Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study

Abstract

Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients' quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD.

Objective: This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls.

Methods: In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants' FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants' smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification.

Results: Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification.

Conclusions: Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity.

Trial registration: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735.

Keywords: FSHD; classification; facioscapulohumeral dystrophy; machine learning; mobile phone; smartphone; wearables.

Conflict of interest statement

Conflicts of Interest: At the time of the study, AC worked at the Centre for Human Drug Research as well as an unpaid member of the scientific advisory board of Facio Therapies. JDM is the Chief Executive Officer of Facio Therapies.

©Ghobad Maleki, Ahnjili Zhuparris, Ingrid Koopmans, Robert J Doll, Nicoline Voet, Adam Cohen, Emilie van Brummelen, Geert Jan Groeneveld, Joris De Maeyer. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.09.2022.

Figures

Figure 1

Feasibility and perceived burden of remote monitoring in patients with facioscapulohumeral dystrophy using smartphone-based technologies.

Figure 2

Selected features for classifying patients with facioscapulohumeral dystrophy and those without facioscapulohumeral dystrophy based on the composite data set using all 6 weeks of data and the least absolute shrinkage and selection operator–penalized logistic regression model. Unstandardized estimated coefficients indicate the direction of the association between the feature and the classification label.

Figure 3

Performance of the incremental classification predictions for 3 classifiers (logistic regression, random forest, and support vector machine). The x-axis shows the time window for training the classification models starting from day 1 to day 42. The error bands represent the SD of the classification performance for the 5-fold cross-validation.

Figure 4

Performance of 3 classifiers (logistic regression, random forest, and support vector machine) trained on the week 1 data and used to predict the classification diagnosis of the subsequent weeks of data. The error bands represent the SD of the classification performance for the 5-fold cross-validation.

Figure 5

Shapley additive explanations (SHAP) summary plot based on a random forest classifier that was trained on the week 1 data. The x-axis shows the feature importance, where features are ranked in descending order. The y-axis shows the SHAP value that illustrates the direction of the association between the feature and facioscapulohumeral dystrophy severity. The color scheme reflects the probability of a participant being classified as a patient with facioscapulohumeral dystrophy.