Evidence map›Paper›PMID 35119373›Full record

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.

Anna L Beukenhorst, Katherine M Burke, Zoe Scheier, Timothy M Miller, Sabrina Paganoni, Mackenzie Keegan, Ella Collins, Kathryn P Connaghan, Anna Tay, James Chan and 2 more

Registry-linked trialOpen access · goldAbstract readObservational StudyRandomized Controlled Trial
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
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.

NCT03168711 phase2completednot on this map

Safety of Urate Elevation in Amyotrophic Lateral Sclerosis (ALS)

TypeinterventionalSponsorMassachusetts General HospitalRan2017 to 2020Enrolled48ConditionsAmyotrophic Lateral SclerosisArmsInosine, Placebo
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 26 citations in OpenAlex.

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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

12 authors at 4 institutions in 2 countries.

Anna L BeukenhorstDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.ORCID 0000-0002-1765-4890
Katherine M BurkeNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0002-5820-5880
Zoe ScheierNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0003-1250-9368
Timothy M MillerDepartment of Neurology, Washington University, Saint Louis, MO, United States.ORCID 0000-0002-3424-5511
Sabrina PaganoniNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0003-0505-1168
Mackenzie KeeganNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0003-1305-8124
Ella CollinsNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0001-9612-2851
Kathryn P ConnaghanMGH Institute of Health Professions, Charlestown, MA, United States.ORCID 0000-0003-0849-1441
Anna TayDepartment of Neurology, Washington University, Saint Louis, MO, United States.ORCID 0000-0002-2765-6515
James ChanBiostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-7173-5029
James D BerryNeurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0003-0898-6081
Jukka-Pekka OnnelaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.ORCID 0000-0001-6613-8668
Massachusetts General Hospital · USHarvard University · USWashington University in St. Louis · USMGH Institute of Health Professions · US

Funding

The impact of dysarthria on communicative participation in people with ALSR15DC018944 · NIDCD · MGH INSTITUTE OF HEALTH PROFESSIONS · PI CONNAGHAN, KATHRYN PATRICIA · 2021 to 2021
$499k
NIDCD NIH HHS R15 DC018944
6 · The paper itself

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.

Indexed as

Mobile ApplicationsSmartphoneActivities of Daily LivingHumansSurveys and QuestionnairesTime Factorsattritiondigital phenotypingmobile healthmobile phonesmartphonestrial

Identifiers

PMID35119373
PMCPMC8857693
OpenAlexW4210743933

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.