Correction: Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

[This corrects the article DOI: 10.2196/45942.].

'Stopping the start': support for proposed tobacco control policies - a population-based survey in Great Britain 2021-2023.

OBJECTIVES: This study assessed public support for four proposed tobacco control policies in Great Britain: (1) Raising the sales age of tobacco by 1 year every year (Smokefree Generation); (2) Raising the sales age of tobacco from 18 years to 21 years; (3) Providing prescription e-cigarettes as smoking cessation aids to adults who smoke; (4) Restricting e-cigarette advertising to prevent youth uptake. DESIGN: Repeat cross-sectional population-based survey weighted to match the population of Great Britain. SETTING: The survey was conducted in England, Scotland and Wales in September 2021, October 2022 and October 2023. PARTICIPANTS: 6541 adults living in Great Britain. MAIN OUTCOME MEASURES: Support for each policy and year and prevalence ratios (PRs) comparing support between years and subgroups. RESULTS: The most popular policy each year was restricting e-cigarette advertising (74%/79%/85%), followed by raising the sales age to 21 years (50%/58%/64%), providing prescription e-cigarettes (45%/44%/47%) and Smokefree Generation (34%/44%/49%). The largest increases were for policies about the age of sale (Smokefree Generation: 2021/2022 PR=1.28, 95% CI 1.18 to 1.40, 2022/2023 PR=1.12, 95% CI 1.04 to 1.20; raising the age to 21 years: 2021/2022 PR=1.16, 95% CI 1.09 to 1.23, 2022/2023 PR=1.11, 95% CI 1.05 to 1.17). Only 30% opposed Smokefree Generation in 2023 down from 41% in 2021. CONCLUSIONS: Support for each policy increased each year, except for providing prescription e-cigarettes. Restricting e-cigarette advertising was the most popular policy, while support for age of sale policies, in particular for a Smokefree Generation, grew most. TRIAL REGISTRATION: The study protocol was published on the Open Science Framework (https://osf.io/46z2c/) prior to starting the analysis.

Smoking prevalence and purchasing of menthol cigarettes since the menthol flavour ban in Great Britain: a population-based survey between 2020 and 2023.

BACKGROUND: Menthol cigarettes have been banned in Great Britain (GB) since May 2020. Still, menthol accessories and unlabelled cigarettes perceived as mentholated are available, and people can buy menthol cigarettes overseas or illicitly. This study assessed: trends in smoking menthol cigarettes among all adults and 18-24-year-olds in GB between October 2020 and March 2023; trends in and differences between England, Scotland and Wales during the same period and purchase sources among people smoking menthol versus non-flavoured cigarettes. METHODS: Population-weighted data were from a monthly cross-sectional survey of adults in GB. Among people smoking cigarettes, we calculated the proportion smoking menthol cigarettes across all adults and 18-24-year-olds, and prevalence ratios (PR) between the first and last quarter. We also calculated the proportions of people smoking menthol/non-flavoured cigarettes by purchase source (including illicit sources). RESULTS: In the first quarter, 16.2% of adults smoking cigarettes reported menthol cigarette smoking with little to no decline throughout the study (PR 0.85, 95% CI 0.71 to 1.01), while it declined among 18-24-year-olds (PR 0.75, 95% CI 0.63 to 0.89). The prevalence of menthol cigarette smoking fell by two-thirds in Wales (PR 0.36, 95% CI 0.19 to 0.62) but remained relatively stable in England (PR 0.88, 95% CI 0.72 to 1.06) and Scotland (PR 0.94, 95% CI 0.59 to 1.53). The main purchasing sources were licit (93.9%), 14.8% reported illicit sources and 11.5% cross-border purchases, without notable differences from people smoking non-flavoured cigarettes. CONCLUSIONS: Roughly one million adults in GB still smoke menthol cigarettes and, with the exception of Wales and young people, there were no noteworthy changes in the post-ban period. There was no indication that the overall persistence of menthol smoking was driven by illicit purchases.

Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

BACKGROUND: The Health eLiteracy for Prevention in General Practice trial is a primary health care-based behavior change intervention for weight loss in Australians who are overweight and those with obesity from lower socioeconomic areas. Individuals from these areas are known to have low levels of health literacy and are particularly at risk for chronic conditions, including diabetes and cardiovascular disease. The intervention comprised health check visits with a practice nurse, a purpose-built patient-facing mobile app (mysnapp), and a referral to telephone coaching. OBJECTIVE: This study aimed to assess mysnapp app use, its user profiles, the duration and frequency of use within the Health eLiteracy for Prevention in General Practice trial, its association with other intervention components, and its association with study outcomes (health literacy and diet) to determine whether they have significantly improved at 6 months. METHODS: In 2018, a total of 22 general practices from 2 Australian states were recruited and randomized by cluster to the intervention or usual care. Patients who met the main eligibility criteria (ie, BMI>28 in the previous 12 months and aged 40-74 years) were identified through the clinical software. The practice staff then provided the patients with details about this study. The intervention consisted of a health check with a practice nurse and a lifestyle app, a telephone coaching program, or both depending on the participants' choice. Data were collected directly through the app and combined with data from the 6-week health check with the practice nurses, the telephone coaching, and the participants' questionnaires at baseline and 6-month follow-up. The analyses comprised descriptive and inferential statistics. RESULTS: Of the 120 participants who received the intervention, 62 (52%) chose to use the app. The app and nonapp user groups did not differ significantly in demographics or prior recent hospital admissions. The median time between first and last app use was 52 (IQR 4-95) days, with a median of 5 (IQR 2-10) active days. App users were significantly more likely to attend the 6-week health check (2-sided Fisher exact test; P<.001) and participate in the telephone coaching (2-sided Fisher exact test; P=.007) than nonapp users. There was no association between app use and study outcomes shown to have significantly improved (health literacy and diet) at 6 months. CONCLUSIONS: Recruitment and engagement were difficult for this study in disadvantaged populations with low health literacy. However, app users were more likely to attend the 6-week health check and participate in telephone coaching, suggesting that participants who opted for several intervention components felt more committed to this study. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12617001508369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373505. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-023239.

Alcohol and smoking brief interventions by socioeconomic position: a population-based, cross-sectional study in Great Britain.

BACKGROUND: Alcohol and smoking brief interventions (BIs) in general practice have been shown to be effective in lowering alcohol and smoking-related harm. AIM: To assess prevalence of self-reported BI receipt among increasing or higher-risk drinkers and past-year smokers in England, Scotland, and Wales, and associations between intervention receipt and socioeconomic position. DESIGN & SETTING: Cross-sectional study using data from a monthly population-based survey in England, Scotland, and Wales. METHOD: The study comprised 47 799 participants (15 573 increasing or higher-risk drinkers [alcohol use disorders identification test consumption score ≥5] and 7791 past-year smokers) surveyed via telephone in 2020-2022 (during the COVID-19 pandemic). All data were self-reported. Prevalence of self-reported BI receipt was assessed descriptively; associations between receipt and socioeconomic position were analysed using logistic regression. RESULTS: Among adults in England, Scotland, and Wales, 32.2% (95% confidence interval [CI] = 31.8 to 32.7) reported increasing or higher-risk drinking and 17.7% (95% CI = 17.3 to 18.1) past-year smoking. Among increasing or higher-risk drinkers, 58.0% (95% CI = 57.1 to 58.9) consulted with a GP in the past year, and of these, 4.1% (95% CI = 3.6 to 4.6) reported receiving BIs. Among past-year smokers, 55.8% (95% CI = 54.5 to 57.1) attended general practice in the past year; of these, 41.0% (95% CI = 39.4 to 42.7) stated receiving BIs. There was a tendency for patients from socioeconomically disadvantaged backgrounds to receive more alcohol (adjusted odds ratio [aOR] 1.38, 95% CI = 1.10 to 1.73) or smoking BIs (aOR 1.11, 95% CI = 0.98 to 1.26), but for the latter the results were statistically non-significant. Results did not differ notably by nation within Great Britain. CONCLUSION: BIs in general practice are more common for smoking than for alcohol. A greater proportion of BIs for alcohol were found to be delivered to people who were from socioeconomically disadvantaged backgrounds and who were increasing or higher-risk drinkers.

Mobile Health Use by Older Individuals at Risk of Cardiovascular Disease and Type 2 Diabetes Mellitus in an Australian Cohort: Cross-sectional Survey Study.

BACKGROUND: The digital transformation has the potential to change health care toward more consumers' involvement, for example, in the form of health-related apps which are already widely available through app stores. These could be useful in helping people understand their risk of chronic conditions and helping them to live more healthily. OBJECTIVE: With this study, we assessed mobile health app use among older Australians in general and among those who were at risk of cardiovascular disease or type 2 diabetes mellitus. METHODS: In this cross-sectional analysis, we used data from the second follow-up wave of the 45 and Up Study. It is a cohort study from New South Wales, Australia, with 267,153 participants aged 45 years and older that is based on a random sample from the Services Australia (formerly the Australian Government Department of Human Services) Medicare enrollment database. The 2019 follow-up questionnaire contained questions about technology and mobile health use. We further used data on prescribed drugs and hospitalizations to identify participants who already had cardiovascular disease or diabetes or who were at risk of these conditions. Our primary outcome measure was mobile health use, defined as having used a mobile health app before. We used descriptive statistics and multivariate logistic regression to answer the research questions. RESULTS: Overall, 31,946 individuals with a median age of 69 (IQR 63-76) years had completed the follow-up questionnaire in 2019. We classified half (16,422/31,946, 51.41%) of these as being at risk of cardiovascular disease or type 2 diabetes mellitus and 38.04% (12,152/31,946) as having cardiovascular disease or type 1 or type 2 diabetes mellitus. The proportion of mobile health app users among the at-risk group was 31.46% (5166/16,422) compared to 29.16% (9314/31,946) in the total sample. Those who used mobile health apps were more likely to be female, younger, without physical disability, and with a higher income. People at risk of cardiovascular disease or type 2 diabetes mellitus were not statistically significantly more likely to use mobile health than were people without risk (odds ratio 1.06, 95% CI 0.97-1.16; P=.18; adjusted for age, sex, income, and physical disability). CONCLUSIONS: People at risk of cardiovascular disease or type 2 diabetes mellitus were not more likely to use mobile health apps than were people without risk. Those who used mobile health apps were less likely to be male, older, with a physical disability, and with a lower income. From the results, we concluded that aspects of equity must be considered when implementing a mobile health intervention to reach all those that can potentially benefit from it.

Remotely Conducted App-Based Intervention for Cardiovascular Disease and Diabetes Risk Awareness and Prevention: Single-Group Feasibility Trial.

BACKGROUND: Cardiovascular disease and type 2 diabetes mellitus are two of the most prevalent chronic conditions worldwide. An unhealthy lifestyle greatly contributes to someone's risk of developing these conditions. Mobile health is an emerging technology that can help deliver health promotion interventions to the population, for example, in the form of health apps. OBJECTIVE: The aim of this study was to test the feasibility of an app-based intervention for cardiovascular and diabetes risk awareness and prevention by measuring nonusage, dropout, adherence to app use, and usability of the app over 3 months. METHODS: Participants were eligible if they were aged 45 years or older, resided in Australia, were free of cardiovascular disease and diabetes, were fluent in English, and owned a smartphone. In the beginning, participants received an email with instructions on how to install the app and a user guide. After 3 months, they received an email with an invitation to an end-of-study survey. The survey included questions about general smartphone use and the user version of the Mobile Application Rating Scale. We analyzed app-generated and survey data by using descriptive and inferential statistics as well as thematic analysis for open-text comments. RESULTS: Recruitment took place between September and October 2021. Of the 46 participants who consented to the study, 20 (44%) never used the app and 15 (33%) dropped out. The median age of the app users at baseline was 62 (IQR 56-67) years. Adherence to app use, that is, using the app at least once a week over 3 months, was 17% (8/46) of the total sample and 31% (8/26) of all app users. The mean app quality rating on the user version of the Mobile Application Rating Scale was 3.5 (SD 0.6) of 5 points. The app scored the highest for the information section and the lowest for the engagement section of the scale. CONCLUSIONS: Nonusage and dropouts were too high, and the adherence was too low to consider the intervention in its current form feasible. Potential barriers that we identified include the research team not actively engaging with participants early in the study to verify that all participants could install the app, the intervention did not involve direct contact with health care professionals, and the app did not have enough interactive features.

A Mobile App for Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus: Development and Usability Study.

BACKGROUND: Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are posing a huge burden on health care systems worldwide. Mobile apps can deliver behavior change interventions for chronic disease prevention on a large scale, but current evidence for their effectiveness is limited. OBJECTIVE: This paper reported on the development and user testing of a mobile app that aims at increasing risk awareness and engaging users in behavior change. It would form part of an intervention for primary prevention of CVD and T2DM. METHODS: The theoretical framework of the app design was based on the Behaviour Change Wheel, combined with the capability, opportunity, and motivation for behavior change system and the behavior change techniques from the Behavior Change Technique Taxonomy (version 1). In addition, evidence from scientific literature has guided the development process. The prototype was tested for user-friendliness via an iterative approach. We conducted semistructured interviews with individuals in the target populations, which included the System Usability Scale. We transcribed and analyzed the interviews using descriptive statistics for the System Usability Scale and thematic analysis to identify app features that improved utility and usability. RESULTS: The target population was Australians aged ≥45 years. The app included 4 core modules (risk score, goal setting, health measures, and education). In these modules, users learned about their risk for CVD and T2DM; set goals for smoking, alcohol consumption, diet, and physical activity; and tracked them. In total, we included 12 behavior change techniques. We conducted 2 rounds of usability testing, each involving 5 participants. The average age of the participants was 58 (SD 8) years. Totally, 60% (6/10) of the participants owned iPhone Operating System phones, and 40% (4/10) of them owned Android phones. In the first round, we identified a technical issue that prevented 30% (3/10) of the participants from completing the registration process. Among the 70% (7/10) of participants who were able to complete the registration process, 71% (5/7) rated the app above average, based on the System Usability Scale. During the interviews, we identified some issues related to functionality, content, and language and clarity. We used the participants' feedback to improve these aspects. CONCLUSIONS: We developed the app using behavior change theory and scientific evidence. The user testing allowed us to identify and remove technical errors and integrate additional functions into the app, which the participants had requested. Next, we will evaluate the feasibility of the revised version of the app developed through this design process and usability testing.

Validation of a lifestyle-based risk score for type 2 diabetes mellitus in Australian adults.

The study aimed to assess the performance of a lifestyle-based prognostic risk model (Diabetes Lifestyle Score) for the prediction of 5-year risk of type 2 diabetes mellitus. The model comprises nine self-reported predictors (sex, age, antihypertensive drugs, body mass index, family history of diabetes, physical activity, fruits, vegetables, and wholemeal/brown bread). We conducted an external validation and update of the model in an Australian cohort including 97,615 residents of New South Wales aged 45 years and older who were free of type 1 and 2 diabetes mellitus at baseline. Of all participants, 4,741 developed type 2 diabetes mellitus over 5 years. We conducted the statistical analyses in RStudio using the programming language R. The area under the receiver operating characteristic curve (AUC) of the original model was 0.726 (95% confidence interval: 0.719, 0.733). After adjusting the calibration intercept and slope, the original model performed reasonably well in the external cohort. The best performance was measured by using the numerical predictors as continuous variables and refitting all coefficients (AUC: 0.741, 95% confidence interval: 0.734, 0.748). The results of the original model after calibration were comparable to those received from the AUSDRISK score which is routinely used in Australian clinical practice. Hence, the lifestyle-based model might be a reasonable alternative for laypersons since the required information is most likely known by these. Further, the risk score may communicate the message about the importance of a healthy diet to reduce the risk of diabetes.

Primary Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus Using Mobile Health Technology: Systematic Review of the Literature.

BACKGROUND: Digital technology is an opportunity for public health interventions to reach a large part of the population. OBJECTIVE: This systematic literature review aimed to assess the effectiveness of mobile health-based interventions in reducing the risk of cardiovascular disease and type 2 diabetes mellitus. METHODS: We conducted the systematic search in 7 electronic databases using a predefined search strategy. We included articles published between inception of the databases and March 2019 if they reported on the effectiveness of an intervention for prevention of cardiovascular disease or type 2 diabetes via mobile technology. One researcher performed the search, study selection, data extraction, and methodological quality assessment. The steps were validated by the other members of the research team. RESULTS: The search yielded 941 articles for cardiovascular disease, of which 3 met the inclusion criteria, and 732 for type 2 diabetes, of which 6 met the inclusion criteria. The methodological quality of the studies was low, with the main issue being nonblinding of participants. Of the selected studies, 4 used SMS text messaging, 1 used WhatsApp, and the remaining ones used specific smartphone apps. Weight loss and reduction in BMI were the most reported successful outcomes (reported in 4 studies). CONCLUSIONS: Evidence on the effectiveness of mobile health-based interventions in reducing the risk for cardiovascular disease and type 2 diabetes is low due to the quality of the studies and the small effects that were measured. This highlights the need for further high-quality research to investigate the potential of mobile health interventions. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019135405; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=135405.