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Background There is a robust market for mobile health (mHealth) apps focused on self-guided interventions to address a high prevalence of mental health disorders and behavioral health needs in the general population. Disseminating mental health interventions via mHealth technologies may help overcome barriers in access to care and has broad consumer appeal. However, development and testing of mental health apps in formal research settings are limited and far outpaced by everyday consumer use. In addition to prioritizing efficacy and effectiveness testing, researchers should examine and test app design elements that impact the user experience, increase engagement, and lead to sustained use over time. Objective The aim of this study was to evaluate the objective and subjective quality of apps that are successful across both research and consumer sectors, and the relationships between objective app quality, subjective user ratings, and evidence-based behavior change techniques. This will help inform user-centered design considerations for mHealth researchers to maximize design elements and features associated with consumer appeal, engagement, and sustainability. Methods We conducted a user-centered design analysis of popular consumer apps with scientific backing utilizing the well-validated Mobile Application Rating Scale (MARS). Popular consumer apps with research support were identified via a systematic search of the App Store iOS (Apple Inc) and Google Play (Google LLC) and literature review. We evaluated the quality metrics of 19 mental health apps along 4 MARS subscales, namely, Engagement, Functionality, Aesthetics, and Information Quality. MARS total and subscale scores range from 1 to 5, with higher scores representing better quality. We then extracted user ratings from app download platforms and coded apps for evidence-based treatment components. We calculated Pearson correlation coefficients to identify associations between MARS scores, App Store iOS/Google Play consumer ratings, and number of evidence-based treatment components. Results The mean MARS score was 3.52 (SD 0.71), consumer rating was 4.22 (SD 0.54), and number of evidence-based treatment components was 2.32 (SD 1.42). Consumer ratings were significantly correlated with the MARS Functionality subscale (r=0.74, P Conclusions In our analysis of popular research-supported consumer apps, objective app quality and subjective consumer ratings were generally high. App functionality and aesthetics were highly consistent with consumer appeal, whereas evidence-based components were not. In addition to designing treatments that work, we recommend that researchers prioritize aspects of app design that impact the user experience for engagement and sustainability (eg, ease of use, navigation, visual appeal). This will help translate evidence-based interventions to the competitive consumer app market, thus bridging the gap between research development and real-world implementation.

Background The use of digital therapeutic solutions for rehabilitation of conditions such as osteoarthritis provides scalable access to rehabilitation. Few validated technological solutions exist to ensure supervision of users while they exercise at home. Motion Coach (Kaia Health GmbH) provides audiovisual feedback on exercise execution in real time on conventional smartphones. Objective We hypothesized that the interrater agreement between physiotherapists and Motion Coach would be noninferior to physiotherapists’ interrater agreement for exercise evaluations in a cohort with osteoarthritis. Methods Patients diagnosed with osteoarthritis of the knee or hip were recruited at a university hospital to perform a set of 6 exercises. Agreement between Motion Coach and 2 physiotherapists’ corrections for segments of the exercises were compared using Cohen κ and percent agreement. Results Participants (n=24) were enrolled and evaluated. There were no significant differences between interrater agreements (Motion Coach app vs physiotherapists: percent agreement 0.828; physiotherapist 1 vs physiotherapist 2: percent agreement 0.833; P Conclusions The results demonstrated that Motion Coach is noninferior to physiotherapist evaluations. Interrater agreement did not differ between 2 physiotherapists or between physiotherapists and the Motion Coach app. This finding was valid for all investigated exercises and subgroups. These results confirm the ability of Motion Coach to detect user form during exercise and provide valid feedback to users with musculoskeletal disorders.

Background Many teenagers in the United States experience challenges with symptoms of depression, and they lack adequate resources for accessing in-person mental health care. Involving teens and clinicians in designing technologies that use evidence-based practices that reduce barriers to accessing mental health care is crucial. Interventions based on behavioral activation (BA) help teens understand the relationship between mood and activity, help them practice goal-directed behaviors to improve mood, and may be particularly well-suited to delivery via internet-based platforms. Objective This study aims to understand the needs and challenges that teens and mental health clinicians face in depression management and involve them in the design process of a remote intervention that uses asynchronous remote communities. Our goal is to understand the benefits and challenges of adapting BA to an internet-based platform that supports the asynchronous remote community approach as a delivery tool for teen depression management. Methods We enrolled mental health clinicians (n=10) and teens (n=8) in separate, private, internet-based groups on Slack (Slack Technologies Inc). They participated in 20-minute design activities for 10 weeks and were then invited to interviews about their experiences in the study. Results Both teen and clinician participants wanted internet-based support for BA as a supplement to in-person therapy. Although participants perceived the asynchronous format as conducive to supporting accessible care, teens and clinicians raised concerns about safety, privacy, and the moderating of the internet-based group. Design decisions that address these concerns need to be balanced with the potential benefits of learning coping skills, increasing access to mental health care, and promoting asynchronous human connection to support teens. Conclusions We discuss considerations for balancing tensions in privacy and safety while designing and selecting internet-based platforms to support remote care and integrating evidence-based support when designing digital technologies for the treatment of teens with depression.

Background Various techniques are used to support contact tracing, which has been shown to be highly effective against the COVID-19 pandemic. To apply the technology, either quarantine authorities should provide the location history of patients with COVID-19, or all users should provide their own location history. This inevitably exposes either the patient’s location history or the personal location history of other users. Thus, a privacy issue arises where the public good (via information release) comes in conflict with privacy exposure risks. Objective The objective of this study is to develop an effective contact tracing system that does not expose the location information of the patient with COVID-19 to other users of the system, or the location information of the users to the quarantine authorities. Methods We propose a new protocol called PRivacy Oriented Technique for Epidemic Contact Tracing (PROTECT) that securely shares location information of patients with users by using the Brakerski/Fan-Vercauteren homomorphic encryption scheme, along with a new, secure proximity computation method. Results We developed a mobile app for the end-user and a web service for the quarantine authorities by applying the proposed method, and we verified their effectiveness. The proposed app and web service compute the existence of intersections between the encrypted location history of patients with COVID-19 released by the quarantine authorities and that of the user saved on the user’s local device. We also found that this contact tracing smartphone app can identify whether the user has been in contact with such patients within a reasonable time. Conclusions This newly developed method for contact tracing shares location information by using homomorphic encryption, without exposing the location information of patients with COVID-19 and other users. Homomorphic encryption is challenging to apply to practical issues despite its high security value. In this study, however, we have designed a system using the Brakerski/Fan-Vercauteren scheme that is applicable to a reasonable size and developed it to an operable format. The developed app and web service can help contact tracing for not only the COVID-19 pandemic but also other epidemics.

Natural dyes have been used from ancient times for multiple purposes, most importantly in the field of textile dying. The increasing demand and excessive costs of natural dye extraction engendered the discovery of synthetic dyes from petrochemical compounds. Nowadays, they are dominating the textile market, with nearly 8 × 105 tons produced per year due to their wide range of color pigments and consistent coloration. Textile industries consume huge amounts of water in the dyeing processes, making it hard to treat the enormous quantities of this hazardous wastewater. Thus, they have harmful impacts when discharged in non-treated or partially treated forms in the environment (air, soil, plants and water), causing several human diseases. In the present work we focused on synthetic dyes. We started by studying their classification which depended on the nature of the manufactured fiber (cellulose, protein and synthetic fiber dyes). Then, we mentioned the characteristics of synthetic dyes, however, we focused more on their negative impacts on the ecosystem (soil, plants, water and air) and on humans. Lastly, we discussed the applied physical, chemical and biological strategies solely or in combination for textile dye wastewater treatments. Additionally, we described the newly established nanotechnology which achieves complete discharge decontamination.

Background There were 2 major incentives introduced by the Chinese government to promote medical informatics in 2009 and 2016. As new drugs are the major source of medical innovation, informatics-related concepts and techniques are a major source of digital medical innovation. However, it is unclear whether the research efforts of medical informatics in China have met the health needs, such as disease management and population health. Objective We proposed an approach to mapping the interplay between different knowledge entities by using the tree structure of Medical Subject Headings (MeSH) to gain insights into the interactions between informatics supply, health demand, and technological applications in digital medical innovation in China. Methods All terms under the MeSH tree parent node “Diseases [C]” or node “Health [N01.400]” or “Public Health [N06.850]” were labelled as H. All terms under the node “Information Science [L]” were labelled as I, and all terms under node “Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]” were labelled as T. The H-I-T interactions can be measured by using their co-occurrences in a given publication. Results The H-I-T interactions in China are showing significant growth and a more concentrated interplay were observed. Computing methodologies, informatics, and communications media (such as social media and the internet) constitute the majority of I-related concepts and techniques used for resolving the health promotion and diseases management problems in China. Generally there is a positive correlation between the burden and informatics research efforts for diseases in China. We think it is not contradictory that informatics research should be focused on the greatest burden of diseases or where it can have the most impact. Artificial intelligence is a competing field of medical informatics research in China, with a notable focus on diagnostic deep learning algorithms for medical imaging. Conclusions It is suggested that technological transfers, namely the functionality to be realized by medical/health informatics (eg, diagnosis, therapeutics, surgical procedures, laboratory testing techniques, and equipment and supplies) should be strengthened. Research on natural language processing and electronic health records should also be strengthened to improve the real-world applications of health information technologies and big data in the future.

The high fermentable sugars content in oil palm frond (OPF) juice makes it an excellent fermentation feedstock for bioethanol production. The major challenge of using the juice as a feedstock is the rapid degradation of the fermentable sugars during storage. Thus, this study was aimed to investigate the effects of OPF juice concentration and mild temperature storage on its glucose content. The OPF juice was obtained by pressing the fresh OPF petiole and it was concentrated by evaporating 30–70% (v/v) of water from the juice. The juice was stored at different temperatures (30–60 °C) for 20 days prior to its use for bioethanol production. The results showed that regardless of the OPF juice concentration, the glucose content remained stable at 50 °C and 60 °C storage. Moreover, bacterial spoilage in the OPF juice was slightly inhibited when stored at 50 °C and it completely disappeared when stored at 60 °C. The production of bioethanol using OPF juice stored at 50 °C gave approximately 15% higher bioethanol production when compared to the use of freshly prepared OPF juice. The results indicate that 50% concentrated OPF juice stored at 50 °C are promising alternative pre-treatment and storage conditions for OPF juice liquid feedstock prior to its use for bioethanol fermentation.

Although urine constitutes less than 1% volume of the total wastewater, it adds up 50–80% of the nutrient load [Phosphates (PO43−) and Nitrates (NO3−), Ammonium (NH4+)] in wastewater treatment plants (WWTPs). Recovery of Phosphorous (P) and Nitrogen (N) from urine diminishes the eutrophication impact as well it could act as a sustainable source of P by lessening dependence on finite phosphorite reservoirs. Due to its environmental (carbon-negative process) and economic (produced from waste) value, biochar is gaining the researcher’s attention in recent years. Many studies are ongoing to introduce novel structures and modifications in the surface properties of biochar for efficient adsorption of P and N. Direct application of urine as fertilizer has been discouraged due to various hygienic pitfalls further forced to explore the alternative nutrient recovery methods. This review article explores the nutrient recovery paradigms from source-separated human urine for abridging the nutrient gap between wastewater treatment and agricultural productivity using pristine/engineered biochar. The influencing factors for P and N’s adsorption process by biochar and its detailed mechanisms have been outlined. Further, it deliberates the possible strategies of engineering the biochar to enhance the recovery of P and N from human urine. Applications of nutrient-loaded biochar in an agricultural field could act as a slow-release fertilizer to releases P and N in addition to soil conditioning. This review emphasized the challenges of storage, transportation, contamination of urine, and possible difficulties in the adsorption process by pristine/engineered biochar.

The repertoire of novel biobased materials is continually expanding as they represent green alternatives to carbon-intensive fossil materials. Lipid-extracted algae (LEA) biomass is a promising feedstock for the production of a spectrum of biobased materials, such as hydrochar and biochar, electrodes in microbial fuel cells (MFCs), supercapacitors, biocomposites, biopolymers, activated carbon including N-doping, and biosorbents. By selecting appropriate process conditions, these renewable products can be designed to possess desirable properties and, at the same time, be more sustainable. Most importantly, we view LEA as a potentially significant additional source of revenue for algae biorefineries that can accelerate the commercial development of algae. The present study assesses the utilization of LEA for the production of biobased materials, their applications and sustainability profile, and future trends.

An extended investigation on the application of blends between diesel oil and used cooking oil methyl esters (UCOME) was developed on an automotive turbocharged diesel engine. Two commercial diesel oils were considered as reference fuels, the first with the conventional content of biodiesel (7%), the second with an amount of 15%. Then, increasing levels of UCOME were added, on a volumetric basis (20%, 40%, 55 or 60%), to analyse the influence of biodiesel content on engine behaviour. Three part load engine operating conditions were selected for tests, measuring average engine parameters and indicated pressure diagrams. Their processing allowed to calculate the most important operating, energy and environmental engine quantities, rate of heat release and related combustion parameters. A proper management of the exhaust gas recirculation circuit, variable nozzle turbine and injection system was applied thanks to the availability of an open electronic control unit. An optimal UCOME content (40%) was identified, leading to highest values of brake thermal efficiency (with maximum increase around 5% referring to baseline fuels) and lowest levels of NOX emissions. For this blend, the analysis of combustion parameters outlined that the use of the renewable fuel corresponded to an enhanced premixed combustion phase, while reducing combustion duration. Lower local temperature levels, related to the decrease of Lower Heating Value, other local effects and biodiesel degree of unsaturation justify NOX reduction. Furthermore, soot emissions were always reduced when increasing biodiesel content, while combustion stability was not affected. All these outcomes were observed with and without EGR.

Automotive engines are encouraged to utilize biofuels to lower the net CO2 and to enhance the usage of renewable energy. With well-proven biodiesel engine characteristics, a comprehensive study about the impact of biodiesel on the fuel filter is lacking in the literature. Hence the current study investigates the comparative impact of higher blends of biodiesel and diesel on the fuel filter. Here, the authors have conducted both experimental and simulation studies on the fuel filter with diesel, two biodiesel fuels namely karanja, and waste cooking oil. Filter blocking tendency (FBT) was measured for the test fuels following ASTM D2068 . Also, a comparative tensile strength analysis of an automotive filter media was examined. The test results reveal that FBT increases exponentially with an increase in biodiesel blend concentration with a maximum FBT value of 30 for karanja biodiesel. Further, the tensile strength of the filter media reduces significantly with soaking in biodiesel compared to fossil diesel. Filter pressure drop measurement results with biodiesel were observed to be 35% higher than diesel. Consequently, effective countermeasures such as fuel preheating, varying the filtration area and ethanol blending are proposed to reduce the filter pressure drop with biodiesel. Filter pressure drop with karanja can be reduced similar to diesel fuel by increasing the fuel temperature by 16 °C or enhancing the filtration area by 35%. Karanja blended with 20% ethanol was also observed to have lowered the pressure drop. The present study concludes that neat biodiesel operation affects the filtration process and the filter media of the conventional fuel filter significantly.

The huge generation of livestock manures has been raised due to the dramatically increasing protein demand. In this study, dairy cattle manure (DCM) was valorized through a pyrolysis platform using CO2 as a co-feedstock, suggesting the conversion of manure to value-added chemicals in a sustainable and renewable way. This work demonstrated that biocrude derived from DCM involves high contents of N-containing hetero-hydrocarbons and aromatic compounds. Because they are not desirable chemicals to be used as fuels due to the challenges during combustion processes, they were further transformed into syngas. CO2 and DCM-derived biochar signified the conversion of biocrude into syngas at ≤ 600 °C, where no gasification reaction works. The gas phase reactions of CO2 with gas phase biocrude led to enhanced CO formation. In addition, alkaline metal(oxide)s in biochar promoted gas phase reactions and chemical bond scissions of biocrude, resulting in more than 5 times higher H2 and CO formations with 3 g DCM biochar than non-catalytic pyrolysis. To evaluate the performance of DCM biochar, pyrolysis of DCM was done with Ni and Co catalysts. The promotion effect of DCM biochar for syngas formation was better than that of equivalent amount of Co catalyst. The performance of DCM biochar was higher than Ni when the additional amount of biochar was used. Considering that DCM is emitted as an agricultural waste material, it is inferred that the waste derived biochar has more economic viability than metal catalysts. Therefore, this study provides that the CO2-driven pyrolysis with DCM biochar could be an environmentally benign means for syngas generation with an economic benefit.

Simultaneously improving coal flotation performance and the subsequent froth breakdown is challenging. The present work addresses this challenge by developing and evaluating a non-chemical approach based on the stabilization effect of acoustic sound on flotation froth. The effectiveness of this approach for improving coking coal flotation at different frother concentrations (10–25 ppm MIBC), collector dosages (0–400 g diesel/t) and aeration rates (1.2–2.2 cm/s) was demonstrated at laboratory scale and in batch mode, with a loudspeaker being placed in air above the flotation cell lip. It was found that at any operating condition tested, the use of sound could improve the flotation performance including the final combustible recovery, apparent flotation rate constant and separation efficiency rate. There was no statistically significant difference in product ash content between the flotation tests with and without applying sound, except at the highest MIBC concentration tested. The images of the product froth taken before and 5-min after being discharged from the flotation cell indicated that the froth stabilization effect induced by sound would occur only within the sound field, demonstrating the potential of the approach in mitigating the overfrothing issue without sacrificing the flotation performance. The possible mechanisms of the improvement of flotation performance with applying sound and its potential of improving coal flotation practice through reducing reagent and air consumption were discussed.

Cement-making is an energy-intensive industrial process that contributes 8% of the global CO2 emissions. This study develops a thermal energy flow model (TEF) for 4200 tonnes of clinker per day, a natural gas-fired cement plant in which 50% of the pre-calciner energy requirements can be supplied by alternative fuels (AF) including biomass, plastics, etc. The TEF shows that the lower heating value (LHV dry), oxygen content (dry basis) and moisture content of the AF, as well as the flue gas oxygen concentration [O2] needed for complete combustion, affect the thermal energy intensity (TEI) and total air demand (AD). Compared to the reference system fueled by natural gas (NG) that burns completely at 1% [O2] in flue gas, all AFs require more total air and thermal energy demand, especially when the solid AFs need a higher [O2] in the flue gas (typically 3%) to ensure complete combustion. Due to the higher carbon (C) intensity, co-firing AFs could increase the total CO2 emissions by 1% to 18%. For the wood dust with 100% biogenic C, 50% NG replacement in pre-calciner could avoid 55.5 and 43.1 kgCO2/t clinker at 1% and 3% [O2] in flue gas respectively, an equivalent of 7.5% and 5.8% decrease in total GHG emissions relative to the reference case. Results of the TEF model from 24 diverse AFs were used to generate regressions that link fuel properties and flue gas oxygen requirement with TEI, making it possible to compare highly diverse AFs for their likely performance in the clinker-making.

Background Analyzing Twitter posts enables rapid access to how issues and experiences are socially shared and constructed among communities of health service users and providers, in ways that traditional qualitative methods may not. Objective To enrich the understanding of mental health crisis care in the United Kingdom, this study explores views on crisis resolution teams (CRTs) expressed on Twitter. We aim to identify the similarities and differences among views expressed on Twitter compared with interviews and focus groups. Methods We used Twitter’s advanced search function to retrieve public tweets on CRTs. A thematic analysis was conducted on 500 randomly selected tweets. The principles of refutational synthesis were applied to compare themes with those identified in a multicenter qualitative interview study. Results The most popular hashtag identified was #CrisisTeamFail, where posts were principally related to poor quality of care and access, particularly for people given a personality disorder diagnosis. Posts about CRTs giving unhelpful self-management advice were common, as were tweets about resource strains on mental health services. This was not identified in the research interviews. Although each source yielded unique themes, there were some overlaps with themes identified via interviews and focus groups, including the importance of rapid access to care. Views expressed on Twitter were generally more critical than those obtained via face-to-face methods. Conclusions Traditional qualitative studies may underrepresent the views of more critical stakeholders by collecting data from participants accessed via mental health services. Research on social media content can complement traditional or face-to-face methods and ensure that a broad spectrum of viewpoints can inform service development and policy.

BackgroundMinimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions.ObjectiveLeveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity.MethodsWe propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process.ResultsTo validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.ConclusionsWe propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines.

Background Evidence-based medicine (EBM) is widely accepted in medicine. It is necessary to improve the knowledge and attitudes of medical students in the use of evidence. In Ethiopia, little is known about medical students’ knowledge and attitudes toward EBM. Objective This study aimed to assess the knowledge and attitudes toward EBM and its associated factors among medical interns in teaching hospitals. Methods A cross-sectional survey was conducted using a random sample of medical interns in teaching hospitals in Ethiopia. Multivariable logistic regression analyses were used to identify the factors associated with the knowledge and attitudes toward EBM. Adjusted odds ratio (AOR) with 95% confidence interval and P≤.05 was used to quantify strength of association between variables. Results Out of a sample of 423 medical interns, 403 completed the questionnaire (95.3% response rate). Overall, 68.0% (274/403 of respondents had a favorable attitude toward EBM and 57.1% (230/403) had good knowledge of EBM. The majority (355/403, 88.1%) of participants had internet access. Only 19.6% (79/403) of respondents had received EBM-related training. Respondents’ knowledge of EBM was associated with previous EBM training (AOR 2.947, 95% CI 1.648-5.268, P Conclusions This study demonstrates a lack of formal EBM training and awareness of basic concepts of EBM among medical interns. Medical intern attitudes toward EBM are relatively good. To enhance EBM knowledge and skills, formal teaching of EBM should be integrated into medical education.

Background The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. Objective This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. Methods We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. Results A higher engagement rate was associated with higher weight loss at 8 weeks (r=−0.59; P Conclusions Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. Trial Registration ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

Background Youper is a widely used, commercially available mobile app that uses artificial intelligence therapy for the treatment of anxiety and depression. Objective Our study examined the acceptability and effectiveness of Youper. Further, we tested the cumulative regulation hypothesis, which posits that cumulative emotion regulation successes with repeated intervention engagement will predict longer-term anxiety and depression symptom reduction. Methods We examined data from paying Youper users (N=4517) who allowed their data to be used for research. To characterize the acceptability of Youper, we asked users to rate the app on a 5-star scale and measured retention statistics for users’ first 4 weeks of subscription. To examine effectiveness, we examined longitudinal measures of anxiety and depression symptoms. To test the cumulative regulation hypothesis, we used the proportion of successful emotion regulation attempts to predict symptom reduction. Results Youper users rated the app highly (mean 4.36 stars, SD 0.84), and 42.66% (1927/4517) of users were retained by week 4. Symptoms decreased in the first 2 weeks of app use (anxiety: d=0.57; depression: d=0.46). Anxiety improvements were maintained in the subsequent 2 weeks, but depression symptoms increased slightly with a very small effect size (d=0.05). A higher proportion of successful emotion regulation attempts significantly predicted greater anxiety and depression symptom reduction. Conclusions Youper is a low-cost, completely self-guided treatment that is accessible to users who may not otherwise access mental health care. Our findings demonstrate the acceptability and effectiveness of Youper as a treatment for anxiety and depression symptoms and support continued study of Youper in a randomized clinical trial.

Background Hypertension affects over 15% of the world’s population and is a significant global public health and socioeconomic challenge. Mobile health (mHealth) services have been increasingly introduced to support hypertensive patients to improve their self-management behaviors, such as adherence to pharmacotherapy and lifestyle modifications. Objective This study aims to explore patients’ perceptions of mHealth services and the mechanisms by which the services support them to self-manage their hypertension. Methods A semistructured, in-depth interview study was conducted with 22 outpatients of the General Hospital of Ningxia Medical University from March to May 2019. In 2015, the hospital introduced an mHealth service to support community-dwelling outpatients with self-management of hypertension. Content analysis was conducted by following a grounded theory approach for inductive thematic extraction. Constant comparison and categorization classified the first-level codes with similar meanings into higher-level themes. Results The patient-perceived mechanisms by which the mHealth service supported their self-management of hypertension were summarized as 6A: access, assessment, assistance, awareness, ability, and activation. With the portability of mobile phones and digitization of information, the mHealth service provided outpatients with easy access to assess their vital signs and self-management behaviors. The assessment results gave the patients real-time awareness of their health conditions and self-management performance, which activated their self-management behaviors. The mHealth service also gave outpatients access to assistance, which included health education and self-management reminders. Both types of assistance could also be activated by abnormal assessment results, that is, uncontrolled or deteriorating blood pressure values, discomfort symptoms, or not using the service for a long period. With its scalable use to handle any possible information and services, the mHealth service provided outpatients with educational materials to learn at their own pace. This led to an improvement in self-management awareness and ability, again activating their self-management behaviors. The patients would like to see further improvements in the service to provide more useful, personalized information and reliable services. Conclusions The mHealth service extended the traditional hypertension care model beyond the hospital and clinician’s office. It provided outpatients with easy access to otherwise inaccessible hypertension management services. This led to process improvement for outpatients to access health assessment and health care assistance and improved their awareness and self-management ability, which activated their hypertension self-management behaviors. Future studies can apply the 6A framework to guide the design, implementation, and evaluation of mHealth services for outpatients to self-manage chronic conditions.

Background Since 2020, peoples’ lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign. Objective We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions. Methods A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as “wheeze” files and 197 as “no-wheeze” files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity. Results Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm. Conclusions The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

Background Compared with White Americans, Black Americans have higher colon cancer mortality rates but lower up-to-date screening rates. Chadwick Boseman was a prominent Black American actor who died of colon cancer on August 28, 2020. As announcements of celebrity diagnoses often result in increased awareness, Boseman’s death may have resulted in greater interest in colon cancer on the internet, particularly among Black Americans. Objective This study aims to quantify the impact of Chadwick Boseman’s death on web-based search interest in colon cancer and determine whether there was an increase in interest in regions of the United States with a greater proportion of Black Americans. Methods We conducted an infoveillance study using Google Trends (GT) and Wikipedia pageview analysis. Using an autoregressive integrated moving average algorithm, we forecasted the weekly relative search volume (RSV) for GT search topics and terms related to colon cancer that would have been expected had his death not occurred and compared it with observed RSV data. This analysis was also conducted for the number of page views on the Wikipedia page for colorectal cancer. We then delineated GT RSV data for the term colon cancer for states and metropolitan areas in the United States and determined how the RSV values for these regions correlated with the percentage of Black Americans in that region. Differences in these correlations before and after Boseman’s death were compared to determine whether there was a shift in the racial demographics of the individuals conducting the searches. Results The observed RSVs for the topics colorectal cancer and colon cancer screening increased by 598% and 707%, respectively, and were on average 121% (95% CI 72%-193%) and 256% (95% CI 35%-814%) greater than expected during the first 3 months following Boseman’s death. Daily Wikipedia page view volume during the 2 months following Boseman’s death was on average 1979% (95% CI 1375%-2894%) greater than expected, and it was estimated that this represented 547,354 (95% CI 497,708-585,167) excess Wikipedia page views. Before Boseman’s death, there were negative correlations between the percentage of Black Americans living in a state or metropolitan area and the RSV for colon cancer in that area (r=−0.18 and r=−0.05, respectively). However, in the 2 weeks following his death, there were positive correlations between the RSV for colon cancer and the percentage of Black Americans per state and per metropolitan area (r=0.73 and r=0.33, respectively). These changes persisted for 4 months and were all statistically significant (P Conclusions There was a significant increase in web-based activity related to colon cancer following Chadwick Boseman’s death, particularly in areas with a higher proportion of Black Americans. This reflects a heightened public awareness that can be leveraged to further educate the public.

Background Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes. Objective To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes). Methods PhenoSSU is an “entity-attribute-value” model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed. Results Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4% (2034/4020) and 21.8% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. Conclusions PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level.

Background The internet is used for information related to health conditions, including low back pain (LBP), but most LBP websites provide inaccurate information. Few studies have investigated the effectiveness of internet resources in changing health literacy or treatment choices. Objective This study aims to evaluate the effectiveness of the MyBackPain website compared with unguided internet use on health literacy, choice of treatments, and clinical outcomes in people with LBP. Methods This was a pragmatic, web-based, participant- and assessor-blinded randomized trial of individuals with LBP stratified by duration. Participants were randomly allocated to have access to the evidence-based MyBackPain website, which was designed with input from consumers and expert consensus or unguided internet use. The coprimary outcomes were two dimensions of the Health Literacy Questionnaire (dimension 2: “having sufficient information to manage my health;” dimension 3: “actively managing my health;” converted to scores 1-100) at 3 months. Secondary outcomes included additional Health Literacy Questionnaire dimensions, quality of treatment choices, and clinical outcomes. Results A total of 453 participants were recruited, and 321 (70.9%) completed the primary outcomes. Access to MyBackPain was not superior to unguided internet use on primary outcomes (dimension 2: mean difference −0.87 units, 95% CI −3.56 to 1.82; dimension 3: mean difference −0.41 units, 95% CI −2.78 to 1.96). Between-group differences in other secondary outcomes had inconsistent directions and were unlikely to be clinically important, although a small improvement of unclear importance in the quality of stated treatment choices at 1 month was found (mean difference 0.93 units, 95% CI 0.03 to 1.84). Conclusions MyBackPain was not superior to unguided internet use for health literacy, but data suggest some short-term improvement in treatment choices. Future research should investigate if greater interactivity and engagement with the website may enhance its impact. Trial Registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001292369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372926 International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2018-027516

Background The monitoring and management of chronic illness has always been a challenge. Patient-reported outcome measures (PROMs) can be powerful tools for monitoring symptoms and guiding treatment of chronic diseases, but the available PROM tools are either too broad or too disease specific for the needs of a primary care practice focused on longitudinal care. Objective In this study we describe the development and preliminary validation of the Parsley Symptom Index (PSI). Methods This prospective cohort study took place from January 5, 2018, to June 05, 2020, among a sample of 4621 adult patients at Parsley Health. After a review of literature, followed by binning and winnowing of potential items, a 45-item PROM that also served as a review of systems (ROS) was developed. The PSI was deployed and completed by patients via an online portal. Construct and face validity was performed by clinicians, tested on patients, and feasibility was measured by response rate, completion rate, and percentage of missing data. Results The response rate for 12,175 collected PSIs was 93.72% (4331/4621) with a 100% item completion rate. A confirmatory factor analysis confirmed the model structure was satisfactory by a Comparative Fit Index of 0.943, Tucker–Lewis index of 0.938, and root mean square error of approximation of 0.028. Conclusions A 45-item ROS-style PROM designed to capture chronic disease symptoms was developed, and preliminary validation suggests that the PSI can be deployed, completed, and helpful to both patients and clinicians.

Background Monitoring public confidence and hesitancy is crucial for the COVID-19 vaccine rollout. Social media listening (infoveillance) can not only monitor public attitudes on COVID-19 vaccines but also assess the dissemination of and public engagement with these opinions. Objective This study aims to assess global hesitancy, confidence, and public engagement toward COVID-19 vaccination. Methods We collected posts mentioning the COVID-19 vaccine between June and July 2020 on Twitter from New York (United States), London (United Kingdom), Mumbai (India), and Sao Paulo (Brazil), and Sina Weibo posts from Beijing (China). In total, we manually coded 12,886 posts from the five global metropolises with high COVID-19 burdens, and after assessment, 7032 posts were included in the analysis. We manually double-coded these posts using a coding framework developed according to the World Health Organization’s Confidence, Complacency, and Convenience model of vaccine hesitancy, and conducted engagement analysis to investigate public communication about COVID-19 vaccines on social media. Results Among social media users, 36.4% (571/1568) in New York, 51.3% (738/1440) in London, 67.3% (144/214) in Sao Paulo, 69.8% (726/1040) in Mumbai, and 76.8% (2128/2770) in Beijing indicated that they intended to accept a COVID-19 vaccination. With a high perceived risk of getting COVID-19, more tweeters in New York and London expressed a lack of confidence in vaccine safety, distrust in governments and experts, and widespread misinformation or rumors. Tweeters from Mumbai, Sao Paulo, and Beijing worried more about vaccine production and supply, whereas tweeters from New York and London had more concerns about vaccine distribution and inequity. Negative tweets expressing lack of vaccine confidence and misinformation or rumors had more followers and attracted more public engagement online. Conclusions COVID-19 vaccine hesitancy is prevalent worldwide, and negative tweets attract higher engagement on social media. It is urgent to develop an effective vaccine campaign that boosts public confidence and addresses hesitancy for COVID-19 vaccine rollouts.

Background Adolescents with irritable bowel syndrome (IBS) are increasingly seeking and sharing information about their symptoms in web-based health care forums. Their posts and those from their parents contain critical insights that can be used by patients, physicians, and caregivers to manage IBS symptoms. Objective The aim of this study is to examine the posts from adolescent patients and their parents in a health forum, IBS Group, to better understand the key challenges, concerns, and issues of interest to young patients with IBS and their caregivers. Methods Using topic modeling and social network analysis, in this study, we analyzed all the messages (over 750 topics and 3400 replies) posted on the IBS Group forum from 2010-2019 by adolescents with IBS aged 13-17 years and parents having children with IBS. We first detected 6 major topics in the posts by adolescent patients and parents on teenagers’ IBS symptoms and the interaction between the topics. Social network analysis was then performed to gain insights into the nature of web-based interaction patterns among patients and caregivers. Results Using the Latent Dirichlet Allocation algorithm and a latent Dirichlet allocation visualization tool, this study revealed 6 leading topics of concern in adolescents with IBS: school life, treatment or diet, symptoms, boys’ ties to doctors, social or friend issues, and girls’ ties to doctors. The top 6 topics in the parents’ discussions were school life, girls’ issues, boys’ issues, diet choice, symptoms, and stress. The analyses show that the adolescent patients themselves are most concerned about the effect of IBS on their everyday activities and social lives. For parents having daughters with IBS, their top concerns were related to the girls’ school performance and how much help they received at school. For their sons, the parents were more concerned about the pain and suffering that their sons had to endure. Both parents and adolescents gained social support from the web-based platform. Topic modeling shows that IBS affects teenagers the most in the areas of pain and school life. Furthermore, the issues raised by parents suggest that girls are bothered more by school performance over pain, whereas boys show exactly the opposite: pain is of greater concern than school performance. Conclusions This study represents the first attempt to leverage both machine learning approaches and social network analysis to identify top IBS concerns from the perspectives of adolescent patients and caregivers in the same health forum. Young patients with IBS must face the challenges of social influences and anxiety associated with this health disorder in addition to physical pain and other symptoms. Boys and girls are affected differently by pain and school performance and view the IBS impacts differently from the parents.

BACKGROUND Mental health and alcohol use problems are among the most common causes of disease burden in young Australians, frequently co-occur (comorbidity), and lead to significant lifetime burden. However, comorbidities remain significantly underdetected and undertreated in health settings. Digital mental health tools designed to identify at-risk individuals, encourage help-seeking, or deliver treatment for comorbidity have the potential to address this service gap. However, despite a strong body of evidence that digital mental health programs provide an effective treatment option for a range of mental health and alcohol use problems in young adults, research shows that uptake rates can be low. Thus, it is important to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e–mental health interventions for comorbidity. OBJECTIVE In this study, we seek to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e–mental health interventions for comorbid alcohol and mood disorders. The aim is to determine the importance of personality (ie, Big Five personality traits and intervention attitudes), affective factors (ie, depression, anxiety, and stress levels), and baseline alcohol consumption in predicting intervention trial engagement at sign-up, satisfaction with the online tool, and quality of life at the end of the iTreAD (Internet Treatment for Alcohol and Depression) trial. METHODS Australian adults (N=411) aged between 18 and 30 years who screened positive for depression and alcohol use problems signed up for the iTreAD project between August 2014 and October 2015. During registration, participants provided information about their personality, current affective state, alcohol use, treatment expectations, and basic demographic information. Subsequent follow-up surveys were used to gauge the ongoing trial engagement. The last follow-up questionnaire, completed at 64 weeks, assessed participants’ satisfaction with web-based treatment and quality-of-life outcomes. RESULTS Multiple linear regression analyses were used to assess the relative influence of predictor variables on trial engagement, treatment satisfaction, and quality-of-life outcomes. The analyses revealed that the overall predictive effects of personality and affective factors were 20% or lower. Neuroticism constituted a unique predictor of engagement with the iTreAD study in that neuroticism facilitated the return of web-based self-assessments during the study. The return of incentivized follow-up assessments predicted treatment satisfaction, and state-based depression predicted variance in quality-of-life reports at study completion. CONCLUSIONS Our findings suggest that traditional predictors of engagement observed in face-to-face research may not be easily transferable to digital health interventions, particularly those aimed at comorbid mental health concerns and alcohol misuse among young adults. More research is needed to identify what determines engagement in this population to optimally design and execute digital intervention studies with multiple treatment aims. CLINICALTRIAL Australian New Zealand Clinical Trials Registry (ACTRN): 12614000310662; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365137&isReview=true. INTERNATIONAL REGISTERED REPORT RR2-10.1186/s12889-015-2365-2

The chemical composition of hop sediments from beer brewing and fermentation gives them the potential for further use. These wastes are not generated in large amounts, but the absence of proper characteristics may lead to processing errors. This study examines the possibility of using hop waste for aerobic biological transformation processes (composting). The study was carried out on two hop sediments from two different technological stages of beer production: hot trub and spent hops. Chemical, microbiological, and biochemical analyses were performed in the composted hop sediments, as was the assessment of phytotoxicity to Lepidium sativum L. The tested feedstocks were partially inhabited by microorganisms and thus safe from an epidemiological point of view, and they were not a source of microbial contamination. Inhibitory properties for plant development were found for hot trub, which most likely result from the organic compound content. If it is only a small portion of its biomass, the mineral composition of hot trub does not exclude the possibility of its composting. Spent hops were characterized by a significant total nitrogen content, which affected the composting process. Composting this sediment required the selection of substrates with a widely C:N ratio.

Background Patients with heart failure (HF) can be affected by disabling symptoms and low quality of life. Furthermore, they may frequently need to visit the emergency department or be hospitalized due to their condition deteriorating. Home telemonitoring can play a role in tracking symptoms, reducing hospital visits, and improving quality of life. Objective Our objective was to conduct a feasibility study of a home health monitoring (HHM) solution for patients with HF in British Columbia, Canada, to prepare for conducting a randomized controlled trial. Methods Patients with HF were recruited from 3 urban hospitals and provided with HHM technology for 60 days of monitoring postdischarge. Participants were asked to monitor their weight, blood pressure, and heart rate and to answer symptomology questions via Bluetooth sensors and a tablet computer each day. A monitoring nurse received this data and monitored the patient’s condition. In our evaluation, the primary outcome was the combination of unscheduled emergency department revisits of discharged participants or death within 90 days. Secondary outcomes included 90-day hospital readmissions, patient quality of life (as measured by Veterans Rand 12-Item Health Survey and Kansas City Cardiomyopathy Scale), self-efficacy (as measured by European Heart Failure Self-Care Behaviour Scale 9), end-user experience, and health system cost-effectiveness including cost reduction and hospital bed capacity. In this feasibility study, we also tested the recruitment strategy, clinical protocols, evaluation framework, and data collection methods. Results Seventy participants were enrolled into this trial. Participant engagement to monitoring was measured at 94% (N=70; ie, data submitted 56/60 days on average). Our evaluation framework allowed us to collect sound data, which also showed encouraging trends: a 79% reduction of emergency department revisits post monitoring, an 87% reduction in hospital readmissions, and a 60% reduction in the median hospital length of stay (n=36). Cost of hospitalization for participants decreased by 71%, and emergency department visit costs decreased by 58% (n=30). Overall health system costs for our participants showed a 56% reduction post monitoring (n=30). HF-specific quality of life (Kansas City Cardiomyopathy Scale) scores showed a significant increase of 101% (n=35) post monitoring (P Conclusions Results from this small-sample feasibility study suggested that our HHM intervention can be beneficial in supporting patients post discharge. Additionally, key insights from the trial allowed us to refine our methods and procedures, such as shifting our recruitment methods to in-patient wards and increasing our scope of data collection. Although these findings are promising, a more rigorous trial design is required to test the true efficacy of the intervention. The results from this feasibility trial will inform our next step as we proceed with a randomized controlled trial across British Columbia. Trial Registration ClinicalTrials.gov NCT03439384; https://clinicaltrials.gov/ct2/show/NCT03439384

Pain that increases from mid to late pregnancy deviates from normative patterns of change over time and is associated with elevated symptoms of postpartum depression., Introduction: Postpartum depression imparts a significant and long-lasting burden on maternal and child health. Successful prevention or early detection of postpartum depression will rely on the identification of early risk factors. Pain during pregnancy (before childbirth) is a key potential predictor of postpartum depression risk. However, longitudinal studies characterizing pregnancy pain, its normal trajectory over time, and its prospective relations with symptoms of postpartum depression are lacking. Methods: We used data from a longitudinal study of maternal emotion that included assessments of pain and depressive symptoms at 3 time points—during the second and third trimester of pregnancy and at 4 months postpartum. Structural equation modelling was used to estimate longitudinal patterns of change in maternal pain over time. Latent growth curve parameters were tested as predictors of symptoms of postpartum depression. Results: Ninety-three healthy pregnant women enrolled in this study. Although the sample comprised women with relatively low-risk pregnancies, more than 90% of participants experienced pregnancy pain. Greater linear increases and less negative quadratic change in maternal pain over time were associated with greater levels of postpartum depression, even when controlling for prenatal depressive symptoms. Interpreting both parameters together, pain that increased in late pregnancy, when normative patterns had either levelled off or begun to decline, was associated with greater levels of postpartum depression. Conclusion: A developmental trajectory of pain experience that did not subside after childbirth was associated with greater postpartum depressive symptoms, suggesting that atypical trajectories of pain may be a risk factor for postpartum depression.

Background Low adherence to real-world online weight loss interventions reduces long-term efficacy. Baseline characteristics and use patterns are determinants of long-term adherence, but we lack cohesive models to guide how to adapt interventions to users’ needs. We also lack information whether very early use patterns (24 hours) help describe users and predict interventions they would benefit from. Objective We aim to understand the impact of users’ baseline characteristics and early (initial 24 hours) use patterns of a web platform for weight loss on user adherence and weight loss in the long term (24 weeks). Methods We analyzed data from the POEmaS randomized controlled trial, a study that compared the effectiveness of a weight loss platform with or without coaching and a control approach. Data included baseline behavior and use logs from the initial 24 hours after platform access. Latent profile analysis (LPA) was used to identify classes, and Kruskal-Wallis was used to test whether class membership was associated with long-term (24 weeks) adherence and weight loss. Results Among 828 participants assigned to intervention arms, 3 classes were identified through LPA: class 1 (better baseline health habits and high 24-hour platform use); class 2 (better than average health habits, but low 24-hour platform use); class 3 (worse baseline health habits and low 24-hour platform use). Class membership was associated with long-term adherence (P Conclusions Baseline questionnaires and use data from the first 24 hours after log-in allowed distinguishing classes, which were associated with long-term adherence. This suggests that this classification might be a useful guide to improve adherence and assign interventions to individual users. Trial Registration ClinicalTrials.gov NCT03435445; https://clinicaltrials.gov/ct2/show/NCT03435445 International Registered Report Identifier (IRRID) RR2-10.1186/s12889-018-5882-y

Background Reduced patient portal use has previously been reported among Black Americans when compared with that of the general population. This statistic is concerning because portals have been shown to improve the control of chronic conditions that are more prevalent and severe in Black Americans. At their very simplest, portals allow patients to access their electronic health records and often provide tools for patients to interact with their own health information, treatment team members, and insurance companies. However, research suggests that Black American patients have greater concerns over a lack of support, loss of privacy, and reduced personalization of care compared with other Americans, which results in a disparity of portal use. Objective This qualitative investigation of primary care experiences of Black Americans from across the United States who participated in remote focus groups in April and May 2020 aims to explore the use and perceived value of patient portals to better understand any barriers to optimized treatment in the primary care setting. Methods We performed an inductive thematic analysis of 8 remote focus group interviews with 29 Black American patients aged 30-60 years to qualitatively assess the experiences of Black American patients with regular access to portals. Results Thematic analysis uncovered the following interrelated themes regarding patient portals in primary care: the optimization of care, patient empowerment, patient-provider communication, and patient burden. Conclusions In contrast to what has been described regarding the reluctance of Black Americans to engage with patient portals, our focus groups revealed the general acceptance of patient portals, which were described overwhelmingly as tools with the potential for providing exceptional, personalized care that may even work to mitigate the unfair burden of disease for Black Americans in primary care settings. Thus, opportunities for better health care will clearly arise with increased communication, experience, and adoption of remote health care practices among Black Americans.

The transformation that pharmaceuticals can undergo during the water reclamation cycle, or by biotic/abiotic reactions when reclaimed water (RW) is used for irrigation, can lead to the presence of transformation products (TPs) in agricultural environments. However, data on TPs in real crops are scarce. Herein, a suspect screening approach was applied for the comprehensive investigation of 262 potential TPs, associated with 20 prioritised pharmaceuticals found in real tomato crops exposed to long-term RW irrigation. The occurrence and fate of the TPs was evaluated by the retrospective analysis of RW, soil, leave and tomato samples from 4 intensive production greenhouses. Sample analysis was accomplished by liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS). Up to 18 TPs were tentatively identified, of which 2 were not previously reported. 7 TPs were finally confirmed with analytical standards. 5 TPs were determined in RW, 15 TPs in soil and 2 TPs in leaves. Remarkably, the investigated TPs were not found in tomato fruits. These results shed light on the variety of TPs that can be found in the water reuse cycle and contribute to the assessment of the global risks of wastewater reuse and the safety of the vegetable and fruit production system.

With the growing demand for macronutrients, such as nitrogen, and environmental issues related to their production, there is increasing need for efficient nutrient recycling. Reject waters from the dewatering of anaerobically digested sewage sludge are potential sources for nutrient recovery due to their high ammonium nitrogen (NH4-N) concentration (ca. 1 gNH4-N L−1) and low volume (ca. 1% of incoming sewage). In this study, a 3-chamber bioelectroconcentration cell was used for NH4-N recovery into a liquid concentrate from both synthetic and real reject water. NH4-N recovery efficiency and rate were optimised based on NH4-N loading rate, varying from 1.4 to 9.4 gNH4-N L−1 d−1 with synthetic reject water. The obtained NH4-N recovery efficiencies are the highest reported to date for bioelectroconcentration, peaking at 75.5 ± 4.6% (recovery rate of 728 ± 117 gN m−3 d−1) at loading rate 1.9 gNH4-N L−1 d−1. A loading rate of 2.9 gNH4-N L−1 d−1 led to the most optimal ratio between NH4-N recovery efficiency (68.2 ± 11.6%) and recovery rate (965 ± 66 gN m−3 d−1), with NH4-N up-concentrated 7.4 ± 0.9 times to 7483 ± 625 mg L−1 in the concentrate. With real reject water, NH4-N recovery efficiency of 53.2 ± 4.0% and recovery rate of 556 ± 37 gN m−3 d−1 were obtained at loading rate 2.5 gNH4-N L−1 d−1, with a specific energy consumption of 6.1 ± 1.1 kWh kgN−1. 16S rRNA amplicon analysis showed the dominance of phyla Bacteroidetes and Firmicutes in the anodic biofilms, with a significant change in the enriched microbial communities after transitioning from synthetic to real reject water. This study indicates the potential of bioelectroconcentration for nitrogen recovery from reject water without the need for an external organic carbon source or other chemical additions. publishedVersion

Background Home is a vulnerable place for accidental child injuries. Unintentional injuries are a leading cause of death, hospitalization, and disabilities. These injuries are considered preventable and if not tackled, they will continue to be a persisting problem. Smartphones have become increasingly important in our everyday life and is an important tool not only for communication but also for other purposes—they have apps that can be used for various purposes. Therefore, an app-based intervention (ChildSafe) was developed to assess and reduce child injury at home. Objective This study aimed to evaluate the acceptance of the ChildSafe smartphone app intervention by parents/guardians. Methods This study was conducted using a qualitative exploratory approach on selected participants of the ChildSafe intervention app study. A total of 27 semistructured in-depth interviews were carried out among parents or guardians who have at least one child between the age of 0 and 59 months in the area of Sungai Buloh, Selangor, between November 2017 and March 2018. Interview questions were developed from the consolidated framework for implementation research (CFIR). Interviews were recorded, transcribed verbatim, and data were thematically analyzed guided by CFIR. Results The study revealed users’ perception on usability, feasibility, and acceptability toward the ChildSafe app. Three CFIR domains were identified: intervention characteristics, inner setting, and characteristics of individuals. A total of 5 constructs were revealed under intervention characteristics: evidence strength and quality, relative advantage, adaptability, trialability, and design quality and packaging; 2 under inner setting: implementation climate and readiness for implementation; and 4 under characteristics of individuals: knowledge and beliefs about the intervention, self-efficacy, individual stage of change, and other personal attributes. In general, participants felt the app is extremely useful and effective, easy to use, and purposeful in achieving home safety assessment via reminders. The app replaces the need for participants to search for information on home safety and dangers, as the app itself was designed as a tool to assess for this specific purpose. Even at the nascent stage and despite its limitations, the app has prompted users to consider and make changes around their own home. However, future versions of the app should be expanded to make it more attractive to users as it lacks interactive feedback and additional features. Conclusions Parents/guardians are accepting the use of the ChildSafe app to prevent child injury at home. However, further expansion and improvements are needed to increase the acceptability of this app by parents/guardians.

Agrochemical wastewater is a potential danger to public health, the environment, and other livings, as it contains hazardous compounds that can harm plants and animals and cause many diseases in humans. In this work, the thermally activated persulfate (TAP) oxidation method, which is an effective and practical approach for degradation of agrochemical wastewater, was carried out. Optimization and modeling of total organic carbon (TOC) and decolorization of the agrochemical wastewater were studied using a central composite design. Persulfate was thermally activated and the effect of the temperature, treatment time, and persulfate concentration on the TAP method was detailed. Temperature and persulfate concentration were the most effective process parameters for TOC removal. Results, model coefficients, and analysis of variance (ANOVA) showed that the resulting model was precise and acceptable for predicting TOC and color removals. The feasibility of energy and reagent consumption was proposed. Under conditions where TOC and color removal rates were reached 70.2% and 100%, the electrical energy and persulfate consumption were 529.5 kWhm−3 and 21.6 kgm−3, respectively. Chemical composition analysis of wastewater and treated samples showed that the TAP method was able to degrade almost all compounds found in this study, including those resistant to oxidation. Ion chromatography analysis showed that the ion concentration of the wastewater samples changes with the applied temperature and the oxidant concentration.

Slagging and fouling are common problems associated with biomass firing. The different nature of the mineral and phase composition of biomass ash makes the vast experience with coal firing insufficient for its translation to biomass fuels, especially when it comes to slagging and fouling behavior. Biomass tends to have lower ash content than coals; however, it is often rich in volatile alkalis. The mineral deposits found on boiler walls and superheater tubes are often comprised of alkali compounds. Numerous studies on ash melting and particle sticking behavior have been conducted. Laboratory observed ash fusion temperatures are commonly used to evaluate the slagging and fouling propensity of fuels. The tests are often time consuming, therefore several predictive indices have been developed to estimate the propensity based on the ash composition alone. Thermodynamic models as well as neural networks have also been applied to this end. However, for practical in the field purposes, the ash fusion tests and predictive indices are preferred because of their convenience. An overview of these indices is presented in this work. A sizeable dataset has been collected in order to statistically evaluate the applicability of the indices and of several AFT prediction formulas. General trends in ash composition on this extensive dataset have also been illustrated. Finally, a more convenient graphical solution is presented for preliminary slagging and fouling predictions.

A novel adsorbent-based coating with zwitterionic functionality (ZwitAd) was employed to treat the heavily polluted hand-drawn batik industrial effluent. The adsorbent was prepared as a coating and supported onto a cotton cloth by applying a simple preparation method based on the combined use of clay-based adsorbent, polymer-based binder, and cationic polyelectrolyte. Batik effluent which is known to have high chemical oxygen demand (COD) (412–2205 mg/L) and colour intensity (500–16900 ADMI) has undertaken treatment process using ZwitAd. The results showed a significant reduction of COD (147 mg/L), colour (128 ADMI), and pH (7.34), which complied with the standard discharge limit for industrial effluent regulations. The finding proved that ZwitAd is applicable to reduce the COD and colour intensity and stabilise the pH of water. This new innovative treatment has simultaneously improved the effluent conditions and minimised operational costs, and minimum secondary waste is generated. The proposed application of ZwitAd at an industrial scale has also been introduced as a functional industrial storage tank with a feasible and practical operation.

The chemical energy contained in wastewater can be considered as a promising sustainable energy source and can be recovered using microbial fuel cell technology by means of electro-active bacteria. There are several brewery industries releasing wastewater into the environment, posing serious environmental issues. Herein, we demonstrated treatment of brewery wastewater and production of bioelectricity simultaneously using double chamber MFCs by inoculating locally isolated microorganisms. Microorganisms were locally isolated from brewery waste sludge (named as BSGB1 and BSGB2), brewery wastewater (BWGB3 and BWGB4) and food processing industry waste sludge (FSGB5 and FSGB6). Total dissolved solids (TDS), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) were determined before and after treatment of brewery wastewater by locally isolated microorganisms. The results revealed that the microorganisms isolated from brewery waste sludge outperformed the bacteria isolated from brewery wastewater and food processing industry waste sludge. The maximum power density of 0.8 W/m3 and 0.35 W/m3 was generated by MFC inoculated with locally isolated microorganisms from brewery waste sludge using the synthetic and real-brewery wastewater, respectively. The removal efficiency of COD was 79−83%, indicating significant treatment of brewery wastewater by locally isolated microorganisms while generating sustainable and clean energy.

In the effort to manage global warming and fossil carbon emissions, many countries are suggesting brand-new policies to make their energy matrix much more renewable. The boost share of bioethanol as a transportation fuel becomes a short-term option since its production technology is fully grown and combined with gasoline. The 2nd generation ethanol still has involvement on the planet biofuels circumstance. In this study, water lettuce (Pistia stratiotes) fresh and dry biomass applied with alkalis (NaOH) and acid (H2SO4) were combined to pretreat the abundant free-floating aquatic plant water lettuce biomass. Naturally, the water lettuce accumulates food in the form of starch, suitable for bioethanol production. The results showed that the highest total sugar (11.350 ± 0.09%), reducing sugar (4.773 ± 0.15%), and ethanol (15. 385 g/L) were obtained after pretreatment of dried water lettuce with cellulase. Consequently, water lettuce was high feasible for sustainable bioethanol production.

To attain the purity standard for carbon sequestration, SOx and NOx must be removed from the flue gas of pressurized oxy-combustion of coal. Studies suggest that at high pressure SOx and NOx can be simultaneously removed in a single column, with water as the working fluid. Additionally, in pressurized oxy-combustion the moisture in the flue gas can be recovered at high temperature. However, to date, experimental investigations in pressurized oxy-combustion environments have been largely limited to laboratory scale and room temperature. This work demonstrates the operation and parametric testing of an experimental pilot-scale direct contact column with a diameter and packing height of 0.2 m and a 2.3 m, respectively, over a temperature range from 24 to 202 °C, O2 concentration from 1 to 4% v/v, and S/N ratio from 0 to 2. Additionally, a model of the direct contact column is developed and simulated in Aspen Plus to complement and interpret the experimental results. The study reveals that NO oxidation is the controlling reaction in the overall scrubbing of SOx and NOx at gas temperatures up to 200 °C. The NOx scrubbing efficiency depends on residence time in the column, with higher than 90% removal at a residence time of 120 s. Oxygen concentration has little effect above a concentration of 1% v/v. At high temperature, the total NOx scrubbing efficiency is reduced by approximately 10%, as compared to room temperature. SOx removal is higher than 93% under all conditions. Scrubbing efficiency at different S/N ratios demonstrate the importance of NOx in sulfur scrubbing and confirm that a low NOx concentration in the flue gas can lead to a significant reduction in SOx scrubbing.

In the last few decades, the production of value-added chemicals from biomass has become a key research focus. Biomass gasification into syngas and further for methane, methanol, and C2+ alcohols production has already proved to be promising for utilization of renewable energy. Therefore, heterogeneous catalyst design and optimization are extremely significant for solving the issues of tar formation during biomass gasification. Apart from alkali metals, natural minerals, and synthetic catalysts, char and char supported catalysts prepared from residual biomass or coal have been already proved to be effective for tar reforming due to their relative chemical inertness, cheap price, excellent pore structures, etc. Advantageously, bio−/coal char could be modified and/or functionalized with heteroatoms doping and metal addition to improve activity in tar reforming. In this review, we systematically discussed the preparation, modification, and application of char catalysts in the reforming of biomass tar and tar model compounds. After that, we reviewed and compared the activity of char catalysts in tar reforming, and then we gave corresponding reactions and deactivation mechanisms over char catalysts in biomass tar reforming. Finally, we proposed existed challenges and shared our perspectives regarding future needs in char catalysts design for accelerating fuel sustainability and green development.

In recent years, the cellular agriculture concept has been proposed as an option to replace livestock proteins. This study presents a conceptual level techno-economic analysis of four concepts where food is produced by microorganisms based on wheat straw. Three single-cell proteins and one recombinant protein process were conceptualized. The process included steam explosion pretreatment, enzymatic hydrolysis, fermentation, and downstream processing. The enzymatic hydrolysis was optimized to minimize the production costs. The minimum protein selling price was determined by net present value using the discounted cash-flow method, which was compared to respective animal and plant proteins. The minimum protein selling prices were 5160–9007 €/ton proposing the processes to be in the feasible range, but the processes still require further development. The sensitivity of the parameters was estimated by sensitivity analysis, which revealed the most critical components in the production to be capacity, investment, interest, and enzyme and raw material costs.

Rapid granulation was realized in a continuous-flow reactor with a two-zone sedimentation tank (CFR-TST), and a novel strategy involving the continuous addition of dewatered sludge during the start-up phase to treat the actual domestic sewage with low concentration. The selection pressure produced by the two-zone sedimentation tank can discharge light flocs with poor settleability and retain the fine particles in the reactor. The continuous addition of dewatered sludge in the start-up phase can increase the small particles in the aeration tank by acting as nuclei to accelerate granulation. The experimental results indicated that aerobic granular sludge (AGS) was formed successfully on day 25 (the sludge volume index at 5 min (SVI5) was 37.7 mL/g, the mean particle size was 0.42 mm, and the average settling velocity was 35.2 m/h); after operated continuously for 45 days, the sedimentation capacity and biomass concentration were further enhanced (SVI5 and the mixed liquor suspended solids (MLSS) were stable in the range of 25.8–31.3 mL/g and 4200−4600 mg/L, respectively). Although the dewatered sludge with low biological activity had a slight negative impact on pollutants degradation in the start-up phase, the biological activity of the sludge was enormously upgraded (compared with those of the inoculated dewatered sludge, the SOUR and dehydrogenase activity of the mature AGS increased 3.72 times and 5.01 times on day 84, respectively), and microbes that promote the secretion of EPS and facilitate the degradation of pollutants were enriched during the whole process of granulation. Moreover, the approach developed in this case was simple and eco-friendly for potential full-scale implementation.

Fruit and vegetables contain molecules that have particular colors, which can potentially be an environmentally attractive substitute for their synthetic counterparts in (non-)food applications. The most sustainable source for such natural colorants would be by the valorization of by-products from the fruit and vegetable industries, but qualitative and quantitative characteristics of food by-products for this purpose remain scarce. Natural dyes also show mediocre stability and affinity toward textile fibers, which questions their potential feasibility for application and level of sustainability to overcome these issues. This review describes three dye classes (i.e., anthocyanins, quinones, and carotenoids) along with their occurrence, mass, and concentration in by-products that are generated from agricultural losses as well as the fruit and vegetable processing industries. To tackle the shortcomings of natural dyes on fibers, several application techniques were collected from the literature. A discussion on techno-economic potential and environmental sustainability is included. The latter is done by including a life cycle assessment (LCA) to investigate the environmental impact of extracting anthocyanins, quinones, and carotenoids from fruit and vegetable processing by-products and their subsequent application to the dyeing process. The mapping of by-products for each natural dye class illustrates the vast availability of agro-food residues (>0.1 Mt annually in the EU-28) with a natural dye content of up to 56 kg/t DW for anthocyanins, 18 kg/t DW for quinones, and 593 kg/t DW for carotenoids. Metallic mordants are mostly favored for improving the fixation of natural dyes but entail potential environmental issues. Greener approaches, such as biomordants and enzymes, still show room for improvement, chemical modification methods might also guarantee dye fixation, though questionable in environmental sustainability. The different valorization scenarios of anthocyanins, quinones, and carotenoids from food waste, analyzed with LCA, showed the environmental competitiveness of these natural dyes, applied as a crude extract, compared to synthetic dyes. The valorization routes design shows that agricultural losses and food processing waste streams are adequate sources of natural dyes, especially to be applied in niche scale applications.

A Life Cycle Analysis (LCA) based on ReCiPe 2016 model of a biorefinery case-study was performed. On the basis of an existing Portuguese Anaerobic Digestion plant, the proposed biorefinery hypothesized the use of (i) Maize Cob Waste (MCW) as co-substrate for Anaerobic co-Digestion (AcoD) with Organic Fraction Municipal Solid Waste (OFMSW), (ii) the use of MCW derived activated carbons (ACs) in a H2S removal unit, and (iii) the biogas upgrading to biomethane in a Pressure Swing Adsorption unit. The main aim was to compare the environmental benefits obtained from distinct uses of biogas, specifically the cogeneration of electricity/heat and biomethane production. Three biogas production configurations were considered: (i) AD of standalone hydrolysed OFMSW (hOFMSW); (ii) AcoD of hOFMSW and MCW pre-treated with H2O2 (hOFMSW + PreMCW); and (iii) AcoD of hOFMSW with non-pre-treated MCW (hOFMSW + MCW). The increase of biogas and methane yields obtained with AcoD of hOFMSW + MCW provided an overall better environmental performance than other configurations. With this configuration the FRS, MRS and GW (hh) categories of impacts provided values of 3.12E-02 and -2.49E-05 US$2013, and 4.87E-09 DALY, respectively, that are 47%, 15% and 97% lower than the corresponding value obtained with AD of standalone OFMSW. The biogas upgrading to biomethane from AcoD of hOFMSW + MCW generated higher environmental impacts than cogeneration, due to the AC production and upgrading processes. If an optimised H2S adsorption capacity is considered, the Fossil Resource Scarcity, Mineral Resource Scarcity, and Global Warming human health impact categories decreased by 20%, 15%, and 17%, respectively, when compared to the base-case upgrading scenario. Further decreases of up to 52%, 23%, and 28% for those impact categories, respectively, are observed when the natural gas used in the OFMSW collection and transportation fleet is substituted by biomethane produced in the biorefinery.

Bio-oil is produced as a promising fuel for improving the economics of biomass. However, bio-oil utilizations suffer from challenges due to its complex compositions. In this research three bio-oil samples (BSR, BPA and BFT) were blended with coal/char to prepare slurries as biofuels. The thermogravimetric analysis of combustion and ash compositions of biofuels were investigated. Results showed that bio-oils have more weight loss peaks than coal/char, and the Tm is concentrated in the range from 476.4 °C to 495.2 °C, which are much lower than char. The contribution of coal/char and bio-oils to biofuel blendings can be clearly seen in the combustion profiles. There is a synergistic effect between bio-oils and coal/char improving the combustion performance. Tb of lignite BSR slurry reduced up to around 40 °C in comparison with lignite. The temperature range for combustion became narrow. By blending BSR, the Ea of lignite, long flame coal and char reduced 5.65 kJ mol−1, 13.56 kJ mol−1 and 6.31 kJ mol−1, respectively. Lignite BSR slurry with the lowest activation energy presented best combustion characteristics among all biofuels. The improved performance may attribute to AAEMs contained in bio-oil and microexplosions occurred in combustion, and AAEMs also help form lower fusion temperature of ashes.

Tremendous growth in the number of automobiles in developed and developing global economies has exorbitantly boosted competition for petroleum products. Petroleum products derived from fossil fuels are predominantly responsible for environmental pollution as unburnt hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx) & carbon dioxide (CO2) emissions are released from the fossil fuel combustion. In the view of increasing environmental pollution and stringent emission norms, the present study is concentrated on using Jatropha biodiesel as an alternate fuel source to run variable compression ratio (VCR) diesel engine. The characteristics of VCR diesel engine emission have been evaluated under different compression ratio (CR), operating conditions of load & pressure of fuel injection. In this research work, Jatropha biodiesel diesel blend B30 (30% biodiesel and 70% diesel) and B0 (100% diesel) have been taken as fuel to run the engine. For conducting experiments, load has been varied from 0 to 12 Kg, CR from 14 to 18 and FIP from 180 to 270 bar as per the model of Response Surface Methodology experiments. The experimental investigation showed that the use of the B30 blend reduces HC & CO emissions by about 16.7% and 24% correspondingly in comparison to diesel. However noteworthy rise in NOx & CO2 emissions rate recorded by using the B30 blend as that of diesel. It has been shown that with enhancing in load & CR, HC&CO emissions decreased significantly however increase in CO2 and NOx observed. Advancing FIP, significantly decreases HC & CO emissions as well as tends to increase NOx and CO2 emissions.

This paper will focus on fixed roof storage tanks and its sub-category cone roof tanks. Paper presents major factors and outputs of tank design process. Theoretical analysis is done for a cone roof tank model and correlation between tank surface, shell thickness and total steel weight used. Detailed algorithm for tank design was realized using MathCAD software using Eurocode standards. Using this algorithm, 432 hypothetical tanks were designed. Data analysis of the collected output data was analysed using R statistics programming language. Using regression models and testing them led to the optimum statistical model. For the purpose of this work, data for 21 real world tanks including fixed roof and floating roof tanks were collected. The data was collected with help of MOL, INA and few other EPC contractors. Paper compares real world data with outputs of the model. Model can be used for quick estimates and control of material consumptions. Taking few input parameters and applying minimal effort can lead to a very accurate steel consumption prediction. With additional real-world data, acceptable ranges of deviation from the mean could be set-up. Thus, allowing for better control on Client part or EPC companies.