Evidence map›Paper›PMID 35684797›Full record

ArticleSensors (Basel, Switzerland)2022

Depressed Mood Prediction of Elderly People with a Wearable Band.

Jinyoung Choi, Soomin Lee, Seonyoung Kim, Dongil Kim, Hyungshin Kim

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

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 7 pooled it
5.3field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 7 syntheses or guidelines pooled it, 35 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Jinyoung ChoiDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Korea.ORCID 0000-0002-9717-5389
Soomin LeeDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Korea.
Seonyoung KimDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Korea.
Dongil KimDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Korea.ORCID 0000-0001-7425-7579
Hyungshin KimDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Korea.ORCID 0000-0001-9615-1644
Chungnam National University · KR

Funding

Chungnam National University BK21 FOUR ProgramKorea Institute for Advancement of Technology National Innovation Cluster R&D program(P0006710_Development of Intelligent Platform for Living Safety Service in the Complex Living Space)Ministry of Trade, Industry and Energy National Innovation Cluster R&D program(P0006710_Development of Intelligent Platform for Living Safety Service in the Complex Living Space)
6 · The paper itself

Abstract

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.

Indexed as

DepressionWearable Electronic DevicesActivities of Daily LivingAffectAgedHumansSurveys and Questionnairesdepressed moodelderly depressionunobtrusive monitoringwearable band

Identifiers

PMID35684797
PMCPMC9185362
OpenAlexW4281750093

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.