Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24-hour blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines.Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure (BP) were obtained with a 24-hour ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long-and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip.Main results: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 * 10 −5 ). This dip was derived from trend estimates of BP which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip.Significance: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24hour measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Background: The recession has increased job insecurity in the European Union which may result

ObjectiveMobile phone-based interventions have been proven to be effective tools for smoking cessation, at least in the short term. Gamification, that is, the use of game-design elements in a non-game context, has been associated with increased engagement and motivation, critical success factors for long-term success of mobile Health solutions. However, to date, no app review has examined the use of gamification in smoking cessation mobile apps. Our review aims to examine and quantify the use of gamification strategies (broad principles) and tactics (on-screen features) among existing mobile apps for smoking cessation in the UK.MethodsThe UK Android and iOS markets were searched in February 2018 to identify smoking cessation apps. 125 Android and 15 iOS apps were tested independently by two reviewers for primary functionalities, adherence to Five A smoking cessation guidelines, and adoption of gamification strategies and tactics. We examined differences between platforms with χ2tests. Correlation coefficients were calculated to explore the relationship between adherence to guidelines and gamification.ResultsThe most common functionality of the 140 mobile apps we reviewed allowed users to track the days since/until the quit date (86.4%). The most popular gamification strategy across both platforms was performance feedback (91.4%). The majority of apps adopted a medium level of gamification strategies (55.0%) and tactics (64.3%). Few adopted high levels of gamification strategies (6.4%) or tactics (5.0%). No statistically significant differences between the two platforms were found regarding level of gamification (p>0.05) and weak correlations were found between adherence to Five A’s and gamification strategies (r=0.38) and tactics (r=0.26).ConclusionThe findings of this review show that a high level of gamification is adopted by a small minority of smoking cessation apps in the UK. Further exploration of the use of gamification in smoking cessation apps may provide insights into its role in smoking cessation.

Background: Decreasing trends in the number of individuals accessing face-to-face support are leaving a significant gap in the treatment options for smokers seeking to quit. Face-to-face behavioral support and other interventions attempt to target psychological factors such as the self-efficacy and motivation to quit of smokers, as these factors are associated with an increased likelihood of making quit attempts and successfully quitting. Although digital interventions, such as smoking cessation mobile apps, could provide a promising avenue to bridge the growing treatment gap, little is known about their impact on psychological factors that are vital for smoking cessation.Objective: This study aims to better understand the possible impact of smoking cessation mobile apps on important factors for successful cessation, such as self-efficacy and motivation to quit. Our aim is to assess the self-efficacy and motivation to quit levels of smokers before and after the use of smoking cessation mobile apps.Methods: Smokers seeking to quit were recruited to participate in a 4-week app-based study. After screening, eligible participants were asked to use a mobile app (Kwit or Quit Genius). The smoking self-efficacy questionnaire and the motivation to stop smoking scale were used to measure the self-efficacy and motivation to quit, respectively. Both were assessed at baseline (before app use), midstudy (2 weeks after app use), and end-study (4 weeks after app use). Paired sample two-tailed t tests were used to investigate whether differences in self-efficacy and motivation between study time points were statistically significant. Linear regression models investigated associations between change in self-efficacy and change in motivation to quit before and after app use with age, gender, and nicotine dependence.Results: A total of 116 participants completed the study, with the majority being male (71/116, 61.2%), employed (76/116, 65.6%), single (77/116, 66.4%), and highly educated (87/116, 75.0%). A large proportion of participants had a low to moderate dependence on nicotine (107/116, 92.2%). A statistically significant increase of 5.09 points (95% CI 1.83-8.34) from 37.38 points at baseline in self-efficacy was found at the end of the study. Statistically significant increases were also found for the subcomponents of self-efficacy (intrinsic and extrinsic self-efficacies). Similarly, a statistically significant increase of 0.38 points (95% CI 0.06-0.70) from 5.94 points at baseline in motivation to quit was found at the end of the study. Gender, age, and nicotine dependence were not statistically significantly associated with changes in self-efficacy and motivation to quit. Conclusions:The assessed mobile apps positively impacted the self-efficacy and motivation to quit of smokers making quit attempts. This has important implications on the possible future use of digitalized interventions and how they could influence important psychological factors for quitting such as self-efficacy and motivation. However, further research...

Background Smoking remains one of the major preventable causes of chronic diseases. Considering the promising evidence on the effectiveness of mobile technology for health behaviour change, along with the increasing adoption of smartphones, this review aims to systematically assess the adherence of popular mobile apps for smoking cessation to evidence-based guidelines. Methods The United Kingdom Android and iOS markets were searched in February 2018 to identify smoking cessation apps. After screening, 125 Android and 15 iOS apps were tested independently by two reviewers for adherence to the National Institute of Care and Excellence (NICE) Smoking Cessation Guidelines for Self-Help Materials and the Five A Guidelines for Smoking Cessation. Pearson chi square tests were run to examine differences between the two operating systems. Results A majority of apps across both operating systems had low adherence (fulfils 1–2 out of 5 guidelines) to the Five A Guidelines (65.7%) and low adherence (fulfils 1–3 out of 9 guidelines) to the NICE Smoking Cessation Guidelines for Self-Help Materials (63.6%). Only 15% of mobile apps provided information about the benefits of nicotine replacement therapy (NRT), and even fewer provided information regarding types of NRT products (7.1%) or how to use them (2.1%). In addition, only a minority of apps arrange follow-up appointments or provide additional support to help smokers quit. Conclusion Similar to previous mobile app reviews dating back to 2014, our findings show that most mobile apps do not follow existing smoking cessation treatment guidelines, indicating little change regarding the availability of evidence-based mobile apps for smoking cessation in the UK market. Smokers seeking to quit, tobacco control policy makers and software developers need to work together to develop apps that are in line with the latest clinical guidelines and strategies to maximise effectiveness. Electronic supplementary material The online version of this article (10.1186/s12889-019-7084-7) contains supplementary material, which is available to authorized users.