Evaluating the QUIT-PRIMO clinical practice ePortal to increase smoker engagement with online cessation interventions: a national hybrid type 2 implementation study

Engagement With Stop Smoking Services After Referral or Signposting: A Mixed-Methods Study

INTRODUCTION

Screening for smoking when people interact with healthcare services and referral of those who smoke to stop smoking services (SSSs) is a key component of efforts to tackle tobacco use. However, little is known about what happens after someone is referred or signposted to SSSs.

METHODS

As part of the Cessation of Smoking Trial in the Emergency Department (NCT04854616), those randomized to intervention (n = 505) were referred to local SSSs (along with receiving brief advice and an e-cigarette starter kit) and those randomized to control (n = 502) were given contact details for the same services (signposted). SSS engagement data were collected: (1) directly from participants and (2) from SSS, additional qualitative data came from 33 participant interviews.

RESULTS

Engagement with SSSs was very low. 3.2% (n = 16) of those in the intervention group and 2.4% (n = 12) in the control group reported attending a one-to-one support session. From SSS data, engagement was also low with 8.9% (n = 43) of those referred engaging and 3.1% (n = 15) going on to quit with SSS support. The majority of the 24 intervention participants interviewed did not recall being contacted by an SSS.

CONCLUSIONS

Referral or signposting to SSSs within an Emergency Department-based trial resulted in very low levels of engagement. Barriers to engagement identified included participants not being contacted by SSSs and the support offered not meeting their needs.

IMPLICATIONS

Referral or signposting of those who smoke to SSSs from the Emergency Department resulted in low rates of engagement in this large multicenter randomized controlled trial. To better support those who smoke, it may be more effective for smoking cessation advice to be offered "in the moment" within clinical settings, and follow-up to be proactively offered rather than relying on people being motivated to contact the services themselves or engaging when contacted.

The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024

The field of behavioral medicine has a long and successful history of leveraging digital health tools to promote health behavior change. Our 2019 summary of the history and future of digital health in behavioral medicine (Arigo in J Behav Med 8: 67-83, 2019) was one of the most highly cited articles in the Journal of Behavioral Medicine from 2010 to 2020; here, we provide an update on the opportunities and challenges we identified in 2019. We address the impact of the COVID-19 pandemic on behavioral medicine research and practice and highlight some of the digital health advances it prompted. We also describe emerging challenges and opportunities in the evolving ecosystem of digital health in the field of behavioral medicine, including the emergence of new evidence, research methods, and tools to promote health and health behaviors. Specifically, we offer updates on advanced research methods, the science of digital engagement, dissemination and implementation science, and artificial intelligence technologies, including examples of uses in healthcare and behavioral medicine. We also provide recommendations for next steps in these areas with attention to ethics, training, and accessibility considerations. The field of behavioral medicine has made meaningful advances since 2019 and continues to evolve with impressive pace and innovation.

The role of mood in shaping reactions to smoking cessation messages among adults who smoke: a multimodal investigation

INTRODUCTION

Mood-tailored communications may help increase the effectiveness of smoking cessation messaging interventions. We used both self-report and psychophysiological measures to test the impact of mood on responses to cessation messages in adults who smoke.

METHODS

In a two-part (crowdsourcing and psychophysiological studies) study, the impact of 30 smoking cessation messages comprised of five themes (i.e., financial, health, quality-of-life, challenges in quitting, motivation to quit) were tested. In a crowdsourcing study, participants (N = 600) were randomly placed into one of three mood induction tasks (i.e., positive, negative, neutral), and then viewed the smoking cessation messages. After each message, they were asked to self-report their motivation to quit, message receptivity, and the perceived relevance of the messages. In an in-lab, psychophysiological study, participants (N = 42) completed the same tasks as the crowdsourcing participants but were monitored for heart rate, skin conductance, and eye-tracking while viewing the cessation messages. Using a multi-attribute decision-making model (MADM) using outcomes from both studies, messages were ranked for each mood state.

RESULTS

The top messages for participants in the positive mood condition included the challenges in quitting, financial costs/rewards, and motivations to quit themes. The top messages for participants assigned to the negative mood condition included the challenges in quitting, quality-of-life, and financial costs/rewards themes. For participants in the neutral mood condition, messages in the challenges in quitting and quality of life themes performed best.

CONCLUSIONS

Variations in the preferences of messages and themes by mood condition suggest that mood-tailored communication may increase the effectiveness of smoking cessation messages.

Strategies to improve the implementation of preventive care in primary care: a systematic review and meta-analysis

BACKGROUND

Action on smoking, obesity, excess alcohol, and physical inactivity in primary care is effective and cost-effective, but implementation is low. The aim was to examine the effectiveness of strategies to increase the implementation of preventive healthcare in primary care.

METHODS

CINAHL, CENTRAL, The Cochrane Database of Systematic Reviews, Dissertations & Theses - Global, Embase, Europe PMC, MEDLINE and PsycINFO were searched from inception through 5 October 2023 with no date of publication or language limits. Randomised trials, non-randomised trials, controlled before-after studies and interrupted time series studies comparing implementation strategies (team changes; changes to the electronic patient registry; facilitated relay of information; continuous quality improvement; clinician education; clinical reminders; financial incentives or multicomponent interventions) to usual care were included. Two reviewers screened studies, extracted data, and assessed bias with an adapted Cochrane risk of bias tool for Effective Practice and Organisation of Care reviews. Meta-analysis was conducted with random-effects models. Narrative synthesis was conducted where meta-analysis was not possible. Outcome measures included process and behavioural outcomes at the closest point to 12 months for each implementation strategy.

RESULTS

Eighty-five studies were included comprising of 4,210,946 participants from 3713 clusters in 71 cluster trials, 6748 participants in 5 randomised trials, 5,966,552 participants in 8 interrupted time series, and 176,061 participants in 1 controlled before after study. There was evidence that clinical reminders (OR 3.46; 95% CI 1.72-6.96; I2 = 89.4%), clinician education (OR 1.89; 95% CI 1.46-2.46; I2 = 80.6%), facilitated relay of information (OR 1.95, 95% CI 1.10-3.46, I2 = 88.2%), and multicomponent interventions (OR 3.10; 95% CI 1.60-5.99, I2 = 96.1%) increased processes of care. Multicomponent intervention results were robust to sensitivity analysis. There was no evidence that other implementation strategies affected processes of care or that any of the implementation strategies improved behavioural outcomes. No studies reported on interventions specifically designed for remote consultations. Limitations included high statistical heterogeneity and many studies did not account for clustering.

CONCLUSIONS

Multicomponent interventions may be the most effective implementation strategy. There was no evidence that implementation interventions improved behavioural outcomes.

TRIAL REGISTRATION

PROSPERO CRD42022350912.

Exploring the content and delivery of feedback facilitation co-interventions: a systematic review

BACKGROUND

Policymakers and researchers recommend supporting the capabilities of feedback recipients to increase the quality of care. There are different ways to support capabilities. We aimed to describe the content and delivery of feedback facilitation interventions delivered alongside audit and feedback within randomised controlled trials.

METHODS

We included papers describing feedback facilitation identified by the latest Cochrane review of audit and feedback. The piloted extraction proforma was based upon a framework to describe intervention content, with additional prompts relating to the identification of influences, selection of improvement actions and consideration of priorities and implications. We describe the content and delivery graphically, statistically and narratively.

RESULTS

We reviewed 146 papers describing 104 feedback facilitation interventions. Across included studies, feedback facilitation contained 26 different implementation strategies. There was a median of three implementation strategies per intervention and evidence that the number of strategies per intervention is increasing. Theory was used in 35 trials, although the precise role of theory was poorly described. Ten studies provided a logic model and six of these described their mechanisms of action. Both the exploration of influences and the selection of improvement actions were described in 46 of the feedback facilitation interventions; we describe who undertook this tailoring work. Exploring dose, there was large variation in duration (15-1800 min), frequency (1 to 42 times) and number of recipients per site (1 to 135). There were important gaps in reporting, but some evidence that reporting is improving over time.

CONCLUSIONS

Heterogeneity in the design of feedback facilitation needs to be considered when assessing the intervention's effectiveness. We describe explicit feedback facilitation choices for future intervention developers based upon choices made to date. We found the Expert Recommendations for Implementing Change to be valuable when describing intervention components, with the potential for some minor clarifications in terms and for greater specificity by intervention providers. Reporting demonstrated extensive gaps which hinder both replication and learning. Feedback facilitation providers are recommended to close reporting gaps that hinder replication. Future work should seek to address the 'opportunity' for improvement activity, defined as factors that lie outside the individual that make care or improvement behaviour possible.

REVIEW REGISTRATION

The study protocol was published at: https://www.protocols.io/private/4DA5DE33B68E11ED9EF70A58A9FEAC02 .

Peer Texting to Promote Quitline Use and Smoking Cessation Among Rural Participants in Vietnam: Randomized Clinical Trial

Objectives: We tested an adapted version of an effective U.S.-based peer-texting intervention to promote Quitline use and smoking cessation among rural participants in Vietnam. Methods: We conducted a two-arm randomized trial with participants recruited at four rural community centers. The intervention included peer messages sent for six months that promoted Quitline use and smoking cessation. Additionally, biweekly two-way text messages assessed participants' interest in Quitline referral and current smoking status. Comparison participants received only the bi-weekly text message assessment of their current smoking status. At six months, we assessed Quitline use and smoking cessation. Smoking cessation was assessed using the 7-day point prevalence question and verified with a carbon monoxide breath monitor (<=6 ppm). Results: Among 750 participants, the intervention had higher Quitline verified use (18%, 95% CI 0.14, 0.22) than comparison (1%, 95% CI .2, 2, p < 0.0001). Carbon-monoxide-verified smoking cessation did not differ between the two groups. However, intervention (28.3%, 95% CI) and comparison (28.1%, 95% CI) participants had substantial rates of carbon monoxide cessation at 6 months (both 28%). Conclusion: Our study highlighted the promise of texting interventions to extend tobacco control efforts in Vietnam.

Identifying behaviour change techniques in 287 randomized controlled trials of audit and feedback interventions targeting practice change among healthcare professionals

BACKGROUND

Audit and feedback (A&F) is among the most widely used implementation strategies, providing healthcare professionals with summaries of their practice performance to prompt behaviour change and optimize care. Wide variability in effectiveness of A&F has spurred efforts to explore why some A&F interventions are more effective than others. Unpacking the variability of the content of A&F interventions in terms of their component behaviours change techniques (BCTs) may help advance our understanding of how A&F works best. This study aimed to systematically specify BCTs in A&F interventions targeting healthcare professional practice change.

METHODS

We conducted a directed content analysis of intervention descriptions in 287 randomized trials included in an ongoing Cochrane systematic review update of A&F interventions (searched up to June 2020). Three trained researchers identified and categorized BCTs in all trial arms (treatment & control/comparator) using the 93-item BCT Taxonomy version 1. The original BCT definitions and examples in the taxonomy were adapted to include A&F-specific decision rules and examples. Two additional BCTs ('Education (unspecified)' and 'Feedback (unspecified)') were added, such that 95 BCTs were considered for coding.

RESULTS

In total, 47/95 BCTs (49%) were identified across 360 treatment arms at least once (median = 5.0, IQR = 2.3, range = 129 per arm). The most common BCTs were 'Feedback on behaviour' (present 89% of the time; e.g. feedback on drug prescribing), 'Instruction on how to perform the behaviour' (71%; e.g. issuing a clinical guideline), 'Social comparison' (52%; e.g. feedback on performance of peers), 'Credible source' (41%; e.g. endorsements from respected professional body), and 'Education (unspecified)' (31%; e.g. giving a lecture to staff). A total of 130/287 (45%) control/comparator arms contained at least one BCT (median = 2.0, IQR = 3.0, range = 0-15 per arm), of which the most common were identical to those identified in treatment arms.

CONCLUSIONS

A&F interventions to improve healthcare professional practice include a moderate range of BCTs, focusing predominantly on providing behavioural feedback, sharing guidelines, peer comparison data, education, and leveraging credible sources. We encourage the use of our A&F-specific list of BCTs to improve knowledge of what is being delivered in A&F interventions. Our study provides a basis for exploring which BCTs are associated with intervention effectiveness.

TRIAL REGISTRATIONS

N/A.

Sustainability, spread, and scale in trials using audit and feedback: a theory-informed, secondary analysis of a systematic review

BACKGROUND

Audit and feedback (A&F) is a widely used implementation strategy to influence health professionals' behavior that is often tested in implementation trials. This study examines how A&F trials describe sustainability, spread, and scale.

METHODS

This is a theory-informed, descriptive, secondary analysis of an update of the Cochrane systematic review of A&F trials, including all trials published since 2011. Keyword searches related to sustainability, spread, and scale were conducted. Trials with at least one keyword, and those identified from a forward citation search, were extracted to examine how they described sustainability, spread, and scale. Results were qualitatively analyzed using the Integrated Sustainability Framework (ISF) and the Framework for Going to Full Scale (FGFS).

RESULTS

From the larger review, n = 161 studies met eligibility criteria. Seventy-eight percent (n = 126) of trials included at least one keyword on sustainability, and 49% (n = 62) of those studies (39% overall) frequently mentioned sustainability based on inclusion of relevant text in multiple sections of the paper. For spread/scale, 62% (n = 100) of trials included at least one relevant keyword and 51% (n = 51) of those studies (31% overall) frequently mentioned spread/scale. A total of n = 38 studies from the forward citation search were included in the qualitative analysis. Although many studies mentioned the need to consider sustainability, there was limited detail on how this was planned, implemented, or assessed. The most frequent sustainability period duration was 12 months. Qualitative results mapped to the ISF, but not all determinants were represented. Strong alignment was found with the FGFS for phases of scale-up and support systems (infrastructure), but not for adoption mechanisms. New spread/scale themes included (1) aligning affordability and scalability; (2) balancing fidelity and scalability; and (3) balancing effect size and scalability.

CONCLUSION

A&F trials should plan for sustainability, spread, and scale so that if the trial is effective, the benefits can continue. A deeper empirical understanding of the factors impacting A&F sustainability is needed. Scalability planning should go beyond cost and infrastructure to consider other adoption mechanisms, such as leadership, policy, and communication, that may support further scalability.

TRIAL REGISTRATION

Registered with Prospero in May 2022. CRD42022332606.

Development and pilot testing of a clinic implementation program delivering physical activity electronic referrals to cancer survivors

Provider physical activity referrals are recommended for cancer survivors, though barriers exist to clinical system integration. To develop and test ActivityChoice, an electronic referral (eReferral) clinic implementation program referring cancer survivors to physical activity programs of their choice. In Phase 1, we conducted semi-structured interviews with Cancer Center clinicians (n = 4) and cancer-focused physical activity program leaders (n = 3) assessing adaptations needed to implement an eReferral previously designed for another context. In Phase 2, we pilot-tested clinician-delivered referrals to survivors in two 12-week Plan, Do, Study, Act (PDSA) cycles. We examined feasibility using descriptive statistics (clinicians' adoption and engagement, patient referrals, and physical activity program enrollment) and acceptability through semi-structured interviews with enrolled clinicians (n = 4) and referred patients (n = 9). ActivityChoice included a secure referral webform, text message/email referral confirmations, clinician training/booster sessions, visual reminders, and referrals to in-person or virtual group physical activity programs. Results for each PDSA cycle respectively included: 41% (n = 7) and 53% (n = 8) of clinicians adopted ActivityChoice; 18 and 36 patients were referred; 39% (n = 7) and 33% (n = 12) of patients enrolled in programs, and 30% (n = 4) and 14% (n = 5) of patients deferred enrollment. Patients and clinicians appreciated the referrals and choices. A printed handout describing both programs was added to the clinic workflow for Cycle 2, which yielded more referrals, but lower program enrollment rates. Clinic-based eReferrals to choices of physical activity programs were feasible and acceptable by clinicians and patients. Added clinic workflow support may facilitate referrals.

Pilot study of implementing the Shared Healthcare Actions & Reflections Electronic systems in Survivorship (SHARE-S) program in coordination with clinical care

INTRODUCTION

Initial cancer survivorship care planning efforts focused on information sharing demonstrated limited impact on patient health outcomes. We designed the Shared Healthcare Actions & Reflections Electronic Systems in survivorship (SHARE-S) program to enhance survivorship guideline implementation by transitioning some effort from clinicians to technology and patients through supporting health self-management (e.g., healthy lifestyles).

METHODS

We conducted a single-group hybrid implementation-effectiveness pilot study. SHARE-S incorporated three strategies: (1) e-referral from the clinical team for patient engagement, (2) three health self-management coach calls, and (3) text messages to enhance coaching. Our primary implementation measure was the proportion of patients e-referred who enrolled (target >30%). Secondary implementation measures assessed patient engagement. We also measured effectiveness by describing changes in patient health outcomes.

RESULTS

Of the 118 cancer survivor patients e-referred, 40 engaged in SHARE-S (proportion enrolled = 34%). Participants had a mean age of 57.4 years (SD = 15.7), 73% were female, 23% were Black/African American, and 5 (12.5%) were from a rural location. Patient-level adherence to coach calls was >90%. Changes from baseline to follow-up showed at least a small effect (Cohen's d = 0.2) for improvements in: mindful attention, alcohol use, physical activity, fruit and vegetable intake, days of mindfulness practice, depressive symptoms, ability to participate in social roles and activities, cancer-specific quality of life, benefits of having cancer, and positive feelings.

CONCLUSION

The SHARE-S program successfully engaged cancer survivor patients. Once enrolled, patients showed promising improvements in health outcomes. Supporting patient self-management is an important component of optimizing delivery of cancer survivorship care.

Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation: A Randomized Clinical Trial

IMPORTANCE

Novel data science and marketing methods of smoking-cessation intervention have not been adequately evaluated.

OBJECTIVE

To compare machine learning recommender (ML recommender) computer tailoring of motivational text messages vs a standard motivational text-based intervention (standard messaging) and a viral peer-recruitment tool kit (viral tool kit) for recruiting friends and family vs no tool kit in a smoking-cessation intervention.

DESIGN, SETTING, AND PARTICIPANTS

This 2 ×2 factorial randomized clinical trial with partial allocation, conducted between July 2017 and September 2019 within an online tobacco intervention, recruited current smokers aged 18 years and older who spoke English from the US via the internet and peer referral. Data were analyzed from March through May 2022.

INTERVENTIONS

Participants registering for the online intervention were randomly assigned to the ML recommender or standard messaging groups followed by partially random allocation to access to viral tool kit or no viral tool kit groups. The ML recommender provided ongoing refinement of message selection based on user feedback and comparison with a growing database of other users, while the standard system selected messages based on participant baseline readiness to quit.

MAIN OUTCOMES AND MEASURES

Our primary outcome was self-reported 7-day point prevalence smoking cessation at 6 months.

RESULTS

Of 1487 participants who smoked (444 aged 19-34 years [29.9%], 508 aged 35-54 years [34.1%], 535 aged ≥55 years [36.0%]; 1101 [74.0%] females; 189 Black [12.7%] and 1101 White [78.5%]; 106 Hispanic [7.1%]), 741 individuals were randomly assigned to the ML recommender group and 746 individuals to the standard messaging group; viral tool kit access was provided to 745 participants, and 742 participants received no such access. There was no significant difference in 6-month smoking cessation between ML recommender (146 of 412 participants [35.4%] with outcome data) and standard messaging (156 of 389 participants [40.1%] with outcome data) groups (adjusted odds ratio, 0.81; 95% CI, 0.61-1.08). Smoking cessation was significantly higher in viral tool kit (177 of 395 participants [44.8%] with outcome data) vs no viral tool kit (125 of 406 participants [30.8%] with outcome data) groups (adjusted odds ratio, 1.48; 95% CI, 1.11-1.98).

CONCLUSIONS AND RELEVANCE

In this study, machine learning-based selection did not improve performance compared with standard message selection, while viral marketing did improve cessation outcomes. These results suggest that in addition to increasing dissemination, viral recruitment may have important implications for improving effectiveness of smoking-cessation interventions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03224520.

Use of a Rapid Qualitative Method to Inform the Development of a Text Messaging Intervention for People With Serious Mental Illness Who Smoke: Formative Research Study

Background

People with serious mental illness are disproportionately affected by smoking and face barriers to accessing smoking cessation treatments in mental health treatment settings. Text-based interventions are cost-effective and represent a widely accessible approach to providing smoking cessation support.

Objective

We aimed to identify key factors for adapting text-based cessation interventions for people with serious mental illness who smoke.

Methods

We recruited 24 adults from mental health programs who had a serious mental illness and currently smoked cigarettes or had quit smoking within the past 5 years. We then conducted virtual qualitative interviews between November 2020 and August 2021. Data were analyzed using the rapid thematic analytic approach.

Results

We identified the following 3 major themes: (1) interplay between smoking and having a serious mental illness, (2) social contextual factors of smoking in adults with serious mental illness, and (3) smoking and quitting behaviors similar to the general population. Participants reported barriers and facilitators to quitting across the 3 themes. Within the “interplay between smoking and having a serious mental illness” theme, barriers included smoking to manage stress and mental health symptoms, and facilitators to quitting included the awareness of the harm of smoking on mental health and patient-provider discussions on smoking and mental health. In the “social contextual factors of smoking in adults with serious mental illness” theme, barriers included high social acceptability of smoking among peers. Positive support and the combined social stigma of smoking and having a mental health condition outside of peer groups motivated individuals to quit. Some participants indicated that low exposure to other smokers during the COVID-19 pandemic helped them to engage in cessation efforts. In the “smoking and quitting behaviors similar to the general population” theme, barriers included smoking after eating, having coffee, drinking alcohol, and experiencing negative social support, and facilitators included health concerns, improvement in the general quality of life, and use of evidence-based tobacco treatments when available.

Conclusions

People with serious mental illness often smoke to cope with intense emotional states, manage mental health symptoms, or maintain social bonds. Text message content emphasizing equally effective and less harmful ways for stress reduction and mental health symptom management may improve quit rates in individuals with serious mental illness.

Effectiveness of a culturally competent care intervention in reducing disparities in Hispanic live donor kidney transplantation: A hybrid trial

Hispanic patients receive disproportionately fewer living donor kidney transplants (LDKTs) than non-Hispanic Whites (NHWs). The Northwestern Medicine Hispanic Kidney Transplant Program (HKTP), designed to increase Hispanic LDKTs, was evaluated as a nonrandomized, implementation-effectiveness hybrid trial of patients initiating transplant evaluation at two intervention and two similar control sites. Using a mixed method, observational design, we evaluated the fidelity of the HKTP implementation at the two intervention sites. We tested the impact of the HKTP intervention by evaluating the likelihood of receiving LDKT comparing pre-intervention (January 2011-December 2016) and postintervention (January 2017-March 2020), across ethnicity and centers. The HKTP study included 2063 recipients. Intervention Site A exhibited greater implementation fidelity than intervention Site B. For Hispanic recipients at Site A, the likelihood of receiving LDKTs was significantly higher at postintervention compared with pre-intervention (odds ratio [OR] = 3.17 95% confidence interval [1.04, 9.63]), but not at the paired control Site C (OR = 1.02 [0.61, 1.71]). For Hispanic recipients at Site B, the likelihood of receiving an LDKT did not differ between pre- and postintervention (OR = 0.88 [0.40, 1.94]). The LDKT rate was significantly lower for Hispanics at paired control Site D (OR = 0.45 [0.28, 0.90]). The intervention significantly improved LDKT rates for Hispanic patients at the intervention site that implemented the intervention with greater fidelity. Registration: ClinicalTrials.gov registered (retrospectively) on September 7, 2017 (NCT03276390).

Contextual influences on implementation of online behavioral obesity treatment in primary care: formative evaluation guided by the consolidated framework for implementation research

Online behavioral obesity treatment is a promising first-line approach to weight management in primary care. However, little is known about contextual influences on implementation. Understand qualitative contextual factors that affect the implementation process, as experienced by key primary care stakeholders implementing the program. Online behavioral obesity treatment was implemented across a 60-clinic primary care practice network. Patients were enrolled by nurse care managers (NCMs; N = 14), each serving 2-5 practices. NCMs were randomized to one of two implementation conditions-"Basic" (standard implementation) or "Enhanced" (i.e., with added patient tracking features and more implementation strategies employed). NCMs completed qualitative interviews guided by the Consolidated Framework for Implementation Research (CFIR). Interviews were transcribed and analyzed via directed content analysis. Emergent categories were summarized by implementation condition and assigned a valence according to positive/negative influence. Individuals in the Enhanced condition viewed two aspects of the intervention as more positively influencing than Basic NCMs: Design Quality & Packaging (i.e., online program aesthetics), and Cost (i.e., no-cost program, clinician time savings). In both conditions, strongly facilitating factors included: Compatibility between intervention and clinical context; Intervention Source (from a trusted local university); and Evidence Strength & Quality supporting effectiveness. Findings highlight the importance of considering stakeholders' perspectives on the most valued types of evidence when introducing a new intervention, ensuring the program aligns with organizational priorities, and considering how training resources and feedback on patient progress can improve implementation success for online behavioral obesity treatment in primary care.

The effectiveness of technology-based interventions for smoking cessation: An umbrella review and quality assessment of systematic reviews

ISSUES

With the advancement and rapid increase in the public's interest in utilisation of Internet and mobile phones, technology-based interventions are being implemented across a range of health conditions to improve patient outcomes. The aim of this review was to summarise findings from systematic reviews that evaluated the effectiveness of technology-based smoking cessation interventions and to critically appraise their methodological qualities.

APPROACH

An umbrella review was conducted using studies identified from a comprehensive literature search of six databases and grey literature. All included systematic reviews were checked for eligibility criteria and quality using the Assessment of Multiple Systematic Reviews tool. The level of evidence for each intervention category was assessed, citation matrices were generated and corrected covered area was calculated.

KEY FINDINGS

Five systematic reviews with a total of 212 randomised controlled trials and 237 760 participants were included. Fourteen intervention approaches were identified and classified into three categories: stand-alone web-based; stand-alone mobile phone-based and multicomponent interventions. Incorporating web and/or mobile-based interventions with face-to-face approach improved the rate of smoking cessation. However, there was no consistent evidence regarding the effectiveness of stand-alone Internet or mobile-based interventions.

IMPLICATIONS

Policymakers are recommended to develop strategies that enable health professionals to integrate these approaches with face-to-face smoking cessation support. Health professionals are recommended to be trained and equipped for online and mobile-based interventions.

CONCLUSION

Adding technology-based intervention to face-to-face smoking cessation support improves smoking cessation. Further research is needed to evaluate stand-alone web-based and mobile phone-based interventions.

mHealth Messaging to Motivate Quitline Use and Quitting: Protocol for a Community-Based Randomized Controlled Trial in Rural Vietnam

BACKGROUND

Tobacco kills more than 8 million people each year, mostly in low- and middle-income countries. In Vietnam, 1 in every 2 male adults smokes tobacco. Vietnam has set up telephone Quitline counseling that is available to all smokers, but it is underused. We previously developed an automated and effective motivational text messaging system to support smoking cessation among US smokers.

OBJECTIVE

The aim of this study is to adapt the aforementioned system for rural Vietnamese smokers to promote cessation of tobacco use, both directly and by increasing the use of telephone Quitline counseling services and nicotine replacement therapy. Moreover, we seek to enhance research and health service capacity in Vietnam.

METHODS

We are testing the effectiveness of our culturally adapted motivational text messaging system by using a community-based randomized controlled trial design (N=600). Participants were randomly allocated to the intervention (regular motivational and assessment text messages) or control condition (assessment text messages only) for a period of 6 months. Trial recruitment took place in four communes in the Hung Yen province in the Red River Delta region of Vietnam. Recruitment events were advertised to the local community, facilitated by community health workers, and occurred in the commune health center. We are assessing the impact of the texting system on 6-month self-reported and biochemically verified smoking cessation, as well as smoking self-efficacy, uptake of the Quitline, and use of nicotine replacement therapy. In addition to conducting the trial, the research team also provided ongoing training and consultation with the Quitline during the study period.

RESULTS

Site preparation, staff training, intervention adaptation, participant recruitment, and baseline data collection were completed. The study was funded in August 2017; it was reviewed and approved by the University of Massachusetts Medical School Institutional Review Board in 2017. Recruitment began in November 2018. A total of 750 participants were recruited from four communes, and 700 (93.3%) participants completed follow-up by March 2021. An analysis of the trial results is in progress; results are expected to be published in late 2022.

CONCLUSIONS

This study examines the effectiveness of mobile health interventions for smoking in rural areas in low- and middle-income countries, which can be implemented nationwide if proven effective. In addition, it also facilitates significant collaboration and capacity building among a variety of international partners, including researchers, policy makers, Quitline counselors, and community health workers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03567993; https://clinicaltrials.gov/ct2/show/NCT03567993.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/30947.

Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement

Background

Motivational messaging is a frequently used digital intervention to promote positive health behavior changes, including smoking cessation. Typically, motivational messaging systems have not actively sought feedback on each message, preventing a closer examination of the user-system engagement. This study assessed the granular user-system engagement around a recommender system (a new system that actively sought user feedback on each message to improve message selection) for promoting smoking cessation and the impact of engagement on cessation outcome.

Methods

We prospectively followed a cohort of current smokers enrolled to use the recommender system for 6 months. The system sent participants motivational messages to support smoking cessation every 3 days and used machine learning to incorporate user feedback (i.e., user’s rating on the perceived influence of each message, collected on a 5-point Likert scale with 1 indicating strong disagreement and 5 indicating strong agreement on perceiving the influence on quitting smoking) to improve the selection of the following message. We assessed user-system engagement by various metrics, including user response rate (i.e., the percent of times a user rated the messages) and the perceived influence of messages. We compared retention rates across different levels of user-system engagement and assessed the association between engagement and the 7-day point prevalence abstinence (missing outcome = smoking) by using multiple logistic regression.

Results

We analyzed data from 731 participants (13% Black; 73% women). The user response rate was 0.24 (SD = 0.34) and user-perceived influence was 3.76 (SD = 0.84). The retention rate positively increased with the user response rate (trend test P < 0.001). Compared with non-response, six-month cessation increased with the levels of response rates: low response rate (odds ratio [OR] = 1.86, 95% confidence interval [CI]: 1.07–3.23), moderate response rate (OR = 2.30, 95% CI: 1.36–3.88), high response rate (OR = 2.69, 95% CI: 1.58–4.58). The association between perceived message influence and the outcome showed a similar pattern.

Conclusions

High user-system engagement was positively associated with both high retention rate and smoking cessation, suggesting that investigation of methods to increase engagement may be crucial to increase the impact of the recommender system for smoking cessation.

Trial registration

Registration Identifier: NCT03224520. Registration date: July 21, 2017.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11803-8.

Strategies to improve smoking cessation rates in primary care

BACKGROUND

Primary care is an important setting in which to treat tobacco addiction. However, the rates at which providers address smoking cessation and the success of that support vary. Strategies can be implemented to improve and increase the delivery of smoking cessation support (e.g. through provider training), and to increase the amount and breadth of support given to people who smoke (e.g. through additional counseling or tailored printed materials).

OBJECTIVES

To assess the effectiveness of strategies intended to increase the success of smoking cessation interventions in primary care settings. To assess whether any effect that these interventions have on smoking cessation may be due to increased implementation by healthcare providers.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and trial registries to 10 September 2020.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) and cluster-RCTs (cRCTs) carried out in primary care, including non-pregnant adults. Studies investigated a strategy or strategies to improve the implementation or success of smoking cessation treatment in primary care. These strategies could include interventions designed to increase or enhance the quality of existing support, or smoking cessation interventions offered in addition to standard care (adjunctive interventions). Intervention strategies had to be tested in addition to and in comparison with standard care, or in addition to other active intervention strategies if the effect of an individual strategy could be isolated. Standard care typically incorporates physician-delivered brief behavioral support, and an offer of smoking cessation medication, but differs across studies. Studies had to measure smoking abstinence at six months' follow-up or longer.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods. Our primary outcome - smoking abstinence - was measured using the most rigorous intention-to-treat definition available. We also extracted outcome data for quit attempts, and the following markers of healthcare provider performance: asking about smoking status; advising on cessation; assessment of participant readiness to quit; assisting with cessation; arranging follow-up for smoking participants. Where more than one study investigated the same strategy or set of strategies, and measured the same outcome, we conducted meta-analyses using Mantel-Haenszel random-effects methods to generate pooled risk ratios (RRs) and 95% confidence intervals (CIs).

MAIN RESULTS

We included 81 RCTs and cRCTs, involving 112,159 participants. Fourteen were rated at low risk of bias, 44 at high risk, and the remainder at unclear risk. We identified moderate-certainty evidence, limited by inconsistency, that the provision of adjunctive counseling by a health professional other than the physician (RR 1.31, 95% CI 1.10 to 1.55; I2 = 44%; 22 studies, 18,150 participants), and provision of cost-free medications (RR 1.36, 95% CI 1.05 to 1.76; I2 = 63%; 10 studies,7560 participants) increased smoking quit rates in primary care. There was also moderate-certainty evidence, limited by risk of bias, that the addition of tailored print materials to standard smoking cessation treatment increased the number of people who had successfully stopped smoking at six months' follow-up or more (RR 1.29, 95% CI 1.04 to 1.59; I2 = 37%; 6 studies, 15,978 participants). There was no clear evidence that providing participants who smoked with biomedical risk feedback increased their likelihood of quitting (RR 1.07, 95% CI 0.81 to 1.41; I2 = 40%; 7 studies, 3491 participants), or that provider smoking cessation training (RR 1.10, 95% CI 0.85 to 1.41; I2 = 66%; 7 studies, 13,685 participants) or provider incentives (RR 1.14, 95% CI 0.97 to 1.34; I2 = 0%; 2 studies, 2454 participants) increased smoking abstinence rates. However, in assessing the former two strategies we judged the evidence to be of low certainty and in assessing the latter strategies it was of very low certainty. We downgraded the evidence due to imprecision, inconsistency and risk of bias across these comparisons. There was some indication that provider training increased the delivery of smoking cessation support, along with the provision of adjunctive counseling and cost-free medications. However, our secondary outcomes were not measured consistently, and in many cases analyses were subject to substantial statistical heterogeneity, imprecision, or both, making it difficult to draw conclusions. Thirty-four studies investigated multicomponent interventions to improve smoking cessation rates. There was substantial variation in the combinations of strategies tested, and the resulting individual study effect estimates, precluding meta-analyses in most cases. Meta-analyses provided some evidence that adjunctive counseling combined with either cost-free medications or provider training enhanced quit rates when compared with standard care alone. However, analyses were limited by small numbers of events, high statistical heterogeneity, and studies at high risk of bias. Analyses looking at the effects of combining provider training with flow sheets to aid physician decision-making, and with outreach facilitation, found no clear evidence that these combinations increased quit rates; however, analyses were limited by imprecision, and there was some indication that these approaches did improve some forms of provider implementation.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that providing adjunctive counseling by an allied health professional, cost-free smoking cessation medications, and tailored printed materials as part of smoking cessation support in primary care can increase the number of people who achieve smoking cessation. There is no clear evidence that providing participants with biomedical risk feedback, or primary care providers with training or incentives to provide smoking cessation support enhance quit rates. However, we rated this evidence as of low or very low certainty, and so conclusions are likely to change as further evidence becomes available. Most of the studies in this review evaluated smoking cessation interventions that had already been extensively tested in the general population. Further studies should assess strategies designed to optimize the delivery of those interventions already known to be effective within the primary care setting. Such studies should be cluster-randomized to account for the implications of implementation in this particular setting. Due to substantial variation between studies in this review, identifying optimal characteristics of multicomponent interventions to improve the delivery of smoking cessation treatment was challenging. Future research could use component network meta-analysis to investigate this further.

Health Recommender Systems: Systematic Review

Background

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

Objective

We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

Methods

We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

Results

Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.

Conclusions

There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Written Advice Given by African American Smokers to Their Peers: Qualitative Study of Motivational Messages

BACKGROUND

Although African Americans have the lowest rates of smoking onset and progression to daily smoking, they are less likely to achieve long-term cessation. Interventions tailored to promote use of cessation resources in African American individuals who smoke are needed. In our past work, we demonstrated the effectiveness of a technology-assisted peer-written message intervention for increasing smoking cessation in non-Hispanic White smokers. In this formative study, we have adapted this intervention to be specific for African American smokers.

OBJECTIVE

We aimed to report on the qualitative analysis of messages written by African American current and former smokers for their peers in response to hypothetical scenarios of smokers facing cessation challenges.

METHODS

We recruited African American adult current and former smokers (n=41) via ResearchMatch between April 2017 and November 2017. We asked participants to write motivational messages for their peers in response to smoking-related hypothetical scenarios. We also collected data on sociodemographic factors and smoking characteristics. Thematic analysis was conducted to identify cessation strategies suggested by the study participants.

RESULTS

Among the study participants, 60% (25/41) were female. Additionally, more than half (23/41, 56%) were thinking about quitting, 29% (12/41) had set a quit date, and 27% (11/41) had used electronic cigarettes in the past 30 days. Themes derived from the qualitative analysis of peer-written messages were (1) behavioral strategies, (2) seeking help, (3) improvements in quality of life, (4) attitudes and expectations, and (5) mindfulness/religious or spiritual practices. Under the behavioral strategies theme, distraction strategies were the most frequently suggested strategies (referenced 84 times in the 318 messages), followed by use of evidence-based treatments/cessation strategies. Within the seeking help theme, subthemes included seeking help or support from family/friends or close social networks (referenced 56 times) and health care professionals (referenced 22 times). The most frequent subthemes that emerged from improvements in the quality of life theme included improving one's health (referenced 22 times) and quality of life (referenced 21 times). Subthemes that emerged from the attitude and expectations theme included practicing positive self-talk (referenced 27 times), autonomy/independence from the smoking habit (referenced six times), and financial cost of smoking (referenced five times). The two subthemes that emerged from the mindfulness/religious or spiritual practices theme were use of self-awareness techniques (referenced 36 times) and religious or spiritual practices to cope (referenced 13 times).

CONCLUSIONS

Our approach to adapt a prior peer-message intervention to African American smokers yielded a set of evidence-based messages that may be suitable for smokers at all phases of motivation to quit (ready to quit or not ready to quit). In future research, we plan to assess the impact of texting these messages to African American smokers in a smoking cessation trial.

Comparing recruitment strategies for a digital smoking cessation intervention: Technology-assisted peer recruitment, social media, ResearchMatch, and smokefree.gov

BACKGROUND

Choosing the right recruitment strategy has implications for the successful conduct of a trial. Our objective was to compare a novel peer recruitment strategy to four other recruitment strategies for a large randomized trial testing a digital tobacco intervention.

METHODS

We compared enrollment rates, demographic and baseline smoking characteristics, and odds of completing the 6-month study by recruitment strategy. Cost of recruitment strategies per retained participant was calculated using staff personnel time and advertisement costs.

FINDINGS

We enrolled 1487 participants between August 2017 and March 2019 from: Peer recruitment n = 273 (18.4%), Facebook Ads n = 505 (34%), Google Ads = 200 (13.4%), ResearchMatch n = 356 (23.9%) and Smokefree.govn = 153 (10.3%). Mean enrollment rate per active recruitment month: 1) Peer recruitment, n = 13.9, 2) Facebook ads, n = 25.3, 3) Google ads, n = 10.51, 4) Research Match, n = 59.3, and 5) Smokefree.gov, n = 13.9. Peer recruitment recruited the greatest number of males (n = 110, 40.3%), young adults (n = 41, 14.7%), participants with a high school degree or less (n = 24, 12.5%) and smokers within one's social network. Compared to peer recruitment (retention rate = 57%), participants from Facebook were less likely (OR 0.46, p < 0.01, retention rate = 40%), and those from ResearchMatch were more likely to complete the study (OR 1.90, p < 0.01, retention rate = 70%). Peer recruitment was moderate in cost per retained participant ($47.18) and substantially less costly than Facebook ($173.60).

CONCLUSIONS

Though peer recruitment had lower enrollment than other strategies, it may provide greater access to harder to reach populations and possibly others who smoke within one's social network while being moderately cost-effective. ClinicalTrials.gov: NCT03224520.

Social norms interventions to change clinical behaviour in health workers: a systematic review and meta-analysis

Background

A social norms intervention seeks to change the clinical behaviour of a target health worker by exposing them to the values, beliefs, attitudes or behaviours of a reference group or person. These low-cost interventions can be used to encourage health workers to follow recommended professional practice.

Objective

To summarise evidence on whether or not social norms interventions are effective in encouraging health worker behaviour change, and to identify the most effective social norms interventions.

Design

A systematic review and meta-analysis of randomised controlled trials.

Data sources

The following databases were searched on 24 July 2018: Ovid MEDLINE (1946 to week 2 July 2018), EMBASE (1974 to 3 July 2018), Cumulative Index to Nursing and Allied Health Literature (1937 to July 2018), British Nursing Index (2008 to July 2018), ISI Web of Science (1900 to present), PsycINFO (1806 to week 3 July 2018) and Cochrane trials (up to July 2018).

Participants

Health workers took part in the study.

Interventions

Behaviour change interventions based on social norms.

Outcome measures

Health worker clinical behaviour, for example prescribing (primary outcome), and patient health outcomes, for example blood test results (secondary), converted into a standardised mean difference.

Methods

Titles and abstracts were reviewed against the inclusion criteria to exclude any that were clearly ineligible. Two reviewers independently screened the remaining full texts to identify relevant papers. Two reviewers extracted data independently, coded for behaviour change techniques and assessed quality using the Cochrane risk-of-bias tool. We performed a meta-analysis and presented forest plots, stratified by behaviour change technique. Sources of variation were explored using metaregression and network meta-analysis.

Results

A total of 4428 abstracts were screened, 477 full texts were screened and findings were based on 106 studies. Most studies were in primary care or hospitals, targeting prescribing, ordering of tests and communication with patients. The interventions included social comparison (in which information is given on how peers behave) and credible source (which refers to communication from a well-respected person in support of the behaviour). Combined data suggested that interventions that included social norms components were associated with an improvement in health worker behaviour of 0.08 standardised mean differences (95% confidence interval 0.07 to 0.10 standardised mean differences) (n = 100 comparisons), and an improvement in patient outcomes of 0.17 standardised mean differences (95% confidence interval 0.14 to 0.20) (n = 14), on average. Heterogeneity was high, with an overall I 2 of 85.4% (primary) and 91.5% (secondary). Network meta-analysis suggested that three types of social norms intervention were most effective, on average, compared with control: credible source (0.30 standardised mean differences, 95% confidence interval 0.13 to 0.47); social comparison combined with social reward (0.39 standardised mean differences, 95% confidence interval 0.15 to 0.64); and social comparison combined with prompts and cues (0.33 standardised mean differences, 95% confidence interval 0.22 to 0.44).

Limitations

The large number of studies prevented us from requesting additional information from authors. The trials varied in design, context and setting, and we combined different types of outcome to provide an overall summary of evidence, resulting in a very heterogeneous review.

Conclusions

Social norms interventions are an effective method of changing clinical behaviour in a variety of health service contexts. Although the overall result was modest and very variable, there is the potential for social norms interventions to be scaled up to target the behaviour of a large population of health workers and resulting patient outcomes.

Future work

Development of optimised credible source and social comparison behaviour change interventions, including qualitative research on acceptability and feasibility.

Study registration

This study is registered as PROSPERO CRD42016045718.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 41. See the NIHR Journals Library website for further project information.

The 'Take a Break' game: Randomized trial protocol for a technology-assisted brief abstinence experience designed to engage lower-motivated smokers

BACKGROUND

While smoking continues to be the most preventable cause of mortality in the United States, most current smokers remain not ready to quit at any given time. Engaging these 'motivation phase' smokers with brief experiences to build confidence and practice skills related to cessation could lead to sooner and more successful quit attempts. Increasingly available mobile technology and gamification can be used to provide smokers with accessible and engaging support.

METHODS

We describe our protocol for conducting a randomized controlled trial evaluating Take a Break, an mHealth-based smoking pre-cessation challenge designed for smokers not ready to quit. Participants in the intervention receive 1) Motivational Messages, 2) text message Challenge Quizzes, 3) Goal-setting with tobacco treatment specialist, 4) Coping Mini-Games apps, and 5) Recognition and Rewards for participation during a 3-week challenge. Access to coping mini-games and motivational messaging continues for 6-months. Both intervention and comparison group participants receive brief Nicotine Replacement Therapy (NRT) sampling and daily smoking assessment text messages for three weeks. Primary outcomes include number of days abstinent during the challenge, change in patient-reported self-efficacy after the challenge, time to first quit attempt following the challenge, and 7-day point prevalent smoking cessation at six months.

CONCLUSION

Take a Break is an innovative approach to engage those not prepared for a quit attempt. Take a Break provides motivation phase smokers with tools and a brief experience to prepare them for a quit attempt, filling a gap in tobacco cessation support and current research.

Secure Asynchronous Communication Between Smokers and Tobacco Treatment Specialists: Secondary Analysis of a Web-Assisted Tobacco Intervention in the QUIT-PRIMO and National Dental PBRN Networks

BACKGROUND

Within a web-assisted tobacco intervention, we provided a function for smokers to asynchronously communicate with a trained tobacco treatment specialist (TTS). Previous studies have not attempted to isolate the effect of asynchronous counseling on smoking cessation.

OBJECTIVE

This study aimed to conduct a semiquantitative analysis of TTS-smoker communication and evaluate its association with smoking cessation.

METHODS

We conducted a secondary analysis of data on secure asynchronous communication between trained TTSs and a cohort of smokers during a 6-month period. Smokers were able to select their preferred TTS and message them using a secure web-based form. To evaluate whether the TTS used evidence-based practices, we coded messages using the Motivational Interviewing Self-Evaluation Checklist and Smoking Cessation Counseling (SCC) Scale. We assessed the content of messages initiated by the smokers by creating topical content codes. At 6 months, we assessed the association between smoking cessation and the amount of TTS use and created a multivariable model adjusting for demographic characteristics and smoking characteristics at baseline.

RESULTS

Of the 725 smokers offered asynchronous counseling support, 33.8% (245/725) messaged the TTS at least once. A total of 1082 messages (TTSs: 565; smokers 517) were exchanged between the smokers and TTSs. The majority of motivational interviewing codes were those that supported client strengths (280/517, 54.1%) and promoted engagement (280/517, 54.1%). SCC code analysis showed that the TTS provided assistance to smokers if they were willing to quit (247/517, 47.8%) and helped smokers prepare to quit (206/517, 39.8%) and anticipate barriers (197/517, 38.1%). The majority of smokers' messages discussed motivations to quit (234/565, 41.4%) and current and past treatments (talking about their previous use of nicotine replacement therapy and medications; 201/565, 35.6%). The majority of TTS messages used behavioral strategies (233/517, 45.1%), offered advice on treatments (189/517, 36.5%), and highlighted motivations to quit (171/517, 33.1%). There was no association between the amount of TTS use and cessation. In the multivariable model, after adjusting for gender, age, race, education, readiness at baseline, number of cigarettes smoked per day at baseline, and the selected TTS, smokers messaging the TTS one or two times had a smoking cessation odds ratio (OR) of 0.8 (95% CI 0.4-1.4), and those that messaged the TTS more than two times had a smoking cessation OR of 1.0 (95% CI 0.4-2.3).

CONCLUSIONS

Our study demonstrated the feasibility of using asynchronous counseling to deliver evidence-based counseling. Low participant engagement or a lack of power could be potential explanations for the nonassociation with smoking cessation. Future trials should explore approaches to increase participant engagement and test asynchronous counseling in combination with other approaches for improving the rates of smoking cessation.

An Implementation Trial to Improve Tobacco Treatment for Cancer Patients: Patient Preferences, Treatment Acceptability and Effectiveness

Continued smoking after a cancer diagnosis increases mortality, risk of recurrence, and negatively impacts treatment effectiveness. However, utilization of tobacco use cessation treatment among cancer patients remains low. We conducted a clinical trial assessing patient preferences, treatment acceptability, and preliminary effectiveness (7-day point prevalence at 12 weeks) of three tobacco treatment options among cancer patients at an academic health center. Implementation strategies included electronic referral and offering the choice of three treatment options: referral to external services, including the quitline (PhoneQuit) and in-person group counseling (GroupQuit), or an internal service consisting of 6-week cognitive behavioral therapy delivered via smartphone video conferencing by a tobacco treatment specialist (SmartQuit). Of 545 eligible patients, 90 (16.5%) agreed to enroll. Of the enrolled patients, 39 (43.3%) chose PhoneQuit, 37 (41.1%) SmartQuit, and 14 (15.6%) GroupQuit. Of patients reached for 12-week follow-up (n = 35), 19 (54.3%) reported receiving tobacco treatment. Of all patients referred, 3 (7.7%) PhoneQuit, 2 (5.4%) SmartQuit, and 2 (14.3%) GroupQuit patients reported 7-day point prevalence abstinence from smoking at 12 weeks. Participants rated the SmartQuit intervention highly in terms of treatment acceptability. Results indicate that more intensive interventions may be needed for this population, and opportunities remain for improving reach and utilization.

Nurse-Driven mHealth Implementation Using the Technology Inpatient Program for Smokers (TIPS): Mixed Methods Study

Background

Smoking is the leading cause of preventable death and disease, yet implementation of smoking cessation in inpatient settings is inconsistent. The Technology Inpatient Program for Smokers (TIPS) is an implementation program designed to reach smokers with a mobile health (mHealth) intervention using stakeholder-supported strategies.

Objective

The purpose of this study was to determine the impact of the TIPS implementation strategies on smoker-level engagement of the mHealth intervention during care transition.

Methods

We examined varying intensities (passive motivational posters only and posters + active nurse-led facilitation) of TIPS strategies on four hospital units located in two sites. Unit-level and smoker-level adoption was monitored during active implementation (30 weeks) and sustainability follow-up (30 weeks). Process measures reflecting the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework, stakeholder reported adaptations of strategies, and formative evaluation data were collected and analyzed.

Results

For our smoker-level reach, 103 smokers signed up for the mHealth intervention in-hospital, with minimal decline during sustainability follow-up. While posters + nurse facilitation did not lead to higher reach than posters alone during active implementation (27 vs 30 signed up), it did lead to higher engagement of smokers (85.2% vs 73.3% completion of the full 2-week intervention). TIPS strategy adoption and fidelity varied by unit, including adoption of motivational posters (range: weeks 1 and 5), fidelity of posters (0.4% to 16.2% of posters missing per unit weekly) and internal facilitation of nurse training sessions (average of 2 vs 7.5 by site). Variable maintenance costs of the program totaled US $6.63 (US $683.28/103) per smoker reached. Reported family-member facilitation of mHealth sign-up was an observation of unintended behavior.

Conclusions

TIPS is a feasible and low-cost implementation program that successfully engages smokers in an mHealth intervention and sustains engagement after discharge. Further testing of nurse facilitation and expanding reach to patient family and friends as an implementation strategy is needed.

Comparing the Efficacy of an Identical, Tailored Smoking Cessation Intervention Delivered by Mobile Text Messaging Versus Email: Randomized Controlled Trial

Background

There is a need to deliver smoking cessation support at a population level, both in developed and developing countries. Studies on internet-based and mobile phone–based smoking cessation interventions have shown that these methods can be as effective as other methods of support, and they can have a wider reach at a lower cost.

Objective

This randomized controlled trial (RCT) aimed to compare, on a population level, the efficacy of an identical, tailored smoking cessation intervention delivered by mobile text messaging versus email.

Methods

We conducted a nationwide 2-arm, double-blinded, fully automated RCT, close to a real-world setting, in Norway. We did not offer incentives to increase participation and adherence or to decrease loss to follow-up. We recruited users of the website, slutta.no, an open, free, multi-component Norwegian internet-based smoking cessation program, from May 2010 until October 2012. Enrolled smokers were considered as having completed a time point regardless of their response status if it was 1, 3, 6, or 12 months post cessation. We assessed 7315 participants using the following inclusion criteria: knowledge of the Norwegian language, age 16 years or older, ownership of a Norwegian cell phone, having an email account, current cigarette smoker, willingness to set a cessation date within 14 days (mandatory), and completion of a baseline questionnaire for tailoring algorithms. Altogether, 6137 participants were eligible for the study and 4378 participants (71.33%) provided informed consent to participate in the smoking cessation trial. We calculated the response rates for participants at the completed 1, 3, 6, and 12 months post cessation. For each arm, we conducted an intention-to-treat (ITT) analysis for each completed time point. The main outcome was 7-day self-reported point prevalence abstinence (PPA) at the completed 6 months post cessation. We calculated effect size of the 7-day self-reported PPA in the text message arm compared with the email arm as odds ratios (ORs) with 95% CIs for the 4 time points post cessation.

Results

At 6 months follow-up, 21.06% (384/1823) of participants in the text message arm and 18.62% (333/1788) in the email arm responded (P=.07) to the surveys. In the ITT analysis, 11.46% (209/1823) of participants in the text message arm compared with 10.96% (196/1788) in the email arm (OR 1.05, 95% CI 0.86-1.30) reported to have achieved 7 days PPA.

Conclusions

This nationwide, double-blinded, large, fully automated RCT found that 1 in 9 enrolled smokers reported 7-day PPA in both arms, 6 months post cessation. Our study found that identical smoking cessation interventions delivered by mobile text messaging and email may be equally successful at a population level.

Trial Registration

ClinicalTrials.gov NCT01103427; https://clinicaltrials.gov/ct2/show/NCT01103427

Dissemination and Effectiveness of the Peer Marketing and Messaging of a Web-Assisted Tobacco Intervention: Protocol for a Hybrid Effectiveness Trial

BACKGROUND

Smoking continues to be the leading preventable cause of death. Digital Interventions for Smoking Cessation (DISCs) are health communication programs accessible via the internet and smartphones and allow for greater reach and effectiveness of tobacco cessation programs. DISCs have led to increased 6-month cessation rates while also reaching vulnerable populations. Despite this, the impact of DISCs has been limited and new ways to increase access and effectiveness are needed.

OBJECTIVE

We are conducting a hybrid effectiveness-dissemination study. We aim to evaluate the effectiveness of a machine learning-based approach (recommender system) for computer-tailored health communication (CTHC) over a standard CTHC system based on quit rates and risk reduction. In addition, this study will assess the dissemination of providing access to a peer recruitment toolset on recruitment rate and variability of the sample.

METHODS

The Smoker-to-Smoker (S2S) study is a 6-month hybrid effectiveness dissemination trial conducted nationally among English-speaking, current smokers aged ≥18 years. All eligible participants will register for the DISC (Decide2quit) and be randomized to the recommender system CTHC or the standard CTHC, followed by allocation to a peer recruitment toolset group or control group. Primary outcomes will be 7-day point prevalence and risk reduction at the 6-month follow-up. Secondary outcomes include recruitment rate, website engagement, and patient-reported outcomes collected via the 6-month follow-up questionnaire. All primary analyses will be conducted on an intent-to-treat basis.

RESULTS

The project is funded from 2017 to 2020 by the Patient Centered Outcomes Research Institute. Enrollment was completed in early 2019, and 6-month follow-ups will be completed by late 2019. Preliminary data analysis is currently underway.

CONCLUSIONS

Conducting a hybrid study with both effectiveness and dissemination hypotheses raises some unique challenges in the study design and analysis. Our study addresses these challenges to test new innovations and increase the effectiveness and reach of DISCs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/14814.

Smoking cessation interventions for potential use in the lung cancer screening setting: A systematic review and meta-analysis

OBJECTIVES

Current guidelines recommend delivery of smoking cessation interventions with lung cancer screening (LCS). Unfortunately, there are limited data to guide clinicians and policy-makers in choosing cessation interventions in this setting. Several trials are underway to fill this evidence gap, but results are not expected for several years.

METHODS AND MATERIALS

We conducted a systematic review and meta-analysis of current literature on the efficacy of smoking cessation interventions among populations eligible for LCS. We searched PubMed, Medline, and PsycINFO for randomized controlled trials of smoking cessation interventions published from 2010-2017. Trials were eligible for inclusion if they sampled individuals likely to be eligible for LCS based on age and smoking history, had sample sizes >100, follow-up of 6- or 12-months, and were based in North America, Western Europe, Australia, or New Zealand.

RESULTS

Three investigators independently screened 3,813 abstracts and identified 332 for full-text review. Of these, 85 trials were included and grouped into categories based on the primary intervention: electronic/web-based, in-person counseling, pharmacotherapy, and telephone counseling. At 6-month follow-up, electronic/web-based (odds ratio [OR] 1.14, 95% CI 1.03-1.25), in-person counseling (OR 1.46, 95% CI 1.25-1.70), and pharmacotherapy (OR 1.53, 95% CI 1.33-1.77) interventions significantly increased the odds of abstinence. Telephone counseling increased the odds but did not reach statistical significance (OR 1.21, 95% CI 0.98-1.50). At 12-months, in-person counseling (OR 1.28 95% CI 1.10-1.50) and pharmacotherapy (OR 1.46, 95% CI 1.17-1.84) remained efficacious, although the decrement in efficacy was of similar magnitude across all intervention categories.

CONCLUSIONS

Several categories of cessation interventions are promising for implementation in the LCS setting.

Gamification to Motivate the Unmotivated Smoker: The "Take a Break" Digital Health Intervention

Objective: Digital health technologies most often reach only those more motivated to engage, particularly when preventive health is targeted. To test whether gamification could be used to engage low-motivation smokers, we conceptualized "Take a Break"-a 3-week technology-assisted challenge for smokers to compete in setting and achieving brief abstinence goals. Materials and Methods: In the feasibility study of the multi-technology Take a Break challenge, low-motivation smokers were given (1) daily motivational messages, (2) brief "challenge quizzes" related to smoking behaviors, (3) a telehealth call to personalize their abstinence goal for the challenge, (4) "coping minigames" to help manage cravings while attempting to achieve their brief abstinence goals, and (5) a leaderboard "webApp," providing comparative feedback on smokers' participation, and allowing for competition. Heterogeneity of engagement was tracked. Results: All 41 smokers initially reported that they were not actively quitting. Over half were employed less than full time (51%), completed less than a 4-year college education (76%), and experienced financial stress (54%). No smokers opted out of the motivational messages, and mean proportion of response to the challenge quizzes was 0.88 (SD = 0.19). Half of the smokers reported using the "coping minigames." Almost all set abstinence goals (78%), with over half lasting 1-2 days (51%); median = 1 day (IQR 1-7). Leaderboard points ranged widely. Conclusions: Rates of smoking in the developed world have declined, and those who remain smokers are complex and have lower motivation to quit. Using a game-inspired challenge, we achieved high levels of engagement from low-motivation smokers.

Which eHealth interventions are most effective for smoking cessation? A systematic review

PURPOSE

To synthesize evidence of the effects and potential effect modifiers of different electronic health (eHealth) interventions to help people quit smoking.

METHODS

Four databases (MEDLINE, PsycINFO, Embase, and The Cochrane Library) were searched in March 2017 using terms that included "smoking cessation", "eHealth/mHealth" and "electronic technology" to find relevant studies. Meta-analysis and meta-regression analyses were performed using Mantel-Haenszel test for fixed-effect risk ratio (RR) and restricted maximum-likelihood technique, respectively. Protocol Registration Number: CRD42017072560.

RESULTS

The review included 108 studies and 110,372 participants. Compared to nonactive control groups (eg, usual care), smoking cessation interventions using web-based and mobile health (mHealth) platform resulted in significantly greater smoking abstinence, RR 2.03 (95% CI 1.7-2.03), and RR 1.71 (95% CI 1.35-2.16), respectively. Similarly, smoking cessation trials using tailored text messages (RR 1.80, 95% CI 1.54-2.10) and web-based information and conjunctive nicotine replacement therapy (RR 1.29, 95% CI 1.17-1.43) may also increase cessation. In contrast, little or no benefit for smoking abstinence was found for computer-assisted interventions (RR 1.31, 95% CI 1.11-1.53). The magnitude of effect sizes from mHealth smoking cessation interventions was likely to be greater if the trial was conducted in the USA or Europe and when the intervention included individually tailored text messages. In contrast, high frequency of texts (daily) was less effective than weekly texts.

CONCLUSIONS

There was consistent evidence that web-based and mHealth smoking cessation interventions may increase abstinence moderately. Methodologic quality of trials and the intervention characteristics (tailored vs untailored) are critical effect modifiers among eHealth smoking cessation interventions, especially for web-based and text messaging trials. Future smoking cessation intervention should take advantages of web-based and mHealth engagement to improve prolonged abstinence.

An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data

Big longitudinal data provide more reliable information for decision making and are common in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important structures for such data. Developing better and more computationally-efficient visualization tool is crucial to guide this technique. This paper proposes an enhanced projection pursuit (EPP) method to better project and visualize the structures (e.g. clusters) of big high-dimensional (HD) longitudinal data on a lower-dimensional plane. Unlike classic PP methods potentially useful for longitudinal data, EPP is built upon nonlinear mapping algorithms to compute its stress (error) function by balancing the paired weights for between and within structure stress while preserving original structure membership in the high-dimensional space. Specifically, EPP solves an NP hard optimization problem by integrating gradual optimization and non-linear mapping algorithms, and automates the searching of an optimal number of iterations to display a stable structure for varying sample sizes and dimensions. Using publicized UCI and real longitudinal clinical trial datasets as well as simulation, EPP demonstrates its better performance in visualizing big HD longitudinal data.

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Background

Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects.

Objective

The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions.

Methods

Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers.

Results

A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review.

Conclusions

Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

What makes online substance-use interventions engaging? A systematic review and narrative synthesis

Background

Online substance-use interventions are effective in producing reductions in harmful-use. However, low user engagement rates with online interventions reduces overall effectiveness of interventions. Identifying optimal strategies with which to engage users with online substance-use interventions may improve usage rates and subsequent effectiveness.

Objectives

(1) To identify the most prevalent engagement promoting strategies utilised to increase use of online substance-use interventions. (2) To determine whether the identified engagement promoting strategies increased said use of online substance-use interventions.

Review methods

The reviewed followed Cochrane methodology. Databases were searched for online substance-use interventions and engagement promoting strategies limited by study type (randomised controlled trial). Due to heterogeneity between engagement promoting strategies and engagement outcomes, meta-analytic techniques were not possible. Narrative synthesis methods were used.

Results

Fifteen studies were included. Five different engagement promoting strategies were identified: (1) tailoring; (2) delivery strategies; (3) incentives; (4) reminders; (5) social support. The most frequently reported engagement promoting strategies was tailoring (47% of studies), followed by reminders and social support (40% of studies) and delivery strategies (33% of studies). The narrative synthesis demonstrated that tailoring, multimedia delivery of content and reminders are potential techniques for promoting engagement. The evidence for social support was inconclusive and negative for incentives.

Conclusions

This review was the first to examine engagement promoting strategies in solely online substance-use interventions. Three strategies were identified that may be integral in promoting engagement with online substance-use interventions. However, the small number of eligible extracted studies, inconsistent reporting of engagement outcomes and diversity of engagement features prevent firmer conclusions. More high-quality trials examining engagement are required.

Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems

Introduction

Although technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook).

Methods

We recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers.

Results

Overall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons).

Conclusions

Peer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research.

Implications

This study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.

Internet-based interventions for smoking cessation

BACKGROUND

Tobacco use is estimated to kill 7 million people a year. Nicotine is highly addictive, but surveys indicate that almost 70% of US and UK smokers would like to stop smoking. Although many smokers attempt to give up on their own, advice from a health professional increases the chances of quitting. As of 2016 there were 3.5 billion Internet users worldwide, making the Internet a potential platform to help people quit smoking.

OBJECTIVES

To determine the effectiveness of Internet-based interventions for smoking cessation, whether intervention effectiveness is altered by tailoring or interactive features, and if there is a difference in effectiveness between adolescents, young adults, and adults.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, which included searches of MEDLINE, Embase and PsycINFO (through OVID). There were no restrictions placed on language, publication status or publication date. The most recent search was conducted in August 2016.

SELECTION CRITERIA

We included randomised controlled trials (RCTs). Participants were people who smoked, with no exclusions based on age, gender, ethnicity, language or health status. Any type of Internet intervention was eligible. The comparison condition could be a no-intervention control, a different Internet intervention, or a non-Internet intervention. To be included, studies must have measured smoking cessation at four weeks or longer.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed and extracted data. We extracted and, where appropriate, pooled smoking cessation outcomes of six-month follow-up or more, reporting short-term outcomes narratively where longer-term outcomes were not available. We reported study effects as a risk ratio (RR) with a 95% confidence interval (CI).We grouped studies according to whether they (1) compared an Internet intervention with a non-active control arm (e.g. printed self-help guides), (2) compared an Internet intervention with an active control arm (e.g. face-to-face counselling), (3) evaluated the addition of behavioural support to an Internet programme, or (4) compared one Internet intervention with another. Where appropriate we grouped studies by age.

MAIN RESULTS

We identified 67 RCTs, including data from over 110,000 participants. We pooled data from 35,969 participants.There were only four RCTs conducted in adolescence or young adults that were eligible for meta-analysis.Results for trials in adults: Eight trials compared a tailored and interactive Internet intervention to a non-active control. Pooled results demonstrated an effect in favour of the intervention (RR 1.15, 95% CI 1.01 to 1.30, n = 6786). However, statistical heterogeneity was high (I2 = 58%) and was unexplained, and the overall quality of evidence was low according to GRADE. Five trials compared an Internet intervention to an active control. The pooled effect estimate favoured the control group, but crossed the null (RR 0.92, 95% CI 0.78 to 1.09, n = 3806, I2 = 0%); GRADE quality rating was moderate. Five studies evaluated an Internet programme plus behavioural support compared to a non-active control (n = 2334). Pooled, these studies indicated a positive effect of the intervention (RR 1.69, 95% CI 1.30 to 2.18). Although statistical heterogeneity was substantial (I2 = 60%) and was unexplained, the GRADE rating was moderate. Four studies evaluated the Internet plus behavioural support compared to active control. None of the studies detected a difference between trial arms (RR 1.00, 95% CI 0.84 to 1.18, n = 2769, I2 = 0%); GRADE rating was moderate. Seven studies compared an interactive or tailored Internet intervention, or both, to an Internet intervention that was not tailored/interactive. Pooled results favoured the interactive or tailored programme, but the estimate crossed the null (RR 1.10, 95% CI 0.99 to 1.22, n = 14,623, I2 = 0%); GRADE rating was moderate. Three studies compared tailored with non-tailored Internet-based messages, compared to non-tailored messages. The tailored messages produced higher cessation rates compared to control, but the estimate was not precise (RR 1.17, 95% CI 0.97 to 1.41, n = 4040), and there was evidence of unexplained substantial statistical heterogeneity (I2 = 57%); GRADE rating was low.Results should be interpreted with caution as we judged some of the included studies to be at high risk of bias.

AUTHORS' CONCLUSIONS

The evidence from trials in adults suggests that interactive and tailored Internet-based interventions with or without additional behavioural support are moderately more effective than non-active controls at six months or longer, but there was no evidence that these interventions were better than other active smoking treatments. However some of the studies were at high risk of bias, and there was evidence of substantial statistical heterogeneity. Treatment effectiveness in younger people is unknown.

TControl: A mobile app to follow up tobacco-quitting patients

BACKGROUND AND OBJECTIVE

Tobacco smoking is a major risk factor for a wide range of respiratory and circulatory diseases in active and passive smokers. Well-designed campaigns are raising awareness to the problem and an increasing number of smokers seeks medical assistance to quit their habit. In this context, there is the need to develop mHealth Apps that assist and manage large smoke quitting programs in efficient and economic ways.

OBJECTIVES

Our main objective is to develop an efficient and free mHealth app that facilitates the management of, and assistance to, people who want to quit smoking. As secondary objectives, our research also aims at estimating the economic effect of deploying that App in the public health system.

METHODS

Using JAVA and XML we develop and deploy a new free mHealth App for Android, called TControl (Tobacco-quitting Control). We deploy the App at the Tobacco Unit of the Santa Maria Hospital in Lleida and determine its stability by following the crashes of the App. We also use a survey to test usability of the app and differences in aptitude for using the App in a sample of 31 patients. Finally, we use mathematical models to estimate the economic effect of deploying TControl in the Catalan public health system.

RESULTS

TControl keeps track of the smoke-quitting users, tracking their status, interpreting it, and offering advice and psychological support messages. The App also provides a bidirectional communication channel between patients and clinicians via mobile text messages. Additionally, registered patients have the option to interchange experiences with each other by chat. The App was found to be stable and to have high performances during startup and message sending. Our results suggest that age and gender have no statistically significant effect on patient aptitude for using TControl. Finally, we estimate that TControl could reduce costs for the Catalan public health system (CPHS) by up to € 400M in 10 years.

CONCLUSIONS

TControl is a stable and well behaved App, typically operating near optimal performance. It can be used independent of age and gender, and its wide implementation could decrease costs for the public health system.

Virtual Patient Technology: Engaging Primary Care in Quality Improvement Innovations

BACKGROUND

Engaging health care staff in new quality improvement programs is challenging.

OBJECTIVE

We developed 2 virtual patient (VP) avatars in the context of a clinic-level quality improvement program. We sought to determine differences in preferences for VPs and the perceived influence of interacting with the VP on clinical staff engagement with the quality improvement program.

METHODS

Using a participatory design approach, we developed an older male smoker VP and a younger female smoker VP. The older male smoker was described as a patient with cardiovascular disease and was ethnically ambiguous. The female patient was younger and was worried about the impact of smoking on her pregnancy. Clinical staff were allowed to choose the VP they preferred, and the more they engaged with the VP, the more likely the VP was to quit smoking and become healthier. We deployed the VP within the context of a quality improvement program designed to encourage clinical staff to refer their patients who smoke to a patient-centered Web-assisted tobacco intervention. To evaluate the VPs, we used quantitative analyses using multivariate models of provider and practice characteristics and VP characteristic preference and analyses of a brief survey of positive deviants (clinical staff in practices with high rates of encouraging patients to use the quit smoking innovation).

RESULTS

A total of 146 clinical staff from 76 primary care practices interacted with the VPs. Clinic staff included medical providers (35/146, 24.0%), nurse professionals (19/146, 13.0%), primary care technicians (5/146, 3.4%), managerial staff (67/146, 45.9%), and receptionists (20/146, 13.7%). Medical staff were mostly male, and other roles were mostly female. Medical providers (OR 0.031; CI 0.003-0.281; P=.002) and younger staff (OR 0.411; CI 0.177-0.952; P=.038) were less likely to choose the younger, female VP when controlling for all other characteristics. VP preference did not influence online patient referrals by staff. In high-performing practices that referred 20 or more smokers to the ePortal (13/76), the majority of clinic staff were motivated by or liked the virtual patient (20/26, 77%).

CONCLUSIONS

Medical providers are more likely motivated by VPs that are similar to their patient population, while nurses and other staff may prefer avatars that are more similar to them.

Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

Background

Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machine learning systems (“recommender systems”) are used to select messages by combining the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles. However, this approach has not been adequately tested for CTHC.

Objective

Our aim was to compare, in a randomized experiment, a standard, evidence-based, rule-based CTHC (standard CTHC) to a novel machine learning CTHC: Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT). We hypothesized that PERSPeCT will select messages of higher influence than our standard CTHC system. This standard CTHC was proven effective in motivating smoking cessation in a prior randomized trial of 900 smokers (OR 1.70, 95% CI 1.03-2.81).

Methods

PERSPeCT is an innovative hybrid machine learning recommender system that selects and sends motivational messages using algorithms that learn from message ratings from 846 previous participants (explicit feedback), and the prior explicit ratings of each individual participant. Current smokers (N=120) aged 18 years or older, English speaking, with Internet access were eligible to participate. These smokers were randomized to receive either PERSPeCT (intervention, n=74) or standard CTHC tailored messages (n=46). The study was conducted between October 2014 and January 2015. By randomization, we compared daily message ratings (mean of smoker ratings each day). At 30 days, we assessed the intervention’s perceived influence, 30-day cessation, and changes in readiness to quit from baseline.

Results

The proportion of days when smokers agreed/strongly agreed (daily rating ≥4) that the messages influenced them to quit was significantly higher for PERSPeCT (73%, 23/30) than standard CTHC (44%, 14/30, P=.02). Among less educated smokers (n=49), this difference was even more pronounced for days strongly agree (intervention: 77%, 23/30; comparison: 23%, 7/30, P<.001). There was no significant difference in the frequency which PERSPeCT randomized smokers agreed or strongly agreed that the intervention influenced them to quit smoking (P=.07) and use nicotine replacement therapy (P=.09). Among those who completed follow-up, 36% (20/55) of PERSPeCT smokers and 32% (11/34) of the standard CTHC group stopped smoking for one day or longer (P=.70).

Conclusions

Compared to standard CTHC with proven effectiveness, PERSPeCT outperformed in terms of influence ratings and resulted in similar cessation rates.

ClinicalTrial

Clinicaltrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 (Archived by WebCite at http://www.webcitation.org/6lEJY1KEd)

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Perceptions of clinicians and staff about the use of digital technology in primary care: qualitative interviews prior to implementation of a computer-facilitated 5As intervention

BACKGROUND

Digital health interventions using hybrid delivery models may offer efficient alternatives to traditional behavioral counseling by addressing obstacles of time, resources, and knowledge. Using a computer-facilitated 5As (ask, advise, assess, assist, arrange) model as an example (CF5As), we aimed to identify factors from the perspectives of primary care providers and clinical staff that were likely to influence introduction of digital technology and a CF5As smoking cessation counseling intervention. In the CF5As model, patients self-administer a tablet intervention that provides 5As smoking cessation counseling, produces patient and provider handouts recommending next steps, and is followed by a patient-provider encounter to reinforce key cessation messages, provide assistance, and arrange follow-up.

METHODS

Semi-structured in-person interviews of administrative and clinical staff and primary care providers from three primary care clinics.

RESULTS

Thirty-five interviews were completed (12 administrative staff, ten clinical staff, and 13 primary care providers). Twelve were from an academic internal medicine practice, 12 from a public hospital academic general medicine clinic, and 11 from a public hospital HIV clinic. Most were women (91 %); mean age (SD) was 42 years (11.1). Perceived usefulness of the CF5As focused on its relevance for various health behavior counseling purposes, potential gains in counseling efficiency, confidentiality of data collection, occupying patients while waiting, and serving as a cue to action. Perceived ease of use was viewed to depend on the ability to accommodate: clinic workflow; heavy patient volumes; and patient characterisitics, e.g., low literacy. Social norms potentially affecting implementation included beliefs in the promise/burden of technology, priority of smoking cessation counseling relative to other patient needs, and perception of CF5As as just "one more thing to do" in an overburdened system. The most frequently cited facilitating conditions were staffing levels and smoking cessation resources and training; the most cited hindering factors were visit time constraints and patients' complex health care needs.

CONCLUSIONS

Integrating CF5As and other technology-enhanced behavioral counseling interventions in primary care requires flexibility to accommodate work flow and perceptions of overload in dynamic environments. Identifying factors that promote and hinder CF5As adoption could inform implementation of other CF behavioral health interventions in primary care.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

BACKGROUND

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.

OBJECTIVE

The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion.

METHODS

We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results.

RESULTS

We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems.

CONCLUSIONS

We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.