Affordable Access

deepdyve-link
Publisher Website

EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration

Authors
  • Manta, Christine
  • Mahadevan, Nikhil
  • Bakker, Jessie
  • Ozen Irmak, Simal
  • Izmailova, Elena
  • Park, Siyeon
  • Poon, Jiat-Ling
  • Shevade, Santosh
  • Valentine, Sarah
  • Vandendriessche, Benjamin
  • Webster, Courtney
  • Goldsack, Jennifer C.
Type
Published Article
Journal
Digital Biomarkers
Publisher
S. Karger AG
Publication Date
May 18, 2021
Volume
5
Issue
2
Pages
127–147
Identifiers
DOI: 10.1159/000515835
PMID: 34179682
PMCID: PMC8215946
Source
PubMed Central
Keywords
Disciplines
  • NODE − Review Article
License
Unknown

Abstract

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.

Report this publication

Statistics

Seen <100 times