Predictors of Patient Engagement in Telehealth-Delivered Tobacco Cessation Treatment during the COVID-19 Pandemic.
Annemarie D JagieloAmy ChiengCindy TranAmy PirklAnn Cao-NasalgaAshley BraggRachelle MirkinTimothy K ThomasPublished in: International journal of environmental research and public health (2024)
Smoking causes one in three cancer deaths and may worsen COVID-19 outcomes. Telehealth tobacco cessation treatment is offered as a covered benefit for patients at the Stanford Cancer Center. We examined predictors of engagement during the COVID-19 pandemic. Data were abstracted from the Electronic Health Record between 3/17/20 (start of pandemic shelter-in-place) and 9/20/22, including patient tobacco use, demographics, and engagement in cessation treatment. Importance of quitting tobacco was obtained for a subset (53%). During the first 2.5 years of the pandemic, 2595 patients were identified as recently using tobacco, and 1571 patients were contacted (61%). Of the 1313 patients still using tobacco (40% women, mean age 59, 66% White, 13% Hispanic), 448 (34%) enrolled in treatment. Patient engagement was greater in pandemic year 1 (42%) than in year 2 (28%) and year 3 (19%). Women (41%) engaged more than men (30%). Patients aged 36-45 (39%), 46-55 (43%), 56-65 (37%), and 66-75 (33%) engaged more than patients aged 18-35 (18%) and >75 (21%). Hispanic/Latinx patients (42%) engaged more than non-Hispanic/Latinx patients (33%). Engagement was not statistically significantly related to patient race. Perceived importance of quitting tobacco was significantly lower in pandemic year 1 than year 2 or 3. Nearly one in three cancer patients engaged in telehealth cessation treatment during the COVID-19 pandemic. Engagement was greater earlier in the pandemic, among women, Hispanic/Latinx individuals, and patients aged 36 to 75. Sheltering-in-place, rather than greater perceived risk, may have facilitated patient engagement in tobacco cessation treatment.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- sars cov
- coronavirus disease
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- social media
- type diabetes
- mental health
- electronic health record
- metabolic syndrome
- machine learning
- polycystic ovary syndrome
- case report
- patient reported outcomes
- social support
- deep learning
- skeletal muscle
- combination therapy
- smoking cessation
- replacement therapy