A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies
Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and dri...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-02-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e58956 |
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| author | Jessie P Bakker Samantha J McClenahan Piper Fromy Simon Turner Barry T Peterson Benjamin Vandendriessche Jennifer C Goldsack |
| author_facet | Jessie P Bakker Samantha J McClenahan Piper Fromy Simon Turner Barry T Peterson Benjamin Vandendriessche Jennifer C Goldsack |
| author_sort | Jessie P Bakker |
| collection | DOAJ |
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Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs—evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable—is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development. |
| format | Article |
| id | doaj-art-8aa3a332475746d0b18b08c948705f7e |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-8aa3a332475746d0b18b08c948705f7e2025-02-07T14:00:34ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-02-0127e5895610.2196/58956A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health TechnologiesJessie P Bakkerhttps://orcid.org/0000-0002-2976-4747Samantha J McClenahanhttps://orcid.org/0000-0002-1792-9700Piper Fromyhttps://orcid.org/0009-0007-7751-7226Simon Turnerhttps://orcid.org/0009-0002-3801-1875Barry T Petersonhttps://orcid.org/0009-0007-4735-0568Benjamin Vandendriesschehttps://orcid.org/0000-0003-0672-0327Jennifer C Goldsackhttps://orcid.org/0000-0003-0461-0183 Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs—evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable—is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development.https://www.jmir.org/2025/1/e58956 |
| spellingShingle | Jessie P Bakker Samantha J McClenahan Piper Fromy Simon Turner Barry T Peterson Benjamin Vandendriessche Jennifer C Goldsack A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies Journal of Medical Internet Research |
| title | A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies |
| title_full | A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies |
| title_fullStr | A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies |
| title_full_unstemmed | A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies |
| title_short | A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies |
| title_sort | hierarchical framework for selecting reference measures for the analytical validation of sensor based digital health technologies |
| url | https://www.jmir.org/2025/1/e58956 |
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