Using Health Information Technology to Engage African American Women on Nutrition and Supplement Use During the Preconception Period

Paula Gardiner, Timothy Bickmore, Leanne Yinusa-Nyahkoon, Matthew Reichert, Clevanne Julce, Nireesha Sidduri, Jessica Martin-Howard, Elisabeth Woodhams, Jumana Aryan, Zhe Zhang, Juan Fernandez, Mark Loafman, Jayakanth Srinivasan, Howard Cabral, Brian W Jack, Paula Gardiner, Timothy Bickmore, Leanne Yinusa-Nyahkoon, Matthew Reichert, Clevanne Julce, Nireesha Sidduri, Jessica Martin-Howard, Elisabeth Woodhams, Jumana Aryan, Zhe Zhang, Juan Fernandez, Mark Loafman, Jayakanth Srinivasan, Howard Cabral, Brian W Jack

Abstract

Importance: Healthy nutrition and appropriate supplementation during preconception have important implications for the health of the mother and newborn. The best way to deliver preconception care to address health risks related to nutrition is unknown.

Methods: We conducted a secondary analysis of data from a randomized controlled trial designed to study the impact of conversational agent technology in 13 domains of preconception care among 528 non-pregnant African American and Black women. This analysis is restricted to those 480 women who reported at least one of the ten risks related to nutrition and dietary supplement use.

Interventions: An online conversational agent, called "Gabby", assesses health risks and delivers 12 months of tailored dialogue for over 100 preconception health risks, including ten nutrition and supplement risks, using behavioral change techniques like shared decision making and motivational interviewing. The control group received a letter listing their preconception risks and encouraging them to talk to a health care provider.

Results: After 6 months, women using Gabby (a) reported progressing forward on the stage of change scale for, on average, 52.9% (SD, 35.1%) of nutrition and supplement risks compared to 42.9% (SD, 35.4) in the control group (IRR 1.22, 95% CI 1.03-1.45, P = 0.019); and (b) reported achieving the action and maintenance stage of change for, on average, 52.8% (SD 37.1) of the nutrition and supplement risks compared to 42.8% (SD, 37.9) in the control group (IRR 1.26, 96% CI 1.08-1.48, P = 0.004). For subjects beginning the study at the contemplation stage of change, intervention subjects reported progressing forward on the stage of change scale for 75.0% (SD, 36.3%) of their health risks compared to 52.1% (SD, 47.1%) in the control group (P = 0.006).

Conclusion: The scalability of Gabby has the potential to improve women's nutritional health as an adjunct to clinical care or at the population health level. Further studies are needed to determine if improving nutrition and supplement risks can impact clinical outcomes including optimization of weight.

Clinical trial registration: ClinicalTrials.gov, identifier NCT01827215.

Keywords: diet; health disparities; health information technology; nutrition; preconception care; supplement use.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Gardiner, Bickmore, Yinusa-Nyahkoon, Reichert, Julce, Sidduri, Martin-Howard, Woodhams, Aryan, Zhang, Fernandez, Loafman, Srinivasan, Cabral and Jack.

Figures

Figure 1
Figure 1
Image of Gabby, the preconception care conversational agent, with response buttons to facilitate dialogue.
Figure 2
Figure 2
Rate ratios and 90%/95% CIs, by subdomain. Rate ratios and confidence intervals are calculated by regressing the rate of each risk achieving action or maintenance or making progress on the stage of change scale on each study arm using logistic regression.
Figure 3
Figure 3
Mean stage of change for nutrition and supplement domain, subdomain, and individual risks at baseline and 12 months for the intervention and control groups. Circle size indicates the relative number of women in the sample who triggered the risk.
Figure 4
Figure 4
Treatment Effect* by risk stage of change at baseline. * Treatment effects confidence intervals are calculated by restricting the data only to subjects that began the study at each stage of change and regressing the percentage of risks making forward progress on the stage of change scale on study arm using OLS regression.

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Source: PubMed

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