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

Maleki, Ghobad; Zhuparris, Ahnjili; Koopmans, Ingrid; Doll, Robert; Voet, Nicoline; Cohen, Adam F.; Brummelen, Emilie van; Groeneveld, Geert Jan; Maeyer, Joris De

doi:10.2196/31775

Cited by 8 publications

(13 citation statements)

References 28 publications

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“…[ 49 ] Individuals with HD spent over 50% of the total time lying down, more than individuals with prodromal HD, PD, and controls [ 47 ] Cambridge Neurotechnology AW4 or Respironics Actiwatch 2 actigraph MPS Children with MPS III had significantly higher activity levels during the early morning hours compared to controls [ 117 ] CHDR Monitoring Remotely (CHDR MORE) platform FSHD The classification between patients with FSHD and controls with 93% accuracy, 100% sensitivity, and 80% specificity. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification [ 39 ] Computational Motor Objective Rater (CMOR) FD Head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS-2 and an adapted version of the Global Dystonia Rating Scale [ 85 ] DynaPort Move Monitor, McRoberts, The Hague, The Netherlands MG Patients perform less vigorous PA, spend more time sedentary and engage in less and shorter durations of MVPA than controls. Habitual PA correlated positively with 6 min walking distance [ 41 ] PAL was lower in patients than in controls.…”

Section: Resultsmentioning

confidence: 99%

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

Poleur

Markati

Servais

2023

Orphanet J Rare Dis

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Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.

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Section: Resultsmentioning

confidence: 99%

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

Poleur

Markati

Servais

2023

Orphanet J Rare Dis

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“…We did not exclude any outliers as none of the data points were viewed as potential measurement errors. In a previous publication, we provided an overview of the proportion of observations that were missing per feature [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…This study is an extension of a previous longitudinal clinical study that investigated the feasibility of monitoring and characterizing patients with FSHD and healthy controls in terms of biometric, physical, and social activities using data sourced from smartphones and other remote monitoring devices. Therefore, additional information regarding the data collection and data quality has been previously published [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

“…Identifying novel objective biomarkers for monitoring disease progression could additionally provide clinically relevant insights and aid drug development. Novel digital end points for neuromuscular disease drug development have already demonstrated to be sensitive to differentiating patients from healthy volunteers and are strongly correlated with clinician assessments [13][14][15]. The widespread adoption of smartphones and wearables could provide new opportunities for objective and continuous monitoring of FSHD disease progression outside the laboratory.…”

Section: Introductionmentioning

confidence: 99%

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Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Zhuparris¹,

Maleki²,

Koopmans³

et al. 2023

JMIR Form Res

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Background Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions. Objective We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic. Methods In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data. Results The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. Conclusions We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. Trial Registration ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735

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“…Supplementary Table 1 illustrates how all the features were aggregated for each data type. The design of these features was based on available data provided by the smartphone and wearable devices, and on a previous published study that had a similar protocol 35 .…”

Section: Methodsmentioning

confidence: 99%

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity

Zhuparris,

Maleki,

van Londen

et al. 2023

Sci Rep

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Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant’s daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology–clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.

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

Cited by 8 publications

References 28 publications

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity

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