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.

Smartphone-derived keystroke dynamics are sensitive to relevant changes in multiple sclerosis.

BACKGROUND: To investigate smartphone keystroke dynamics (KD), derived from regular typing, on sensitivity to relevant change in disease activity, fatigue, and clinical disability in multiple sclerosis (MS). METHODS: Preplanned interim analysis of a cohort study with 102 MS patients assessed at baseline and 3-month follow-up for gadolinium-enhancing lesions on magnetic resonance imaging, relapses, fatigue and clinical disability outcomes. Keyboard interactions were unobtrusively collected during typing using the Neurokeys App. From these interactions 15 keystroke features were derived and aggregated using 16 summary and time series statistics. Responsiveness of KD to clinical anchor-based change was assessed by calculating the area under the receiver operating characteristic curve (AUC). The optimal cut-point was used to determine the minimal clinically important difference (MCID) and compared to the smallest real change (SRC). Commonly used clinical measures were analyzed for comparison. RESULTS: A total of 94 patients completed the follow-up. The five best performing keystroke features had AUC-values in the range 0.72-0.78 for change in gadolinium-enhancing lesions, 0.67-0.70 for the Checklist Individual Strength Fatigue subscale, 0.66-0.79 for the Expanded Disability Status Scale, 0.69-0.73 for the Ambulation Functional System, and 0.72-0.75 for Arm function in MS Questionnaire. The MCID of these features exceeded the SRC on group level. KD had higher AUC-values than comparative clinical measures for the study outcomes, aside from ambulatory function. CONCLUSIONS: Keystroke dynamics demonstrated good responsiveness to changes in disease activity, fatigue, and clinical disability in MS, and detected important change beyond measurement error on group level. Responsiveness of KD was better than commonly used clinical measures.

The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis.

BACKGROUND: Typing on smartphones, which has become a near daily activity, requires both upper limb and cognitive function. Analysis of keyboard interactions during regular typing, that is, keystroke dynamics, could therefore potentially be utilized for passive and continuous monitoring of function in patients with multiple sclerosis. OBJECTIVE: To determine whether passively acquired smartphone keystroke dynamics correspond to multiple sclerosis outcomes, we investigated the association between keystroke dynamics and clinical outcomes (upper limb and cognitive function). This association was investigated longitudinally in order to study within-patient changes independently of between-patient differences. METHODS: During a 1-year follow-up, arm function and information processing speed were assessed every 3 months in 102 patients with multiple sclerosis with the Nine-Hole Peg Test and Symbol Digit Modalities Test, respectively. Keystroke-dynamics data were continuously obtained from regular typing on the participants' own smartphones. Press-and-release latency of the alphanumeric keys constituted the fine motor score cluster, while latency of the punctuation and backspace keys constituted the cognition score cluster. The association over time between keystroke clusters and the corresponding clinical outcomes was assessed with linear mixed models with subjects as random intercepts. By centering around the mean and calculating deviation scores within subjects, between-subject and within-subject effects were distinguished. RESULTS: Mean (SD) scores for the fine motor score cluster and cognition score cluster were 0.43 (0.16) and 0.94 (0.41) seconds, respectively. The fine motor score cluster was significantly associated with the Nine-Hole Peg Test: between-subject ß was 15.9 (95% CI 12.2-19.6) and within-subject ß was 6.9 (95% CI 2.0-11.9). The cognition score cluster was significantly associated with the Symbol Digit Modalities Test between subjects (between-subject ß -11.2, 95% CI -17.3 to -5.2) but not within subjects (within-subject ß -0.4, 95% CI -5.6 to 4.9). CONCLUSIONS: Smartphone keystroke dynamics were longitudinally associated with multiple sclerosis outcomes. Worse arm function corresponded with longer latency in typing both across and within patients. Worse processing speed corresponded with higher latency in using punctuation and backspace keys across subjects. Hence, keystroke dynamics are a potential digital biomarker for remote monitoring and predicting clinical outcomes in patients with multiple sclerosis. TRIAL REGISTRATION: Netherlands Trial Register NTR7268; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7268.

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.

Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis.

Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person's smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.

Towards individualized monitoring of cognition in multiple sclerosis in the digital era: A one-year cohort study.

BACKGROUND: Cognitive impairment is frequent in multiple sclerosis (MS), but reliable, sensitive and individualized monitoring in clinical practice is still limited. Smartphone-adapted tests may enhance the assessment of function as tests can be performed more frequently and within the daily living environment. The objectives were to prove reproducibility of a smartphone-based Symbol Digit Modalities Test (sSDMT), its responsiveness to relevant change in clinical cognitive outcomes, and develop an individual-based monitoring method for cognition. METHODS: In a one-year cohort study with 102 patients with MS, weekly sSDMTs were performed and analyzed on reproducibility parameters: the standard error of measurement (SEM) and smallest detectable change (SDC). Responsiveness of the sSDMT to relevant change in the 3-monthly clinically assessed SDMT (i.e. 4-point change) was quantified with the area under the receiver operating characteristic curve (AUC). Curve fitting of the weekly sSDMT scores of individual patients was performed with a local linear trend model to estimate and visualize the de-noised cognitive state and 95% confidence interval (CI). The optimal assessment frequency was determined by analyzing the CI bandwidth as a function of sSDMT assessment frequency. RESULTS: Weekly sSDMT showed improved reproducibility estimates (SEM=2.94, SDC=8.15) compared to the clinical SDMT. AUC-values did not exceed 0.70 in classifying relevant change in cSDMT. However, utilizing weekly sSDMT measurements, estimated state curves and the 95% CI were plotted showing detailed changes within individuals over time. With a test frequency of once per 12 days, 4-point changes in sSDMT can be detected. CONCLUSION: A local linear trend model applied on sSDMT scores of individual patients increases the signal-to-noise ratio substantially, which improves the detection of statistically reliable changes. Therefore, this fine-grained individual-based monitoring approach can be used to complement current clinical assessment to enhance clinical care in MS. TRIAL REGISTRATION: Netherlands Trial Register NL7070; https://www.trialregister.nl/trial/7070.

Project Y: The search for clues explaining phenotype variability in MS.

BACKGROUND: To study phenotypic variability in MS patients, well-defined unbiased cohort studies are necessary. The most common and probably most important confounding factor when studying disease phenotype in MS is age. OBJECTIVE: To describe study design and subject characteristics of a unique birth cohort (Project Y). The overall aim of Project Y is to identify determinants associated with phenotypic variability in MS, eliminating the possibility of confounding by age. METHODS: Project Y is a population-based cross-sectional study of all people with MS born in the Netherlands in 1966. Patients and healthy controls were subjected to comprehensive examinations: functional and static imaging, physical and cognitive measurements, and lifestyle factors early and later in life. In addition body fluids were collected and stored for future biomarker research. RESULTS: 452 eligible MS patients were identified. Between December 2017 and January 2021, 367 MS patients and 125 healthy controls participated. The total number of identified cases results in a current prevalence of at least 189/100.000 for people born in the year 1966 in The Netherlands. CONCLUSION: Project Y is a unique cohort designed to identify factors associated with phenotypic variability in MS patients without the confounding effects of age. This first description of the Project Y cohort indicates that the prevalence of MS in the Netherlands might be higher than previously presumed. Various studies using Project Y data are ongoing and results will be published in upcoming years.

Improving Detection of Change in Motor Functioning in Multiple Sclerosis Using Video-Assisted Composite Measures.

BACKGROUND: Assessing motor functioning is important to monitor the disease course of multiple sclerosis (MS). Video-assisted rating of classic neurologic tests and activities of daily living may improve the detection of changes in motor functioning. We investigated the value of using video-assisted composite measures for the detection of changes in mobility and upper extremity function (UEF). METHODS: Forty-three patients with MS were recorded performing motor function tests before and during treatment with fampridine. Patients were classified as improved or not improved on mobility composite (MOB-COM) and UEF composite (UEF-COM) measures based on neurologists' ratings of the tests. The proportional agreements between the composite measures and the conventional measures-the Timed 25-Foot Walk test (T25FW) and the Nine-Hole Peg Test (NHPT)-were determined and compared with patient-perceived improvement, which was determined using patient-reported ratings of changes in mobility and UEF. RESULTS: Agreement between MOB-COM and T25FW was 79.5%, and agreement between UEF-COM and NHPT was 82.1%. Twenty-six of 39 patients (66.7%) reported mobility improvement; 6 of these reports were confirmed by both T25FW and MOB-COM, 4 were confirmed by T25FW only, and 2 were confirmed by MOB-COM only. For UEF, 13 of 39 patients (33.3%) reported improvement; 3 of these were confirmed by the NHPT and 3 were confirmed by the UEF-COM. CONCLUSIONS: Compared with the conventional NHPT measure, the video-assisted composite measure of UEF detected additional patient-perceived improvement. This was less evident for mobility measures. Video-assisted composite measures may enhance the detection of treatment effects in MS clinical practice and trials.

The Potential Impact of Digital Biomarkers in Multiple Sclerosis in The Netherlands: An Early Health Technology Assessment of MS Sherpa.

(1) Background: Monitoring of Multiple Sclerosis (MS) with eHealth interventions or digital biomarkers provides added value to the current care path. Evidence in the literature is currently scarce. MS sherpa is an eHealth intervention with digital biomarkers, aimed at monitoring symptom progression and disease activity. To show the added value of digital biomarker-based eHealth interventions to the MS care path, an early Health Technology Assessment (eHTA) was performed, with MS sherpa as an example, to assess the potential impact on treatment switches. (2) Methods: The eHTA was performed according to the Dutch guidelines for health economic evaluations. A decision analytic MS model was used to estimate the costs and benefits of MS standard care with and without use of MS sherpa, expressed in incremental cost-effectiveness ratios (ICERs) from both societal and health care perspectives. The efficacy of MS sherpa on early detection of active disease and the initiation of a treatment switch were modeled for a range of assumed efficacy (5%, 10%, 15%, 20%). (3) Results: From a societal perspective, for the efficacy of 15% or 20%, MS sherpa became dominant, which means cost-saving compared to the standard of care. MS sherpa is cost-effective in the 5% and 10% scenarios (ICERs EUR 14,535 and EUR 4069, respectively). From the health care perspective, all scenarios were cost-effective. Sensitivity analysis showed that increasing the efficacy of MS sherpa in detecting active disease early leading to treatment switches be the most impactful factor in the MS model. (4) Conclusions: The results indicate the potential of eHealth interventions to be cost-effective or even cost-saving in the MS care path. As such, digital biomarker-based eHealth interventions, like MS sherpa, are promising cost-effective solutions in optimizing MS disease management for people with MS, by detecting active disease early and helping neurologists in decisions on treatment switch.

Predictors of quality of life 1 year after minor stroke or TIA: a prospective single-centre cohort study.

OBJECTIVES: In patients after a transient ischaemic attack (TIA) or minor stroke, dysfunction is often underestimated by clinical measures due to invisible symptoms, including cognitive and emotional problems. Many of these patients need stroke care programme, but others do not. In this study, we aim to identify potential predictors of quality of life (QoL) in patients with TIA or minor stroke 1 year poststroke to be able to select which of these patients will need aftercare. DESIGN: Prospective observational cohort study. SETTING: Single-centre hospital in the Netherlands. PARTICIPANTS: 120 patients, diagnosed with TIA or minor stroke and discharged without rehabilitation treatment, completed the study. PRIMARY AND SECONDARY OUTCOME MEASURES: QoL (RAND-36), anxiety and depressive symptoms (Hospital Anxiety and Depression scale), the degree of disability or functional dependence after stroke (modified Rankin Scale (mRS)) and symptoms of anxiety and depression specific to stroke (SSADQ) were assessed at baseline (2-6 weeks poststroke) and compared with follow-up at 1 year poststroke. RESULTS: Depression (B=-1.35, p<0.001) and anxiety (B=-0.57, p=0.041) at baseline predicted a worse mental component of QoL after 1 year. Depression (B=-1.100, p<0.001) at baseline, but also age (B=-0.261, p=0.002) and female sex (B=4.101, p=0.034) predicted a worse physical component of QoL after 1 year. CONCLUSION: With the identification of these predictors, we might be able to select more efficiently and timely the patients with TIA or minor stroke who need stroke aftercare.

Validity of the PROMIS-10 Global Health assessed by telephone and on paper in minor stroke and transient ischaemic attack in the Netherlands.

OBJECTIVES: Dysfunction after transient ischaemic attack (TIA) and minor stroke is often underestimated by clinical measures. Patient-reported outcome measures used in value-based healthcare may help in detecting these problems. The Patient-Reported Outcomes Measurement Information System 10-Question Short Form (PROMIS-10 Global Health) is a concise patient-centred outcome measuring tool proposed for assessing health status in patients who had stroke. This study aims to address the validity of the Dutch PROMIS-10 in patients who had stroke in the Netherlands and also aims to compare telephone versus on-paper assessment. DESIGN: Observational cohort study. SETTING: Single-centre hospital in the Netherlands. PARTICIPANTS: 75 patients who were diagnosed with TIA or minor stroke and discharged without rehabilitation treatment 1 year ago (between December 2014 and January 2016) completed the study. PRIMARY AND SECONDARY OUTCOME MEASURES: PROMIS-10 physical (PH) and mental health (MH) scores assessed 1 year poststroke on paper (n=37) and by telephone (n=38) was compared with RAND-36 physical and mental component scores assessed on paper. RESULTS: PROMIS-10 and RAND-36 correlated significantly in PH, r=0.81 (95% CI 0.69 to 0.88), and MH, r=0.76 (95% CI 0.64 to 0.85). Paper-and-pencil assessed correlations were r=0.87 and 0.79 for PH and MH, respectively. Telephone assessed correlations were r=0.76 and 0.73 for PH and MH, respectively. Internal consistency analysis indicated high reliabilities for both health components of the PROMIS-10, all Cronbach's α>0.70. CONCLUSIONS: The Dutch PROMIS-10 was found to strongly correlate with the RAND-36. Paper-and-pencil assessment was found to have a higher correlation than telephone assessment. This study provides support for the use of the Dutch PROMIS-10 in assessing health status in patients after TIA and minor stroke.