An ecological momentary intervention for smoking cessation: The associations of just-in-time, tailored messages with lapse risk factors

Smartphone-Based Smoking Cessation Intervention (OKquit) for Oklahoma Tobacco Helpline Users: Protocol for a Randomized Controlled Trial

BACKGROUND

Tobacco quitlines provide effective resources (eg, nicotine replacement therapy, smoking cessation counseling, and text and web-based support) for those who want to quit smoking in the United States. However, quitlines reach approximately only 1%-3% of people who smoke each year. Novel, smartphone-based, and low-burden interventions that offer 24/7 access to smoking cessation resources that are tailored to current readiness to quit may increase appeal, reach, and effectiveness of smoking cessation interventions.

OBJECTIVE

This study will examine the efficacy of OKquit, a low-burden smartphone-based app for smoking cessation.

METHODS

Approximately 500 people who smoke cigarettes and access the Oklahoma Tobacco Helpline (OTH) will be randomized to receive standard OTH care (SC) or SC plus the novel OKquit smartphone app for smoking cessation (OKquit). All participants will use a smartphone app to complete study surveys (ie, baseline, 27 weekly surveys, brief daily check-ins, and 27-week follow-up). Upon completion of daily check-ins and weekly surveys, participants will receive either trivia type messages (SC) or messages that are tailored to current readiness to quit smoking and currently experienced lapse triggers (OKquit). In addition, those assigned to receive the OKquit app will have access to on-demand smoking cessation content (eg, quit tips, smoking cessation medication tips). It is hypothesized that participants assigned to OKquit will be more likely to achieve biochemically verified 7-day point prevalence abstinence than those assigned to SC at 27 weeks post enrollment. In addition, participants who use more OTH resources (eg, more cessation coaching sessions completed) or more OKquit resources (eg, access more quit tips) will have greater biochemically verified smoking cessation rates.

RESULTS

Data collection began in September 2022 and final follow-ups are expected to be completed by May 2025.

CONCLUSIONS

Data from this randomized controlled trial will determine whether the OKquit smartphone app combined with OTH care will increase smoking cessation rates over standard OTH care alone. If successful, OKquit could provide tailored intervention content at a fraction of the cost of traditional interventions. Furthermore, this type of low-burden intervention may offer a way to reach underserved populations of adults who smoke and want to quit.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05539209; https://clinicaltrials.gov/study/NCT05539209.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56827.

Mobile Health Interventions for Substance Use Disorders

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions.

Towards Predicting Smoking Events for Just-in-time Interventions

Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.

Associations between morning affect and later-day smoking urges and behavior

OBJECTIVE

Affective experiences are associated with smoking urges and behavior. Few studies have examined the temporal nature of these associations within a day, such as whether positive and negative affect in the morning are associated with smoking urges and behavior later in the day.

METHOD

Participants (N = 63; MAge = 50 years, 48% female; 60% White) were randomized into one of three smoking cessation interventions and answered up to five daily ecological momentary assessments for 28 days during a quit attempt (M = 21.0 days, SD = 7.1). Before analysis, scores for morning positive and negative affect and later-day smoking urges and behavior were calculated.

RESULTS

On days when individuals' morning positive affect was higher than usual, later-day smoking urges tended to be lower than usual. In contrast, on days when individuals' morning negative affect was higher than usual, later-day smoking urges tended to be higher than usual, and smoking was more likely. Further, individuals who had higher characteristic morning positive affect tended to have less intense later-day smoking urges, whereas those who tended to have higher characteristic morning negative affect tended to have more intense later-day smoking urges.

CONCLUSIONS

Morning positive and negative affect were associated with later-day smoking urges, and morning negative affect was related to later-day smoking behavior. Future research should examine whether interventions that boost positive affect on mornings when it is lower than usual and attenuate negative affect on mornings when it is higher than usual, may reduce the intensity of smoking urges and the likelihood of smoking later in the day. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Temporal associations linking alcohol and cannabis use to cigarette smoking in young adults engaged in a tobacco cessation and relapse monitoring study

Young adulthood remains a developmental period in which cigarette smoking initiation and progression to dependence and regular use is common. Moreover, co-use of alcohol and/or cannabis with tobacco is common in this age group and may have detrimental effects on tobacco use rates and cessation outcomes. Although young adults are interested in quitting smoking, achieving abstinence remains difficult, even with evidence-based treatment strategies. Understanding proximal associations between other substance use (e.g., alcohol and cannabis) and smoking may have important treatment implications. This exploratory analysis investigated the role of alcohol and/or cannabis use in contributing to smoking events on the same day or next day among young adults engaged in a smoking cessation and relapse monitoring study. We used ecological momentary assessment (EMA) data from 43 young adults (ages 18-25; 932 observations) who smoked cigarettes daily and agreed to participate in a 5-week study that included a 2-day smoking quit attempt and provision of tobacco treatment in the form of nicotine replacement therapy, brief cessation counseling, and financial incentives for abstinence (incentives were provided only during the 2-day quit attempt). We tested multilevel time-series models of daily associations between alcohol use, cannabis use, and smoking. Consistent with hypotheses, days on which participants were more likely to drink alcohol predicted increased likelihood of smoking the next day (OR = 2.27, p =.003). This effect was significant after controlling for both the one-day lagged effect of smoking (i.e., autoregression) and the concurrent (i.e., same day) effects of drinking and cannabis use. Although there was a positive concurrent effect of cannabis use on smoking (OR = 12.86, p =.003), the one-day lagged effect of cannabis use and the concurrent effect of drinking was not significant, contrary to hypotheses. Results indicate that alcohol use presents a potential threat to successful smoking cessation that extends to the following day. This suggests a risk-window in which treatment could be supplemented with just-in-time interventions and extending the focus on co-use to include this lagged impact on cessation outcomes.

Effectiveness of personalized smoking cessation intervention based on ecological momentary assessment for smokers who prefer unaided quitting: protocol for a randomized controlled trial

Introduction

Ecological momentary assessment (EMA)-based smoking cessation intervention may help personalize intervention for smokers who prefer to quit smoking unaided. This study aims to evaluate the effectiveness of EMA-based phone counseling and instant messaging for smoking cessation.

Methods/design

This is a two-arm, accessor-blinded, simple individual randomized controlled trial (allocation ratio 1:1). Participants will be recruited from community sites and online platforms in Hong Kong. Interventions will be delivered via a phone call and instant messaging. Current adult smokers who (1) self-report no intention to use smoking cessation services and medication in the coming month and (2) have not used smoking cessation services or nicotine replacement therapy in the past 7 days will be recruited. Recruited participants will be randomized to intervention or control groups via an online randomizer. All participants will be required to complete EMAs (five times per day for 7 consecutive days). The intervention group (n = 220) will receive a nurse-led brief phone counseling immediately after the 1-week EMAs and 10-week EMA-based advice via instant messaging applications (e.g., WhatsApp, WeChat). The 10-week EMA-based advice covers a summary of the 1-week EMAs, and tailored cessation support focused on personalized smoking triggers. The control group (n = 220) will not receive any intervention during the same period. The primary outcomes are participants' progression toward smoking cessation assessed by the Incremental Behavior Change toward Smoking Cessation (IBC-S) and biochemically validated abstinence at the 3-month follow-up. Secondary outcomes include self-reported and biochemically validated tobacco abstinence at the 6-month follow-up.

Discussion

The findings will provide evidence that the EMA-based tailored smoking cessation intervention can be adapted as a new health promotion strategy for current smokers who are unwilling to use smoking cessation aids.

Clinical trial registration

https://classic.clinicaltrials.gov/ct2/show/NCT05212220, identifier: NCT05212220.

The Craving-Manager smartphone app designed to diagnose substance use/addictive disorders, and manage craving and individual predictors of relapse: a study protocol for a multicenter randomized controlled trial

Background

The rate of individuals with addiction who are currently treated are low, and this can be explained by barriers such as stigma, desire to cope alone, and difficulty to access treatment. These barriers could be overcome by mobile technologies. EMI (Ecological Momentary Intervention) is a treatment procedure characterized by the delivery of interventions (messages on smartphones) to people in their daily lives. EMI presents opportunities for treatments to be available to people during times and in situations when they are most needed. Craving is a strong predictor of relapse and a key target for addiction treatment. Studies using Ecological Momentary Assessment (EMA) method have revealed that, in daily life, person-specific cues could precipitate craving, that in turn, is associated with a higher probability to report substance use and relapse in the following hours. Assessment and management of these specific situations in daily life could help to decrease addictive use and avoid relapse. The Craving-Manager smartphone app has been designed to diagnose addictive disorders, and assess and manage craving as well as individual predictors of use/relapse. It delivers specific and individualized interventions (counseling messages) composed of evidence-based addiction treatments approaches (cognitive behavioral therapy and mindfulness). The Craving-Manager app can be used for any addiction (substance or behavior). The objective of this protocol is to evaluate the efficacy of the Craving-Manager app in decreasing use (of primary substance(s)/addictive behavior(s)) over 4 weeks, among individuals on a waiting list for outpatient addiction treatment.

Methods/design

This multicenter double-blind randomized controlled trial (RCT) will compare two parallel groups: experimental group (full interventional version of the app, 4 weeks, EMA + EMI), versus control group (restricted version of the app, 4 weeks, only EMA). Two hundred and seventy-four participants will be recruited in 6 addiction treatment centers in France.

Discussion

This RCT will provide indication on how the Craving-Manager app will reduce addictive use (e.g., better craving management, better stimulus control) in both substance and behavioral addictions. If its efficacy is confirmed, the app could offer the possibility of an easy to use and personalized intervention accessible to the greatest number of individuals with addiction.

Clinical Trial Registration

ClinicalTrials.gov: NCT04732676.

Socio-economic status moderates within-person associations of risk factors and smoking lapse in daily life

BACKGROUND AND AIMS

Individuals of lower socio-economic status (SES) display a higher prevalence of smoking and have more diffxiculty quitting than higher SES groups. The current study investigates whether the within-person associations of key risk (e.g. stress) and protective (self-efficacy) factors with smoking lapse varies by facets of SES.

DESIGN AND SETTING

Observational study using ecological momentary assessment to collect data for a 28-day period following a smoking quit attempt. Multi-level mixed models (i.e. generalized linear mixed models) examined cross-level interactions between lapse risk and protective factors and indicators of SES on smoking lapse.

PARTICIPANTS

A diverse sample of 330 adult US smokers who completed a larger study examining the effects of race/ethnicity and social/environmental influences on smoking cessation.

MEASUREMENTS

Risk factors included momentary urge, negative affect, stress; protective factors included positive affect, motivation, abstinence self-efficacy; SES measures: baseline measures of income and financial strain; the primary outcome was self-reported lapse.

FINDINGS

Participants provided 43 297 post-quit observations. Mixed models suggested that income and financial strain moderated the effect of some risk factors on smoking lapse. The within-person association of negative [odds ratio (OR) = 0.967, 95% CI= 0.945, 0.990, P < 0.01] and positive affect (OR = 1.023, 95% CI = 1.003, 1.044, P < 0.05) and abstinence self-efficacy (OR = 1.020, 95% CI = 1.003, 1.038, P < 0.05) on lapse varied with financial strain. The within-person association of negative affect (OR = 1.005, 95% CI = 1.002, 1.008, P < 0.01), motivation (OR = 0.995, 95% CI = 0.991, 0.999, P < 0.05) and abstinence self-efficacy (OR = 0.996, 95% CI = 0.993, 0.999, P < 0.01) on lapse varied by income. The positive association of negative affect with lapse was stronger among individuals with higher income and lower financial strain. The negative association between positive affect and abstinence self-efficacy with lapse was stronger among individuals with lower financial strain, and the negative association between motivation and abstinence self-efficacy with lapse was stronger among those with higher income. The data were insensitive to detect statistically significant moderating effects of income and financial strain on the association of urge or stress with lapse.

CONCLUSION

Some risk factors (e.g. momentary negative affect) exert a weaker influence on smoking lapse among lower compared to higher socio-economic status groups.

Adding Mobile Elements to Online Physical Activity Interventions for Adults Aged Over 50 Years: Prototype Development Study

BACKGROUND

Only a minority of adults aged over 50 years meet physical activity (PA) guidelines of the World Health Organization (WHO). eHealth interventions are proven effective tools to help this population increase its PA levels in the short term, among which the Active Plus and I Move interventions have been developed by our own research group. To achieve long-term effects, increase intervention use, and decrease dropout rates, 3 emergent but different mobile elements (an activity tracker, an ecological momentary intervention [EMI] program, and a chatbot) were added separately to Active Plus and I Move. In this study, the prototype development and pilot-testing of these interventions is described.

OBJECTIVE

This study aims to enhance 2 existing PA-stimulating computer-based interventions with 3 mobile elements (an activity tracker, an EMI program, or a chatbot) and test the prototypes on usability and appreciation within a target population of adults aged over 50 years.

METHODS

A systematic design protocol consisting of development, evaluation, and adaptation procedures was followed with involvement of the target population. Literature searches separated per mobile element and interviews with the target population (N=11) led to 6 prototypes: Active Plus or I Move including (1) an activity tracker, (2) EMI, or (3) a chatbot. These prototypes were tested on usability and appreciation during pilot tests (N=47) and subsequently fine-tuned based on the results.

RESULTS

The literature searches and interviews provided important recommendations on the preferences of the target population, which enabled us to develop prototypes. The subsequent pilot tests showed that the mobile elements scored moderate to good on usability, with average System Usability Scale (SUS) scores of 52.2-82.2, and moderate to good on enjoyment and satisfaction, with average scores ranging from 5.1 to 8.1 on a scale of 1-10. The activity tracker received the best scores, followed by EMI, followed by the chatbot. Based on the findings, the activity tracker interventions were fine-tuned and technical difficulties regarding EMI and the chatbot were solved, which is expected to further improve usability and appreciation.

CONCLUSIONS

During this study, 6 prototypes of online PA interventions with added mobile elements were developed and tested for usability and appreciation. Although all prototypes scored moderate to high on usability, enjoyment, and satisfaction, it can be concluded that the integration of an activity tracker with a computer-based PA intervention is the most promising option among the 3 mobile elements tested during this study. The prototype development steps of the systematic design protocol followed can be considered useful and successful for the purposes of this study. The interventions can now be evaluated on a larger scale through a randomized controlled trial.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/31677.

A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings

Smoking cessation treatments that are easily accessible and deliver intervention content at vulnerable moments (e.g., high negative affect) have great potential to impact tobacco abstinence. The current study examined the feasibility and acceptability of a multi-component Just-In-Time Adaptive Intervention (JITAI) for smoking cessation. Daily smokers interested in quitting were consented to participate in a 6-week cessation study. Visit 1 occurred 4 days pre-quit, Visit 2 was on the quit day, Visit 3 occurred 3 days post-quit, Visit 4 was 10 days post-quit, and Visit 5 was 28 days post-quit. During the first 2 weeks (Visits 1-4), the JITAI delivered brief mindfulness/motivational strategies via smartphone in real-time based on negative affect or smoking behavior detected by wearable sensors. Participants also attended 5 in-person visits, where brief cessation counseling (Visits 1-4) and nicotine replacement therapy (Visits 2-5) were provided. Outcomes were feasibility and acceptability; biochemically-confirmed abstinence was also measured. Participants (N = 43) were 58.1 % female (AgeMean = 49.1, mean cigarettes per day = 15.4). Retention through follow-up was high (83.7 %). For participants with available data (n = 38), 24 (63 %) met the benchmark for sensor wearing, among whom 16 (67 %) completed at least 60 % of strategies. Perceived ease of wearing sensors (Mean = 5.1 out of 6) and treatment satisfaction (Mean = 3.6 out of 4) were high. Biochemically-confirmed abstinence was 34 % at Visit 4 and 21 % at Visit 5. Overall, the feasibility of this novel multi-component intervention for smoking cessation was mixed but acceptability was high. Future studies with improved technology will decrease participant burden and better detect key intervention moments.

Psychosocial factors influencing smoking relapse among youth experiencing homelessness: A qualitative study

OBJECTIVES

In the United States, up to 70% of youth experiencing homelessness smoke cigarettes. Many are interested in quitting; however, little is known about psychosocial factors influencing smoking relapse in this population. This study, part of a larger project to develop an optimized smoking cessation intervention for youth experiencing homelessness, aimed to describe how psychosocial factors influence smoking relapse in this group.

METHODS

This study describes the smoking relapse experiences of 26 youth tobacco users, aged 14-24 years, who were recruited from a homeless drop-in center in Ohio. We conducted semi-structured interviews to understand how stress, opportunity, and coping contribute to smoking relapse.

RESULTS

Five themes emerged from the data: (1) smoking as a lapse in emotional self-regulation in response to stress; (2) smoking as active emotional self-regulation in response to stress; (3) social opportunities facilitate smoking in the context of emotion-focused stress coping; (4) problem-focused stress coping; and (5) opportunity facilitates smoking relapse.

CONCLUSIONS

Stress was a primary driver of smoking relapse among youth experiencing homelessness, yet social and environmental opportunities to smoke also precipitated relapse. Interventions to improve abstinence among this population should target foundational stressors, coping skills, social supports, and nicotine dependence.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review

BACKGROUND AND AIMS

Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use.

METHODS

Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.

FINDINGS

We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.

CONCLUSIONS

Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

Within-person examination of the exercise intention-behavior gap among women in midlife with elevated cardiovascular disease risk

Engaging in moderate-to-vigorous intensity physical activity (MVPA) is important for protecting cardiovascular health among women in midlife (i.e., ages 40-60), particularly if they have already developed conditions that increase their risk for cardiovascular disease (e.g., hypertension). Although the gap between MVPA intentions and behavior is well documented in other populations, little is known about the intention-behavior gap in this at-risk group - particularly as it plays a role in daily life. The present study employed an ecological momentary assessment design to examine the relation between women's MVPA intentions and behavior in the subsequent 3 hours, as well as momentary moderators of this relation (i.e., affective states and body satisfaction). Surveys sent to women's smartphones 5 times per day for 10 days while they wore ActiGraph GT3X waistband accelerometers. Women achieved their exercise intentions at only 13% of occasions on which they set intentions. Although the most common intended exercise was walking, women engaged in more minutes of MVPA after setting intentions to do yoga or Pilates than any other type of exercise (sr = 0.25). Multilevel models showed a modest within-person relation between minutes of intended MVPA and observed MVPA in the next 3 hours (sr = 0.20). This relation was moderated within-person by the reported extent of positive affect (particularly contentment) and body satisfaction (srs = 0.35 and 0.28, respectively). Findings extend knowledge about the physical activity intention-behavior gap to an at-risk population of women and identify positive affect and body satisfaction as potential contextual influences for this group, which could inform improvements to existing interventions (e.g., delivering intervention content at times with lower-than-usual body satisfaction).

GAMBLINGLESS IN-THE-MOMENT: Protocol for a micro-randomised trial of a gambling Just-In-Time Adaptive Intervention (Preprint)

Background

The presence of discrete but fluctuating precipitants, in combination with the dynamic nature of gambling episodes, calls for the development of tailored interventions delivered in real time, such as just-in-time adaptive interventions (JITAIs). JITAIs leverage mobile and wireless technologies to address dynamically changing individual needs by providing the type and amount of support required at the right time and only when needed. They have the added benefit of reaching underserved populations by providing accessible, convenient, and low-burden support. Despite these benefits, few JITAIs targeting gambling behavior are available.

Objective

This study aims to redress this gap in service provision by developing and evaluating a theoretically informed and evidence-based JITAI for people who want to reduce their gambling. Delivered via a smartphone app, GamblingLess: In-The-Moment provides tailored cognitive-behavioral and third-wave interventions targeting cognitive processes explicated by the relapse prevention model (cravings, self-efficacy, and positive outcome expectancies). It aims to reduce gambling symptom severity (distal outcome) through short-term reductions in the likelihood of gambling episodes (primary proximal outcome) by improving craving intensity, self-efficacy, or expectancies (secondary proximal outcomes). The primary aim is to explore the degree to which the delivery of a tailored intervention at a time of cognitive vulnerability reduces the probability of a subsequent gambling episode.

Methods

GamblingLess: In-The-Moment interventions are delivered to gamblers who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability via ecological momentary assessments 3 times a day. The JITAI will tailor the type, timing, and amount of support for individual needs. Using a microrandomized trial, a form of sequential factorial design, each eligible participant will be randomized to a tailored intervention condition or no intervention control condition at each ecological momentary assessment across a 28-day period. The microrandomized trial will be supplemented by a 6-month within-group follow-up evaluation to explore long-term effects on primary (gambling symptom severity) and secondary (gambling behavior, craving severity, self-efficacy, and expectancies) outcomes and an acceptability evaluation via postintervention surveys, app use and engagement indices, and semistructured interviews. In all, 200 participants will be recruited from Australia and New Zealand.

Results

The project was funded in June 2019, with approval from the Deakin University Human Research Ethics Committee (2020-304). Stakeholder user testing revealed high acceptability scores. The trial began on March 29, 2022, and 84 participants have been recruited (as of June 24, 2022). Results are expected to be published mid-2024.

Conclusions

GamblingLess: In-The-Moment forms part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions. This trial will provide important empirical data that can be used to facilitate the JITAI’s optimization to make it a more effective, efficient, and scalable tailored intervention.

Trial Registration

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000490774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380757&isClinicalTrial=False

International Registered Report Identifier (IRRID)

PRR1-10.2196/38958

You Don't Need an App-Conducting Mobile Smoking Research Using a Qualtrics-Based Approach

With the increasing availability of smartphones, many tobacco researchers are exploring smartphone-delivered mobile smoking interventions as a disseminable means of treatment. Most effort has been focused on the development of smartphone applications (apps) to conduct mobile smoking research to implement and validate these interventions. However, developing project-specific smartphone apps that work across multiple mobile platforms (e.g., iOS and Android) can be costly and time-consuming. Here, using a hypothetical study, we present an alternate approach to demonstrate how mobile smoking cessation and outcome evaluation can be conducted without the need of a dedicated app. Our approach uses the Qualtrics platform, a popular online survey host that is used under license by many academic institutions. This platform allows researchers to conduct device-agnostic screening, consenting, and administration of questionnaires through Qualtrics's native survey engine. Researchers can also collect ecological momentary assessment data using text messaging prompts with the incorporation of Amazon Web Services' Pinpoint. Besides these assessment capabilities, Qualtrics has the potential for delivering personalized behavioral interventions through the use of JavaScript code. By customizing the question's web elements in Qualtrics (e.g., using texts, images, videos, and buttons), researchers can integrate interactive web-based interventions and complicated behavioral and cognitive tasks into the survey. In conclusion, this Qualtrics-based methodology represents a novel and cost-effective approach for conducting mobile smoking cessation and assessment research.

Smartphone-Delivered Ecological Momentary Interventions Based on Ecological Momentary Assessments to Promote Health Behaviors: Systematic Review and Adapted Checklist for Reporting Ecological Momentary Assessment and Intervention Studies

Background

Healthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life.

Objective

This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives.

Methods

We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies.

Results

We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs.

Conclusions

The use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots).

Trial Registration

PROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Operationalizing the Population Health Framework: Clinical Characteristics, Social Context, and the Built Environment

As a framework, population health emphasizes health outcomes for entire populations, the broad range of determinants of these outcomes, and the comparative effectiveness of medical and public health interventions. In practice, however, many contemporary population health programs instead focus on small subsets of patients who account for a disproportionate share of health care utilization, often with disappointing results. The authors proposed a new approach to operationalize population health in clinical settings, with the example of tobacco use. Electronic health record (EHR) data from a mid-Atlantic health system were used to: (1) define and describe a hospital-based population of current smokers, (2) analyze the demographic characteristics of the population to consider how the social context may impact treatment, and (3) join EHR data with public licensing data on tobacco retail locations to assess the relationship between the built environment and smoking status. Out of a total of 20,310 unique adult admissions to the health system, 3749 (18.5%) were current smokers. Compared to never smokers, current smokers were significantly younger, more likely to be male, more likely to be Black/African American, less likely to be Hispanic/Latino/a, and more likely to be on Medicaid or be self-pay. Current vs. former smokers had significantly higher exposure to tobacco retail locations, even after adjusting for demographic and other covariates. By defining populations around leading modifiable medical determinants of health, and accounting for the larger context of sociodemographic factors and the built environment, health systems can invest in comprehensive programs designed to produce the greatest population health returns.

Mobile Ecological Momentary Assessment and Intervention and Health Behavior Change Among Adults in Rakai, Uganda: Pilot Randomized Controlled Trial

BACKGROUND

An extraordinary increase in mobile phone ownership has revolutionized the opportunities to use mobile health approaches in lower- and middle-income countries (LMICs). Ecological momentary assessment and intervention (EMAI) uses mobile technology to gather data and deliver timely, personalized behavior change interventions in an individual's natural setting. To our knowledge, there have been no previous trials of EMAI in sub-Saharan Africa.

OBJECTIVE

To advance the evidence base for mobile health (mHealth) interventions in LMICs, we conduct a pilot randomized trial to assess the feasibility of EMAI and establish estimates of the potential effect of EMAI on a range of health-related behaviors in Rakai, Uganda.

METHODS

This prospective, parallel-group, randomized pilot trial compared health behaviors between adult participants submitting ecological momentary assessment (EMA) data and receiving behaviorally responsive interventional health messaging (EMAI) with those submitting EMA data alone. Using a fully automated mobile phone app, participants submitted daily reports on 5 different health behaviors (fruit consumption, vegetable consumption, alcohol intake, cigarette smoking, and condomless sex with a non-long-term partner) during a 30-day period before randomization (P1). Participants were then block randomized to the control arm, continuing EMA reporting through exit, or the intervention arm, EMA reporting and behavioral health messaging receipt. Participants exited after 90 days of follow-up, divided into study periods 2 (P2: randomization + 29 days) and 3 (P3: 30 days postrandomization to exit). We used descriptive statistics to assess the feasibility of EMAI through the completeness of data and differences in reported behaviors between periods and study arms.

RESULTS

The study included 48 participants (24 per arm; 23/48, 48% women; median age 31 years). EMA data collection was feasible, with 85.5% (3777/4418) of the combined days reporting behavioral data. There was a decrease in the mean proportion of days when alcohol was consumed in both arms over time (control: P1, 9.6% of days to P2, 4.3% of days; intervention: P1, 7.2% of days to P3, 2.4% of days). Decreases in sex with a non-long-term partner without a condom were also reported in both arms (P1 to P3 control: 1.9% of days to 1% of days; intervention: 6.6% of days to 1.3% of days). An increase in vegetable consumption was found in the intervention (vegetable: 65.6% of days to 76.6% of days) but not in the control arm. Between arms, there was a significant difference in the change in reported vegetable consumption between P1 and P3 (control: 8% decrease in the mean proportion of days vegetables consumed; intervention: 11.1% increase; P=.01).

CONCLUSIONS

Preliminary estimates suggest that EMAI may be a promising strategy for promoting behavior change across a range of behaviors. Larger trials examining the effectiveness of EMAI in LMICs are warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04375423; https://www.clinicaltrials.gov/ct2/show/NCT04375423.

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

Consider the Source: Examining Attrition Rates, Response Rates, and Preliminary Effects of eHealth Mindfulness Messages and Delivery Framing in a Randomized Trial with Young Adult Cancer Survivors

Purpose: Young adults with cancer often experience stress, depression, and anxiety. Mindfulness meditation is an effective intervention for these outcomes, and maintenance support may be needed for long-term improvements. eHealth technologies provide a promising delivery strategy for maintenance interventions. Methods: Following an 8-week mindfulness-based stress reduction (MBSR) course, 62 young adult cancer survivors were randomized to 8 weeks of instructor-framed messages, peer-framed messages, or no messages. On average, participants were 33.6 years old. The majority of participants were college-educated Caucasian females. We examined attrition rates between participants who received messages and those who did not, and compared response rates from different perceived sources. In addition, we evaluated the preliminary effects of eHealth support on mindfulness and associated outcomes. Results: No significant differences in attrition or message response rates across groups were observed. Repeated measures models revealed significant group by time interactions on perceived stress, anxiety, and depression. There were no differences between the groups that received eHealth messages and the group that did not. There was a significant difference in anxiety symptoms from post-MBSR to post-messaging between messaging groups. Individuals who received instructor-framed messages reported increased symptoms of anxiety over time. Conclusion: Attrition and response rates did not differ across groups, suggesting that eHealth may be a feasible strategy for providing maintenance support. However, further evaluation of feasibility, acceptability, and optimal content and dose of such an intervention is needed. Additionally, young adult cancer survivors may be more likely to benefit from eHealth interventions that are not delivered by authority figures.

Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of “imminent drinking” (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant’s current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness.

Comparative effects of varenicline or combination nicotine replacement therapy versus patch monotherapy on candidate mediators of early abstinence in a smoking cessation attempt

BACKGROUND AND AIMS

The phase-based model of smoking cessation treatment suggests that treatment needs may vary across phases (e.g. pre-cessation, cessation). This study tested the comparative effects of varenicline and combination nicotine replacement therapy (C-NRT) relative to nicotine patch monotherapy on pre-cessation and cessation phase candidate withdrawal, expectancy and motivation mediators; relations between mediators and abstinence; and indirect effects of enhanced treatments on abstinence via candidate mediators.

DESIGN

Secondary mediation analysis of data from the open-label, randomized Wisconsin Smokers' Health Study 2, a comparative effectiveness trial of varenicline or C-NRT, versus patch monotherapy, in adults who smoked, recruited via media and community outreach.

SETTING

Research clinics in Madison and Milwaukee, Wisconsin, USA.

PARTICIPANTS

A total of 1051 daily smokers motivated to quit smoking (52.5% female; mean age = 48.1, standard deviation = 11.6).

INTERVENTIONS

Twelve weeks of varenicline (n = 407) or 12 weeks of combination nicotine patch and nicotine lozenge therapy (n = 421), both compared with 12 weeks of patch control condition (n = 230), with individual smoking cessation counseling.

MEASUREMENTS

The primary abstinence outcome was biochemically verified 7-day point-prevalence abstinence 4 weeks post-target quit day (TQD). Candidate mediators (craving, positive smoking expectancies, withdrawal symptoms, and quitting motivation) were assessed via ecological momentary assessment from 1 week prior (pre-cessation phase) to 4 weeks after (cessation phase) the TQD.

FINDINGS

Pre-cessation and cessation mean levels and slopes of craving [adjusted odds ratio (aOR) = 0.34-0.79], smoking expectancies (aOR = 0.46-0.79) and quitting motivation (aOR = 1.35-7.21) significantly predicted 4-week post-TQD abstinence (P < 0.05). Significant varenicline mediation occurred via greater suppression in pre-cessation craving [mediated effect (ab) = 0.09, standard error (SE) = 0.03, 95% confidence interval (CI) = 0.04-0.14] and smoking expectancies (ab = 0.06, SE = 0.02, 95% CI = 0.02-0.12). C-NRT mediation occurred via greater reduction in pre-post-TQD changes in craving (ab = 0.04, SE = 0.02, 95% CI = 0.01-0.08) and expectancies (ab = 0.03, SE = 0.02, 95% CI = 0.001-0.07), relative to patch monotherapy.

CONCLUSION

Among adult smokers seeking to quit, varenicline seems to work through its effects on suppression of craving and smoking expectancies pre-cessation while combination nicotine replacement therapy mediation seems to work through cessation-related reduction in craving and smoking expectancies changes.

The Third Rail of Pediatric Communication: Discussing Firearm Risk and Safety in Well-Child Exams

This research endeavors to understand how pediatricians and parents discuss - or do not discuss - firearm risks for children during well-child visits. Through individual semi-structured interviews with 16 pediatric providers and 20 parents, the research explores discursive barriers to open conversation, perspectives on anticipatory guidance, and new ideas for culturally competent messaging. The research focuses particularly on how parents' and providers' perspectives on firearm risk communication are tied to cultural norms and expectations. One salient theme that emerged is that the American Academy of Pediatrics recommendation that pediatricians ask parents about ownership status is deemed undesirable by pediatricians and parents because of the delicate intercultural setting. Born out of pediatric and parent experiences, and mindful of culturally salient barriers, this study offers alternative strategies for discussing firearm risk in well-child exams.

A Pilot and Feasibility Mobile Health Intervention to Support Healthy Behaviors in African American Breast Cancer Survivors

African American breast cancer (AA BC) survivors are more likely to have cancer-related comorbidities compared with other women, ultimately putting them at higher risk for overall mortality and breast cancer-specific mortality. Survivorship care guidelines emphasize the importance of attention to obesity, weight management, and physical activity. Mobile technologies have been effective for improving health behaviors among cancer survivors, though few studies have focused on AA BC survivors. Creating Healthy Actions through Technology (CHAT) was a 4-week pilot intervention that employed an ecological momentary assessment (EMA) to improve survivors' physical activity and diet behaviors. We evaluated the acceptability, feasibility, and impact of a mHealth intervention for AA BC survivors. Participants (N = 22) were randomized to intervention (n = 13) or control (n = 9). All participants completed daily EMAs via smartphone for 4 weeks and wore accelerometers for seven consecutive days at baseline, 4, and 8 weeks. Intervention participants additionally received tailored health messages. Diet was measured using a self-reported questionnaire and physical activity with accelerometers. Participant engagement was high. Of 84 EMA assessments, the average response was 63 (SD 16.1). Participant accelerometer wear was at least 6 of the 7 days (SD 1.7) for each assessment. Eighty-five percent of participants reported the intervention helped change behaviors. Intervention participants reduced their sedentary time by 4.37 (SD = 7.14) hours/day versus controls (p = .05), reduced fast food intake by 1.5 servings (p = 0.008), and increased vigorous activity by 0.56 (SD = 28.10) minutes, which was non-significant (p = 0.959). Findings show feasibility and acceptability and potential of the intervention to positively impact physical activity among AA BC survivors.

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study

BACKGROUND

Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors.

OBJECTIVE

The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes.

METHODS

The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria.

RESULTS

Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered "unhealthy" and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior.

CONCLUSIONS

The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

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.

Cognitive Behavioral and Mindfulness-Based Interventions for Smoking Cessation: a Review of the Recent Literature

PURPOSE OF REVIEW

Cigarette smoking is the primary cause of cancer and is the leading preventable cause of morbidity and mortality. Cognitive behavioral therapy (CBT) is one of the most well-established and efficacious interventions for smoking cessation. The study of mindfulness-based interventions (MBIs) has increased exponentially in recent years, showing efficacy for smoking cessation as well. This review highlights research from the past 5 years examining CBT and MBIs for smoking cessation.

RECENT FINDINGS

Both CBT and MBIs are efficacious for special populations (e.g., low SES; pregnant smokers) and have shown initial efficacy when delivered via mhealth/ehealth. CBT has shown efficacy when combined with another behavioral treatment (e.g., ACT). Continued research is needed on CBT and MBIs that have high potential for scalability. Understanding whether they are beneficial for certain populations (e.g., cancer survivors), along with determining for whom CBT vs MBIs are most effective, is also needed.

Smartphone Apps for the Treatment of Mental Disorders: Systematic Review

BACKGROUND

Smartphone apps are an increasingly popular means for delivering psychological interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective.

OBJECTIVE

This study aimed to systematically review the literature on the use of smartphones for psychological interventions. Our systematic review has the following objectives: (1) analyze the coverage of mental disorders in research articles per year; (2) study the types of assessment in research articles per mental disorder per year; (3) map the use of advanced technical features, such as sensors, and novel software features, such as personalization and social media, per mental disorder; (4) provide an overview of smartphone apps per mental disorder; and (5) provide an overview of the key characteristics of empirical assessments with rigorous designs (ie, randomized controlled trials [RCTs]).

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed. We performed searches in Scopus, Web of Science, American Psychological Association PsycNET, and Medical Literature Analysis and Retrieval System Online, covering a period of 6 years (2013-2018). We included papers that described the use of smartphone apps to deliver psychological interventions for known mental disorders. We formed multidisciplinary teams, comprising experts in psychology and computer science, to select and classify articles based on psychological and technical features.

RESULTS

We found 158 articles that met the inclusion criteria. We observed an increasing interest in smartphone-based interventions over time. Most research targeted disorders with high prevalence, that is, depressive (31/158,19.6%) and anxiety disorders (18/158, 11.4%). Of the total, 72.7% (115/158) of the papers focused on six mental disorders: depression, anxiety, trauma and stressor-related, substance-related and addiction, schizophrenia spectrum, and other psychotic disorders, or a combination of disorders. More than half of known mental disorders were not or very scarcely (<3%) represented. An increasing number of studies were dedicated to assessing clinical effects, but RCTs were still a minority (25/158, 15.8%). From a technical viewpoint, interventions were leveraging the improved modalities (screen and sound) and interactivity of smartphones but only sparingly leveraged their truly novel capabilities, such as sensors, alternative delivery paradigms, and analytical methods.

CONCLUSIONS

There is a need for designing interventions for the full breadth of mental disorders, rather than primarily focusing on most prevalent disorders. We further contend that an increasingly systematic focus, that is, involving RCTs, is needed to improve the robustness and trustworthiness of assessments. Regarding technical aspects, we argue that further exploration and innovative use of the novel capabilities of smartphones are needed to fully realize their potential for the treatment of mental health disorders.

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

Background

Smartphone apps for smoking cessation could offer easily accessible, highly tailored, intensive interventions at a fraction of the cost of traditional counseling. Although there are hundreds of publicly available smoking cessation apps, few have been empirically evaluated using a randomized controlled trial (RCT) design. The Smart-Treatment (Smart-T2) app is a just-in-time adaptive intervention that uses ecological momentary assessments (EMAs) to assess the risk for imminent smoking lapse and tailors treatment messages based on the risk of lapse and reported symptoms.

Objective

This 3-armed pilot RCT aimed to determine the feasibility and preliminary efficacy of an automated smartphone-based smoking cessation intervention (Smart-T2) relative to standard in-person smoking cessation clinic care and the National Cancer Institute’s free smoking cessation app, QuitGuide.

Methods

Adult smokers who attended a clinic-based tobacco cessation program were randomized into groups and followed for 13 weeks (1 week prequitting through 12 weeks postquitting). All study participants received nicotine patches and gum and were asked to complete EMAs five times a day on study-provided smartphones for 5 weeks. Participants in the Smart-T2 group received tailored treatment messages after the completion of each EMA. Both Smart-T2 and QuitGuide apps offer on-demand smoking cessation treatment.

Results

Of 81 participants, 41 (50%) were women and 55 (68%) were white. On average, participants were aged 49.6 years and smoked 22.4 cigarettes per day at baseline. A total of 17% (14/81) of participants were biochemically confirmed 7-day point prevalence abstinent at 12 weeks postquitting (Smart-T2: 6/27, 22%, QuitGuide: 4/27, 15%, and usual care: 4/27, 15%), with no significant differences across groups (P>.05). Participants in the Smart-T2 group rated the app positively, with most participants agreeing that they can rely on the app to help them quit smoking, and endorsed the belief that the app would help them stay quit, and these responses were not significantly different from the ratings given by participants in the usual care group.

Conclusions

Dynamic smartphone apps that tailor intervention content in real time may increase user engagement and exposure to treatment-related materials. The results of this pilot RCT suggest that smartphone-based smoking cessation treatments may be capable of providing similar outcomes to traditional, in-person counseling.

Trial Registration

ClinicalTrials.gov NCT02930200; https://clinicaltrials.gov/show/NCT02930200

Digital assessment of working memory and processing speed in everyday life: Feasibility, validation, and lessons-learned

OBJECTIVES

Cognitive functioning is often impaired in mental and neurological conditions and might fluctuate throughout the day. An existing experience-sampling tool was upgraded to assess individual's cognition in everyday life. The objectives were to test the feasibility and validity of two momentary cognition tasks.

METHODS

The momentary Visuospatial Working Memory Task (mVSWMT) and momentary Digit Symbol Substitution Task (mDSST) were add-ons to an experience sampling method (ESM) smartphone app. Healthy adults (n = 49) between 19 and 73 years of age performed the tasks within an ESM questionnaire 8 times a day, over 6 consecutive days. Feasibility was determined through completion rate and participant experience. Validity was assessed through contextualization of cognitive performance within intrapersonal and situational factors in everyday life.

FINDINGS

Participants experienced the tasks as pleasant, felt motivated, and the completion rate was high (71%). Social context, age, and distraction influenced cognitive performance in everyday life. The mVSWMT was too difficult as only 37% of recalls were correct and thus requires adjustments (i.e. fixed time between encoding and recall; more trials per moment). The mDSST speed outcome seems the most sensitive outcome measure to capture between- and within-person variance.

CONCLUSIONS

Short momentary cognition tasks for repeated assessment are feasible and hold promise, but more research is needed to improve validity and applicability in different samples. Recommendations for teams engaging in the field include matching task design with traditional neuropsychological tests and involving a multidisciplinary team as well as users. Special attention for individual needs can improve motivation and prevent frustration. Finally, tests should be attractive and competitive to stimulate engagement, but still reflect actual cognitive functioning.

Stress and opioid use disorder: A systematic review

Medication assisted treatment (MAT) is highly effective in reducing illicit opioid use and preventing overdose in individuals with opioid use disorder (OUD); however, treatment retention of patients engaged in MAT is a significant clinical concern. The experience of stress may contribute to a decision to drop out of treatment. The current study is a systematic review conducted across multiple databases of empirical studies on primary appraisal of stress and its relationship to opioid craving, opioid use, and OUD treatment outcomes. Primary appraisal of stress is defined as an explicit inquiry into individual perception of feeling stressed using a self-report measure administered in laboratory, clinical, or naturalistic environment. A total of 21 included studies were organized into three categories: observed stress, experimentally-induced stress, and stress-focused interventions. Appraised stress was generally associated with greater craving, but associations with opioid use and treatment retention were mixed. All but one study included MAT samples and every study sample included some form of drug counseling. These findings suggest that individuals experience considerable stress in spite of receiving standard treatment for OUD. Psychopharmacological interventions targeting stress were promising, but no behavioral interventions specific to stress management were found. The preliminary results with clonidine and lofexidine targeting stress in individuals with OUD warrant further studies. To better understand the impact of stress in OUD, future research should consider using repeated assessment of stress in the context of daily life. Utilization of behavioral treatments specifically targeting stress could have benefits in improving OUD outcomes.

Momentary precipitants connecting stress and smoking lapse during a quit attempt

OBJECTIVE

Most attempts at smoking cessation are unsuccessful, and stress is frequently characterized both as a momentary precipitant of smoking lapse and a predictor of subsequent changes in other key precipitants of lapse. The current study examined longitudinal associations among stress, multiple precipitants of lapse, and lapse among smokers attempting to quit.

METHOD

Ecological momentary assessments (EMAs) were gathered from a multiethnic, gender-balanced sample of 370 adults enrolled in a smoking cessation program. EMAs (N = 32,563) assessed smoking lapse and precipitants of lapse, including stress, negative affect, smoking urge, abstinence self-efficacy, motivation to quit, difficulty concentrating, coping outcome expectancies, and smoking outcome expectancies. A multilevel structural equation model simultaneously estimated within-subject paths from stress to multiple precipitants and subsequent smoking lapse. Indirect effects of stress to smoking lapse through precipitants were computed.

RESULTS

Results indicated that increased stress was significantly associated with all precipitants of lapse, consistent with a greater risk for lapse (i.e., increased negative affect, smoking urge, difficulty concentrating, and smoking outcome expectancies and reduced abstinence self-efficacy, motivation to quit, and coping outcome expectancies). All precipitants were significantly associated with subsequent lapse. Indirect effects indicated that stress was uniquely connected to lapse through negative affect, smoking urge, abstinence self-efficacy, coping outcome expectancies, and smoking outcome expectancies.

CONCLUSIONS

Results of this study highlight the broad importance of stress for smoking lapse during a quit attempt. Smoking cessation programs should pay close attention to the role of stress in exacerbating key precipitants of lapse to improve cessation success rates. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Playing Around with Quitting Smoking: A Randomized Pilot Trial of Mobile Games as a Craving Response Strategy

Objective: Strong cravings to smoke are an obstacle to cessation success. Unfortunately, cessation medication and counseling only modestly quell craving. This pilot study was designed to examine the feasibility of mobile games as a response strategy to craving and whether a fully powered trial is warranted. Materials and Methods: Smokers interested in quitting (N = 30) were offered 4 weeks of nicotine patch plus counseling and randomized to quit with (games-on) versus without (games-off) access to 11 commercial mobile games. Outcomes included post-target quit day (TQD) game play, craving, smoking, and quitting. Almost all P's were >0.05; outcomes should be interpreted with caution due to the small N. Results: Of games-on participants (n = 16), one played games ≥80% of days post-TQD (22/28 days); 38% played >1/3 of days; 25% did not play. Games-on participants reported games moderately helped them cope with cravings; M = 3.22 on a scale from 1 (not at all) to 5 (very much). Also, games-on participants showed a slight decrease in craving from baseline to 1-week post-TQD (2.35-2.25 on a 0-5 point-scale), whereas games-off participants showed an increase (2.01-2.53). Games-on participants showed greater decreases in craving after playing a game than after the passage of time (when an app imposed a 2-minute wait period following their game request), but there was little evidence games-on versus games-off participants differed in mean post-TQD cigarettes/day. Games-on participants reported modestly but not significantly higher continuous abstinence through day 28 (31.3% vs. 21.4%). Conclusion: Feasibility results encourage a fully powered trial of this easily disseminable intervention. Clinical Trial Registration: ClinicalTrials.gov NCT02164383.

Context-Aware Systems for Chronic Disease Patients: Scoping Review

Background

Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work.

Objective

This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures.

Methods

A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients’ health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted.

Results

The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results.

Conclusions

The use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.

Behavioral economics implementation: Regret lottery improves mHealth patient study adherence

Background

Nonadherence to study protocols reduces the generalizability, validity, and statistical power of longitudinal studies.

Purpose

To determine whether an automated electronically-delivered regret lottery would improve adherence to an intensive mHealth self-monitoring protocol as part of a longitudinal observational study.

Methods

We enrolled 77 adults into a 52-week study requiring five daily ecologic momentary assessments (EMA) of stress and daily accelerometer use. We performed a pre/post single-arm study to evaluate the efficacy of a lottery intervention in improving adherence to this protocol. Midway through the study, participants were invited to enter a weekly regret lottery ($50 prize, expected value <$1) in which prize collection was contingent upon meeting adherence thresholds for the prior week. Study protocol adherence before and after lottery initiation were compared using mixed models repeated measures analysis of variance.

Results

62 participants consented to lottery participation. In the 12 weeks prior to lottery initiation, weekly adherence was declining (slope −1.4%/week). The weekly per-participant probability of adherence was higher after lottery initiation when comparing the 4-week (32% pre-lottery vs 50% post-lottery, p < 0.001), 8-week (37% vs 49%, p < 0.001), and 12-week periods (39% vs 45%, p = 0.001) before and after lottery initiation. However, the rate of decline in adherence over time was unchanged.

Conclusion

The implementation of an automated, electronically-delivered weekly regret lottery improved adherence with an intensive self-monitoring study protocol. Regret lotteries may represent a cost-effective tool to improve adherence and reduce bias caused by dropout or nonadherence.

Use of Ecological Momentary Assessment and Intervention in Treatment With Adults

This article reviews the use of ecological momentary assessment (EMA) and ecological momentary intervention (EMI) in clinical research applications. EMA refers to a method of data collection that attempts to capture respondents' activities, emotions, and thoughts in the moment, in their natural environment. It typically uses prompts administered through a personal electronic device, such as a smartphone or tablet. EMI extends this technique and includes the use of microlevel interventions administered through personal electronic devices. These technological developments hold promise for enhancing psychological treatments by prompting the patient outside of therapy sessions in his or her day-to-day environment. Research suggests that EMI may be beneficial to participants and that this effect is amplified when EMI is delivered in the context of ongoing psychotherapy. EMI may reflect a cost-effective mechanism to enhance therapeutic outcomes.

Mobile Applications for the Treatment of Tobacco Use and Dependence

PURPOSE OF REVIEW

Smoking remains a leading preventable cause of premature death in the world; thus, developing effective and scalable smoking cessation interventions is crucial. This review uses the Obesity-Related Behavioral Intervention Trials (ORBIT) model for early phase development of behavioral interventions to conceptually organize the state of research of mobile applications (apps) for smoking cessation, briefly highlight their technical and theory-based components, and describe available data on efficacy and effectiveness.

RECENT FINDINGS

Our review suggests that there is a need for more programmatic efforts in the development of mobile applications for smoking cessation, though it is promising that more studies are reporting early phase research such as user-centered design. We identified and described the app features used to implement smoking cessation interventions, and found that the majority of the apps studied used a limited number of mechanisms of intervention delivery, though more effort is needed to link specific app features with clinical outcomes. Similar to earlier reviews, we found that few apps have yet been tested in large well-controlled clinical trials, although progress is being made in reporting transparency with protocol papers and clinical trial registration.

SUMMARY

ORBIT is an effective model to summarize and guide research on smartphone apps for smoking cessation. Continued improvements in early phase research and app design should accelerate the progress of research in mobile apps for smoking cessation.

"Like you are fooling yourself": how the "Stoptober" temporary abstinence campaign supports Dutch smokers attempting to quit

Background

The Stoptober temporary abstinence campaign challenges smokers to engage in a collective quit attempt for 28 days. The campaign is based on social contagion theory, SMART (i.e., Specific, Measurable, Attainable, Realistic and Time-sensitive) goal setting and PRIME (i.e., Plans, Responses, Impulses, Motives and Evaluations) theory. Although Stoptober was found to yield impressive 28-day quit rates, relapse rates remained substantial. Therefore, we examined how Stoptober supported smokers in their attempt to quit and how the campaign’s effectiveness could be strengthened.

Methods

In 2016, we conducted semi-structured interviews with 23 Stoptober participants in the Netherlands. Data were analyzed thematically.

Results

Respondents explained how social contagion-based components had familiarized them with Stoptober, motivated them to participate, and created a pro-smoking cessation social norm. Setting SMART goals was reported as “fooling yourself”, since it distracted respondents from their goal of quitting for good and helped them perceive that temporary abstinence was achievable. Respondents also illustrated the usefulness of PRIME theory. They typically used an individual selection of available supports that varied over time. To achieve long-term abstinence, respondents expressed the need for additional social network support and interactive, personalized and professional support during and after the campaign.

Conclusions

Stoptober supports smokers in their attempts to quit and generally according to the campaign’s theoretical principles. Added to available evidence, this finding supports the continuation and wider implementation of Stoptober, while connecting the campaign to social networks and regular smoking-cessation services to help improve long-term abstinence rates.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6833-y) contains supplementary material, which is available to authorized users.

Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial

Background

Smoking is considered the main cause of preventable illness and early deaths worldwide. The treatment usually prescribed to people who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt.

Objective

We present a study protocol of a 12-month randomized open-label parallel-group trial whose primary objective is to analyze the efficacy and efficiency of usual psychopharmacological therapy plus the Social-Local-Mobile app (intervention group) applied to the smoking cessation process compared with usual psychopharmacological therapy alone (control group).

Methods

The target population consists of adult smokers (both male and female) attending the Smoking Cessation Unit at Virgen del Rocío University Hospital, Seville, Spain. Social-Local-Mobile is an innovative intervention based on mobile technologies and their capacity to trigger behavioral changes. The app is a complement to pharmacological therapies to quit smoking by providing personalized motivational messages, physical activity monitoring, lifestyle advice, and distractions (minigames) to help overcome cravings. Usual pharmacological therapy consists of bupropion (Zyntabac 150 mg) or varenicline (Champix 0.5 mg or 1 mg). The main outcomes will be (1) the smoking abstinence rate at 1 year measured by means of exhaled carbon monoxide and urinary cotinine tests, and (2) the result of the cost-effectiveness analysis, which will be expressed in terms of an incremental cost-effectiveness ratio. Secondary outcome measures will be (1) analysis of the safety of pharmacological therapy, (2) analysis of the health-related quality of life of patients, and (3) monitoring of healthy lifestyle and physical exercise habits.

Results

Of 548 patients identified using the hospital’s electronic records system, we excluded 308 patients: 188 declined to participate and 120 did not meet the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive usual psychopharmacological therapy, while the intervention group (n=120) will receive usual psychopharmacological therapy plus the So-Lo-Mo app. The project was approved for funding in June 2015. Enrollment started in October 2016 and was completed in October 2017. Data gathering was completed in November 2018, and data analysis is under way. The first results are expected to be submitted for publication in early 2019.

Conclusions

Social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking habits as well. As part of the SmokeFreeBrain H2020 European Commission project, this study aims at elucidating the potential role of these technologies when used as an extra aid to quit smoking.

Trial Registration

ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/record/NCT03553173 (Archived by WebCite at http://www.webcitation.org/74DuHypOW).

International Registered Report Identifier (IRRID)

PRR1-10.2196/12464

Engagement features judged by excessive drinkers as most important to include in smartphone applications for alcohol reduction: A mixed-methods study

Objective

Engagement with smartphone applications (apps) for alcohol reduction is necessary for their effectiveness. This study explored (1) the features that are ranked as most important for engagement by excessive drinkers and (2) why particular features are judged to be more important for engagement than others.

Methods

Two studies were conducted in parallel. The first was a focus group study with adult excessive drinkers, interested in reducing alcohol consumption using an app (n_groups = 3). Participants individually ranked their top 10 features from a pre-specified list and subsequently discussed their rankings. The second was an online study with a new sample (n = 132). Rankings were analysed using the intraclass correlation coefficient (ICC) to assess the level of agreement between raters for each study. Qualitative data were analysed using inductive thematic analysis.

Results

There was low agreement between participants in their rankings, both in the focus groups (ICC = 0.15, 95% confidence interval (CI) = 0.03–0.38) and the online sample (ICC = 0.11, 95% CI = 0.06–0.23). ‘Personalisation’, ‘control features’ and ‘interactive features’ were most highly ranked in the focus groups. These were expected to elicit a sense of benefit and usefulness, adaptability, provide motivational support or spark users’ interest. Results from the online study partly corroborated these findings.

Conclusion

There was little agreement between participants, but on average, the features judged to be most important for inclusion in smartphone apps for alcohol reduction were personalisation, interactive features and control features. Tailoring on users’ underlying psychological needs may promote engagement with alcohol reduction apps.