Evidence map›Paper›PMID 32547805›Full record

ReviewHealthcare informatics research2020

Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health.

Bens Pardamean, Haryono Soeparno, Arif Budiarto, Bharuno Mahesworo, James Baurley

Abstract readReview
In one paragraph

Review in Healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Review
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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.

Bens PardameanComputer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia.ORCID 0000-0002-7404-9005
Haryono SoeparnoComputer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia.ORCID 0000-0001-9766-0352
Arif BudiartoBioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.ORCID 0000-0001-8875-6694
Bharuno MahesworoBioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.ORCID 0000-0003-1017-2917
James BaurleyBioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.ORCID 0000-0003-4116-7723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesRecently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness.

methodsA thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using "wearable device research" as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers).

resultsAll the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular disorders and post-stroke treatment.

conclusionsDespite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.

Indexed as

ExerciseFitness TrackerMedical InformaticsMental HealthWearable Electronic Device

Identifiers

PMID32547805
PMCPMC7278513

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.