Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention.

The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials.

The use of digital tools in rare neurological diseases towards a new care model: a narrative review.

Rare neurological diseases as a whole share peculiar features as motor and/or cognitive impairment, an elevated disability burden, a frequently chronic course and, in present times, scarcity of therapeutic options. The rarity of those conditions hampers both the identification of significant prognostic outcome measures, and the development of novel therapeutic approaches and clinical trials. Collection of objective clinical data through digital devices can support diagnosis, care, and therapeutic research. We provide an overview on recent developments in the field of digital tools applied to rare neurological diseases, both in the care setting and as providers of outcome measures in clinical trials in a representative subgroup of conditions, including ataxias, hereditary spastic paraplegias, motoneuron diseases and myopathies.

Digital Patient-Reported Outcome Measures Assessing Health-Related Quality of Life in Skull Base Diseases-Analysis of Feasibility and Pitfalls Two Years after Implementation.

Health-related quality of life (HRQoL) assessment is becoming increasingly important in neurosurgery following the trend toward patient-centered care, especially in the context of skull base diseases. The current study evaluates the systematic assessment of HRQoL using digital patient-reported outcome measures (PROMs) in a tertiary care center specialized in skull base diseases. The methodology and feasibility to conduct digital PROMs using both generic and disease-specific questionnaires were investigated. Infrastructural and patient-specific factors affecting participation and response rates were analyzed. Since August 2020, 158 digital PROMs were implemented in skull base patients presenting for specialized outpatient consultations. Reduced personnel capacity led to significantly fewer PROMs being conducted during the second versus (vs.) the first year after introduction (mean: 0.77 vs. 2.47 per consultation day, p = 0.0002). The mean age of patients not completing vs. those completing long-term assessments was significantly higher (59.90 vs. 54.11 years, p = 0.0136). Follow-up response rates tended to be increased with recent surgery rather than with the wait-and-scan strategy. Our strategy of conducting digital PROMs appears suitable for assessing HRQoL in skull base diseases. The availability of medical personnel for implementation and supervision was essential. Response rates during follow-up tended to be higher both with younger age and after recent surgery.

Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living.

BACKGROUND: Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). OBJECTIVE: To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. METHODS: We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC. RESULTS: Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79-0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. CONCLUSION: Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.

Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms.

BACKGROUND: We aimed to develop a Human Activity Recognition (HAR) model using a wrist-worn device to assess patient activity in relation to negative symptoms of schizophrenia. METHODS: Data were analyzed in a randomized, three-way cross-over, proof-of-mechanism study (ClinicalTrials.gov: NCT02824055) comparing two doses of RG7203 with placebo, given as adjunct to stable antipsychotic treatment in patients with chronic schizophrenia and moderate levels of negative symptoms. Baseline negative symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and Brief Negative Symptom Scale (BNSS). Patients were given a GeneActiv™ wrist-worn actigraphy device to wear over a 15-week period. For this analysis, actigraphy data and behavioral and clinical assessments obtained during placebo treatment were used. Motivated behavior was evaluated with a computerized effort-choice task. A trained HAR model was used to classify activity and an activity-time ratio was derived. Gesture events and features were inferred from the HAR-detected activities and the acceleration signal. RESULTS: Thirty-three patients were enrolled: mean (±SD) age 36.6 ± 7 years; mean (±SD) baseline PANSS negative symptom factor score 23.0 ± 3.5; and mean (±SD) baseline BNSS total score 36.0 ± 11.5. Activity data were collected for 31 patients with a median monitoring time of 1,859 h per patient, equating to ~11 weeks or 74% monitoring ratio. The trained HAR model demonstrated >95% accuracy in separating ambulatory and stationary activities. A positive correlation was seen between the activity-time ratio and the percent of high-effort choices (Spearman r = 0.58; P = 0.002) in the effort-choice task. Median daily gesture counts correlated negatively with the BNSS total score (Spearman r = -0.44; P = 0.03), specifically with the diminished expression sub-score (Spearman r = -0.42; P = 0.03). Gesture features also correlated negatively with the BNSS total score and diminished expression sub-scores. Activity measures showed similar correlations with PANSS negative symptom factor but did not reach significance. CONCLUSION: Our findings support the use of wrist-worn devices to derive activity and gesture-based digital outcome measures for patients with schizophrenia with negative symptoms in a clinical trial setting.