An Automated Text-Messaging Platform for Enhanced Retention and Data Collection in a Longitudinal Birth Cohort: Cohort Management Platform Analysis.

data collection longitudinal studies mobile health text messaging

Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
02 Apr 2019
Historique:
received: 23 07 2018
accepted: 14 12 2018
revised: 05 12 2018
entrez: 3 4 2019
pubmed: 3 4 2019
medline: 3 4 2019
Statut: epublish

Résumé

Traditional methods for recruiting and maintaining contact with participants in cohort studies include print-based correspondence, which can be unidirectional, labor intensive, and slow. Leveraging technology can substantially enhance communication, maintain engagement of study participants in cohort studies, and facilitate data collection on a range of outcomes. This paper provides an overview of the development process and design of a cohort management platform (CMP) used in the Newborn Epigenetic STudy (NEST), a large longitudinal birth cohort study. The platform uses short message service (SMS) text messaging to facilitate interactive communication with participants; it also semiautomatically performs many recruitment and retention procedures typically completed by research assistants over the course of multiple study follow-up visits. Since February 2016, 302 participants have consented to enrollment in the platform and 162 have enrolled with active engagement in the system. Daily reminders are being used to help improve adherence to the study's accelerometer wear protocol. At the time of this report, 213 participants in our follow-up study who were also registered to use the CMP were eligible for the accelerometer protocol. Preliminary data show that texters (138/213, 64.8%), when compared to nontexters (75/213, 35.2%), had significantly longer average accelerometer-wearing hours (165.6 hours, SD 56.5, vs 145.3 hours, SD 58.5, P=.01) when instructed to wear the devices for 1 full week. This platform can serve as a model for enhancing communication and engagement with longitudinal study cohorts, especially those involved in studies assessing environmental exposures.

Sections du résumé

BACKGROUND BACKGROUND
Traditional methods for recruiting and maintaining contact with participants in cohort studies include print-based correspondence, which can be unidirectional, labor intensive, and slow. Leveraging technology can substantially enhance communication, maintain engagement of study participants in cohort studies, and facilitate data collection on a range of outcomes.
OBJECTIVE OBJECTIVE
This paper provides an overview of the development process and design of a cohort management platform (CMP) used in the Newborn Epigenetic STudy (NEST), a large longitudinal birth cohort study.
METHODS METHODS
The platform uses short message service (SMS) text messaging to facilitate interactive communication with participants; it also semiautomatically performs many recruitment and retention procedures typically completed by research assistants over the course of multiple study follow-up visits.
RESULTS RESULTS
Since February 2016, 302 participants have consented to enrollment in the platform and 162 have enrolled with active engagement in the system. Daily reminders are being used to help improve adherence to the study's accelerometer wear protocol. At the time of this report, 213 participants in our follow-up study who were also registered to use the CMP were eligible for the accelerometer protocol. Preliminary data show that texters (138/213, 64.8%), when compared to nontexters (75/213, 35.2%), had significantly longer average accelerometer-wearing hours (165.6 hours, SD 56.5, vs 145.3 hours, SD 58.5, P=.01) when instructed to wear the devices for 1 full week.
CONCLUSIONS CONCLUSIONS
This platform can serve as a model for enhancing communication and engagement with longitudinal study cohorts, especially those involved in studies assessing environmental exposures.

Identifiants

pubmed: 30938689
pii: v5i2e11666
doi: 10.2196/11666
pmc: PMC6465978
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e11666

Subventions

Organisme : NIEHS NIH HHS
ID : P01 ES022831
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD084487
Pays : United States

Informations de copyright

©Caroline M Barry, Aditi Sabhlok, Victoria C Saba, Alesha D Majors, Julia C Schechter, Erica L Levine, Martin Streicher, Gary G Bennett, Scott H Kollins, Bernard F Fuemmeler. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 02.04.2019.

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Auteurs

Caroline M Barry (CM)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Aditi Sabhlok (A)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Victoria C Saba (VC)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Alesha D Majors (AD)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Julia C Schechter (JC)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Erica L Levine (EL)

Global Digital Health Science Center, Global Health Institute, Duke University, Durham, NC, United States.

Martin Streicher (M)

Global Digital Health Science Center, Global Health Institute, Duke University, Durham, NC, United States.

Gary G Bennett (GG)

Global Digital Health Science Center, Global Health Institute, Duke University, Durham, NC, United States.

Scott H Kollins (SH)

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Duke University, Durham, NC, United States.

Bernard F Fuemmeler (BF)

Cancer Prevention and Control, Department of Health Behavior and Policy, Virginia Commonwealth University, Richmond, VA, United States.

Classifications MeSH