Smoking cessation with smartphone applications (SWAPP): study protocol for a randomized controlled trial

Comparable cigarette consumption data collected using timeline follow-back and digital diary among treatment-seeking smokers

OBJECTIVE

The timeline follow-back interview is a common method of collecting daily cigarette consumption (cigarettes per day [CPD]) in smoking research. However, it may be subject to recall bias due to its reliance on retrospective reports. The increasing ownership of smartphones allows researchers to administer app-based digital diaries (DD) to collect CPD, which is expected to have less recall bias. Several studies have compared these two methods and found a noticeable discrepancy between them. However, these studies have mainly focused on the time window when smokers were smoking ad libitum. In this study, we wanted to determine the comparability of these two methods when treatment-seeking smokers are attempting to quit smoking.

METHOD

In a cessation trial, treatment-seeking smokers (n = 251) reported their CPD using the timeline follow-back and DD methods over a 12-week treatment period. To evaluate the comparability, we used the Bland-Altman comparison approach for agreement, correlational analysis between CPD and biochemical measures, digit bias, and logistic regression for predicting abstinence.

RESULTS

We found that the two methods exhibited good agreement, and the agreement did not vary as a function of consumption levels. Consistent with this agreement, CPD data from both methods showed similar correlations with biochemical measures of smoking and predicted 6-month abstinence in a comparable fashion. Despite the agreement, the DD method appeared to be more precise by having a lower digit bias than the timeline follow-back method.

CONCLUSIONS

Capturing smoking behavior using either TLFB or DD approaches yields similar data while smokers are attempting to quit smoking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effectiveness of the QuitSure Smartphone App for Smoking Cessation: Findings of a Prospective Single Arm Trial

BACKGROUND

Digital therapies, especially smartphone apps for active and continuous smoking cessation support, are strongly emerging as an alternative smoking cessation therapy. In the Indian context, there is a growing interest in the use of app-based smoking cessation programs; however, there is limited evidence regarding their effectiveness in achieving long-term continuous abstinence.

OBJECTIVE

This study aimed to evaluate the long-term abstinence effect (up to 30-d abstinence postprogram completion) of a smartphone app, QuitSure, for smoking cessation in active smokers from India.

METHODS

In this prospective single-arm study, participants who signed up for the QuitSure app were enrolled in this study. The primary end point was the prolonged abstinence (PA) rate from weeks 1 to 4 (day 7 to day 30). Furthermore, data for withdrawal symptoms, relapse reasons, and reasons for not continuing the program were also assessed.

RESULTS

The quit rate was calculated considering only the participants who followed up and completed the survey sent to them (per protocol) at day 7 and at day 30, respectively. The PA rate at day 7 was found to be 64.5% (111/172; 95% CI 56% to 72%), and the PA rate at day 30 was found to be 55.8% (72/129; 95% CI 45% to 65%). Within the 7-day abstinence period, 60.4% (67/111) of the participants did not have any withdrawal symptoms. The most common mild withdrawal symptoms were mild sleep disturbance (21/111, 18.9%), mild digestive changes (19/111, 17.1%), and coughing (17/111, 15.3%). Severe withdrawal symptoms were rare, with only 5.4% (6/111) experiencing them. For those achieving 30-day postprogram abstinence, 85% (61/72) had no mild withdrawal symptoms, and 99% (71/72) had no severe withdrawal symptoms. Among successful quitters at day 7, a total of 72.1% (80/111) reported minimal to no cravings, which increased to 88% (63/72) at day 30. Furthermore, 78% (56/72) of those with PA at day 30 reported no change in weight or reduced weight. Among participants experiencing relapse, 48% (28/58) cited intense cravings, 28% (16/58) mentioned facing a tragedy, and 26% (15/58) reported relapsing due to alcohol consumption. The PA rates as a result of the QuitSure program were found to be better than those reported in the results of other smoking-cessation app programs' studies.

CONCLUSIONS

The QuitSure app yields high PA rates and ameliorates symptoms associated with smoking cessation. In order to obtain conclusive evidence regarding the effectiveness and efficacy of the QuitSure program, future research should include appropriate control measures. Nevertheless, the QuitSure program can serve as a valuable adjunct to a conventional smoking cessation treatment program to aid sustained abstinence.

Awareness and Needs of Smoking Cessation Services for Female Emotional Laborers, Parcel Delivery Workers, Transportation Workers, and Construction Workers in South Korea

Although South Korea has implemented various smoking cessation services, women who are emotional laborers, as well as parcel delivery, transportation, and construction workers, have poor access to these services. This study evaluated the smoking-related characteristics of workers in these four occupations as well as the awareness of and need for smoking cessation services. In total 808 workers in these four occupations aged 19 years and above were recruited nationwide and had their data analyzed. The participants' age, marital status, number of work hours per week, job-related stress, age when they started smoking, average number of cigarettes a day, types of tobacco products, close relationships to others who smoke, number of attempts to quit smoking, plans to quit smoking, awareness of cessation services, prior utilization of cessation services, and need for cessation services were surveyed. Compared with parcel delivery workers, female emotional laborers and transportation and construction workers had more attempts to quit smoking, plans to quit smoking, and prior utilization of smoking cessation services, moreover, construction workers had a significantly lower awareness of smoking cessation services. Parcel delivery workers need smoking cessation programs, mobile applications to help them quit smoking, and improvements in their work environments. Cessation services and education should be promoted at workplaces and among managers.

Update on Cyber Health Psychology: Virtual Reality and Mobile Health Tools in Psychotherapy, Clinical Rehabilitation, and Addiction Treatment

(1) Background: we investigated and analyzed the most recent implementations of technology in the fields of psychotherapy, clinical rehabilitation, and addiction treatment. (2) Methods: from December 2021 to January 2022, we conducted a review aimed at identifying the recent implementations of technology in cyber health psychology, with particular reference to Virtual Reality in psychotherapy, mHealth tools in clinical rehabilitation, and smartphone applications in the treatment of addiction to substances of abuse, searching for relevant studies in the databases PubMed, Web of Science, Google Scholar, Health & Medical Collection, and APA PsycArticles. (3) Results: the tools analyzed are in constant development and are increasingly used, with good results, and further technological progress could lead to even better treatment outcomes; as far as mHealth tools and smartphone applications are concerned, anti-smoking Apps are the most widespread, followed by those for the treatment of alcohol use disorder, and there is no presence of Apps for the treatment of heroin, cocaine, or crack addiction. (4) Conclusions: the results of the review indicate that these technological tools are increasingly used and are, in principle, effective and have numerous advantages, including low cost and versatility.

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Effectiveness of a Dyadic Buddy App for Smoking Cessation: Randomized Controlled Trial

Background

Tobacco smoking is one of the biggest public health threats. Smartphone apps offer new promising opportunities for supporting smoking cessation in real time. This randomized controlled trial investigated the effectiveness of an app that encourages individuals to quit smoking with the help of a social network member (buddy) in daily life.

Objective

The objective of this study is to test the effectiveness of the SmokeFree buddy app compared with a control group with self-reported smoking abstinence and carbon monoxide (CO)–verified smoking abstinence as primary outcomes and self-reports of smoked cigarettes per day (CPD) as a secondary outcome.

Methods

A total of 162 adults who smoked participated in this single-blind, two-arm, parallel-group, intensive longitudinal randomized controlled trial. Around a self-set quit date (ie, 7 days before the self-set quit date and 20 days after) and 6 months later, participants of the intervention and control groups reported on daily smoking abstinence and CPD in end-of-day diaries. Daily smoking abstinence was verified via daily exhaled CO assessments. This assessment was administered via an app displaying results of exhaled CO, thus addressing self-monitoring in both groups. In addition, participants in the intervention group used the SmokeFree buddy app, a multicomponent app that facilitates social support from a buddy of choice.

Results

A significant reduction in CPD from baseline to the 6-month follow-up was observed among participants in both groups. Multilevel analyses revealed no significant intervention effect on self-reported and CO-verified daily smoking abstinence at the quit date and 3 weeks later. However, CPD was lower at the quit date and 3 weeks later in the intervention group than in the control group. No significant differences between groups were found for any outcome measures 6 months after the quit date. Overall, low app engagement and low perceived usefulness were observed.

Conclusions

Despite some encouraging short-term findings on the amount of smoking, the SmokeFree buddy app did not have beneficial effects on smoking abstinence over and above the self-monitoring control condition. Future studies should examine whether and what support processes can be effectively stimulated and how app use can be improved to better achieve this goal.

Trial Registration

ISRCTN Registry 11154315; https://www.isrctn.com/ISRCTN11154315

International Registered Report Identifier (IRRID)

RR2-10.1186/s12889-019-7723-z

Are Machine Learning Methods the Future for Smoking Cessation Apps?

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

Methods Used in Smoking Cessation and Reduction Attempts: Findings from Help-Seeking Smokers

In addition to traditional smoking cessation methods like nicotine replacement therapy (NRT), new methods such as mobile applications and e-cigarettes have been added to the toolbox. The purpose of this study was to examine which methods smokers currently use in quit or reduction attempts and map characteristics of users of the various methods. In this study, participants were smokers who visited a website or called a quit line for smoking cessation and who were currently in quit or reduction attempts (N = 740). Data were collected in Norway in 2013-2017 through a web survey. Most smokers were currently trying to quit, and the most frequently used methods were a smoking cessation app for mobile phones, nicotine replacement therapies (NRTs), and e-cigarettes. Logistic regression analyses identified older daily smokers with high cigarette consumption as NRT users, while the users of a cessation app were younger females. The use of e-cigarettes was associated with older, low educated smokers with low cigarette consumption. The use of the mobile phone app was associated with having made several recent quit attempts. The study provides insight into help-seeking smokers' preferences for smoking cessation methods and user characteristics. This knowledge is relevant for further work in smoking cessation planning and policies.

Digital Health in Cardiac Rehabilitation and Secondary Prevention: A Search for the Ideal Tool

Digital health is becoming more integrated in daily medical practice. In cardiology, patient care is already moving from the hospital to the patients' homes, with large trials showing positive results in the field of telemonitoring via cardiac implantable electronic devices (CIEDs), monitoring of pulmonary artery pressure via implantable devices, telemonitoring via home-based non-invasive sensors, and screening for atrial fibrillation via smartphone and smartwatch technology. Cardiac rehabilitation and secondary prevention are modalities that could greatly benefit from digital health integration, as current compliance and cardiac rehabilitation participation rates are low and optimisation is urgently required. This viewpoint offers a perspective on current use of digital health technologies in cardiac rehabilitation, heart failure and secondary prevention. Important barriers which need to be addressed for implementation in medical practice are discussed. To conclude, a future ideal digital tool and integrated healthcare system are envisioned. To overcome personal, technological, and legal barriers, technological development should happen in dialog with patients and caregivers. Aided by digital technology, a future could be realised in which we are able to offer high-quality, affordable, personalised healthcare in a patient-centred way.

Effectiveness of mobile applications to quit smoking: Systematic review and meta-analysis

INTRODUCTION

Tobacco is the leading cause of preventable mortality. The use of mobile phones has grown exponentially, becoming a powerful tool to be used in health care.

METHODS

In order to assess the effectiveness of mobile phones to quit smoking, we have carried out a systematic review and meta-analysis of randomized clinical trials evaluating interventions based on mobile applications for smartphones, that were not a smaller version of the same application, against other types of therapy. To address this, a bibliographic search was carried out in MEDLINE, EMBASE and COCHRANE LIBRARY. To obtain the combined effect, the relative risk and the 95% confidence interval were used. A heterogeneity and sensitivity analysis were also conducted.

RESULTS

A total of nine studies were identified, but five were excluded. Qualitative review was performed with four selected studies, but quantitative analysis was carried out for only three, given the impossibility of calculating the RR in one of the studies. After combining the results, an RR of 0.901 (95% CI: 0.57-1.423) was calculated comparing the effectiveness of mobile applications versus others type of interventions. This measure was robust, as shown by the sensitivity analysis.

CONCLUSIONS

According to the results, it cannot be concluded that apps are effective for quitting tobacco. There are very few clinical trials published evaluating the effectiveness of mobile applications compared to other alternatives. Several clinical trials are still in progress, therefore their results have not been included in the present meta-analysis.