Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

Area of Science:

• Neurology
• Biomedical Engineering
• Digital Health

Background:

• Traditional methods for measuring Amyotrophic Lateral Sclerosis (ALS) progression in clinical trials rely on staff-administered scales.
• There is a need for more objective and frequent measures of disease progression in ALS therapeutic development.
• Mobile applications and wearable devices offer potential for active and passive data collection.

Purpose of the Study:

• To evaluate the feasibility of using mobile apps and wearable devices to quantify ALS disease progression.
• To assess the utility of active (surveys) and passive (sensor) data collection in ambulatory adults with ALS.

Main Methods:

• Forty ambulatory adults with ALS participated in a 6-month study.
• The Beiwe mobile application was used for self-administered surveys: ALS Functional Rating Scale-Revised (ALSFRS-RSE) and Rasch Overall ALS Disability Scale (ROADS).
• Participants continuously wore wrist-worn or ankle-worn activity monitors to collect passive sensor data.

Main Results:

• High correlation was observed between the ALS Functional Rating Scale-Revised (ALSFRS-R) and the self-entry version (ALSFRS-RSE).
• Several daily physical activity measures from wearable devices showed statistically significant changes over time.
• Wearable device data demonstrated associations with ALSFRS-RSE and ROADS scores, indicating disease progression.

Conclusions:

• Active and passive digital data collection using mobile apps and wearable devices are feasible in ALS patients.
• These digital tools show promise as novel outcome measures for future Amyotrophic Lateral Sclerosis clinical trials.
• This approach may enhance the precision and efficiency of evaluating therapeutic efficacy in ALS.