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A feasibility study of mHealth and wearable technology in late onset GM2 gangliosidosis (Tay-Sachs and Sandhoff Disease)

BACKGROUND: As part of a late onset GM2 gangliosidosis natural history study, digital health technology was utilized to monitor a group of patients remotely between hospital visits. This approach was explored as a means of capturing continuous data and moving away from focusing only on episodic data...

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Detalles Bibliográficos
Autores principales: Davies, Elin Haf, Johnston, Jean, Toro, Camilo, Tifft, Cynthia J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397597/
https://www.ncbi.nlm.nih.gov/pubmed/32746863
http://dx.doi.org/10.1186/s13023-020-01473-x
Descripción
Sumario:BACKGROUND: As part of a late onset GM2 gangliosidosis natural history study, digital health technology was utilized to monitor a group of patients remotely between hospital visits. This approach was explored as a means of capturing continuous data and moving away from focusing only on episodic data captured in traditional study designs. A strong emphasis was placed on real-time capture of symptoms and mobile Patient Reported Outcomes (mPROs) to identify the disease impact important to the patients themselves; an impact that may not always correlate with the measured clinical outcomes assessed during patient visits. This was supported by passive, continuous data capture from a wearable device. RESULTS: Adherence rate for wearing the device and completing the mPROs was 84 and 91%, respectively, resulting in a rich multidimensional dataset. As expected for a six-month proof-of-concept study in a disease that progresses slowly, statistically significant changes were not expected or observed in the clinical, mPROs, or wearable device data. CONCLUSIONS: The study demonstrated that patients were very enthusiastic and motivated to engage with the technology as demonstrated by excellent compliance. The combination of mPROs and wearables generates feature-rich datasets that could be a useful and feasible way to capture remote, real-time insight into disease burden.