Improving the health of young African American women in the preconception period using health information technology: a randomised controlled trial.


Journal

The Lancet. Digital health
ISSN: 2589-7500
Titre abrégé: Lancet Digit Health
Pays: England
ID NLM: 101751302

Informations de publication

Date de publication:
09 2020
Historique:
received: 23 06 2020
revised: 17 07 2020
accepted: 24 07 2020
entrez: 17 12 2020
pubmed: 18 12 2020
medline: 28 1 2021
Statut: ppublish

Résumé

Preconception care focuses on improving women's health before pregnancy as a means to improve their health and future pregnancy outcomes. How to effectively deliver such care is unknown. The aim of this research was to assess the impact of an embodied conversational agent system on preconception risks among African American and Black women. We did an open-label, randomised controlled trial of women aged 18-34 years, self-identified as African American or Black, or both, and not pregnant, recruited from 35 states in the USA. Sealed allocation envelopes (in permuted blocks of six and eight, prepared using a random number generator) were opened after enrolment. Intervention participants received an online conversational agent called Gabby that assessed 102 preconception risks and delivered 12 months of tailored dialogue using synthesised speech, non-verbal behaviour, visual aids, and health behaviour change techniques such as motivational interviewing. The control group received a letter listing their preconception risks and encouraging them to talk with a clinician. The primary outcome was the proportion of identified risks at the action or maintenance stage of change at months 6 and 12. The study is registered with ClinicalTrials.gov, NCT01827215. From March 11, 2014, through July 8, 2018, 528 women recruited from 35 states and 242 cities across the USA received the Gabby intervention (n=262) or were assigned to the control group (n=266). Participants identified a mean of 21 preconception risks per woman (SD 9·9). In the intention-to-treat analysis, at 6 months, intervention women reported reaching the action or maintenance stage of change for 50·0% (SD 28·9) of those preconception risks identified compared with 42·7% (28·3) in the control group (incidence rate ratio 1·16, 95% CI 1·07-1·26; p=0·0004). This result persisted at 12 months. The Gabby system has the potential to improve women's preconception health. Further research is needed to determine if improving preconception risks impacts outcomes such as preterm delivery. National Institute for Minority Health and Health Disparities.

Sections du résumé

BACKGROUND
Preconception care focuses on improving women's health before pregnancy as a means to improve their health and future pregnancy outcomes. How to effectively deliver such care is unknown. The aim of this research was to assess the impact of an embodied conversational agent system on preconception risks among African American and Black women.
METHODS
We did an open-label, randomised controlled trial of women aged 18-34 years, self-identified as African American or Black, or both, and not pregnant, recruited from 35 states in the USA. Sealed allocation envelopes (in permuted blocks of six and eight, prepared using a random number generator) were opened after enrolment. Intervention participants received an online conversational agent called Gabby that assessed 102 preconception risks and delivered 12 months of tailored dialogue using synthesised speech, non-verbal behaviour, visual aids, and health behaviour change techniques such as motivational interviewing. The control group received a letter listing their preconception risks and encouraging them to talk with a clinician. The primary outcome was the proportion of identified risks at the action or maintenance stage of change at months 6 and 12. The study is registered with ClinicalTrials.gov, NCT01827215.
FINDINGS
From March 11, 2014, through July 8, 2018, 528 women recruited from 35 states and 242 cities across the USA received the Gabby intervention (n=262) or were assigned to the control group (n=266). Participants identified a mean of 21 preconception risks per woman (SD 9·9). In the intention-to-treat analysis, at 6 months, intervention women reported reaching the action or maintenance stage of change for 50·0% (SD 28·9) of those preconception risks identified compared with 42·7% (28·3) in the control group (incidence rate ratio 1·16, 95% CI 1·07-1·26; p=0·0004). This result persisted at 12 months.
INTERPRETATION
The Gabby system has the potential to improve women's preconception health. Further research is needed to determine if improving preconception risks impacts outcomes such as preterm delivery.
FUNDING
National Institute for Minority Health and Health Disparities.

Identifiants

pubmed: 33328115
pii: S2589-7500(20)30189-8
doi: 10.1016/S2589-7500(20)30189-8
pii:
doi:

Banques de données

ClinicalTrials.gov
['NCT01827215']

Types de publication

Journal Article Randomized Controlled Trial Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e475-e485

Informations de copyright

Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Brian W Jack (BW)

Department of Family Medicine, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA; Institute for Health Systems Innovation and Policy, Boston University, Boston, MA, USA. Electronic address: bjack@bu.edu.

Timothy Bickmore (T)

Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.

Leanne Yinusa-Nyahkoon (L)

College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.

Matthew Reichert (M)

Department of Government, Harvard University, Cambridge, MA, USA.

Clevanne Julce (C)

Department of Family Medicine, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA.

Nireesha Sidduri (N)

Department of Family Medicine, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA.

Jessica Martin-Howard (J)

Department of Family Medicine, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA; Institute for Health Systems Innovation and Policy, Boston University, Boston, MA, USA.

Zhe Zhang (Z)

Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.

Elisabeth Woodhams (E)

Department of Obstetrics and Gynecology, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA.

Juan Fernandez (J)

Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.

Mark Loafman (M)

Department of Family Medicine, Cook County Health System, Chicago, IL, USA.

Howard J Cabral (HJ)

Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.

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Classifications MeSH