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Background Mycoplasma pneumoniae ( M. pneumoniae ) is a common pathogen of community-acquired pneumonia (CAP). Epidemics occur every 3–7 years especially in pediatric patients. We collected data from a large laboratory network in Germany to define the epidemiological dynamics in the pre- and post-COVID-19 pandemic period. Methods In this retrospective cohort study we included all patients that obtained targeted or multiplex PCR for M. pneumoniae from nasopharyngeal swabs, sputum or bronchoalveolar fluids from 2015 to 2024. Demographic data (age, sex, place of residence, in- or outpatient status) were compared between M. pneumoniae positive and negative patients and co-infections with bacterial or viral pathogens analyzed. Results We screened 38,204 patients for M. pneumoniae . We identified 1448 cases (3.8%) of M. pneumoniae (48.8% females). Pediatric patients ≤ 18 years represented 75.7% of M. pneumoniae patients and 2.3% were ≥ 60 years. Incidence of M. pneumoniae increased in fourth quartile 2015 (16.2%), second quartile 2018 (14.8%) and fourth quartile 2023 (13.4%). No cases were detected during COVID-19 pandemic 2021. Young age (aOR 0.98 95%-CI 0.97–0.98), outpatient status (aOR 0.56 95%-CI 0.43–0.71) and year of testing (OR dependent on year of testing) were predictors of M. pneumoniae detection in multivariate analysis ( p  < 0.001). We observed a significant increase in outpatients with M. pneumoniae after COVID-19 pandemic (86.7 vs. 96.5%, p = < 0.001, aOR 0.25, 95% CI 0.15–0.4). Conclusions Empirical treatment of CAP patients often does not include coverage of M. pneumoniae . A more thorough implementation of available surveillance data into clinical routine, respective therapies could be adapted more quickly during epidemic outbreaks of M. pneumoniae infections.

Effective disease self-management lowers asthma's burden of disease for both individual patients and health care systems. In principle, mobile health (mHealth) apps could enable effective asthma self-management interventions that improve a patient's quality of life while simultaneously reducing the overall treatment costs for health care systems. However, prior reviews in this field have found that mHealth apps for asthma lack clinical evaluation and are often not based on medical guidelines. Yet, beyond the missing evidence for clinical efficacy, little is known about the potential apps might have for improving asthma self-management. The aim of this study was to assess the potential of publicly available and well-adopted mHealth apps for improving asthma self-management. The Apple App store and Google Play store were systematically searched for asthma apps. In total, 523 apps were identified, of which 38 apps matched the selection criteria to be included in the review. Four requirements of app potential were investigated: app functions, potential to change behavior (by means of a behavior change technique taxonomy), potential to promote app use (by means of a gamification components taxonomy), and app quality (by means of the Mobile Application Rating Scale [MARS]). The most commonly implemented functions in the 38 reviewed asthma apps were tracking (30/38, 79%) and information (26/38, 68%) functions, followed by assessment (20/38, 53%) and notification (18/38, 47%) functions. On average, the reviewed apps applied 7.12 of 26 available behavior change techniques (standard deviation [SD]=4.46) and 4.89 of 31 available gamification components (SD=4.21). Average app quality was acceptable (mean=3.17/5, SD=0.58), whereas subjective app quality lied between poor and acceptable (mean=2.65/5, SD=0.87). Additionally, the sum scores of all review frameworks were significantly correlated (lowest correlation: r =.33, P=.04 between number of functions and gamification components; highest correlation: r =.80, P<.001 between number of behavior change techniques and gamification components), which suggests that an app's potential tends to be consistent across review frameworks. Several apps were identified that performed consistently well across all applied review frameworks, thus indicating the potential mHealth apps offer for improving asthma self-management. However, many apps suffer from low quality. Therefore, app reviews should be considered as a decision support tool before deciding which app to integrate into a patient's asthma self-management. Furthermore, several research-practice gaps were identified that app developers should consider addressing in future asthma apps.

Background Tissue-nonspecific alkaline phosphatase (TNSALP) encoded by the ALPL gene is of particular importance for bone mineralization. Mutation in the ALPL gene can lead to persistent low ALP activity resulting in the rare disease Hypophosphatasia (HPP) that is characterized by disturbed bone and dental mineralization. While severe forms are extremely rare with an estimated prevalence of 1/100.000, recent studies suggest that moderate form caused by heterozygous mutations are much more frequent with an estimated prevalence of 1/508. The purpose of this study was to estimate the prevalence of low AP levels in the population based on laboratory measurements. Methods In this study, the prevalence of low AP activity and elevated pyridoxal-5-phosphate (PLP) levels was analyzed in 6.918.126 measurements from 2011 to 2016 at a single laboratory in northern Germany. Only laboratory values of subjects older than 18 years of age were included. Only the first measurement was included, all repeated values were excluded. Results In total, 8.46% of the measurements of a total of 6.918.126 values showed a value < 30 U/L. 0.59% of the subjects with an ALP activity below 30 U/L had an additional PLP measurement. Here, 6.09% showed elevated pyridoxal-5-phosphate (PLP) levels. This suggest that 0.52% (1:194) of subjects show laboratory signs of HPP. Conclusion These data support the genetic estimation that the prevalence of moderate forms of HPP may be significantly higher than expected. Based on these data, we recommend automatically measurement of PLP in the case of low ALP activity and a notification to the ordering physician that HPP should be included in the differential diagnosis and further exploration is recommended.

In most countries the majority of patients are in outpatient care. In difference to hospitalized patients, their blood samples often take hours after collection to centrifugation. The study investigates the release of potassium and the development of pseudohyperkalemia in lithium heparin (Li-Hep) and serum blood collection tubes over time. From 201 donors 4 serum and 4 Li-Hep blood collection tubes were taken each. After 0.5, 4, 6 and 8h whole blood was centrifuged, and potassium levels were determined. To simulate the preanalytic conditions, the samples with a storage time >0.5h were shaken on a standard shaker for 1h and stored at 4-8°C for the remaining time. Over time, significant more potassium was released before centrifugation from the Li-Hep plasma than from serum (1.21 vs 0.94 mmol/L). After 6h, the two groups were no longer highly statistically significantly different (potassium mean: 5.01 mmol/L in serum group, 4.92 mmol/L in Li-Hep group). In the Li-Hep group 164 donors developed a pseudohyperkalemia after 8h, compared to 76 in the serum group. The decision as to which material is best suited should not only be based on which value comes closest to the physiological situation immediately after blood collection. The subsequent preanalytic circumstances must also be considered. Serum tubes appear to be at least as suitable for potassium determination as Li-Hep tubes. In terms of patient blood management, serum provides the possibility of performing a wider range of analyses in the outpatient setting.

The Sabatier reaction (that is, CO 2 methanation) is undergoing a revival for two main reasons. First, the power-to-gas concept offers the prospect of large-scale recycling of (point source) CO 2 emissions, in combination with the use of large quantities of renewable energy to form methane. When this can be achieved in a cost-effective manner, it can use the gas distribution infrastructure that already exists. However, methanation is no simple panacea to the detrimental environmental effect of CO 2 emissions, and reaction products other than methane should also be targeted. Second, methanation has been identified as an important reaction to facilitate long-term space exploration missions by space agencies, such as NASA. This Perspective discusses the current understanding of CO 2 hydrogenation within these concepts, from fundamental mechanistic aspects to several parameters that will ultimately define its technical and economic feasibility on Earth and in space, as we transition into the era of small-molecule activation. The hydrogenation of CO 2 to form methane has been known for over a century. However, given increased interest in small-molecule activation for energy storage, and improved catalysts and understanding of the process, it is worthwhile to look again at the reaction. This Perspective discusses recent work on the fundamentals of the Sabatier reaction and also the potential for large-scale applications.

Providing feedback is a technique to promote health behavior that is emphasized by behavior change theories. However, these theories make contradicting predictions regarding the effect of the feedback sign-that is, whether the feedback signals success or failure. Thus, it is unclear whether positive or negative feedback leads to more favorable behavior change in a health behavior intervention. The aim of this study was to examine the effect of the feedback sign in a health behavior change intervention. Data from participants (N=1623) of a 6-month physical activity intervention was used. Participants received a feedback email at the beginning of each month. Feedback was either positive or negative depending on the participants' physical activity in the previous month. In an exploratory analysis, change in monthly step count averages was used to evaluate the feedback effect. The feedback sign did not predict the change in monthly step count averages over the course of the intervention (b=-84.28, P=.28). Descriptive differences between positive and negative feedback can be explained by regression to the mean. The feedback sign might not influence the effect of monthly feedback emails sent out to participants of a large-scale physical activity intervention. However, randomized studies are needed to further support this conclusion. Limitations as well as opportunities for future research are discussed.

Increasing the level of automation in Air Traffic Management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, Air Traffic Controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out‐Of‐The‐Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de‐skilling. A countermeasure to this phenomenon has been identified in the Adaptive Automation, i.e. a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo’s mental state to be used as control logic for Adaptive Automation-based systems. In this paper it is presented the so-called “Vigilance and Attention Controller”, a system based on Electroencephalography (EEG) and Eye Tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human machine interface and to use this measure to adapt the Level of Automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) Adaptive Automation was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of Adaptive Automation.

Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood. The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators. We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool. Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence. mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects.

This paper is written in response to the paper “How green is blue hydrogen?” by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain. This paper is written in response to the paper “How green is blue hydrogen?” by R. W. Howarth and M. Z. Jacobson and aims at highlighting some flaws in the method and assumptions of that paper and at providing a more balanced perspective on blue hydrogen. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. We conclude that for blue hydrogen to have a role in the transition to a renewable net‐zero economy, it is necessary to: (i) adopt hydrogen production processes and CO2 capture technologies ensuring high CO2 capture rate, possibly above 90%; (ii) develop a low‐emission natural gas supply chain, and (iii) adopt a life cycle approach based on reliable accounting of the methane leakage from the supply chain

Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d). DERR1-10.2196/11540.