MapMySmoke: feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting

Effectiveness, feasibility, and acceptability of behaviour change tools used by family doctors: a global systematic review

BACKGROUND

Priority patients in primary care include people from low-income, rural, or culturally and linguistically diverse communities, and First Nations people.

AIM

To describe the effectiveness, feasibility, and acceptability of behaviour change tools that have been tested by family doctors working with priority patients.

DESIGN AND SETTING

A global systematic review.

METHOD

Five databases were searched for studies published from 2000 to 2021, of any design, that tested the effectiveness or feasibility of tangible, publicly available behaviour change tools used by family doctors working with priority patients. The methodological quality of each study was appraised using the Mixed Methods Appraisal Tool.

RESULTS

Thirteen of 4931 studies screened met the eligibility criteria, and described 12 tools. The health-related behaviours targeted included smoking, diet and/or physical activity, alcohol and/or drug use, and suicidal ideation. Six tools had an online/web/app-based focus; the remaining six utilised only printed materials and/or in-person training. The effectiveness of the tools was assessed in 11 studies, which used diverse methods, with promising results for enabling behaviour change. The nine studies that assessed feasibility found that the tools were easy to use and enhanced the perceived quality of care.

CONCLUSION

Many of the identified behaviour change tools were demonstrated to be effective at facilitating change in a target behaviour and/or feasible for use in practice. The tools varied across factors, such as the mode of delivery and the way the tool was intended to influence behaviour. There is clear opportunity to build on existing tools to enable family doctors to assist priority patients towards achieving healthier lifestyles.

Smokers' user experience of smoking cessation apps: A systematic review

OBJECTIVES

To explore how smokers view common functions and characteristics of smoking cessation apps.

DESIGN

Systematic review.

SEARCH SOURCES

CINAHL PLUS, MEDLINE, PsycINFO, EMBASE, IEEE Xplore, ACM Digital Library, and Google Scholar.

REVIEW METHODS

Seven digital databases were searched separately using relevant search terms. Search results were uploaded to Covidence. Inclusion and exclusion criteria were identified with the expert team in advance. Titles, abstracts, and full texts were screened by two reviewers independently. Any disagreements were discussed in research meetings. Pertinent data were extracted and analysed using qualitative content analysis. Findings were presented in a narrative approach.

RESULTS

28 studies were included in this review. The overarching themes were app functionality and app characteristics. Under app "functionality", six subthemes emerged: 1) education; 2) tracking; 3) social support; 4) compensation; 5) distraction, and 6) reminding. Under "app characteristics", five subthemes emerged: 1) simplification, 2) personalisation, 3) diverse content forms, 4) interactivity, and 5) privacy and security.

CONCLUSION

Understanding user needs and expectations is crucial for developing a programme theory for smoking cessation app interventions. Relevant needs identified in this review should be linked to broader theories of smoking cessation and app-based intervention.

The associations between smartphone addiction and self-esteem, self-control, and social support among Chinese adolescents: A meta-analysis

Background

Smartphone addiction has become a social problem that affects the healthy growth of adolescents, and it is frequently reported to be correlated with self-esteem, self-control, and social support among adolescents.

Methods

A meta-analysis was conducted by searching the PubMed, Web of Science, Embase, PsycINFO, PsycArticles, China National Knowledge Infrastructure (CNKI), WANFANG DATA, and Chongqing VIP Information Co., Ltd. (VIP) databases. Stata 16.0 was used to analyse the overall effect and test the moderating effect.

Results

Fifty-six studies were included, involving a total of 42,300 participants. Adolescents' smartphone addiction had a moderately negative correlation with self-esteem (r = -0.25, 95% CI = -0.29 to -0.22, p < 0.001), a strong negative correlation with self-control (r = -0.48, 95% CI = -0.53 to -0.42, p < 0.001), and a weak negative correlation with social support (r = -0.16, 95% CI = -0.23 to -0.09, p < 0.001). Moderation analysis revealed that the correlation between adolescents' smartphone addiction and self-esteem was strongest when smartphone addiction was measured with the Mobile Phone Addiction Tendency Scale for College Students (MPATS; r = -0.38). The correlation between adolescents' smartphone addiction and self-control was strongest when self-control was measured with the Middle school students' Self-control Ability Questionnaire (MSAQ; r = -0.62). The effect of dissertations on smartphone addiction, self-control, and social support among adolescents was significantly larger than that of journal articles. The correlation between adolescents' smartphone addiction and social support was strongest when smartphone addiction was measured with the Mobile Phone Addiction Index (MPAI; r = -0.24). However, the correlations between adolescents' smartphone addiction and self-esteem, self-control, and social support were not affected by age or gender.

Conclusion

There was a strong relationship between smartphone addiction and self-esteem, self-control, and social support among adolescents. In the future, longitudinal research should be carried out to better investigate the dynamic changes in therelationship between smartphone addiction and self-esteem, self-control, and social support.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022300061.

Real-time prediction of smoking activity using machine learning based multi-class classification model

Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6-axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11042-022-12349-6.

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.

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.

Mobile Applications for the Treatment of Tobacco Use and Dependence

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Relative effectiveness of a full versus reduced version of the 'Smoke Free' mobile application for smoking cessation: an exploratory randomised controlled trial

Background: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm exploratory randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual 'badges' and daily 'missions' with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users': device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version.

Relative effectiveness of a full versus reduced version of the 'Smoke Free' mobile application for smoking cessation: a randomised controlled trial

Background: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual 'badges' and daily 'missions' with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users': device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version.

Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research

Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.

Smartphone app uses loyalty point incentives and push notifications to encourage influenza vaccine uptake

PURPOSE

Carrot Rewards is a free, incentive-based, smartphone health app available in participating provinces in Canada. One feature of Carrot was designed to incentivize influenza vaccine education messages and encourage vaccine uptake for users in the province of British Columbia. This study aimed to evaluate the uptake of the Carrot Flu Campaign educational quiz and to determine if mobile "push" notifications, plus loyalty point incentives, resulted in users visiting a sponsored pharmacy to discuss and receive the influenza vaccine.

METHODS

The Carrot Flu Campaign delivered an in-app quiz, educating users on the importance of the influenza vaccine. Push notifications were then sent to users when they came within 200 m of a sponsored pharmacy. Those who visited the pharmacy collected bonus points and completed a follow up quiz tracking influenza vaccine behaviour. A sub-sample of users completed the Flu Campaign between their baseline and follow up Health Risk Assessment (HRA), a survey which asked about influenza vaccine uptake behaviour. Descriptive statistics were summarized.

RESULTS

A total of 38.1% (30,538/80,228) registered Carrot users completed the Flu Campaign quiz. Of those in participating cities (n = 21,469), 41% clicked on the map to show the nearest sponsored pharmacy and 78% enabled their smartphone's "locations" feature, allowing them to receive the push notifications. A small number of users spoke to a pharmacist (n = 96) and less than half reported receiving the influenza vaccine (38/96; 39.6%). From the HRA sub-sample (n = 3693), approximately 5% more users reported receiving the influenza vaccine during the 2017 influenza season compared to the previous year.

CONCLUSIONS

Carrot Rewards used a novel delivery method to educate the general population and showed geolocation could be used to facilitate influenza vaccine uptake. Future iterations could tailor content to target those most at risk and should consider more robust evaluation methods to determine the app's effectiveness.