Computational Model for Behavior Shaping as an Adaptive Health Intervention Strategy

Adaptive behavioral interventions that automatically adjust in real-time to participants’ changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated

Developing and Selecting Auditory Warnings for a Real-Time Behavioral Intervention

Real-time sensing and computing technologies are increasingly used in the delivery of real-time health behavior interventions. Auditory signals play a critical role in many of these interventions, impacting not only behavioral response but also treatment adherence and participant retention. Yet, few behavioral interventions that employ auditory feedback report the characteristics of sounds used and even fewer design signals specifically for their intervention. This paper describes a four-step process used in developing and selecting auditory warnings for a behavioral trial designed to reduce indoor secondhand smoke exposure. In step one, relevant information was gathered from ergonomic and behavioral science literature to assist a panel of research assistants in developing criteria for intervention-specific auditory feedback. In step two, multiple sounds were identified through internet searches and modified in accordance with the developed criteria, and two sounds were selected that best met those criteria. In step three, a survey was conducted among 64 persons from the primary sampling frame of the larger behavioral trial to compare the relative aversiveness of sounds, determine respondents\u27 reported behavioral reactions to those signals, and assess participant’s preference between sounds. In the final step, survey results were used to select the appropriate sound for auditory warnings. Ultimately, a single-tone pulse, 500 milliseconds (ms) in length that repeats every 270 ms for three cycles was chosen for the behavioral trial. The methods described herein represent one example of steps that can be followed to develop and select auditory feedback tailored for a given behavioral intervention

Randomized Trial to Reduce Air Particle Levels in Homes of Smokers and Children

Introduction Exposure to fine particulate matter in the home from sources such as smoking, cooking, and cleaning may put residents, especially children, at risk for detrimental health effects. A randomized clinical trial was conducted from 2011 to 2016 to determine whether real-time feedback in the home plus brief coaching of parents or guardians could reduce fine particle levels in homes with smokers and children. Design A randomized trial with two groups—intervention and control. Setting/participants A total of 298 participants from predominantly low-income households with an adult smoker and a child aged \u3c14 years. Participants were recruited during 2012–2015 from multiple sources in San Diego, mainly Women, Infants and Children Program sites. Intervention The multicomponent intervention consisted of continuous lights and brief sound alerts based on fine particle levels in real time and four brief coaching sessions using particle level graphs and motivational interviewing techniques. Motivational interviewing coaching focused on particle reduction to protect children and other occupants from elevated particle levels, especially from tobacco-related sources. Main outcome measures In-home air particle levels were measured by laser particle counters continuously in both study groups. The two outcomes were daily mean particle counts and percentage time with high particle concentrations (\u3e15,000 particles/0.01 ft3). Linear mixed models were used to analyze the differential change in the outcomes over time by group, during 2016–2017. Results Intervention homes had significantly larger reductions than controls in daily geometric mean particle concentrations (18.8% reduction vs 6.5% reduction, p\u3c0.001). Intervention homes’ average percentage time with high particle concentrations decreased 45.1% compared with a 4.2% increase among controls (difference between groups p\u3c0.001). Conclusions Real-time feedback for air particle levels and brief coaching can reduce fine particle levels in homes with smokers and young children. Results set the stage for refining feedback and possible reinforcing consequences for not generating smoke-related particles. Trial registration This study is registered at www.clinicaltrials.gov NCT01634334

Randomised Controlled Trial of Real-Time Feedback and Brief Coaching to Reduce Indoor Smoking

Background: Previous secondhand smoke (SHS) reduction interventions have provided only delayed feedback on reported smoking behaviour, such as coaching, or presenting results from child cotinine assays or air particle counters. Design: This SHS reduction trial assigned families at random to brief coaching and continuous real-time feedback (intervention) or measurement-only (control) groups. Participants: We enrolled 298 families with a resident tobacco smoker and a child under age 14. Intervention: We installed air particle monitors in all homes. For the intervention homes, immediate light and sound feedback was contingent on elevated indoor particle levels, and up to four coaching sessions used prompts and praise contingent on smoking outdoors. Mean intervention duration was 64 days. Measures: The primary outcome was \u27particle events\u27 (PEs) which were patterns of air particle concentrations indicative of the occurrence of particle-generating behaviours such as smoking cigarettes or burning candles. Other measures included indoor air nicotine concentrations and participant reports of particle-generating behaviour. Results: PEs were significantly correlated with air nicotine levels (r=0.60) and reported indoor cigarette smoking (r=0.51). Interrupted time-series analyses showed an immediate intervention effect, with reduced PEs the day following intervention initiation. The trajectory of daily PEs over the intervention period declined significantly faster in intervention homes than in control homes. Pretest to post-test, air nicotine levels, cigarette smoking and e-cigarette use decreased more in intervention homes than in control homes. Conclusions: Results suggest that real-time particle feedback and coaching contingencies reduced PEs generated by cigarette smoking and other sources

Modeling Residential Exposure to Secondhand Tobacco Smoke

We apply a simulation model to explore the effect of a house's multicompartment character on a nonsmoker's inhalation exposure to secondhand tobacco smoke (SHS). The model tracks the minute-by-minute movement of people and pollutants among multiple zones of a residence and generates SHS pollutant profiles for each room in response to room-specific smoking patterns. In applying the model, we consider SHS emissions of airborne particles, nicotine, and carbon monoxide in two hypothetical houses, one with a typical 4-room layout and one dominated by a single large space. We use scripted patterns of room-to-room occupant movement and a cohort of 5,000 activity patterns sampled from a US nationwide survey. The results for scripted and cohort simulation trials indicate that the multicompartment nature of homes, manifested as inter-room differences in pollutant levels and the movement of people among zones, can cause substantial variation in nonsmoker SHS exposure