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Ann Clin Transl Neurol. 2019 Apr 3;6(5):873-881. doi: 10.1002/acn3.770. eCollection 2019 May.

Design and results of a smartphone-based digital phenotyping study to quantify ALS progression.

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School of Medicine Harvard Medical School Boston Massachusetts.
Neurological Clinical Research Institute Department of Neurology Massachusetts General Hospital Boston Massachusetts.
Department of Physical Medicine and Rehabilitation Spaulding Rehabilitation Hospital Boston Massachusetts.
Department of Biostatistics Harvard T.H. Chan School of Public Health Boston Massachusetts.
MGH Institute of Health Professions Charlestown Massachusetts.
Department of Neurology Henry and Allison McCance Center for Brain Health Massachusetts General Hospital Boston Massachusetts.
Department of Biostatistics Massachusetts General Hospital Boston Massachusetts.



The amyotrophic lateral sclerosis (ALS) trial outcome measures are clinic based. Active and passive smartphone data can provide important longitudinal information about ALS progression outside the clinic.


We used Beiwe, a research platform for smartphone-based digital phenotyping, to collect active (self-report ALSFRS-R surveys and speech recordings) and passive (phone sensors and logs) data from patients with ALS for approximately 24 weeks. In clinics, at baseline and every 3 months, we collected vital capacity, ALSFRS-R, and ALS-CBS at enrollment, week 12, and week 24. We also collected ALSFRS-R by telephone at week 6.


Baseline in-clinic ALSFRS-R and smartphone self-report correlation was 0.93 (P < 0.001). ALSFRS-R slopes were equivalent and within-subject standard deviation was smaller for smartphone-based self-report (0.26 vs. 0.56). Use of Beiwe afforded weekly collection of speech samples amenable to a variety of analyses, and we found mean pause time to increase by 0.02 sec per month across the sample.


Smartphone-based digital phenotyping in people with ALS is feasible and informative. Self-administered smartphone ALSFRS-R scores correlate highly with clinic-based ALSFRS-R scores, have low variability, and could be used in clinical trials. More research is required to fully analyze speech recordings and passive data, and to identify optimal digital markers for use in future ALS clinical trials.

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