{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:37:28Z","timestamp":1791488248981,"version":"4.3.4"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Digital Health Technologies (DHTs) have been under investigation for many years as innovative tools for Parkinson's disease motor symptoms given their inherent high-frequency, sensitive, and objective measurement properties. DHTs used in drug development, can be defined as Drug Development Tools (DDT), though some DHTs may also be categorized as medical devices. The recent rapid increase in use of DHTs in clinical trials has been accompanied by a rapidly evolving regulatory landscape, resulting in a challenging environment for widespread implementation of DHTs in applications that will provide clear impact on pharmaceutical company drug development pipelines. Parkinson's disease represents a disease of escalating burden with high unmet need for therapies that are disease modifying. Early intervention is a key area of focus, yet the heterogeneity of symptoms and lack of biomarkers poses challenges for drug development. Furthermore, the technologies and device platforms, both hardware and software, are rapidly evolving, and the companies developing the underlying devices frequently have objectives and timelines that may not align with those of the pharmaceutical industry. DHTs therefore have a unique set of challenges in terms of devising meaningful measures, standardization of data collected, responding to evolving regulatory expectations, and ensuring alignment across stakeholders. There is a growing need for new models of collaboration to bring together diverse stakeholders required to achieve regulatory endorsement of DHTs for use as DDTs. Collaborations between stakeholders working on DHTs need to be firmly anchored in the regulatory ecosystem as many regulatory challenges in DHTs have parallels in other technologies. Furthermore, there is an especially urgent need to define the pre-competitive space in which DHT data can be shared, data collection standards devised, and novel analysis approaches that are robust to residual variability developed. Critical Path for Parkinson's Consortium's (CPP) Digital Drug Development Tool (3DT) initiative is highlighted as a case example to illustrate how pre-competitive public private partnerships can advance the regulatory maturity of digital health technology measures for use in clinical trials.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1415202","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:37:35Z","timestamp":1761197855000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Aligning with regulatory agencies for the use of digital health technologies in drug development: a case study from Parkinson's disease"],"prefix":"10.3389","volume":"7","author":[{"given":"Derek L.","family":"Hill","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camile","family":"Carroll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramona","family":"Belfiore-Oshan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diane","family":"Stephenson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/S1474-4422(19)30397-7","article-title":"Long-term unsupervised mobility assessment in movement disorders","volume":"19","author":"Warmerdam","year":"2020","journal-title":"Lancet Neurol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41746-023-00767-1","article-title":"Evidence from ClinicalTrials.gov on the growth of digital health technologies in neurology trials","volume":"6","author":"Masanneck","year":"2023","journal-title":"NPJ Digit Med"},{"key":"B3","article-title":"Digital Health Technologies for Remote Data Acquisition in Clinical Investigations Guidance for Industry, Investigators, and Other Stakeholders","year":"2023"},{"key":"B4","doi-asserted-by":"publisher","first-page":"581","DOI":"10.3233\/JPD-150570","article-title":"Precompetitive data sharing as a catalyst to address unmet needs in Parkinson\u2019s disease","volume":"5","author":"Stephenson","year":"2015","journal-title":"J Park Dis"},{"key":"B5","article-title":"Proposal for a Regulation laying down harmonised rules on artificial intelligence | Shaping Europe\u2019s digital future","year":"2021"},{"key":"B6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/cpt.3398","article-title":"Regulatory pathways for qualification and acceptance of digital health technology-derived clinical trial endpoints: considerations for sponsors","volume":"117","author":"Bakker","year":"2025","journal-title":"Clin Pharmacol Ther"},{"key":"B7","doi-asserted-by":"publisher","first-page":"104388","DOI":"10.1016\/j.drudis.2025.104388","article-title":"Insights from the European medicines agency on digital health technology derived endpoints","volume":"30","author":"Jadoenathmisier","year":"2025","journal-title":"Drug Discov Today"},{"key":"B8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/s41597-023-01930-9","article-title":"Mobility recorded by wearable devices and gold standards: the mobilise-D procedure for data standardization","volume":"10","author":"Palmerini","year":"2023","journal-title":"Sci Data"},{"key":"B9","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack CR","year":"2008","journal-title":"J Magn Reson Imaging"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jalz.2014.07.003","article-title":"The centiloid project: standardizing quantitative amyloid plaque estimation by PET","volume":"11","author":"Klunk","year":"2015","journal-title":"Alzheimers Dement"},{"key":"B11","article-title":"Research C for DE and. 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