A Two-Minute Walking Test With a Smartphone App for Persons With Multiple Sclerosis: Validation Study

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

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.

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

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

BACKGROUND

Remote smartphone-based 2-minute walking tests (s2MWTs) allow frequent and potentially sensitive measurements of ambulatory function.

OBJECTIVE

To investigate the s2MWT on assessment of, and responsiveness to change in ambulatory function in MS.

METHODS

One hundred two multiple sclerosis (MS) patients and 24 healthy controls (HCs) performed weekly s2MWTs on self-owned smartphones for 12 and 3 months, respectively. The timed 25-foot walk test (T25FW) and Expanded Disability Status Scale (EDSS) were assessed at 3-month intervals. Anchor-based (using T25FW and EDSS) and distribution-based (curve fitting) methods were used to assess responsiveness of the s2MWT. A local linear trend model was used to fit weekly s2MWT scores of individual patients.

RESULTS

A total of 4811 and 355 s2MWT scores were obtained in patients (n = 94) and HC (n = 22), respectively. s2MWT demonstrated large variability (65.6 m) compared to the average score (129.5 m), and was inadequately responsive to anchor-based change in clinical outcomes. Curve fitting separated the trend from noise in high temporal resolution individual-level data, and statistically reliable changes were detected in 45% of patients.

CONCLUSIONS

In group-level analyses, clinically relevant change was insufficiently detected due to large variability with sporadic measurements. Individual-level curve fitting reduced the variability in s2MWT, enabling the detection of statistically reliable change in ambulatory function.