Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct 29;4(1):217.
doi: 10.1038/s43856-024-00653-1.

At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich's Ataxia

Ram Kinker Mishra #  1 , Adonay S Nunes #  1 , Ana Enriquez  1 , Victoria R Profeta  2   3 , McKenzie Wells  2   3 , David R Lynch  4   5 , Ashkan Vaziri  6
Affiliations

At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich's Ataxia

Ram Kinker Mishra et al. Commun Med (Lond). .

Abstract

Background: Friedreich ataxia (FRDA) results in progressive impairment in gait, upper extremity coordination, and speech. Currently, these symptoms are assessed through expert examination at clinical visits. Such in-clinic assessments are time-consuming, subjective, of limited sensitivity, and provide only a limited perspective of the daily disability of patients.

Methods: In this study, we recruited 39 FRDA patients and remotely monitored their physical activity and upper extremity function using a set of wearable sensors for 7 consecutive days. We compared the sensor-derived metrics of lower and upper extremity function as measured during activities of daily living with FRDA clinical measures (e.g., mFARS and FA-ADL) and biological biomarkers of disease severity (guanine-adenine-adenine (GAA) and frataxin (FXN) levels), using Spearman correlation analyses.

Results: The results show significant correlations with moderate to high effect sizes between multiple sensor-derived metrics and the FRDA clinical and biological outcomes. In addition, we develop multiple machine learning-based models to predict disease severity in FRDA using demographic, biological, and sensor-derived metrics. When sensor-derived metrics are included, the model performance enhances 1.5-fold and 2-fold in terms of explained variance, R², for predicting FRDA clinical measures and biological biomarkers of disease severity, respectively.

Conclusions: Our results establish the initial clinical validity of using wearable sensors in assessing disease severity and monitoring motor dysfunction in FRDA.

Plain language summary

Friedreich ataxia (FRDA) is a condition that impairs movement and coordination. Current clinical assessments are subjective, highlighting the need for better ways to monitor disease severity. By using wearable devices to track symptoms in everyday life, we can gain better insights into how patients function outside the clinical environment, offering a more comprehensive understanding of the disease’s impact. In this study, 39 patients were observed using wearable sensors for a week to track their physical activity and arm movements. The data collected was compared with traditional clinical tests and biological markers of the disease. The findings demonstrate that wearable sensors can accurately predict disease severity, offering continuous real-world monitoring that could enhance patient care and treatment outcomes.

PubMed Disclaimer

Conflict of interest statement

At the time of performing the study, R.K.M., A.S.N., A.E., and A.V. were employees of BioSensics LLC, the developer and manufacturer of the wearable sensors used in this study. D.R.L. receives funding from the National Institute of Health, Friedrich’s Ataxia Research Alliance (FARA), Reata Pharmaceuticals Inc., PTC Therapeutics Inc., and Design Therapeutics. V.R.P. and M.W. declare no competing interests.

Figures

Fig. 1
Fig. 1. Correlations between sensor-derived physical activity metrics and clinical scores.
A Correlation map displaying correlation coefficients (r or ρ) between 45 sensor-derived measures of physical activity and clinical scores of FRDA, as well as disease duration, GAA and FXN. Spearman correlation analysis was used to quantify the relationship between sensor-derived metrics and clinical scores, including FA-ADL, FA-ADL LL, mFARS, and mFARS Section E. Meanwhile, Pearson correlation analysis was employed to evaluate the association between sensor-derived metrics and biological outcomes, such as 25FWT, GAA, FXN, and disease duration. B Median steps per walking bout versus FA-ADL, mFARS, and mFARS Section E. Data represent n = 39 participants. All physical activity metrics are averaged daily values measured over 7 consecutive days.
Fig. 2
Fig. 2. Correlations between sensor-derived GDM and clinical scores and biological biomarkers.
A Correlation map displaying correlation coefficients (r or ρ) between 27 sensor-derived measures of GDM and clinical scores of FRDA, as well as disease duration, GAA and FXN. Spearman correlation analysis was used to quantify the relationship between sensor-derived metrics and clinical scores, including FA-ADL, FA-ADL UL, mFARS, and mFARS Subsection B. Meanwhile, Pearson correlation analysis was employed to evaluate the association between sensor-derived metrics and biological outcomes, such as GAA, FXN, average HPT, and disease duration. B Velocity root mean squared of GDMs versus FA-ADL, mFARS, and 9 HPT. Data represent n = 39 participants. All GDM metrics are averaged daily values measured over 7 consecutive days.
Fig. 3
Fig. 3. Machine learning-based models for predicting mFARS.
A mFARS predictions from Models 1 (n = 33), 2 (n = 39), and 5 (n = 32) versus the clinical scores. B The explained variance for mFARS prediction for models 1-5. C mFARS Section E predictions from Models 1 (n = 33), 2 (n = 39), and 5 (n = 33) versus the clinical scores. D The explained variance for mFARS Section E prediction for models 1, 2, 3, and 5. Model 4 was not trained for the prediction mFARS Section E as it uses sensor-derived GDM metrics.
Fig. 4
Fig. 4. Machine learning-based models for predicting biological biomarkers.
A GAA predictions from Models 1 (n = 33), 2 (n = 34), and 5 (n = 33) versus the actual GAA. B The explained variance for GAA prediction for models 1-5. C FXN predictions from Models 1 (n = 23), 2 (n = 28), and 5 (n = 26) versus the actual FXN. D The explained variance for FXN prediction for models 1-5.
See this image and copyright information in PMC

References

    1. Lynch, D. R., Schadt, K., Kichula, E., McCormack, S. & Lin, K. Y. Friedreich ataxia: multidisciplinary clinical care. J. Multidiscip. Healthcare14, 1645–1658 (2021). - PMC - PubMed
    1. Strawser, C. et al. Pharmacological therapeutics in Friedreich ataxia: the present state. Expert Rev. Neurotherapeutics17, 895–907 (2017). - PubMed
    1. Harding, I. H., Lynch, D. R., Koeppen, A. H. & Pandolfo, M. Central nervous system therapeutic targets in Friedreich ataxia. Hum. Gene Ther.31, 1226–1236 (2020). - PMC - PubMed
    1. Lynch, D. R. et al. Measuring Friedreich ataxia: complementary features of examination and performance measures. Neurology66, 1711–1716 (2006). - PubMed
    1. Lynch, D. R. et al. Efficacy of Omaveloxolone in Friedreich’s Ataxia: Delayed‐Start Analysis of the MOXIe Extension. Mov. Disord.38, 313–320 (2023). - PubMed

LinkOut - more resources