This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (825) Novel Sensors and Data Acquisition Methods in Cardiology (191)

Published on in Vol 6, No 12 (2018): December

Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

Authors of this article:

Jia Zhang1 Author Orcid Image ;   Laura Tüshaus1 Author Orcid Image ;   Néstor Nuño Martínez2, 3 Author Orcid Image ;   Monica Moreo1 Author Orcid Image ;   Hector Verastegui4 Author Orcid Image ;   Stella M Hartinger2, 3, 4 Author Orcid Image ;   Daniel Mäusezahl2, 3 Author Orcid Image ;   Walter Karlen1 Author Orcid Image
Article Authors Cited by (4) Tweetations (8) Metrics

Journals

  1. Zhang J, Mihai C, Tüshaus L, Scebba G, Distler O, Karlen W. Wound Image Quality From a Mobile Health Tool for Home-Based Chronic Wound Management With Real-Time Quality Feedback: Randomized Feasibility Study. JMIR mHealth and uHealth 2021;9(7):e26149 View
  2. Afshar A, Li Y, Chen Z, Chen Y, Lee J, Irani D, Crank A, Singh D, Kanter M, Faraday N, Kharrazi H. An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database. JAMIA Open 2021;4(3) View
  3. Ilhan E, Jola L, van der Zalm M, Bernstein M, Goussard P, Redfern A, Hesseling A, Hoddinott G, McCollum E, King C. Designing a Smartphone-Based Pulse Oximeter for Children in South Africa (Phefumla Project): Qualitative Analysis of Human-Centered Design Workshops With Health Care Workers. JMIR Human Factors 2024;11:e54983 View