Automated Digital Interventions and Smoking Cessation: Systematic Review and Meta-analysis Relating Efficiency to a Psychological Theory of Intervention Perspective

Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors

Introduction

Smoking, obesity, and insufficient physical activity are modifiable health risk behaviors. Self-regulation is one fundamental behavior change mechanism often incorporated within digital therapeutics as it varies momentarily across time and contexts and may play a causal role in improving these health behaviors. However, the role of momentary self-regulation in achieving behavior change has been infrequently examined. Using a novel momentary self-regulation scale, this study examined how targeting self-regulation through a digital therapeutic impacts adherence to the therapeutic and two different health risk behavioral outcomes.

Methods

This prospective interventional study included momentary data for 28 days from 50 participants with obesity and binge eating disorder and 50 participants who smoked regularly. An evidence-based digital therapeutic, called Laddr™, provided self-regulation behavior change tools. Participants reported on their momentary self-regulation via ecological momentary assessments and health risk behaviors were measured as steps taken from a physical activity tracker and breathalyzed carbon monoxide. Medical regimen adherence was assessed as daily Laddr usage. Bayesian dynamic mediation models were used to examine moment-to-moment mediation effects between momentary self-regulation subscales, medical regimen adherence, and behavioral outcomes.

Results

In the binge eating disorder sample, the perseverance [β 1 = 0.17, 95% CI = (0.06, 0.45)] and emotion regulation [β 1 = 0.12, 95% CI = (0.03, 0.27)] targets of momentary self-regulation positively predicted Laddr adherence on the following day, and higher Laddr adherence was subsequently a positive predictor of steps taken the same day for both perseverance [β 2 = 0.335, 95% CI = (0.030, 0.717)] and emotion regulation [β 2 = 0.389, 95% CI = (0.080, 0.738)]. In the smoking sample, the perseverance target of momentary self-regulation positively predicted Laddr adherence on the following day [β = 0.91, 95% CI = (0.60, 1.24)]. However, higher Laddr adherence was not a predictor of CO values on the same day [β 2 = -0.09, 95% CI = (-0.24, 0.09)].

Conclusions

This study provides evidence that a digital therapeutic targeting self-regulation can modify the relationships between momentary self-regulation, medical regimen adherence, and behavioral health outcomes. Together, this work demonstrated the ability to digitally assess the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and pro-health behavioral outcomes.

Clinical Trial Registration

ClinicalTrials.gov, identifier (NCT03774433).

Mapping the Landscape of Digital Health Intervention Strategies: 25-Year Synthesis

BACKGROUND

Digital health interventions have emerged as promising tools to promote health behavior change and improve health outcomes. However, a comprehensive synthesis of strategies contributing to these interventions is lacking.

OBJECTIVE

This study aims to (1) identify and categorize the strategies used in digital health interventions over the past 25 years; (2) explore the differences and changes in these strategies across time periods, countries, populations, delivery methods, and senders; and (3) serve as a valuable reference for future researchers and practitioners to improve the effectiveness of digital health interventions.

METHODS

This study followed a systematic review approach, complemented by close reading and text coding. A comprehensive search for published English academic papers from PubMed, Web of Science, and Scopus was conducted. The search employed a combination of digital health and intervention-related terms, along with database-specific subject headings and filters. The time span covered 25 years, from January 1, 1999, to March 10, 2024. Sample papers were selected based on study design, intervention details, and strategies. The strategies were identified and categorized based on the principles of Behavior Change Techniques and Behavior Strategies.

RESULTS

A total of 885 papers involving 954,847 participants met the eligibility criteria. We identified 173 unique strategies used in digital health interventions, categorized into 19 themes. The 3 most frequently used strategies in the sample papers were "guide" (n=492, 55.6%), "monitor" (n=490, 55.4%), and "communication" (n=392, 44.3%). The number of strategies employed in each paper ranged from 1 to 32. Most interventions targeted clients (n=844, 95.4%) and were carried out in hospitals (n=268, 30.3%). High-income countries demonstrated a substantially higher number and diversity of identified strategies than low- and middle-income countries, and the number of studies targeting the public (n=647, 73.1%) far exceeded those focusing on vulnerable groups (n=238, 26.9%).

CONCLUSIONS

Digital health interventions and strategies have undergone considerable development over the past 25 years. They have evolved from simple approaches to sophisticated, personalized techniques and are trending toward multifaceted interventions, leveraging advanced technologies for real-time monitoring and feedback. Future studies should focus on rigorous evaluations, long-term effectiveness, and tailored approaches for diverse populations, and more attention should be given to vulnerable groups.

Tobacco cessation: screening and interventions

BACKGROUND

Tobacco use has been the leading cause of disease and death in South Korea. Early detection of tobacco use and evidence-based interventions play pivotal roles in facilitating tobacco cessation.

METHODS

In accordance with the earlier iterations of the Lifetime Health Maintenance Program (2009) and recent recommendations from the United States Preventive Services Task Force (USPSTF; 2021), two themes were chosen for investigation: the identification of and intervention for tobacco use. The USPSTF recommendations were formulated by conducting an overview of reviews. In this study, literature searches and quality assessments of reviews were conducted.

RESULTS

The findings highlighted the efficacy of physician-led identification and advising in promoting tobacco cessation, with robust evidence supporting the implementation of behavioral and pharmacological interventions. These interventions significantly increased the likelihood of successful cessation compared with usual care. Digital interventions, such as internet- or mobile-based interventions, showed additive effects for quitting.

CONCLUSION

Identification and targeted interventions are essential for tobacco cessation. By leveraging evidencebased strategies and enhancing access to resources, healthcare providers can empower individuals to achieve successful tobacco cessation and improve overall health outcomes.

Presenting and Evaluating a Smartwatch-Based Intervention for Smoking Relapse (StopWatch): Feasibility and Acceptability Study

BACKGROUND

Despite the benefits of smoking cessation, maintaining abstinence during a quit attempt is difficult, and most attempts result in relapse. Innovative, evidence-based methods of preventing relapse are needed. We present a smartwatch-based relapse prevention system that uses passive detection of smoking to trigger just-in-time smoking cessation support.

OBJECTIVE

This study aims to evaluate the feasibility of hosting just-in-time smoking cessation support on a smartwatch and the acceptability of the "StopWatch" intervention on this platform.

METHODS

The person-based approach for intervention development was used to design the StopWatch smoking relapse prevention intervention. Intervention delivery was triggered by an algorithm identifying hand movements characteristic of smoking from the smartwatch's motion sensors, and the system-generated intervention messages (co-designed by smokers) were delivered on the smartwatch screen. A total of 18 smokers tested the intervention over a 2-week period, and at the end of this period, they provided qualitative feedback on the acceptability of both the intervention and the smartwatch platform.

RESULTS

Participants reported that the smartwatch intervention increased their awareness of smoking and motivated them to quit. System-generated intervention messages were generally felt to be relevant and timely. There were some challenges with battery life that had implications for intervention adherence, and the bulkiness of the device and the notification style reduced some participants' acceptability of the smartwatch platform.

CONCLUSIONS

Our findings indicate our smoking relapse prevention intervention and the use of a smartwatch as a platform to host a just-in-time behavior change intervention are both feasible and acceptable to most (12/18, 66%) participants as a relapse prevention intervention, but we identify some concerns around the physical limitations of the smartwatch device. In particular, the bulkiness of the device and the battery capacity present risks to adherence to the intervention and the potential for missed detections. We recommend that a longer-term efficacy trial be carried out as the next step.

The Feasibility and Acceptability of Using a Digital Conversational Agent (Chatbot) for Delivering Parenting Interventions: Systematic Review

BACKGROUND

Parenting interventions are crucial for promoting family well-being, reducing violence against children, and improving child development outcomes; however, scaling these programs remains a challenge. Prior reviews have characterized the feasibility, acceptability, and effectiveness of other more robust forms of digital parenting interventions (eg, via the web, mobile apps, and videoconferencing). Recently, chatbot technology has emerged as a possible mode for adapting and delivering parenting programs to larger populations (eg, Parenting for Lifelong Health, Incredible Years, and Triple P Parenting).

OBJECTIVE

This study aims to review the evidence of using chatbots to deliver parenting interventions and assess the feasibility of implementation, acceptability of these interventions, and preliminary outcomes.

METHODS

This review conducted a comprehensive search of databases, including Web of Science, MEDLINE, Scopus, ProQuest, and Cochrane Central Register of Controlled Trials. Cochrane Handbook for Systematic Review of Interventions and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to conduct the search. Eligible studies targeted parents of children aged 0 to 18 years; used chatbots via digital platforms, such as the internet, mobile apps, or SMS text messaging; and targeted improving family well-being through parenting. Implementation measures, acceptability, and any reported preliminary measures of effectiveness were included.

RESULTS

Of the 1766 initial results, 10 studies met the inclusion criteria. The included studies, primarily conducted in high-income countries (8/10, 80%), demonstrated a high mean retention rate (72.8%) and reported high acceptability (10/10, 100%). However, significant heterogeneity in interventions, measurement methods, and study quality necessitate cautious interpretation. Reporting bias, lack of clarity in the operationalization of engagement measures, and platform limitations were identified as limiting factors in interpreting findings.

CONCLUSIONS

This is the first study to review the implementation feasibility and acceptability of chatbots for delivering parenting programs. While preliminary evidence suggests that chatbots can be used to deliver parenting programs, further research, standardization of reporting, and scaling up of effectiveness testing are critical to harness the full benefits of chatbots for promoting family well-being.

Smartphone application versus written material for smoking reduction and cessation in individuals undergoing low-dose computed tomography (LDCT) screening for lung cancer: a phase II open-label randomised controlled trial

Background

Counseling, nicotine replacement, and other cessation medications have been proven effective in smoking cessation. The wide-scale adoption of smartphones and other mobile devices has opened new possibilities for scalable and personalized smoking cessation approaches. The study investigated whether a smartphone application would be more effective than written material for smoking cessation and reduction in smoking in individuals undergoing low-dose computed tomography (LDCT) screening for lung cancer (NCT05630950).

Methods

This randomized controlled trial enrolled 201 current smokers with marked smoking history (smoked ≥15 cigarettes/day for ≥25 years or smoked ≥10 cigarettes/day for ≥30 years). Participants were stratified by age and pack-years and randomized in 1:1 fashion to the developed smartphone application (experimental arm) or written material (standard of care). All the subjects underwent LDCT screening. Self-reported smoking cessation at three and six months were the primary endpoints of the study. The smoking-related secondary endpoints of the study were the percentage of individuals who had reduced the number of smoked cigarettes/d from the baseline.

Findings

Between Nov 18, 2022, and Apr 14, 2023, 201 patients were screened at Oulu University Hospital, Finland, of whom all were randomly assigned to smartphone application (n = 101) or written cessation material (n = 100); 200 were included in the full analysis set. Study arms were well-balanced for all the studied demographic factors. Subjects randomized to the smartphone application arm had significantly higher rates for self-reported smoking cessation at three (19.8 versus 7.1%; OR 3.175 CI 95% 1.276-7.899) and six months (18.8 versus 7.1%; OR 2.847 CI 95% 1.137-7.128). In the experimental arm, individuals with a frequent use of the application had a higher chance for smoking cessation at three (p < 0.001) and six months (p = 0.003).

Interpretation

The study showed that the developed smartphone application increases the likelihood for smoking cessation in individuals undergoing lung cancer LDCT screening.

Funding

AstraZeneca, Roche, and Cancer Foundation Finland.

Mobile Health Interventions for Substance Use Disorders

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

A Digital Cognitive-Physical Intervention for Attention-Deficit/Hyperactivity Disorder: Randomized Controlled Trial

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders among children. Pharmacotherapy has been the primary treatment for ADHD, supplemented by behavioral interventions. Digital and exercise interventions are promising nonpharmacologic approaches for enhancing the physical and psychological health of children with ADHD. However, the combined impact of digital and exercise therapies remains unclear.

OBJECTIVE

The aim of this study was to determine whether BrainFit, a novel digital intervention combining gamified cognitive and exercise training, is efficacious in reducing ADHD symptoms and executive function (EF) among school-aged children with ADHD.

METHODS

This 4-week prospective randomized controlled trial included 90 children (6-12 years old) who visited the ADHD outpatient clinic and met the diagnostic criteria for ADHD. The participants were randomized (1:1) to the BrainFit intervention (n=44) or a waitlist control (n=46) between March and August 2022. The intervention consisted of 12 30-minute sessions delivered on an iPad over 4 weeks with 3 sessions per week (Monday, Wednesday, and Friday after school) under the supervision of trained staff. The primary outcomes were parent-rated symptoms of attention and hyperactivity assessed according to the Swanson, Nolan, and Pelham questionnaire (SNAP-IV) rating scale and EF skills assessed by the Behavior Rating Inventory of Executive Function (BRIEF) scale, evaluated pre and post intervention. Intention-to-treat analysis was performed on 80 children after attrition. A nonparametric resampling-based permutation test was used for hypothesis testing of intervention effects.

RESULTS

Among the 145 children who met the inclusion criteria, 90 consented and were randomized; ultimately, 80 (88.9%) children completed the study and were included in the analysis. The participants' average age was 8.4 (SD 1.3) years, including 63 (78.8%) male participants. The most common ADHD subtype was hyperactive/impulsive (54/80, 68%) and 23 (29%) children had severe symptoms. At the endpoint of the study, the BrainFit intervention group had a significantly larger improvement in total ADHD symptoms (SNAP-IV total score) as compared to those in the control group (β=-12.203, 95% CI -17.882 to -6.523; P<.001), owing to lower scores on the subscales Inattention (β=-3.966, 95% CI -6.285 to -1.647; P<.001), Hyperactivity/Impulsivity (β=-5.735, 95% CI -8.334 to -3.137; P<.001), and Oppositional Defiant Disorder (β=-2.995, 95% CI -4.857 to -1.132; P=.002). The intervention was associated with significant reduction in the Metacognition Index (β=-6.312, 95% CI -10.973 to -1.650; P=.006) and Global Executive Composite (β=-5.952, 95% CI -10.214 to -1.690; P=.003) on the BRIEF. No severe intervention-related adverse events were reported.

CONCLUSIONS

This novel digital cognitive-physical intervention was efficacious in school-age children with ADHD. A larger multicenter effectiveness trial with longer follow-up is warranted to confirm these findings and to assess the durability of treatment effects.

TRIAL REGISTRATION

Chinese Clinical Trial Register ChiCTR2300070521; https://www.chictr.org.cn/showproj.html?proj=177806.

Do medications increase the efficacy of digital interventions for smoking cessation? Secondary results from the iCanQuit randomized trial

BACKGROUND AND AIMS

iCanQuit is a smartphone application (app) proven efficacious for smoking cessation in a Phase III randomized controlled trial (RCT). This study aimed to measure whether medications approved by the US Food and Drug Administration (FDA) for smoking cessation would further enhance the efficacy of iCanQuit, relative to its parent trial comparator-the National Cancer Institute's (NCI's) QuitGuide app.

DESIGN

Secondary analysis of the entire parent trial sample of a two-group (iCanQuit and QuitGuide), stratified, doubled-blind RCT.

SETTING

United States.

PARTICIPANTS

Participants who reported using an FDA-approved cessation medication on their own (n = 619) and those who reported no use of cessation medications (n = 1469).

INTERVENTIONS

Participants were randomized to receive iCanQuit app or NCI's QuitGuide app.

MEASUREMENTS

Use of FDA-approved medications was measured at 3 months post-randomization. Smoking cessation outcomes were measured at 3, 6 and 12 months. The primary outcome was 12-month self-reported 30-day point prevalence abstinence (PPA).

FINDINGS

The data retention rate at the 12-month follow-up was 94.0%. Participants were aged 38.5 years, 71.0% female, 36.6% minority race/ethnicity, 40.6% high school or less education, residing in all 50 US States and smoking 19.2 cigarettes/day. The 29.6% of all participants who used medications were more likely to choose nicotine replacement therapy (NRT; 78.8%) than other cessation medications (i.e. varenicline or bupropion; 18.3 and 10.5%, respectively) and use did not differ by app treatment assignment (all P > 0.05). There was a significant (P = 0.049) interaction between medication use and app treatment assignment on PPA. Specifically, 12-month quit rates were 34% for iCanQuit versus 20% for QuitGuide [odds ratio (OR) = 2.36, 95% confidence interval (CI) = 1.59, 3.49] among participants reporting any medication use, whereas among participants reporting no medication use, quit rates were 28% for iCanQuit versus 22% for QuitGuide (OR = 1.41, 95% CI = 1.09, 1.82). Results were stronger for those using only NRT: 40% quit rates for iCanQuit versus 18% quit rates for QuitGuide (OR = 3.57, 95% CI = 2.20, 5.79).

CONCLUSIONS

The iCanQuit smartphone app for smoking cessation was more efficacious than the QuitGuide smartphone app, regardless of whether participants used medications to aid cessation. Smoking cessation medications, especially nicotine replacement therapy, might enhance the efficacy of the iCanQuit app.

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Mobile Phone-Based Interventions for Smoking Cessation Among Young People: Systematic Review and Meta-Analysis

Background

Mobile phone-based cessation interventions have emerged as a promising alternative for smoking cessation, while evidence of the efficacy of mobile phone-based smoking cessation programs among young people is mixed.

Objective

This study aimed to determine the efficacy of mobile phone-based interventions compared to usual practice or assessment-only controls on smoking cessation in young people.

Methods

In this systematic review and meta-analysis, we searched Cochrane Library, Embase, PubMed, and Web of Science on March 8, 2023. We included randomized controlled trials that examined the efficacy of mobile phone-based interventions on smoking cessation in young people (age ≤30 years). The risk of bias was assessed with Cochrane Risk of Bias 2.

Results

A total of 13 eligible studies, comprising 27,240 participants, were included in this analysis. The age range of the participants was between 16 and 30 years. Nine studies were SMS text messaging interventions, and 4 studies were app-based interventions. The duration of the smoking cessation intervention varied from 5 days to 6 months. The included studies were conducted in the following countries: the United States, China, Sweden, Canada, Switzerland, and Thailand. The meta-analysis revealed that SMS text messaging interventions significantly improved continuous abstinence rates compared to inactive control conditions (risk ratio [RR] 1.51, 95% CI 1.24-1.84). The subgroup analysis showed pooled RRs of 1.90 (95% CI 1.29-2.81), 1.64 (95% CI 1.23-2.18), and 1.35 (95% CI 1.04-1.76) for continuous abstinence at the 1-, 3-, and 6- month follow-up, respectively. Pooling across 7 studies, SMS text messaging interventions showed efficacy in promoting 7-day point prevalence abstinence (PPA), with an RR of 1.83 (95% CI 1.34-2.48). The subgroup analysis demonstrated a significant impact at the 1- and 3-month follow-ups, with pooled RRs of 1.72 (95% CI 1.13-2.63) and 2.54 (95% CI 2.05-3.14), respectively, compared to inactive control conditions. However, at the 6-month follow-up, the efficacy of SMS text messaging interventions in promoting 7-day PPA was not statistically significant (RR 1.45, 95% CI 0.92-2.28). In contrast, app-based interventions did not show significant efficacy in promoting continuous abstinence or 7-day PPA. However, it is important to note that the evidence for app-based interventions was limited.

Conclusions

SMS text messaging-based smoking cessation interventions compared to inactive controls were associated with abstinence among young people and could be considered a viable option for smoking cessation in this population. More research is needed on smoking cessation apps, especially apps that target young people. Future research should focus on identifying the most effective mobile phone-based cessation approaches and on developing strategies to increase their uptake and intention.

The Impact of Digital Health on Smoking Cessation

BACKGROUND

Smartphones have become useful tools for medicine, with the use of specific apps making it possible to bring health care closer to inaccessible areas, continuously monitor a patient's pathology at any time and place, promote healthy habits, and ultimately improve patients' quality of life and the efficiency of the health care system. Since 2020, the use of smartphones has reached unprecedented levels. There are more than 350,000 health apps, according to a 2021 IQVIA Institute report, that address, among other things, the management of patient appointments; communication among different services or professionals; the promotion of lifestyle changes related to adopting healthy habits; and the monitoring of different pathologies and chronic conditions, including smoking cessation. The number of mobile apps for quitting smoking is high. As early as 2017, a total of 177 unique smoking cessation-relevant apps were identified in the iPhone App Store, 139 were identified in Google Play, 70 were identified in the BlackBerry app store, and 55 were identified in the Windows Phone Store, but very few have adequate scientific support. It seems clear that efforts are needed to assess the quality of these apps, as well as their effectiveness in different population groups, to have tools that offer added value to standard practices.

OBJECTIVE

This viewpoint aims to highlight the benefits of mobile health (mHealth) and its potential as an adjuvant tool in health care.

METHODS

A review of literature and other data sources was performed in order to show the current status of mobile apps that can offer support for smoking cessation. For this purpose, the PubMed, Embase, and Cochrane databases were explored between May and November 2022.

RESULTS

In terms of smoking cessation, mHealth has become a powerful coadjuvant tool that allows health workers to perform exhaustive follow-ups for the process of quitting tobacco and provide support anytime and anywhere. mHealth tools are effective for different groups of smokers (eg, pregnant women, patients with chronic obstructive pulmonary disease, patients with mental illness, and the general population) and are cost-effective, generating savings for the health system. However, there are some patient characteristics that can predict the success of using mobile apps in the smoking cessation process, such as the lower age of patients, dependence on tobacco, the number of quit attempts, and the previous use of mobile apps, among others. Therefore, it is preferable to offer these tools to patients with a higher probability of quitting tobacco.

CONCLUSIONS

mHealth is a promising tool for helping smokers in the smoking cessation process. There is a need for well-designed clinical studies and economic evaluations to jointly assess the effectiveness of new interventions in different population groups, as well as their impact on health care resources.