Trial reportJMIR mHealth and uHealth2022
Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies.
Trial report in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03168711 (Safety of Urate Elevation in Amyotrophic Lateral Sclerosis), which is not on this map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Safety of Urate Elevation in Amyotrophic Lateral Sclerosis (ALS)
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Digital Remote Assessment of Motor and Speech Changes in Amyotrophic Lateral Sclerosis: Longitudinal Observational Study.JMIR formative research · 2026Observational
- Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students.JMIR formative research · 2026Observational
- Short prescribed exercises can quantify upper limb functioning in neurodegenerative disease.Journal of neuroengineering and rehabilitation · 2026Article
- Design considerations for C9orf72 disease prevention trials.Brain : a journal of neurology · 2025Review
- A Preliminary Investigation of Acoustic Features for Remote Monitoring of Respiration in ALS.Muscle & nerve · 2025Article
- Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients.Frontiers in digital health · 2025Article
- Listener effort measures clinically meaningful change of dysarthria in amyotrophic lateral sclerosis.Brain communications · 2025Article
- Access for ALL in ALS: A large-scale, inclusive, collaborative consortium to unlock the molecular and genetic mechanisms of amyotrophic lateral sclerosis.Muscle & nerve · 2024Review
- Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.JMIR public health and surveillance · 2024Observational
- Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data.Annals of clinical and translational neurology · 2024Article
- Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures.NPJ digital medicine · 2023Article
- Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies.BMC musculoskeletal disorders · 2022Review
- Feasibility and acceptability of remote smartphone cognitive testing in frontotemporal dementia research.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundSmartphone 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.
objectiveThe 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.
methodsWe 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.
resultsTime-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%).
conclusionsThese 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 registrationClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.