Evidence map›Paper›PMID 26644316›Full record

SynthesisScientific reports2015

The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications.

Chu Qin, Lin Tao, Yik Hui Phang, Cheng Zhang, Shang Ying Chen, Peng Zhang, Ying Tan, Yu Yang Jiang, Yu Zong Chen

Abstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  5. Review
  6. Article
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

9 authors.

Chu QinShenzhen Kivita Innovative Drug Discovery Institute, and the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, P. R. China.
Lin TaoDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.
Yik Hui PhangDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.
Cheng ZhangDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.
Shang Ying ChenDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.
Peng ZhangDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.
Ying TanShenzhen Kivita Innovative Drug Discovery Institute, and the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, P. R. China.
Yu Yang JiangShenzhen Kivita Innovative Drug Discovery Institute, and the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, P. R. China.
Yu Zong ChenDepartment of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543 Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobile health technologies to detect physiological and simple-analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage, and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance, and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products, and discuss potential issues of implementing this strategy.

Indexed as

BiomarkersBiomedical TechnologyDelivery of Health CareDisease ManagementElectronic Data ProcessingHumansMass ScreeningMolecular Diagnostic TechniquesPrecision MedicinePrognosisTelemedicineBiomarkers

Identifiers

PMID26644316
PMCPMC4672303

What OpenQuestion holds

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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.