The utility of personal wearable data in long COVID and personalized patient care (2024)

Access to the expanded dataset of personal wearable data comes with limitations, including time constraints for providers who already feel overwhelmed by the amount of data available for each patient¹⁴, concerns over the quality and consistency of data produced across smart devices¹⁵, and uncertainties on how these data should influence patient care¹⁶. Furthermore, inequities persist in who can access and pay for fitness trackers and smart devices, raising concerns about how differences in access could lead to differences in the quality of care between patients with and without personal wearable data¹⁷. The decision of which biomarkers to prioritize in clinical healthcare of all those collected by personal wearables also brings challenges, as certain digital markers may capture risk patterns for some diseases better than for others¹⁸. Clearly, the incorporation of personal wearable data into research and clinical spaces will require adaptations. As a start, challenges with implementation and inequities could be addressed by:

1. i.

   Use of artificial intelligence (AI) models to streamline wearable data analysis. AI models can rapidly sort and analyze vast amounts of patient information¹⁹, and could be employed to decrease provider burden in the analysis of data collected by personal wearable devices. Already, machine learning models have proven capable of analyzing large amounts of patient data to stratify disease risk²⁰, and personal wearable data could enhance such AI prediction tools without imposing additional requirements on providers.

2. ii.

   Increased collaboration, compatibility, and data sharing among technology companies in the personal wearable sector. Despite the high prevalence of personal wearable devices, the wearable industry remains fragmented²¹. Data sharing across companies could validate the reliability of models and improve the consistency and quality of data across devices. Furthermore, interoperability of different wearables would allow for easier integration of wearable technology into the healthcare system and easier interpretation of wearable data generated by difference devices¹⁷.

3. iii.

   Increased regulation of data quality and collection techniques for smart-watch and fitness tracker companies. Should physicians use personal wearable data to guide patient care, FDA oversight must be extended, requiring additional regulatory guidance and quality standards currently lacking in the field. This oversight would presumably produce mandated standards, thereby further increasing interoperability across wearable devices, as well.

4. iv.

   A push for insurance companies to cover smartwatches and fitness trackers as they do other medical devices. While the FDA classification of personal wearables as medical devices would make such coverage more likely, a policy shift of this scope seems likely to require further evidence of improved patient outcomes and reduced insurance costs associated with the use of personal wearable data. More research into the health and cost effects of incorporating personal wearable data into the clinic is needed to propel such potential coverage forward.

5. v.

   Identification of digital biomarkers most sensitive to change and appropriate for different patient populations. Digital biomarkers that measure resilience (i.e., heart rate variability) have been proposed as markers to assess a variety of distinct conditions, including vascular homeostasis, glucose regulation, and chronic inflammation¹⁸. Similarly, markers that capture movement data have been proposed as prediction tools to determine mood disorder status²² and disease progression in neuromuscular disorders²³. Given patients’ unique histories and baseline risk profiles, it is likely that different digital biomarkers will prove most useful in different clinical contexts. Further research is again required to identify which markers prove most universally important, and which will serve as critical tools in risk stratification for specific patient populations.

The challenges of incorporating personal wearable data are certainly formidable. Nonetheless, as Radin et al.’s⁷ work reveals, personal wearable data provides temporal insight that isolated clinical and research data threaten to miss. The potential for wearables to provide increasingly comprehensive and personalized care necessitates conversation within the healthcare industry about how personal device data can be incorporated into healthcare, and the above adjustments offer initial guidance on the changes that must be considered to realize the potential of these data in personalizing care.

Authors and Affiliations

1. Harvard Medical School, Boston, MA, USA

   Elizabeth J. Enichen,Kimia Heydari,Serena Wang,Grace C. Nickel&Joseph C. Kvedar

Contributions

E.E. wrote the first draft of the manuscript. K.H., G.N., and S.W. contributed to the first draft and provided critical revisions. J.C.K. provided critical revisions. All authors have read and approved of the final manuscript.

Corresponding author

Correspondence to Elizabeth J. Enichen.

Competing interests

J.C.K. is the editor-in-chief of npj Digital Medicine. All other authors declare no competing interests.

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