Abstract
Postpartum relapse to cigarette smoking (PRS) rate has not substantially improved for more than two decades. Over 55% of women successfully quit smoking during pregnancy; however, half (50%) return to smoking within three months of childbirth and 90% relapse within a year. The identification of effective PRS prevention interventions are needed, especially since factors related to PRS risk factors vary by person, time, and context. In this paper, a prototype risk estimation system using daily ecological momentary assessment data is proposed to develop an adaptive intervention system which will consider multiple risk factors. The risk estimator is designed using a hierarchical fuzzy inference system design scheme to capture human knowledge. A particle swarm optimization scheme is also applied. The simulation results show the feasibility of the proposed estimator for the PRS prevention intervention system.
Original language | English (US) |
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Pages (from-to) | 192-202 |
Number of pages | 11 |
Journal | Simulation Series |
Volume | 53 |
Issue number | 2 |
State | Published - 2021 |
Event | 2021 Annual Modeling and Simulation Conference, ANNSIM 2021 - Virtual, Online Duration: Jul 19 2021 → Jul 22 2021 |
Keywords
- Cigarette smoking
- Ecologically momentary assessment
- Hierarchical fuzzy inference system
- Particle swarm optimization
- Postpartum relapse prevention
ASJC Scopus subject areas
- Computer Networks and Communications