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BackgroundThe availability and use of health apps continues to increase, revolutionizing the way mobile health interventions are delivered. Apps are increasingly used to prevent disease, improve well-being, and promote healthy behavior. On a similar rise is the incidence of skin cancers. Much of the underlying risk can be prevented through behavior change and adequate sun protection. Self-monitoring apps have the potential to facilitate prevention by measuring risk (eg, sun intensity) and encouraging protective behavior (eg, seeking shade). ObjectiveOur aim was to assess health care consumer preferences for sun protection with a self-monitoring app that tracks the duration and intensity of sun exposure and provides feedback on when and how to protect the skin. MethodsWe conducted an unlabeled discrete choice experiment with 8 unique choice tasks, in which participants chose among 2 app alternatives, consisting of 5 preidentified 2-level attributes (self-monitoring method, privacy control, data sharing with health care provides, reminder customizability, and costs) that were the result of a multistep and multistakeholder qualitative approach. Participant preferences, and thus, the relative importance of attributes and their levels were estimated using conditional logit modeling. Analyses consisted of 200 usable surveys, yielding 3196 observations. ResultsOur respondents strongly preferred automatic over manually operated self-monitoring (odds ratio [OR] 2.37, 95% CI 2.06-2.72) and no cost over a single payment of 3 Swiss francs (OR 1.72, 95% CI 1.49-1.99). They also preferred having over not having the option of sharing their data with a health care provider of their choice (OR 1.66, 95% CI 1.40-1.97), repeated over single user consents, whenever app data are shared with commercial thirds (OR 1.57, 95% CI 1.31-1.88), and customizable over noncustomizable reminders (OR 1.30, 95% CI 1.09-1.54). While most participants favored thorough privacy infrastructures, the attribute of privacy control was a relatively weak driver of app choice. The attribute of self-monitoring method significantly interacted with gender and perceived personal usefulness of health apps, suggesting that female gender and lower perceived usefulness are associated with relatively weaker preferences for automatic self-monitoring. ConclusionsBased on the preferences of our respondents, we found that the utility of a self-monitoring sun protection app can be increased if the app is simple and adjustable; requires minimal effort, time, or expense; and has an interoperable design and thorough privacy infrastructure. Similar features might be desirable for preventive health apps in other areas, paving the way for future discrete choice experiments. Nonetheless, to fully understand these preference dynamics, further qualitative or mixed method research on mobile self-monitoring-based sun protection and broader preventive mobile self-monitoring is required. International Registered Report Identifier (IRRID)RR2-10.2196/16087

BackgroundVaccine-serotype (VT) invasive pneumococcal disease (IPD) rates declined substantially following introduction of 7-valent pneumococcal conjugate vaccine (PCV7) into national immunization programs. Increases in non-vaccine-serotype (NVT) IPD rates occurred in some sites, presumably representing serotype replacement. We used a standardized approach to describe serotype-specific IPD changes among multiple sites after PCV7 introduction.Methods and findingsOf 32 IPD surveillance datasets received, we identified 21 eligible databases with rate data ≥ 2 years before and ≥ 1 year after PCV7 introduction. Expected annual rates of IPD absent PCV7 introduction were estimated by extrapolation using either Poisson regression modeling of pre-PCV7 rates or averaging pre-PCV7 rates. To estimate whether changes in rates had occurred following PCV7 introduction, we calculated site specific rate ratios by dividing observed by expected IPD rates for each post-PCV7 year. We calculated summary rate ratios (RRs) using random effects meta-analysis. For children

Little evidence on the validity of simple and widely applicable tools to predict mortality in patients with chronic obstructive pulmonary disease (COPD) exists. To conduct a large international study to validate the ADO index that uses age, dyspnoea and FEV(1) to predict 3-year mortality and to update it in order to make prediction of mortality in COPD patients as generalisable as possible. Individual subject data analysis of 10 European and American cohorts (n=13 914). Population-based, primary, secondary and tertiary care. COPD GOLD stages I-IV. We validated the original ADO index. We then obtained an updated ADO index in half of our cohorts to improve its predictive accuracy, which in turn was validated comprehensively in the remaining cohorts using discrimination, calibration and decision curve analysis and a number of sensitivity analyses. 1350 (9.7%) of all subjects with COPD (60% male, mean age 61 years, mean FEV(1) 66% predicted) had died at 3 years. The original ADO index showed high discrimination but poor calibration (p

BackgroundAsthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. ObjectiveThe objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. MethodsWe used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. ResultsWe recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. ConclusionsOur study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.

BackgroundThe incidence of sun-exposure-related skin conditions, such as melanoma, is a gradually increasing and largely preventable public health problem. Simultaneously, the availability of mobile apps that enable the self-monitoring of health behavior and outcomes is ever increasing. Inevitably, recent years have seen an emerging volume of electronic patient-generated health data (PGHD), as well as their targeted application across primary prevention areas, including sun protection and skin health. Despite their preventive potential, the actual impact of these apps relies on the engagement of health care consumers, who are primarily responsible for recording, sharing, and using their PGHD. Exploring preferences is a key step toward facilitating consumer engagement and ultimately realizing their potential. ObjectiveThis paper describes an ongoing research project that aims to elicit the preferences of health care consumers for sun protection via app-based self-monitoring. MethodsA discrete choice experiment (DCE) will be conducted to explore how healthy consumers choose between two alternative preventive self-monitoring apps. DCE development and attribute selection were built on extensive qualitative work, consisting of the secondary use of a previously conducted scoping review, a rapid review of reviews, 13 expert interviews, and 12 health care consumer interviews, the results of which are reported in this paper. Following D-optimality criteria, a fractional factorial survey design was generated. The final DCE will be administered in the waiting room of a travel clinic, targeting a sample of 200 participants. Choice data will be analyzed with conditional logit and multinomial logit models, accounting for individual participant characteristics. ResultsAn ethics approval was waived by the Ethics Committee Zurich. The study started in September 2019 and estimated data collection and completion is set for January 2020. Five two-level attributes have been selected for inclusion in the DCE, addressing (1) data generation methods, (2) privacy control, (3) data sharing with general practitioner, (4) reminder timing, and (5) costs. Data synthesis, analysis, and reporting are planned for January and February 2020. Results are expected to be submitted for publication by February 2020. ConclusionsOur results will target technology developers, health care providers, and policy makers, potentially offering some guidance on how to design or use sun-protection-focused self-monitoring apps in ways that are responsive to consumer preferences. Preferences are ultimately linked to engagement and motivation, which are key elements for the uptake and success of digital health. Our findings will inform the design of person-centered apps, while also inspiring future preference-eliciting research in the field of emerging and complex eHealth services. International Registered Report Identifier (IRRID)PRR1-10.2196/16087

Background Seroprevalence and the proportion of people with neutralizing activity (functional immunity) against SARS-CoV-2 variants were high in early 2022. In this prospective, population- based, multi-region cohort study, we assessed the development of functional and hybrid immunity (induced by vaccination and infection) in the general population during this period of high incidence of infections with Omicron variants. Methods We randomly selected and assessed individuals aged ≥16 years from the general population in southern (n  = 739) and north-eastern (n  = 964) Switzerland in March 2022. We assessed them again in June/July 2022, supplemented with a random sample from western (n  = 850) Switzerland. We measured SARS-CoV-2 specific IgG antibodies and SARS-CoV-2 neutralizing antibodies against three variants (ancestral strain, Delta, Omicron). Results Seroprevalence remained stable from March 2022 (97.6%, n  = 1894) to June/July 2022 (98.4%, n  = 2553). In June/July, the percentage of individuals with neutralizing capacity against ancestral strain was 94.2%, against Delta 90.8% and against Omicron 84.9%, and 50.6% developed hybrid immunity. Individuals with hybrid immunity had highest median levels of anti-spike IgG antibodies titres [4518 World Health Organization units per millilitre (WHO U/mL)] compared with those with only vaccine- (4304 WHO U/mL) or infection- (269 WHO U/mL) induced immunity, and highest neutralization capacity against ancestral strain (hybrid: 99.8%, vaccinated: 98%, infected: 47.5%), Delta (hybrid: 99%, vaccinated: 92.2%, infected: 38.7%) and Omicron (hybrid: 96.4%, vaccinated: 79.5%, infected: 47.5%). Conclusions This first study on functional and hybrid immunity in the Swiss general population after Omicron waves showed that SARS-CoV-2 has become endemic. The high levels of antibodies and neutralization support the emerging recommendations of some countries where booster vaccinations are still strongly recommended for vulnerable persons but less so for the general population. [ABSTRACT FROM AUTHOR]