Evidence map›Paper›PMID 31527640›Full record

ArticleScientific reports2019

Touchscreen typing pattern analysis for remote detection of the depressive tendency.

Rafail-Evangelos Mastoras, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Seada Kassie, Taoufik Alsaadi, Ahsan Khandoker, Leontios J Hadjileontiadis

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

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 6 pooled it
7.6field-weighted citation impact, top 2% 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

44 citing papers in PubMed, 6 syntheses or guidelines pooled it, 102 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

8 authors at 2 institutions in 2 countries.

Rafail-Evangelos MastorasDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Dimitrios IakovakisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Stelios HadjidimitriouDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Vasileios CharisisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Seada KassieAmerican Center for Psychiatry and Neurology, Abu Dhabi, UAE.ORCID http://orcid.org/0000-0001-6546-6168
Taoufik AlsaadiAmerican Center for Psychiatry and Neurology, Abu Dhabi, UAE.ORCID http://orcid.org/0000-0002-7513-5706
Ahsan KhandokerHealthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Leontios J HadjileontiadisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece. leontios.hadjileontiadis@ku.ac.ae.
Aristotle University of Thessaloniki · GRKhalifa University of Science and Technology · AE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients' routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects' status. The best-performing pipeline achieved an AUC = 0.89 (0.72-1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.

Indexed as

AdultDepressive DisorderDiagnosis, Computer-AssistedFemaleHumansMalePattern Recognition, AutomatedSmartphoneSurveys and QuestionnairesTelemedicineYoung Adult

Identifiers

PMID31527640
PMCPMC6746713
OpenAlexW2973854560

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.