TY - JOUR
T1 - Mobile Health Tobacco Cessation Interventions to Promote Health Equity
T2 - Current Perspectives
AU - Battalio, Samuel L.
AU - Pfammatter, Angela F.
AU - Kershaw, Kiarri N.
AU - Hernandez, Alexis
AU - Conroy, David E.
AU - Spring, Bonnie
N1 - Publisher Copyright:
Copyright © 2022 Battalio, Pfammatter, Kershaw, Hernandez, Conroy and Spring.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - Although US tobacco use trends show overall improvement, social disadvantage continues to drive significant disparities. Traditional tobacco cessation interventions and public policy initiatives have failed to equitably benefit socially-disadvantaged populations. Advancements in mobile digital technologies have created new opportunities to develop resource-efficient mobile health (mHealth) interventions that, relative to traditional approaches, have greater reach while still maintaining comparable or greater efficacy. Their potential for affordability, scalability, and efficiency gives mHealth tobacco cessation interventions potential as tools to help redress tobacco use disparities. We discuss our perspectives on the state of the science surrounding mHealth tobacco cessation interventions for use by socially-disadvantaged populations. In doing so, we outline existing models of health disparities and social determinants of health (SDOH) and discuss potential ways that mHealth interventions might be optimized to offset or address the impact of social determinants of tobacco use. Because smokers from socially-disadvantaged backgrounds face multi-level barriers that can dynamically heighten the risks of tobacco use, we discuss cutting-edge mHealth interventions that adapt dynamically based on context. We also consider complications and pitfalls that could emerge when designing, evaluating, and implementing mHealth tobacco cessation interventions for socially-disadvantaged populations. Altogether, this perspective article provides a conceptual foundation for optimizing mHealth tobacco cessation interventions for the socially-disadvantaged populations in greatest need.
AB - Although US tobacco use trends show overall improvement, social disadvantage continues to drive significant disparities. Traditional tobacco cessation interventions and public policy initiatives have failed to equitably benefit socially-disadvantaged populations. Advancements in mobile digital technologies have created new opportunities to develop resource-efficient mobile health (mHealth) interventions that, relative to traditional approaches, have greater reach while still maintaining comparable or greater efficacy. Their potential for affordability, scalability, and efficiency gives mHealth tobacco cessation interventions potential as tools to help redress tobacco use disparities. We discuss our perspectives on the state of the science surrounding mHealth tobacco cessation interventions for use by socially-disadvantaged populations. In doing so, we outline existing models of health disparities and social determinants of health (SDOH) and discuss potential ways that mHealth interventions might be optimized to offset or address the impact of social determinants of tobacco use. Because smokers from socially-disadvantaged backgrounds face multi-level barriers that can dynamically heighten the risks of tobacco use, we discuss cutting-edge mHealth interventions that adapt dynamically based on context. We also consider complications and pitfalls that could emerge when designing, evaluating, and implementing mHealth tobacco cessation interventions for socially-disadvantaged populations. Altogether, this perspective article provides a conceptual foundation for optimizing mHealth tobacco cessation interventions for the socially-disadvantaged populations in greatest need.
UR - http://www.scopus.com/inward/record.url?scp=85134205896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134205896&partnerID=8YFLogxK
U2 - 10.3389/fdgth.2022.821049
DO - 10.3389/fdgth.2022.821049
M3 - Article
C2 - 35847415
AN - SCOPUS:85134205896
SN - 2673-253X
VL - 4
JO - Frontiers in Digital Health
JF - Frontiers in Digital Health
M1 - 821049
ER -