Dataset Information

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

ABSTRACT: Objective: 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.

Methods: 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.

Results: 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.

Interpretation: 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.

SUBMITTER: Berry JD

PROVIDER: S-EPMC6529832 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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

Berry James D JD Paganoni Sabrina S Carlson Kenzie K Burke Katherine K Weber Harli H Staples Patrick P Salinas Joel J Chan James J Green Jordan R JR Connaghan Kathryn K Barback Josh J Onnela Jukka Pekka JP

Annals of clinical and translational neurology 20190403 5

<h4>Objective</h4>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.<h4>Methods</h4>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 baseli ...[more]

PMID: 31139685

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Project description:IntroductionPerinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications.Methods and analysisAll Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible.Ethics and disseminationEthical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.

Dataset Information

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

Publications

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

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