A Mobile Game to Support Smoking Cessation: Prototype Assessment

Background: Cigarette smoking results in an estimated seven million deaths annually. Almost half of all smokers attempt to quit each year, yet only approximately 6% are successful. Although there are multiple effective interventions that can increase these odds, substantial room remains for improvement. One effective approach to helping smokers quit is contingency management, where quitting is incentivized with the delivery of monetary rewards in exchange for objective evidence (e.g., exhaled carbon monoxide levels) of abstinence. Objective: We assessed the feasibility and promise of Inspired, a contingency management mobile app for smoking cessation that uses game-based rewards to incentivize abstinence from smoking instead of the monetary (or material) rewards typically used. We sought participant feedback and limited objective data on: the features and design of Inspired, interest in using Inspired when it becomes available, the likelihood of Inspired being an effective cessation aid, and the rank order preference of Inspired relative to other familiar smoking cessation aids. Methods: Twenty-eight treatment-seeking smokers participated in this study. Participants attended a single one-hour session in which they received an overview of the goals of the Inspired mobile game, practiced submitting breath carbon monoxide (CO) samples, and played representative levels of the game. Participants were then told that they could play an extra level, or they could stop, complete an outcome survey, receive payment, and be dismissed. A sign-up sheet requesting personal contact information was available for those who wished to be notified when the full version of Inspired becomes available. Results: Using binary criteria for endorsement, participants indicated that, assuming it was currently available and fully developed, they would be more likely to use Inspired than: any other smoking cessation aid (21/28, 75%), the nicotine patch (23/28, 82%), a drug designed to reduce smoking cravings (23/28, 82%), or a program involving attendance in training sessions or support group meetings (27/28, 96%). In the questionnaire, participants indicated that both the Inspired program (26/28, 93%) and the Inspired game would be “Fun” (28/28, 100%), and 71% (20/28) reported that the program would help them personally quit smoking. Fifty-eight percent of participants (15/26) chose to continue playing the game rather than immediately collecting payment for participation and leaving. Eighty-two percent of participants (23/28) signed up to be notified when the full version of Inspired becomes available. Conclusions: This was the first study to evaluate a game-based contingency management app that uses game-based virtual goods as rewards for smoking abstinence. The outcomes suggest that the completed app has potential to be an effective smoking cessation aid that would be widely adopted by smokers wishing to quit

Identifying Video Game Preferences Among Adults Interested in Quitting Smoking Cigarettes: Survey Study.

BACKGROUND: Smoking is the most prevalent cause of morbidity and mortality in the United States. Although most individuals who smoke express a desire to quit smoking, only a small percentage are successful. Serious games have become popular in health sectors as a potential avenue for delivering a scalable treatment that is both accessible and engaging for the smoking population. Several smoking cessation games have already been developed, but these games feature a broad range of gameplay elements and are not necessarily based on existing video game preferences in the general or smoking population. OBJECTIVE: To better inform treatment development, this study aims to evaluate video game genre preferences among treatment-seeking individuals who smoke (N=473). METHODS: Participants responded to a screening survey to enroll in a larger, serious game intervention for smoking cessation. During this screening survey, participants were asked to disclose their favorite video games, which resulted in 277 unique game titles. These titles were coded for genre categories based on publisher listings and game features. The genres were then analyzed for the frequency of reporting overall and across age groups. RESULTS: Action, Role-Playing, and Action-Adventure were the most reported genres among adults aged ≤34 years; Action, Action-Adventure, and Logic were the most reported genres among adults aged 35-44 years; and Logic and Action were the most reported genres among adults aged ≥45 years. CONCLUSIONS: These data indicate that treatment-seeking individuals who smoke have different game preferences across age groups, and the data provide novel information to inform the development of future serious games targeting the smoking population that are tailored to the preferences of their age group. TRIAL REGISTRATION: ClinicalTrials.gov NCT03929003; https://clinicaltrials.gov/ct2/show/NCT03929003

Laboratory Validation of Inertial Body Sensors to Detect Cigarette Smoking Arm Movements

Cigarette smoking remains the leading cause of preventable death in the United States. Traditional in-clinic cessation interventions may fail to intervene and interrupt the rapid progression to relapse that typically occurs following a quit attempt. The ability to detect actual smoking behavior in real-time is a measurement challenge for health behavior research and intervention. The successful detection of real-time smoking through mobile health (mHealth) methodology has substantial implications for developing highly efficacious treatment interventions. The current study was aimed at further developing and testing the ability of inertial sensors to detect cigarette smoking arm movements among smokers. The current study involved four smokers who smoked six cigarettes each in a laboratory-based assessment. Participants were outfitted with four inertial body movement sensors on the arms, which were used to detect smoking events at two levels: the puff level and the cigarette level. Two different algorithms (Support Vector Machines (SVM) and Edge-Detection based learning) were trained to detect the features of arm movement sequences transmitted by the sensors that corresponded with each level. The results showed that performance of the SVM algorithm at the cigarette level exceeded detection at the individual puff level, with low rates of false positive puff detection. The current study is the second in a line of programmatic research demonstrating the proof-of-concept for sensor-based tracking of smoking, based on movements of the arm and wrist. This study demonstrates efficacy in a real-world clinical inpatient setting and is the first to provide a detection rate against direct observation, enabling calculation of true and false positive rates. The study results indicate that the approach performs very well with some participants, whereas some challenges remain with participants who generate more frequent non-smoking movements near the face. Future work may allow for tracking smoking in real-world environments, which would facilitate developing more effective, just-in-time smoking cessation interventions

Weight Management Preferences in a Non-Treatment Seeking Sample

Background: Obesity is a serious public health issue in the United States, with the CDC reporting that most adult Americans are now either overweight or obese. Little is known about the comparative acceptability of available weight management approaches in non-treatment seeking samples. Method: This report presents preliminary survey data collected from an online sample on weight management preferences for 8 different weight management strategies including a proposed incentive-based program. Participants were 72 individuals (15 men, 55 women and 2 transgendered individuals) who self-re-ported being overweight or obese, or who currently self-reported a normal weight but had attempted to lose weight in the past. Results: ANOVA and Pair-wise comparison indicated clear preferences for certain treatments over others in the full sample; most notably, the most popular option in our sample for managing weight was to diet and exercise without professional assistance. Several differences in preference between the three weight groups were also observed. Conclusions: Dieting and exercising without any professional assistance is the most highly endorsed weight management option among all groups. Overweight and obese individuals may find self-management strategies for weight loss less attractive than normal weight individuals, but still prefer it to other alternatives. This has implications for the development and dissemination of empirically based self-management strategies for weight

Article Laboratory Validation of Inertial Body Sensors to Detect Cigarette Smoking Arm Movements

electronic