Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis

Lam, Ka-Hoo; Bucur, Ioan Gabriel; Oirschot, Pim van; Graaf, Frank de; Strijbis, Eva; Uitdehaag, Bernard M. J.; Heskes, Tom; Killestein, Joep; Groot, Vincent de

doi:10.1177/13524585231152433

Cited by 2 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

Molenaar,

Noteboom,

van Nederpelt

et al. 2024

J Neurol

View full text Add to dashboard Cite

Background Digital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression. Objective To study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS. Methods In this prospective study, 92 PwMS were included. Clinical tests [Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk test (T25FW), 9-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT)] and structural MRI scans were performed at baseline (M0) and 12-month follow-up (M12). Active monitoring included the smartphone-based Symbol Digit Modalities Test (sSDMT) and 2 Minute Walk Test (s2MWT), while passive monitoring was based on smartphone keystroke dynamics (KD). Linear regression analyses were used to determine cross-sectional and longitudinal relations between digital and clinical outcomes and brain volumes, with age, disease duration and sex as covariates. Results In PwMS, both sSDMT and SDMT were associated with thalamic volumes and lesion volumes. KD were related to brain, ventricular, thalamic and lesion volumes. No relations were found between s2MWT and MRI volumes. NHPT scores were associated with lesion volumes only, while EDSS and T25FW were not related to MRI. No longitudinal associations were found for any of the outcome measures between M0 and M12. Conclusion Our results show clear cross-sectional correlations between digital biomarkers and brain volumes in PwMS, which were not all present for conventional clinical outcomes, supporting the potential added value of digital monitoring tools.

show abstract

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

Molenaar,

Noteboom,

van Nederpelt

et al. 2024

J Neurol

View full text Add to dashboard Cite

show abstract

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments

Xia,

Chikersal,

Venkatesh

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's own environment may improve self-monitoring and clinical management for people with MS (pwMS). Objective: We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers. Methods: We divide the collected data into discrete periods for each patient. For each prediction period, we first extract patient-level behavioral features from the current period (action features) and the previous period (context features). Then, we apply a machine learning (ML) approach based on Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every two weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every four weeks). Results: Between November 16, 2019, and January 24, 2021, 104 pwMS (84.6% women, 93.3% non-Hispanic White, 44.0(SD=1.8) years age from a clinic-based MS cohort completed 12-weeks of data collection, including a subset of 44 pwMS (88.6% women, 95.5% non-Hispanic White, 45.7 (SD=11.2) years) who completed 24-weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of response to two brief questions on the day before the prediction point. Conclusions: Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS (and potentially other chronic neurological disorders).

show abstract

Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis

Cited by 2 publications

References 40 publications

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments

Contact Info

Product

Resources

About