WhatsApp embedded in routine service delivery for smoking cessation: effects on abstinence rates in a randomized controlled study

Effectiveness of digital tools for smoking cessation in Asian countries: a systematic review

AIM

The use of tobacco is responsible for many preventable diseases and deaths worldwide. Digital interventions have greatly improved patient health and clinical care and have proven to be effective for quitting smoking in the general population due to their flexibility and potential for personalization. However, there is limited evidence on the effectiveness of digital interventions for smoking cessation in Asian countries.

METHODS

Three major databases - Web of Science (WOS), Scopus, and PubMed - for relevant studies published between 1 January 2010 and 12 February 2023 were searched for studies evaluating the effectiveness of digital intervention for smoking cessation in Asian countries.

RESULTS

A total of 25 studies of varying designs were eligible for this study collectively involving a total of n = 22,005 participants from 9 countries. Among different digital tools for smoking cessation, the highest abstinence rate (70%) was reported with cognitive behavioural theory (CBT)-based smoking cessation intervention via Facebook followed by smartphone app (60%), WhatsApp (59.9%), and Pharmacist counselling with Quit US smartphone app (58.4%). However, WhatsApp was preferred over Facebook intervention due to lower rates of relapse. WeChat was responsible for 15.6% and 41.8% 7-day point prevalence abstinence. For telephone/text messaging abstinence rate ranged from 8-44.3% and quit rates from 6.3% to 16.8%. Whereas, no significant impact of media/multimedia messages and web-based learning on smoking cessation was observed in this study.

CONCLUSION

Based on the study findings the use of digital tools can be considered an alternative and cost-effective smoking cessation intervention as compared to traditional smoking cessation interventions.

Conversational Chatbot for Cigarette Smoking Cessation: Report of the User-Centered Design Eleven Step Development Process

BACKGROUND

Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.

OBJECTIVE

To describe the user-centered design development process for a novel and comprehensive quit smoking conversational chatbot called "QuitBot."

METHODS

The four years of formative research for developing QuitBot followed an eleven-step process: (1) specifying a conceptual model, (2) conducting content analysis of existing interventions (63 hours of intervention transcripts), (3) assessing user needs, (4) developing the chat's persona ("personality"), (5) prototyping content and persona, (6) developing full functionality, (7) programming the QuitBot, (8) conducting a diary study, (9) conducting a pilot randomized trial, (10) reviewing results of the trial, and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot trial results. The process of adding a QnA function itself involved a three-step process: (a) generating QnA pairs, (b) fine tuning Large Language Models (LLMs) on QnA pairs, and (c) evaluating the LLM model outputs.

RESULTS

A quit smoking program spanning 42 days of 2 to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing FDA-approved cessation medications, coping with triggers, and recovering from lapses/relapses. In a pilot randomized trial with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute's SmokefreeTXT (SFT) text messaging program-particularly among those who viewed all 42 days of program content: 30-day complete-case, point prevalence abstinence (PPA) rates at three-month follow-up were 63% (39/62) for QuitBot vs. 38% (45/117) for SFT (OR = 2.58; 95% CI: 1.34, 4.99; P =.005). However, Facebook Messenger (FM) intermittently blocked participants' access to QuitBot so we transitioned from FM to a standalone smartphone app as the communication channel. Participants' frustration with QuitBot's inability to answer their open-ended questions lead to us develop a core conversational feature enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft's Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT3.5 responds to questions that are not within our library of QnA pairs.

CONCLUSIONS

The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions.

CLINICALTRIAL

ClinicalTrials.gov Identifier, NCT03585231.

Effect of Adding Personalized Instant Messaging Apps to a Brief Smoking Cessation Model in Community Smokers in Hong Kong: Pragmatic Randomized Clinical Trial

BACKGROUND

While text messaging has proven effective for smoking cessation (SC), engagement in the intervention remains suboptimal.

OBJECTIVE

This study aims to evaluate whether using more interactive and adaptive instant messaging (IM) apps on smartphones, which enable personalization and chatting with SC advisors, can enhance SC outcomes beyond the provision of brief SC advice and active referral (AR) to SC services.

METHODS

From December 2018 to November 2019, we proactively recruited 700 adult Chinese daily cigarette users in Hong Kong. Participants were randomized in a 1:1 ratio. At baseline, all participants received face-to-face brief advice on SC. Additionally, they were introduced to local SC services and assisted in selecting one. The intervention group received an additional 26 personalized regular messages and access to interactive chatting through IM apps for 3 months. The regular messages aimed to enhance self-efficacy, social support, and behavioral capacity for quitting, as well as to clarify outcome expectations related to cessation. We developed 3 sets of messages tailored to the planned quit date (within 30 days, 60 days, and undecided). Participants in the intervention group could initiate chatting with SC advisors on IM themselves or through prompts from regular messages or proactive inquiries from SC advisors. The control group received 26 SMS text messages focusing on general health. The primary outcomes were smoking abstinence validated by carbon monoxide levels of <4 parts per million at 6 and 12 months after the start of the intervention.

RESULTS

Of the participants, 505/700 (72.1%) were male, and 450/648 (69.4%) were aged 40 or above. Planning to quit within 30 days was reported by 500/648 (77.2%) participants, with fewer intervention group members (124/332, 37.3%) reporting previous quit attempts compared with the control group (152/335, 45.4%; P=.04). At the 6- and 12-month follow-ups (with retention rates of 456/700, 65.1%, and 446/700, 63.7%, respectively), validated abstinence rates were comparable between the intervention (14/350, 4.0%, and 19/350, 5.4%) and control (11/350, 3.1% and 21/350, 6.0%) groups. Compared with the control group, the intervention group reported greater utilization of SC services at 12 months (RR 1.26, 95% CI 1.01-1.56). Within the intervention group, engaging in chat sessions with SC advisors predicted better validated abstinence at 6 months (RR 3.29, 95% CI 1.13-9.63) and any use of SC services (RR 1.66, 95% CI 1.14-2.43 at 6 months; RR 1.67, 95% CI 1.26-2.23 at 12 months).

CONCLUSIONS

An IM-based intervention, providing support and assistance alongside brief SC advice and AR, did not yield further increases in quitting rates but did encourage the utilization of SC services. Future research could explore whether enhanced SC service utilization leads to improved long-term SC outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03800719; https://clinicaltrials.gov/ct2/show/NCT03800719.

Systematic review and meta-analysis of text messaging interventions to support tobacco cessation

OBJECTIVE

To review randomised controlled trials (RCTs) investigating the effectiveness of text message-based interventions for smoking cessation, including the effects of dose (number of text messages) and concomitant use of behavioural or pharmacological interventions.

DATA SOURCES

We searched seven databases (PubMed, CINAHL, PsycINFO, Scopus, EMBASE, Cochrane Library and Web of Science), Google Scholar and the reference lists of relevant publications for RCTs. Eligible studies included participants aged ≥15 years who smoked tobacco at enrolment.

STUDY SELECTION

One reviewer screened titles and abstracts and two reviewers independently screened full texts of articles.

DATA EXTRACTION

One of three reviewers independently extracted data on study and intervention characteristics and smoking abstinence rates using Qualtrics software.

DATA SYNTHESIS

30 of the 40 included studies reported higher rates of smoking cessation among those receiving text messaging interventions compared with comparators, but only 10 were statistically significant. A meta-analysis of seven RCTs found that participants receiving text messages were significantly more likely to quit smoking compared with participants in no/minimal intervention or 'usual care' conditions (risk ratio 1.87, 95% CI 1.52 to 2.29, p <0.001). Three trials found no benefit from a higher dose of text messages on smoking cessation. Two trials that tested the added benefit of text messaging to pharmacotherapy reported outcomes in favour of adding text messaging.

CONCLUSIONS

Findings suggest that text messaging-based interventions are effective at promoting smoking cessation. Further research is required to establish if any additional benefit is gained from an increased number of text messages or concurrent pharmacotherapy or behavioural counselling.

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials

Background

It is uncertain whether app-based interventions add value to existing mental health care.

Objective

To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions.

Methods

We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545.

Results

We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power.

Discussion

App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes.

Efficacy of e-health interventions for smoking cessation management in smokers: a systematic review and meta-analysis

Background

Smoking is one of the major risk factors for shortened lifespan and disability, while smoking cessation is currently the only guaranteed method to reduce the harm caused by smoking. E-health is a field that utilizes information and communication technology to support the health status of its users. The emergence of this digital health approach has provided a new way of smoking cessation support for smokers seeking help, and an increasing number of researchers are attempting to use e-health for a wide range of effective smoking cessation interventions. We conducted a systematic review and meta-analysis of studies that used e-health as a smoking cessation support tool.

Methods

This systematic review and meta-analysis searched the PubMed, Embase, and Cochrane Library databases until December 2022. The included studies were randomized controlled trials (RCTs) comparing the use of e-health interventions and traditional offline smoking cessation care interventions. The primary outcome of the studies was the point smoking cessation rate (7-day and 30-day), and the secondary outcome was sustained smoking cessation rates. Studies were excluded if there was no clear e-health intervention described or if standard-compliant cessation outcomes were not clearly reported. Fixed-effects meta-analysis and meta-regression analyses were performed on the included study data to evaluate the effectiveness of the interventions. The meta-analysis outcome was the risk ratio (RR) and a 95% confidence interval. The study was registered with PROSPERO, CRD42023388667.

Findings

We collectively screened 2408 articles, and ultimately included 39 articles with a total of 17,351 eligible participants, of which 44 studies were included in the meta-analysis. The meta-analysis revealed that compared to traditional smoking cessation interventions, e-health interventions can increase point quit rates (RR 1.86, 95% CI 1.69-2.04) as well as sustained quit rates in the long-term (RR 1.79, 95% CI 1.60-2.00) among smokers. Subgroup analysis showed that text and telephone interventions in e-health significantly improved short-term quit rates for up to 7 days (RR 2.10, 95% CI 1.77-2.48). Website and app interventions also had a positive impact on improving short-term quit rates for up to 7 days (RR 1.74, 95% CI 1.56-1.94). The heterogeneity of the study results was low, demonstrating the significant smoking cessation advantages of e-health interventions.

Interpretation

We have found that personalized e-health interventions can effectively help smokers quit smoking. The diverse remote intervention methods of e-health can provide more convenient options for further customization. Additionally, further follow-up research is needed to evaluate the sustained effectiveness of interventions on smokers' continuous abstinence over a longer period (greater than one year). In the future, e-health can further optimize smoking cessation strategies.

Funding

No funding.

Effectiveness of eHealth Smoking Cessation Interventions: Systematic Review and Meta-Analysis

BACKGROUND

Rapid advancements in eHealth and mobile health (mHealth) technologies have driven researchers to design and evaluate numerous technology-based interventions to promote smoking cessation. The evolving nature of cessation interventions emphasizes a strong need for knowledge synthesis.

OBJECTIVE

This systematic review and meta-analysis aimed to summarize recent evidence from randomized controlled trials regarding the effectiveness of eHealth-based smoking cessation interventions in promoting abstinence and assess nonabstinence outcome indicators, such as cigarette consumption and user satisfaction, via narrative synthesis.

METHODS

We searched for studies published in English between 2017 and June 30, 2022, in 4 databases: PubMed (including MEDLINE), PsycINFO, Embase, and Cochrane Library. Two independent reviewers performed study screening, data extraction, and quality assessment based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. We pooled comparable studies based on the population, follow-up time, intervention, and control characteristics. Two researchers performed an independent meta-analysis on smoking abstinence using the Sidik-Jonkman random-effects model and log risk ratio (RR) as the effect measurement. For studies not included in the meta-analysis, the outcomes were narratively synthesized.

RESULTS

A total of 464 studies were identified through an initial database search after removing duplicates. Following screening and full-text assessments, we deemed 39 studies (n=37,341 participants) eligible for this review. Of these, 28 studies were shortlisted for meta-analysis. According to the meta-analysis, SMS or app text messaging can significantly increase both short-term (3 months) abstinence (log RR=0.50, 95% CI 0.25-0.75; I2=0.72%) and long-term (6 months) abstinence (log RR=0.77, 95% CI 0.49-1.04; I2=8.65%), relative to minimal cessation support. The frequency of texting did not significantly influence treatment outcomes. mHealth apps may significantly increase abstinence in the short term (log RR=0.76, 95% CI 0.09-1.42; I2=88.02%) but not in the long term (log RR=0.15, 95% CI -0.18 to 0.48; I2=80.06%), in contrast to less intensive cessation support. In addition, personalized or interactive interventions showed a moderate increase in cessation for both the short term (log RR=0.62, 95% CI 0.30-0.94; I2=66.50%) and long term (log RR=0.28, 95% CI 0.04-0.53; I2=73.42%). In contrast, studies without any personalized or interactive features had no significant impact. Finally, the treatment effect was similar between trials that used biochemically verified or self-reported abstinence. Among studies reporting outcomes besides abstinence (n=20), a total of 11 studies reported significantly improved nonabstinence outcomes in cigarette consumption (3/14, 21%) or user satisfaction (8/19, 42%).

CONCLUSIONS

Our review of 39 randomized controlled trials found that recent eHealth interventions might promote smoking cessation, with mHealth being the dominant approach. Despite their success, the effectiveness of such interventions may diminish with time. The design of more personalized interventions could potentially benefit future studies.

TRIAL REGISTRATION

PROSPERO CRD42022347104; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347104.

Effectiveness of including weight management in smoking cessation treatments: A meta-analysis of behavioral interventions

INTRODUCTION

The potential of weight gain after smoking cessation reduces the incentive to quit. This meta-analysis examines the efficacy of behavioral interventions for smoking cessation that also address post-cessation weight gain.

METHODS

Medline, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials were searched for randomized controlled trials on behavioral treatments targeting both health outcomes. Six separate meta-analyses were undertaken to assess treatment efficacy on smoking abstinence and weight outcomes at end of treatment (EOT), short-term, and long-term follow-up. Individual and treatment moderators were examined as well as methodological quality and publication bias of studies.

RESULTS

A total of 28 studies were included in the meta-analysis. There was a statistically significant positive impact of treatments addressing both targets on smoking outcomes at EOT (RR = 1.279, 95% CI: 1.096, 1.492, p = .002), but not at follow-ups. Age impacted on EOT abstinence rates Q (1) = 4.960, p = .026) while increasing the number of sessions significantly improved EOT abstinence rates (p = .020). There was no statistically significant impact of these treatments on weight at EOT (Hedges' g = -0.015, 95% CI: -.164, 0.135, p = .849) or follow-ups (short term: Hedges' g = 0.055, 95% CI: -0.060, 0.170, p = .347; long term: Hedges' g = -0.320, 95% CI: -.965, 0.325, p = .331). There were minimal impacts of publication bias, mostly related to sample size, meaning studies including small sample sizes revealed larger effect sizes on abstinence at EOT.

DISCUSSION

Addressing post-cessation weight management in treatments for smoking cessation significantly enhances tobacco abstinence at EOT though it was not found to have a lasting impact after treatment.

Assessment of a Text Message-Based Smoking Cessation Intervention for Adult Smokers in China: A Randomized Clinical Trial

IMPORTANCE

Successful smoking cessation strategies are an important part of reducing tobacco use. However, providing universal smoking cessation support can be a challenge for most countries because it requires sufficient resources. One way to expand access is to use mobile technologies to provide cessation support.

OBJECTIVE

To assess the efficacy of a behavior change theory-based smoking cessation intervention using personalized text messages.

DESIGN, SETTING, AND PARTICIPANTS

This study was a 2-arm double-blind randomized clinical trial conducted in 5 cities in China. Daily or weekly smokers 18 years or older were eligible for inclusion if they owned a mobile phone and used the WeChat social media app. A total of 722 participants were randomized to the intervention or control group between April 1 and July 27, 2021.

INTERVENTIONS

Intervention group participants received a personalized text message-based smoking cessation intervention that was based on the transtheoretical model and protection motivation theory and developed by this study's investigators. Control group participants received a nonpersonalized text message-based smoking cessation intervention developed by the US National Cancer Institute. Both groups received 1 to 2 text messages per day for 3 months through the app.

MAIN OUTCOMES AND MEASURES

The primary outcome was the biochemically verified 6-month sustained abstinence rate, defined as the self-report of no smoking of any cigarettes after the designated quit date, which was validated biochemically by an expired air carbon monoxide level of less than 6 ppm at each follow-up point.

RESULTS

A total of 722 participants (mean [SD] age, 41.5 [12.7] years; 716 men [99.2%]; all of Chinese ethnicity) were randomly assigned to the intervention group (360 participants) or the control group (362 participants). Biochemically verified continuous abstinence at 6 months was 6.9% in the intervention group and 3.0% in the control group (odds ratio [OR], 2.66; 95% CI, 1.21-5.83). Among smokers with low nicotine dependence, the intervention group had significantly better abstinence rates for most of the indicators after adjusting for covariates (eg, biochemically verified 24-hour point prevalence of abstinence at 1 month: adjusted OR, 2.15; 95% CI, 1.05-4.38). Among smokers with moderate and high nicotine dependence, only the biochemically verified 24-hour point prevalence of abstinence at 6 months was statistically significant (adjusted OR, 4.17; 95% CI, 1.34-3.00). The pattern was similar for quitting intention, and the personalized text message-based intervention was more effective for smokers who had strong quitting intention than for those who had weak quitting intention.

CONCLUSIONS AND RELEVANCE

In this study, the behavior change theory-based smoking cessation intervention using personalized text messages was more effective than an intervention using nonpersonalized text messages. The intervention was most effective among smokers with low nicotine dependence and strong quitting intention. This study's findings also provide further evidence regarding the potential benefits of mobile health interventions for other behaviors.

TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100041942.

Digital interventions for smoking abstinence: a systematic review and meta-analysis of randomized control trials

OBJECTIVES

Technological advancements have improved patients' health and clinical care through digital interventions. This study investigated the effects of digital interventions on smoking abstinence.

METHODS

PubMed, the Cochrane Library, and Scopus were systematically searched from inception until December 2021. Meta-analysis was carried out using a random-effects model. The degree of heterogeneity, quality, and publication bias of the selected studies was further evaluated.

RESULTS

A total of 43 randomized control trial studies were eligible for this study. 38,814 participants from 18 countries were included in the analysis. Digital interventions on seven-day point prevalence abstinence (1 month) showed increased smoking abstinence. The odds ratio was 2.02 and confidence interval (CI) was 1.67-2.43; p < 0.001; I² = 55.1%) . The result for a 30-day point prevalence abstinence (1 month) was 1.63 (CI 1.09-2.46; p = 0.018; I² = 0%). Digital intervention also had a significant effect on continuous abstinence (odds ratio = 1.68; CI 1.29-2.18; p < 0.001; I² = 70.1%) and prolonged abstinence (odds ratio = 1.60; CI 1.19-2.15; p = 0.002; I² = 53.6%). There was evidence of heterogeneity and publication bias.

CONCLUSIONS

Digital interventions led to increased smoking abstinence and can be a valuable tool in smoking cessation. Further research is required to evaluate the long-term impact of digital interventions on outcomes related to smoking cessation.

Telehealth interventions for substance use disorders in low- and- middle income countries: A scoping review

The increasing prevalence and magnitude of harmful effects of substance use disorders (SUDs) in low- and middle-income countries (LMICs) make it imperative to embrace interventions which are acceptable, feasible, and effective in reducing this burden. Globally, the use of telehealth interventions is increasingly being explored as possible effective approaches in the management of SUDs. Using a scoping review of literature, this article summarizes and evaluates evidence for the acceptability, feasibility, and effectiveness of telehealth interventions for SUDs in LMICs. Searches were conducted in five bibliographic databases: PubMed, Psych INFO, Web of Science, Cumulative Index of Nursing and Allied Professionals and the Cochrane database of systematic review. Studies from LMICs which described a telehealth modality, identified at least one psychoactive substance use among participants, and methods that either compared outcomes using pre- and post-intervention data, treatment versus comparison groups, post-intervention data, behavioral or health outcome, and outcome of either acceptability, feasibility, and/or effectiveness were included. Data is presented in a narrative summary using charts, graphs, and tables. The search produced 39 articles across 14 countries which fulfilled our eligibility criteria over a period of 10 years (2010 to 2020). Research on this topic increased remarkably in the latter five years with the highest number of studies in 2019. The identified studies were heterogeneous in their methods and various telecommunication modalities were used to evaluate substance use disorder, with cigarette smoking as the most assessed. Most studies used quantitative methods. The highest number of included studies were from China and Brazil, and only two studies from Africa assessed telehealth interventions for SUDs. There has been an increasingly significant body of literature which evaluates telehealth interventions for SUDs in LMICs. Overall, telehealth interventions showed promising acceptability, feasibility, and effectiveness for SUDs. This article identifies gaps and strengths and suggests directions for future research.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.

The effect of an instant message-based lifestyle and stress management intervention on the reduction of cardiovascular disease risk

AIM

This study investigated the effectiveness of an instant message-based lifestyle and stress management intervention delivered by nurses on cardiovascular disease risk reduction.

METHODS

In this nonrandomized concurrent controlled trial conducted from March 2013 to September 2013, 164 eligible employees in two companies were assigned to the intervention (n = 83) and control (n = 81) groups based on their worksites. Only participants were blinded to group assignment. All participants received two education sessions during 1 month, and the intervention group also received an instant message-based lifestyle and stress management intervention for 5 months. The primary outcome was the Framingham Risk Score, and the data were collected at the first month and the sixth month.

RESULTS

The final analysis included 80 participants in the intervention group and 76 in the control group. After the intervention, significant intervention effects were found for the mean value and the changes of the Framingham Risk Score and the proportion of participants who improved their diet and exercise (P < 0.05). There were trends for improvement in the proportion of smoking and levels of stress, but statistically significant levels (P > 0.05) were not met.

CONCLUSION

An instant message-based lifestyle and stress management intervention can reduce cardiovascular disease risk in high-risk individuals.

Mobile Delivery of Mindfulness-Based Smoking Cessation Treatment Among Low-Income Adults During the COVID-19 Pandemic: Pilot Randomized Controlled Trial

BACKGROUND

Smoking is the leading cause of premature death, and low-income adults experience disproportionate burden from tobacco. Mindfulness interventions show promise for improving smoking cessation. A text messaging program "iQuit Mindfully" was developed to deliver just-in-time support for quitting smoking among low-income adults. A pilot study of iQuit Mindfully was conducted in spring 2020, during the COVID-19 pandemic, among low-income and predominantly African American smokers.

OBJECTIVE

This pilot study examined the acceptability and feasibility of delivering Mindfulness-Based Addiction Treatment via mHealth during the COVID-19 pandemic.

METHODS

Participants were adult cigarette smokers (n=23), of whom 8 (34.8%) were female, 19 (82.6%) were African American, and 18 (78.3%) had an annual income of <US $24,000. They were randomly assigned to either 8 weeks of iQuit Mindfully as a fully automated standalone intervention or iQuit Mindfully in combination with therapist-led in-person group treatment. For participant safety, in-person mindfulness groups were transitioned to the internet and assessments also took place over the internet. Survey questions asked participants about changes in their stress, smoking habits and quit attempts, and their perceptions of the mindfulness and text messaging intervention in the context of the pandemic.

RESULTS

Most participants (n=15 of 21, 71.4%) indicated a change in stress due to the pandemic, of whom 14 (93.3%) indicated higher stress. Participants shared concerns about finances, homelessness, health, and social isolation. Most (n=17 of 21, 80.9%) believed that smoking increases the risk of contracting COVID-19, and although that was motivating for some, others expressed lower motivation to quit smoking because of higher stress. Most (n=18 of 21, 85.7%) stated that practicing mindfulness was helpful during the pandemic. Mean ratings of the helpfulness of text messages and the extent to which they would recommend the program to others were 7.1 (median 8 on a 10-point scale, SD 2.9) and 8.2 (median 9, SD 2.5), respectively. Through open-ended program evaluations, participants shared details about how mindfulness practices and the text messages helped them manage stress and feel a sense of social support during the pandemic. Moreover, 10 of 19 (52.6%) of participants achieved 7-day abstinence from smoking, with no differences between conditions.

CONCLUSIONS

This study supports the promise of text messaging and the use of teleconferencing to provide mindfulness and smoking cessation services to underserved populations during a pandemic.

mHealth Technology and CVD Risk Reduction

PURPOSE OF REVIEW

To review existing mHealth-based interventions and examine their efficacy in reducing cardiovascular disease (CVD) risk factors.

RECENT FINDINGS

A total of 50 articles are included in this review. The majority of the mHealth interventions targeted a specific CVD risk factor, while 4 addressed 2 or more CVD risk factors. Of the 9 mHealth-supported weight loss intervention trials, 4 resulted in significant weight loss. Four out of 7 RCTs targeting improvement in physical activity reported significant improvement, while 4 of the 8 mHealth-supported smoking cessation intervention trials resulted in smoking abstinence. Of the 10 mHealth-based diabetes intervention trials, 5 reported significant reductions in HbA1c; however, only 3 out of the 9 antihypertension interventions resulted in significant reductions in blood pressure. There is a growing body of literature focused on mHealth interventions that address CVD risk factors. Despite the immense potential of mHealth interventions, evidence of their efficacy in mitigating cardiovascular risk is heterogeneous.

Efficacy of tailored recommendations to promote healthy lifestyles: a post hoc analysis of a randomized controlled trial

Prevention is the key to stopping the ravages of cardiovascular diseases, the main cause of death worldwide. The objective was to analyze the efficacy of tailored recommendations to promote healthy lifestyles. Parallel-arm randomized controlled trial with 1 year follow-up. Individuals aged 35-74 years from Girona (Spain) randomly selected from a population with no cardiovascular diseases at baseline were included. Participants in the intervention group received a brochure with tailored healthy choices according to the individual risk profile and a trained nurse explained all recommendations in detail in a 30 min consultation. One year changes in smoking, Mediterranean diet adherence, physical activity, and weight were analyzed with McNemar, Student's t, Wilcoxon, and Fisher exact tests according to an intention-to-treat strategy. Of 955 individuals (52.3% women; mean age 50 [±10] years) randomly allocated to the intervention or control group, one participant in each group presented a cardiovascular event and 768 (81%) were reexamined at 1 year follow-up. The prevalence of nonsmokers increased in both the intervention and control groups (78.1%-82.5%, p = <.001, and 76.7% to 78.8%, p = .015, respectively); however, significance persisted only in the intervention group when stratified by sex, age group, and educational level. Adherence to a Mediterranean diet increased in the intervention group (22.3%-26.5%, p = .048). In conclusion, a brief personalized intervention with science-based recommendations according to individual risk profiles appears to improve healthy lifestyles, particularly nonsmoking and adherence to a Mediterranean diet. This promising intervention system offers evidence-based recommendations to develop healthy lifestyles.

Artificial Intelligence in Medical Sensors for Clinical Decisions

Due to the limited ability of conventional methods and the limited perspective of human diagnostics, patients are often diagnosed incorrectly or at a late stage as their disease condition progresses. They may then undergo unnecessary treatments due to inaccurate diagnoses. In this Perspective, we offer a brief overview on the integration of nanotechnology-based medical sensors and artificial intelligence (AI) for advanced clinical decision support systems to help decision-makers and healthcare systems improve how they approach information, insights, and the surrounding contexts, as well as to promote the uptake of personalized medicine on an individualized basis. Relying on these milestones, wearable sensing devices could enable interactive and evolving clinical decisions that could be used for evidence-based analysis and recommendations as well as for personalized monitoring of disease progress and treatment. We present and discuss the ongoing challenges and future opportunities associated with AI-enabled medical sensors in clinical decisions.

Mobile phone-based messaging for tobacco cessation in low and middle-income countries: A systematic review

INTRODUCTION

Mobile phone-based tobacco cessation (mCessation) interventions are effective in high-income countries but their effectiveness in low and middle-income countries (LMICs) is unclear. We aimed to assess the evidence-base for mCessation interventions in LMICs by synthesizing study characteristics and to describe intervention characteristics and content.

METHODS

Studies were included in this review if they evaluated an intervention that targeted tobacco users, were conducted in an LMIC, measured tobacco cessation as a primary or secondary outcome, and were primarily delivered using mobile phone (text or app-based) messaging. Data were extracted on fields pertaining to study and intervention characteristics and study quality was assessed using the Effective Public Health Practice Project tool. Screening, extraction and quality assessment were conducted by two independent reviewers.

RESULTS

Of 606 unique records, 12 articles were included. The majority of studies were methodologically weak. Methodological limitations included small sample sizes, short follow-up durations and use of self-reported outcomes. Most evaluations were conducted in upper middle-income countries with urban, adult smokers intending to quit smoking. Approximately half the interventions were bidirectional (enabled two-way messaging) and fully automated. Almost all interventions were delivered via SMS. Treatment offerings of the intervention and control groups varied widely.

CONCLUSIONS

More rigorous large-scale randomized controlled trials are needed to conclusively establish the efficacy of mCessation interventions in LMICs. Interventions also need to be tested across more diverse populations and settings. Future studies should test the relative effectiveness of different intervention characteristics.

Effectiveness of digital psychological interventions for mental health problems in low-income and middle-income countries: a systematic review and meta-analysis

BACKGROUND

The effectiveness of digital psychological interventions in low-income and middle-income countries (LMICs) remains unclear. We aimed to systematically investigate the available evidence for digital psychological interventions in reducing mental health problems in LMICs.

METHODS

In this systematic review and meta-analysis, we searched PubMed, PsycINFO, Embase, and Cochrane databases for articles published in English from database inception to March 9, 2020. We included randomised controlled trials investigating digital psychological interventions in individuals with mental health problems in LMICs. We extracted data on demographics, inclusion and exclusion criteria, details of the intervention, including the setting, digital delivery method, control group conditions, number of sessions, therapeutic orientation (eg, cognitive therapy or behaviour therapy), presence or absence of guidance, and length of follow-up, and statistical information to calculate effect sizes. If a study reported insufficient data to calculate effect sizes, the corresponding authors were contacted to provide data that could be aggregated. We did random-effects meta-analyses, and calculated the standardised mean difference in scores of digital psychological interventions versus control conditions (Hedges'g). Quality of evidence was assessed by use of the Grading of Recommendations Assessment, Development, and Evaluation approach. The primary outcome was post-intervention mental health problems, as measured by self-reporting instruments or clinical interviews. This study is registered with PROSPERO, CRD42019137755.

FINDINGS

We identified 22 eligible studies that were included in the meta-analysis. The included studies involved a total of 4104 participants (2351 who received a digital psychological intervention and 1753 who were in the control group), and mainly focused on young adults (mean age of the study population was 20-35 years) with depression or substance misuse. The results showed that digital psychological interventions are moderately effective when compared with control interventions (Hedges'g 0·60 [95% CI 0·45-0·75]; Hedges'g with treatment as usual subgroup for comparison 0·54 [0·35-0·73]). Heterogeneity between studies was substantial (I²=74% [95% CI 60-83]). There was no evidence of publication bias, and the quality of evidence according to the GRADE criteria was generally high.

INTERPRETATION

Digital psychological interventions, which have been mostly studied in individuals with depression and substance misuse, are superior to control conditions, including usual care, and are moderately effective in LMICs. However, the considerable heterogeneity observed in our analysis highlights the need for more studies to be done, with standardised implementation of digital psychological intervention programmes to improve their reproducibility and efficiency. Digital psychological interventions should be considered for regions where usual care for mental health problems is minimal or absent.

FUNDING

None.

TRANSLATIONS

For the Persian, Chinese, Hindi, Portuguese, Bahasa, Turkish, Romanian, Spanish and Thai translations of the abstract see Supplementary Materials section.

Wildfire-Like Effect of a WhatsApp Campaign to Mobilize a Group of Predominantly Health Professionals With a University Degree on a Health Issue: Infodemiology Study

BACKGROUND

Online interactions within a closed WhatsApp group can influence the attitudes and behaviors of the users in relation to health issues.

OBJECTIVE

This study aimed to analyze the activity of the members of a WhatsApp group initiated to raise awareness of the possible health effects of 5G mobile networks and mobilize members to sign the related petition.

METHODS

We retrospectively analyzed data from the WhatsApp group of 205 members that was active during 4 consecutive days in August 2019. The messages exchanged were collected, anonymized, and analyzed according to their timing and content.

RESULTS

The WhatsApp group members were invited to the group from the administrator's contacts; 91% (187/205) had a university degree, 68% (140/205) were medical professionals, and 24% (50/205) held academic positions. Approximately a quarter of the members (47/205, 23%) declared in their messages they signed the corresponding petition. The intense message exchange had wildfire-like features, and the majority of messages (126/133, 95%) were exchanged during the first 26 hours. Despite the viral activity and high rate of members openly declaring that they signed the petition, only 8 (8/133, 6%) messages from the group members, excluding the administrator, referred to the health issue, which was the topic of the group. No member expressed an opposite opinion to those presented by the administrator, and there was no debate in the form of exchanging opposite opinions.

CONCLUSIONS

The wildfire-like activity of the WhatsApp group and open declaration of signing the petition as a result of the mobilization campaign were not accompanied by any form of a debate related to the corresponding health issue, although the group members were predominantly health professionals, with a quarter of holding academic positions.

Relapse prevention interventions for smoking cessation

BACKGROUND

A number of treatments can help smokers make a successful quit attempt, but many initially successful quitters relapse over time. Several interventions have been proposed to help prevent relapse.

OBJECTIVES

To assess whether specific interventions for relapse prevention reduce the proportion of recent quitters who return to smoking.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group trials register, clinicaltrials.gov, and the ICTRP in May 2019 for studies mentioning relapse prevention or maintenance in their title, abstracts, or keywords.

SELECTION CRITERIA

Randomised or quasi-randomised controlled trials of relapse prevention interventions with a minimum follow-up of six months. We included smokers who quit on their own, were undergoing enforced abstinence, or were participating in treatment programmes. We included studies that compared relapse prevention interventions with a no intervention control, or that compared a cessation programme with additional relapse prevention components with a cessation programme alone.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane.

MAIN RESULTS

We included 81 studies (69,094 participants), five of which are new to this update. We judged 22 studies to be at high risk of bias, 53 to be at unclear risk of bias, and six studies to be at low risk of bias. Fifty studies included abstainers, and 30 studies helped people to quit and then tested treatments to prevent relapse. Twenty-eight studies focused on special populations who were abstinent because of pregnancy (19 studies), hospital admission (six studies), or military service (three studies). Most studies used behavioural interventions that tried to teach people skills to cope with the urge to smoke, or followed up with additional support. Some studies tested extended pharmacotherapy. We focused on results from those studies that randomised abstainers, as these are the best test of relapse prevention interventions. Of the 12 analyses we conducted in abstainers, three pharmacotherapy analyses showed benefits of the intervention: extended varenicline in assisted abstainers (2 studies, n = 1297, risk ratio (RR) 1.23, 95% confidence interval (CI) 1.08 to 1.41, I² = 82%; moderate-certainty evidence), rimonabant in assisted abstainers (1 study, RR 1.29, 95% CI 1.08 to 1.55), and nicotine replacement therapy (NRT) in unaided abstainers (2 studies, n = 2261, RR 1.24, 95% Cl 1.04 to 1.47, I² = 56%). The remainder of analyses of pharmacotherapies in abstainers had wide confidence intervals consistent with both no effect and a statistically significant effect in favour of the intervention. These included NRT in hospital inpatients (2 studies, n = 1078, RR 1.23, 95% CI 0.94 to 1.60, I² = 0%), NRT in assisted abstainers (2 studies, n = 553, RR 1.04, 95% CI 0.77 to 1.40, I² = 0%; low-certainty evidence), extended bupropion in assisted abstainers (6 studies, n = 1697, RR 1.15, 95% CI 0.98 to 1.35, I² = 0%; moderate-certainty evidence), and bupropion plus NRT (2 studies, n = 243, RR 1.18, 95% CI 0.75 to 1.87, I² = 66%; low-certainty evidence). Analyses of behavioural interventions in abstainers did not detect an effect. These included studies in abstinent pregnant and postpartum women at the end of pregnancy (8 studies, n = 1523, RR 1.05, 95% CI 0.99 to 1.11, I² = 0%) and at postpartum follow-up (15 studies, n = 4606, RR 1.02, 95% CI 0.94 to 1.09, I² = 3%), studies in hospital inpatients (5 studies, n = 1385, RR 1.10, 95% CI 0.82 to 1.47, I² = 58%), and studies in assisted abstainers (11 studies, n = 5523, RR 0.98, 95% CI 0.87 to 1.11, I² = 52%; moderate-certainty evidence) and unaided abstainers (5 studies, n = 3561, RR 1.06, 95% CI 0.96 to 1.16, I² = 1%) from the general population.

AUTHORS' CONCLUSIONS

Behavioural interventions that teach people to recognise situations that are high risk for relapse along with strategies to cope with them provided no worthwhile benefit in preventing relapse in assisted abstainers, although unexplained statistical heterogeneity means we are only moderately certain of this. In people who have successfully quit smoking using pharmacotherapy, there were mixed results regarding extending pharmacotherapy for longer than is standard. Extended treatment with varenicline helped to prevent relapse; evidence for the effect estimate was of moderate certainty, limited by unexplained statistical heterogeneity. Moderate-certainty evidence, limited by imprecision, did not detect a benefit from extended treatment with bupropion, though confidence intervals mean we could not rule out a clinically important benefit at this stage. Low-certainty evidence, limited by imprecision, did not show a benefit of extended treatment with nicotine replacement therapy in preventing relapse in assisted abstainers. More research is needed in this area, especially as the evidence for extended nicotine replacement therapy in unassisted abstainers did suggest a benefit.