Feasibility, Acceptability, and Potential Impact of a Novel mHealth App for Smokers Ambivalent About Quitting: Randomized Pilot Study.

BACKGROUND: Most smokers are ambivalent about quitting-they want to quit someday, but not now. Interventions are needed that can engage ambivalent smokers, build their motivation for quitting, and support future quit attempts. Mobile health (mHealth) apps offer a cost-effective platform for such interventions, but research is needed to inform their optimal design and assess their acceptability, feasibility, and potential effectiveness. OBJECTIVE: This study aims to assess the feasibility, acceptability, and potential impact of a novel mHealth app for smokers who want to quit smoking someday but are ambivalent about quitting in the near term. METHODS: We enrolled adults across the United States who smoked more than 10 cigarettes a day and were ambivalent about quitting (n=60). Participants were randomly assigned to 1 of 2 versions of the GEMS app: standard care (SC) versus enhanced care (EC). Both had a similar design and identical evidence-based, best-practice smoking cessation advice and resources, including the ability to earn free nicotine patches. EC also included a series of exercises called experiments designed to help ambivalent smokers clarify their goals, strengthen their motivation, and learn important behavioral skills for changing smoking behavior without making a commitment to quit. Outcomes were analyzed using automated app data and self-reported surveys at 1 and 3 months post enrollment. RESULTS: Participants who installed the app (57/60, 95%) were largely female, White, socioeconomically disadvantaged, and highly nicotine dependent. As expected, key outcomes trended in favor of the EC group. Compared to SC users, EC participants had greater engagement (mean sessions 19.9 for EC vs 7.3 for SC). An intentional quit attempt was reported by 39.3% (11/28) of EC users and 37.9% (11/29) of SC users. Seven-day point prevalence smoking abstinence at the 3-month follow-up was reported by 14.7% (4/28) of EC users and 6.9% (2/29) of SC users. Among participants who earned a free trial of nicotine replacement therapy based on their app usage, 36.4% (8/22) of EC participants and 11.1% (2/18) of SC participants requested the treatment. A total of 17.9% (5/28) of EC and 3.4% (1/29) of SC participants used an in-app feature to access a free tobacco quitline. Other metrics were also promising. EC participants completed an average of 6.9 (SD 3.1) out of 9 experiments. Median helpfulness ratings for completed experiments ranged from 3 to 4 on a 5-point scale. Finally, satisfaction with both app versions was very good (mean 4.1 on a 5-point Likert scale) and 95.3% (41/43) of all respondents would recommend their app version to others. CONCLUSIONS: Ambivalent smokers were receptive to the app-based intervention, but the EC version, which combined best-practice cessation advice with self-paced, experiential exercises, was associated with greater use and evidence of behavior change. Further development and evaluation of the EC program is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT04560868; https://clinicaltrials.gov/ct2/show/NCT04560868.

Construction of a Pediatrics Risk Score to Predict High Health Care Costs Among a Community Health Center Cohort.

Risk-stratification strategies are needed for ambulatory pediatric populations. The authors sought to develop age-specific risk scores that predict high health care costs among an urban population. A retrospective cohort study was performed of children ages 1-18 years who received care at Fair Haven Community Health Care (FHCHC), a community health center in New Haven, Connecticut. Cost was estimated from charges in the electronic health record (EHR), which is shared with the only hospital system in the city. Using multivariable logistic regression models, independent predictors of being in the top decile of total charges during the 2017 calendar year were identified, drawing from covariates collected from the EHR prior to 2017. Random forest modeling was used to verify the feature importance of significant covariates and model performance from 2017 cost data were compared to those using 2018 cost data. Regression models were used to construct age-specific nomograms to predict cost. Among 8960 children who received care at FHCHC in the 18 months prior to 2017, covariate frequencies clustered in age groups 1-5 years, 6-11 years, and 12-18 years, so 3 age-specific models were constructed. Prior utilization variables predicted future costs, as did younger children who received specialty care and older children with behavioral health diagnoses. Final models for each age group had C statistics ≥0.68 using both 2017 and 2018 cost data. Prediction models can draw from elements accessible in the EHR to predict cost of ambulatory pediatric patients. Strategies to impact utilization among high-risk children are needed.