.org/0000-0001-8021-9640
Gillian GrennanDepartment of Psychiatry, University of California, San Diego, CA, USA.
Mariam Zafar-KhanDepartment of Psychiatry, University of California, San Diego, CA, USA.
Fahad AlimDepartment of Psychiatry, University of California, San Diego, CA, USA.ORCID http://orcid.org/0000-0002-8832-5066
Sujit DeyMobile Systems Design Lab, Dept. of Electrical and Computer Engineering, University of California, San Diego, CA, USA.
Dhakshin RamanathanDepartment of Psychiatry, University of California, San Diego, CA, USA.
Jyoti MishraDepartment of Psychiatry, University of California, San Diego, CA, USA. jymishra@health.ucsd.edu.ORCID http://orcid.org/0000-0001-6612-4557
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

Indexed as

Machine LearningWearable Electronic DevicesAnxiety DisordersEcological Momentary AssessmentExerciseHumans

Identifiers

PMID34103481
PMCPMC8187630
OpenAlexW3170475262

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.