While IBM is credited with inventing the first smartphone twenty-five years ago, the advent of the iPhone in 2007 gave birth to apps as we know them in modern times. Digital health has followed as a natural outgrowth of the ubiquitous nature of phones and how we rely on them daily.
Digital health, from online learning in the early 2000s to digital therapeutics, which are a more recent evolution, has slowly built the evidence base to demonstrate their effectiveness. The speed of adoption of digital tools in the pandemic underscores the related need to speed up the pace of scientific discovery to learn more about where these tools can be optimally deployed.
A new commentary by Joy Ku and Ida Sim from Stanford and USCF respectively appears in the latest issue of Nature Digital Medicine provides an overview of three recent papers that offer guidance on translating research into practice.
The authors reflect that over 85,000 health apps are available to consumers, but few have been studied in research trials, and fewer still have demonstrated evidence of benefit. In 2016, mHealth Connect was started as a collaboration between the National Institutes of Health’s Big Data To Knowledge (BD2K)- Centers for Excellence- the Mobilize Center and the Mobile-Sensor-To-Knowledge (MD2K) to explore alignment for evidence generation for mobile health. This effort aimed to bring various stakeholders together to address critical issues like data use, data sharing, and clinical use case development to close the knowledge gap and advance the field.
mHealth Connect
An early focus of this work is the use of app-generated data outside of traditional healthcare settings. The reviewed papers do not include data derived in healthcare settings (e.g., Holter monitor) or app data generated data from educational apps. The articles include data from mobile apps that consumers are using to manage and monitor their health with or without the support of a care team. The authors reflect that work by mHealth Connect leads to the creation of large data sets that researchers can standardize and leverage for insight mining and study design.
The Clinical Trials Transformation Initiative (CTTI) provides mhealth developers a comprehensive set of guidelines, including novel endpoints and protocols for device-supported data collection. Having standards helps offer benchmarks to app companies to design for, as today we have a lot of noise and not enough signal. These efforts lend themselves to advancing standards, so app-to-app comparisons will be possible if common data architecture is used.
Work is also underway to deploy mhealth in clinical care settings, and the pandemic likely accelerated this work via virtual care and remote patient monitoring. Integrating these tools into clinical care goes beyond demonstrating mhealth’s value as clinical workflows, and EMR functionality is also a significant impediment to adoption. What kind of data is being collected? What access does the care team have to this data, and how might they act on the metrics? Is the care team configured to act on this data?
It has been 13 years since the first iPhone was released; when we juxtapose that with the clinical pace of knowledge transfer, often quoted as taking 14 years to move from journal article to front line practice, this work is still in its infancy. These apps are now in consumers’ hands; a home blood pressure monitor allows someone with hypertension to monitor their blood pressure at home; in real-time, they can also share these readings with their doctor. This was not possible before the smartphone.
New models of care will emerge when these tools are more deeply deployed in clinical care. Building the evidence base is contingent on multiple stakeholders working together to create the research agendas necessary to demonstrate the value of this new therapeutic class. Funding for mhealth tools often comes from venture capitalists who aren’t in the business of funding research, so NIH’s efforts in the USA and related efforts globally will be significant to the advancement and longevity of the field.
Thanks for reading – Trina
(Opinions are my own)
References
Ku JP, Sim I. Mobile Health: making the leap to research and clinics. NPJ Digit Med. 2021 May 14;4(1):83. doi: 10.1038/s41746-021-00454-z. PMID: 33990671; PMCID: PMC8121913.
NIH Big Data to Knowledge Center
Ku JP, et al. The Mobilize Center: an NIH Big Data to Knowledge Center to advance human movement research and improve mobility. J. Am. Med. Inform. Assoc. 2015;22:1120–1125. doi: 10.1093/jamia/ocv071. – DOI – PMC – PubMed
Kumar S, et al. Center of excellence for mobile sensor data-to-knowledge (MD2K) IEEE Pervasive Comput. 2017;16:18–22. doi: 10.1109/MPRV.2017.29. – DOI – PMC – PubMed
Clinical Trials Transformation Initiative. Mobile Clinical Trials (MCT).https://www.ctti-clinicaltrials.org/programs/mobile-clinical-trials (2016).
