A Mobile Just-in-Time Adaptive Intervention for Smoking Cessation: Pilot Randomized Controlled Trial

Identifying mechanisms that link pain to smoking relapse during a quit attempt

OBJECTIVE

Emerging evidence suggests that adults with chronic pain have poor smoking cessation outcomes, but the exact mechanisms are less understood. This study examined whether depression, anxiety, stress, and then, positive outcome expectancy for smoking mediated the association between pain and smoking relapse during a quit attempt.

METHODS

This study is a secondary data analysis of a three-armed randomized clinical trial that compared in-person and smartphone-based smoking cessation interventions. Participants (N = 81) self-reported the amount of bodily pain they experienced in the past 4 weeks at baseline. Depression, anxiety, stress, and positive outcome expectancy for smoking were measured daily, via a smartphone app, throughout the first week of the quit attempt, and were aggregated to the week level for analyses. Biochemically verified smoking abstinence was assessed 4 weeks postquit date.

RESULTS

Sequential mediation analyses showed that pain was indirectly associated with smoking relapse through greater feelings of stress and then higher expectations that smoking would improve mood (B = 0.22 [95% CI = 0.03, 0.65]). The pathways for depression and anxiety were not significant mediators of pain and smoking relapse.

CONCLUSION

Findings from this study indicate that pain is indirectly associated with smoking relapse through feelings of stress and then positive outcome expectancy for smoking. Smoking cessation treatment for adults who experience high levels of bodily pain should include psychoeducation that teaches adaptive coping responses, such as mindfulness, to manage stress, and challenge expectations about the ability of smoking to improve mood. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

What differs former, light and heavy smokers? Evidence from a post-conflict setting

BACKGROUND

Evidence suggests that people who live in regions affected by the armed conflict are more likely to smoke.

OBJECTIVE

The purpose of this study was to assess factors associated with smoking status in a sample of students in the northern Kosovo province.

MATERIALS AND METHODS

A total of 514 students enrolled in University in Kosovska Mitrovica, Kosovo, were recruited between April to June 2015 at Student Public Health Center during mandatory health checks. Participants filled in socio-demographic and behavioral questionnaire and Beck Depression Inventory (BDI). Based on responses about smoking, students were categorized in non-smokers, former smokers, light smokers (1-13 cigarettes/day) and heavy smokers (> 13 cigarettes/day).

RESULTS

Of 514 students, 116 (22.6%) classified themselves as smokers. Higher education level of fathers (Odds ratio [OR]=2.89, 95% confidence interval [CI] 1.30-6.44, p=0.009), not living with smokers (OR=0.42, 95%CI 0.15-0.97, p=0.017) and longer exposure to second hand smoke (OR=1.07, 95%CI 1.01-1.13, p=0.036) was associated with former smoking. Studying medical and natural sciences (OR=2.07, 95%CI 1.05-4.18, p=0.040), consuming alcohol (OR=2.98, 95%CI 1.19-10.03, p=0.020), living with smokers (OR=2.88, 95%CI 1.49-5.56, p=0.002), longer exposure to second hand smoke (OR=1.06, 95%CI 1.01-1.11, p=0.019) and having a more intense depressive symptoms (OR=1.08, 95%CI 1.03-1.13, p=0.002) was associated with light smoking. Being male (OR=0.22, 95%CI 0.07-0.41, p=0.001), older (OR=1.47, 95%CI 1.21-1.78, p=0.001), living with smokers (OR=3.78, 95%CI 1.69-8.07, p=0.001), longer daily exposure to second-hand smoke (OR=1.10, 95%CI 1.04-1.16, p=0.001), and having more severe depressive symptoms (OR=1.12, 95%CI 1.07-1.18, p=0.001) were associated with heavy smoking.

CONCLUSION

Smoking prevention and cessation programs should include the entire community, because exposure to environmental second hand smoke may facilitate initiation and more intense smoking. Screening of student smokers for depression should be prioritized in the process of rebuilding the framework for primary and secondary prevention in the post-conflict period.

Using Digital Technologies to Reach Tobacco Users Who Want to Quit: Evidence From the National Cancer Institute's Smokefree.gov Initiative

The rapid growth of smartphone ownership and broadband access has created new opportunities to reach smokers with cessation information and support using digital technologies. These technologies can both complement and be integrated with traditional support modalities such as telephone quitlines and 1-on-1 clinical cessation counseling. The National Cancer Institute's Smokefree.gov Initiative provides free, evidence-based cessation support to the public through a multimodal suite of digital interventions, including several mobile-optimized websites, text messaging programs, and 2 mobile applications. In addition to digital resources directed at the general population, the Smokefree.gov Initiative includes population-specific resources targeted to adolescents, women, military veterans, Spanish speakers, older adults, and other populations. This paper describes the reach and use of the Smokefree.gov Initiative's resources over a 5-year period between 2014 and 2018, including how users interact with the program's digital content in ways that facilitate engagement with live counseling support. Use of Smokefree.gov Initiative resources has grown steadily over time; in 2018 alone, approximately 7-8 million people accessed Smokefree.gov Initiative web- and mobile-based resources. Smokefree.gov Initiative utilization data show that people take advantage of the full range of technology tools and options offered as part of the Smokefree.gov Initiative's multiplatform intervention. The Smokefree.gov Initiative experience suggests that offering different, complementary technology options to meet the needs and preferences of smokers has the potential to meaningfully expand the reach of cessation treatment.

Predicting the first smoking lapse during a quit attempt: A machine learning approach

BACKGROUND

Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate decision rules that determine when someone is most likely to lapse. The primary goal of the present study was to identify the strongest predictors of first lapse among smokers undergoing a quit attempt.

METHODS

Smokers attending a clinic-based smoking cessation program (n = 74) were asked to complete ecological momentary assessments five times daily on study-provided smartphones for 4 weeks post-quit. A three-stage modeling process utilized Cox proportional hazards regression to examine time to lapse a function of 31 predictors. First, univariate models evaluated the relationship between each predictor and time to lapse. Second, the elastic net machine learning algorithm was used to select the best predictors. Third, backwards elimination further reduced the set of predictors to optimize parsimony.

RESULTS

Univariate models identified seven predictors significantly related to time to lapse. The elastic net algorithm retained five: perceived odds of smoking today, confidence in ability to avoid smoking, motivation to avoid smoking, urge to smoke, and cigarette availability. The reduced model demonstrated inadequate approximation to the non-penalized baseline model.

CONCLUSIONS

Accurate estimation of moments of high risk for smoking lapse remains an important goal in the development of JITAI. These results demonstrate the utility of exploratory data-driven approaches to variable selection. The results of this study can inform future JITAI by highlighting targets for intervention.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

INTRODUCTION

We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

METHODS

We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

RESULTS

Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

CONCLUSION

There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science

PURPOSE OF REVIEW

Addiction is a serious and prevalent problem across the globe. An important challenge facing intervention science is how to support addiction treatment and recovery while mitigating the associated cost and stigma. A promising solution is the use of mobile health (mHealth) just-in-time adaptive interventions (JITAIs), in which intervention options are delivered in situ via a mobile device when individuals are most in need.

RECENT FINDINGS

The present review describes the use of mHealth JITAIs to support addiction treatment and recovery, and provides guidance on when and how the micro-randomized trial (MRT) can be used to optimize a JITAI. We describe the design of five mHealth JITAIs in addiction and three MRT studies, and discuss challenges and future directions.

SUMMARY

This review aims to provide guidance for constructing effective JITAIs to support addiction treatment and recovery.

Digital Health and Addiction

Digital technologies are rapidly changing how we understand and promote health. A robust and growing line of research has examined how digital health may enhance our understanding and treatment of addiction. This manuscript highlights innovations in the application of digital health approaches to addiction medicine, with a particular emphasis on advances in (1) real-time measurement of drug use events, (2) real-time measurement of the confluence of factors that surround drug use events, and (3) research examining how real-time measurement can inform responsive, in-the-moment interventions to prevent and treat substance use disorder. Although this manuscript focuses on addiction medicine as one exemplar of the striking impact of digital health, science-based digital health offers generalizable solutions to scaling-up unprecedented models of precision healthcare delivery across a broad spectrum of diseases across the globe.

Digital health for assessment and intervention targeting tobacco and cannabis co-use

Purpose of review

This article aims to summarize current research on digital health for assessment and intervention targeting tobacco and cannabis co-use and to answer the following questions: Which digital tools have been used? Which populations have been targeted? And what are implications for future research?

Recent findings

Ecological Momentary Assessment (EMA) via text messages or interactive voice response calls has been used to capture co-use patterns within a time window or co-administration of both substances via blunts among young adults. Feasibility of multicomponent interventions targeting dual cessation of both substances among adult co-users with cannabis use disorder, delivered via smartphone apps, online, and computer modules has been demonstrated.

Summary

Digital tools, particularly those using EMAs and mobile sensors, should be expanded to assess co-use of emerging tobacco and cannabis products. Digital cessation interventions should be tailored to different groups of co-users and address specific mechanisms underlying different co-use patterns.