Article Text
Abstract
Objective The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using inbuilt smartphone sensors.
Methods Systematic Review—MEDLINE, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until 15 December 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used inbuilt sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the reporting quality with the ’STAndards for the Reporting of Diagnostic accuracy studies' (STARD) statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarised.
Results We screened 3296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % CI 0.56 to 0.94) and 0.89 (95 %CI 0.70 to 0.97) for specificity.
Conclusions The diagnostic evidence of available health apps on Apple’s and Google’s app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them.
PROSPERO registration number 42016033049.
- mobile health apps
- evidence-based medicine
- systematic review
- diagnostic research
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Statistics from Altmetric.com
Footnotes
RB and LF contributed equally.
Contributors RB, LF, LMB, KRL, NSB and MAT obtained and appraised data. LMB and MKS wrote the paper with considerable input from OJ, MAT, RB and KRL. All coauthors provided intellectual input and approved the final manuscript. LMB was responsible for the design and the statistical analysis of the study and is the study guarantor.
Funding The work presented in this paper was funded by Medignition Inc, a privately owned company in Switzerland providing health technology assessments for the public and private sectors, via an unrestricted research grant.
Competing interests LMB holds shares of Medignition.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement The dataset containing all abstracted data of included studies is available from the Dryad repository: doi:10.5061/dryad.900f8.