Evidence map›Paper›PMID 39015513›Full record

ArticleDigital biomarkers

Self-Recording of Eye Movements in Amyotrophic Lateral Sclerosis Patients Using a Smartphone Eye-Tracking App.

Pouya Barahim Bastani, Ali S Saber Tehrani, Shervin Badihian, Hector Rieiro, David Rastall, Nathan Farrell, Max Parker, Jorge Otero-Millan, Ahmed Hassoon, David Newman-Toker and 4 more

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Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Pouya Barahim BastaniDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ali S Saber TehraniDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Shervin BadihianArmstrong Institute Center for Diagnostic Excellence, Baltimore, MD, USA.
Hector RieiroDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
David RastallDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Nathan FarrellDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Max ParkerDepartment of Neurology, NYU Langone Health, New York, NY, USA.
Jorge Otero-MillanDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ahmed HassoonDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
David Newman-TokerDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Lora L ClawsonDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Alpa UchilDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Kristen RileyDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Steven R ZeilerDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Amyotrophic lateral sclerosis (ALS) can affect various eye movements, making eye tracking a potential means for disease monitoring. In this study, we evaluated the feasibility of ALS patients self-recording their eye movements using the "EyePhone," a smartphone eye-tracking application. Methods: We prospectively enrolled ten participants and provided them with an iPhone equipped with the EyePhone app and a PowerPoint presentation with step-by-step recording instructions. The goal was for the participants to record their eye movements (saccades and smooth pursuit) without the help of the study team. Afterward, a trained physician administered the same tests using video-oculography (VOG) goggles and asked the participants to complete a questionnaire regarding their self-recording experience. Results: All participants successfully completed the self-recording process without assistance from the study team. Questionnaire data indicated that participants viewed self-recording with EyePhone favorably, considering it easy and comfortable. Moreover, 70% indicated that they prefer self-recording to being recorded by VOG goggles. Conclusion: With proper instruction, ALS patients can effectively use the EyePhone to record their eye movements, potentially even in a home environment. These results demonstrate the potential for smartphone eye-tracking technology as a viable and self-administered tool for monitoring disease progression in ALS, reducing the need for frequent clinic visits.

Indexed as

Amyotrophic lateral sclerosisEye movementsHealth technologySmartphone eye-tracking application

Identifiers

PMID39015513
PMCPMC11250669

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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.