Mackenzie Keegan

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with an active trial landscape that relies on the sensitivity of selected clinical trial endpoints. Traditional clinical outcome assessments perform well in trials but lack strong psychometric properties and may not detect small but clinically meaningful disease progression. Digital health technologies offer a promising alternative for tracking ALS disease progression. Objective: This study assessed the feasibility of remote digital monitoring in ALS using a comprehensive battery of prescribed home-based assessments via a smartphone, a wearable device, and a computer-based mouse-clicking task. Methods: Participants completed weekly remote assessments, including motor and speech tasks via a smartphone app and a computer mouse-clicking task for 24 weeks. They also participated in 3 remote telephone visits in weeks 1, 13, and 25. Reliability, minimal detectable change, and correlations with self-reported ALS Functional Rating Scale–Revised subdomain scores were calculated for 8 features across the speech, fine motor, and gross motor smartphone app tasks and for all 32 features from the computer mouse-clicking task. Sensitivity to longitudinal change was assessed for the 8 smartphone-derived features and for a representative subset of 8 computer mouse-clicking features. Results: Forty-two participants (19 with ALS and 23 controls) completed 10,237 smartphone assessments and 459 computer mouse-clicking sessions. Baseline discriminative models differentiated ALS from controls with AUC values of 0.75‐0.92. Digital measures correlated strongly with self-reported ALS Functional Rating Scale–Revised subdomain scores. Both participants with ALS and controls demonstrated improvement in fine motor and speech measures, with the exception of nondominant-hand pegboard performance, which declined in the ALS group. Improvements were smaller in participants with ALS, leading to increasing group differences over time, although only one feature showed a statistically significant separation over the 24 weeks. Gait and balance performance declined in both groups, with greater but nonsignificant separation observed for balance measures. Conclusions: These findings support the feasibility of digital remote assessments in ALS, demonstrate the ability to discriminate between ALS and controls based on certain features collected from speech, fine, and gross motor tasks, and in some cases, quantify functional decline over time. Further research is necessary to explore the natural history of these features longitudinally in larger cohorts of participants with ALS over extended periods to enable their potential integration into clinical trials.

Introduction/Aims: Expanded access protocols (EAPs) allow individuals ineligible for clinical trials to receive investigational products. EAP data can be collected in parallel to randomized clinical trials (RCTs) and serve as a source of evidence in clinical practice. Here, we present the results of a National Institutes of Health (NIH)‐funded EAP for amyotrophic lateral sclerosis (ALS). Methods: Participants received trehalose, a drug studied in a parallel RCT, for up to 24 weeks; clinical and biomarker data were collected throughout the study. Results: Seventy participants were enrolled at 20 study centers across the United States. Treatment with trehalose did not affect the levels of neurofilament light chain [estimated flat slope per month was −0.005, SE = 0.0078; 95% CI (−0.021, 0.011)] or disease progression [estimated least square mean change of the ALS Functional Rating Scale—Revised total score and slow vital capacity (percent predicted) from baseline to Week 24 were −5.6 (0.67); 95% CI (−7.0, −4.3) and −4.53 (4.308); 95% CI (−13.55, 4.48)], respectively. No unexpected treatment‐related risks were identified. Serious adverse events were deemed not related to trehalose (20 occurrences in 13 [18.6%] participants with eight deaths). Discussion: This EAP establishes a framework for implementing multi‐center EAPs that complement data collected from RCTs. Additional NIH‐funded EAPs are currently underway. Data and additional serum samples collected in this study are available to the research community for further study. Trial Registration: ClinicalTrials.gov: NCT05597436

Introduction/aims: Improved functional outcome measures in amyotrophic lateral sclerosis (ALS) would aid ALS trial design and help hasten drug discovery. We evaluate the longitudinal performance of the Rasch‐Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) compared to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised for Self‐Entry (ALSFRS‐RSE) as patient reported outcomes of functional status in people with ALS.Methods: Participants completed the ROADS and the ALSFRS‐RSE questionnaires at baseline, 3‐, 6‐, and 12‐ mo using Research Electronic Data Capture as part of a prospective, longitudinal, remote, online survey study of fatigue in ALS from 9/2020 to 12/2021. The scales were compared cross‐sectionally (at baseline) and longitudinally. Correlation coefficients, coefficients of variation, and descriptive statistics were assessed.Results: A total of 182 adults with ALS consented to the study. This volunteer sample was comprised of predominantly White, non‐Hispanic, non‐smoking participants. Consented participant survey completion was approximately 90% at baseline and greater than 40% at 12 mo. The ALSFRS‐RSE and the ROADS had high, significant agreement at 3 and 6 mo by Cohen's kappa ≥71% (p < 0.001); the number of functional increases or plateaus on the two scales were not significantly different; and the coefficient of variation of functional decline was similar at the 6‐month mark, though higher for the ROADS at 3 mo and lower at 12 mo.Discussion: Although the ROADS performed similarly to the ALSFRS‐RSE in an observational cohort, it has psychometric advantages, such as Rasch‐modeling and unidimensionality. It merits further investigation as a patient reported outcome of overall disability and efficacy outcome measure in ALS trials.

Background: Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term. Objective: The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis. Methods: We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer. Results: Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%). Conclusions: These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful. Trial Registration: ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

BACKGROUND Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term. OBJECTIVE The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis. METHODS We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer. RESULTS Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%). CONCLUSIONS These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful. CLINICALTRIAL ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

Mapping vertebrate incompatibility alleles: Deleterious gene interactions may underlie the observed hybrid incompatibilities. However, few genes underlying hybrid incompatibilities have been identified, and most of these involve species that do not hybridize in natural conditions. Powell et al. used genome sequencing to map genes likely responsible for incompatibilities that reduce fitness in naturally occurring hybrid swordtail fish. These gene combinations result in malignant melanoma, which is found in naturally hybridizing populations but is not present in the parental populations (see the Perspective by Dagilis and Matute). Using genome and population resequencing, the authors performed a genome-wide association study to identify potentially causative mutations. Using an admixture mapping approach that assessed introgression between multiple swordtail fish species, the authors suggest that lineages carry different genes that interact with the same candidate gene, resulting in the observed melanomas and providing insight into convergent hybrid incompatibles that arise between species. Science , this issue p. 731 ; see also p. 710