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A ‘new’ group of pathogenic agents, enterohemorrhagic Escherichia coli (EHEC) (particularly the strains of O157 serogroup), emerged in the last 20 years causing an increased number of sporadic and epidemic diarrhoeal diseases with hemorrhagic enterocolitis as a most common clinical manifestation of the infection. As a consequence of the absorption and cytotoxic effect of the main virulence factor of these bacteria - verotoxin (shiga-toxin), in about 10% of the affected persons extraintestinal complications, most frequently hemolytic-uremic syndrome (HUS), occurred 7-14 days after an episode of diarrhoeal disease. The first case of hemorrhagic enterocolitis with the documented EHEC O157 infection in Yugoslavia is presented in this paper. Considering the existing expansion trend of these carriers, practitioners should be aware of them in case of the occurrence of diarrhoeal disease (particularly hemorrhagic enterocolitis), and keep these patients under control during the reconvalescence period because of potential development of extraintestinal complications, such as HUS.

Kikuchi-Fujimoto disease (KFD), also know as histiocytic necrotizing lymphadenitis, is a benign disorder characterized histologically by necrotic foci surrounded by histiocytic aggregates, and with the absence of neutrophils. KFD was recognized in Japan, where it was first described in 1972. The disease is most commonly affecting young women. The cause of the disease is unknown, and its exact pathogenesis has not yet been clarified. Many investigators have postulated viral etiology of KFD, connecting it with Epstein Barr virus, human herpes simplex virus 6 parvo B 19, but also with toxoplasmic infection. Kikuchi-Fujimoto disease is usually manifested with lymphadenopathy and high fever, and is associated with lymphopenia splenomegaly, and hepatomegaly with abnormal liver function tests arthralgia, and weight loss. The disease has the tendency of spontaneous remission, with mean duration of three months. Single recurrent episodes of KFD have been reported with many years’ pauses between the episodes. Kikuchi-Fujimoto disease may reflect systemic lupus erythematosus (SLE), and self-limited SLE-like conditions. Final diagnosis could only be established on the basis of typical morphological changes in the lymph node, and lymph node biopsy is needed for establishing the diagnosis. Lymphadenopathy in a patient with fever of the unknown origin could provide a clue to the diagnosis of lymphoma, tuberculosis, metastatic carcinoma, toxoplasmosis and infectious mononucleosis. As KFD does not have any classical clinical features and laboratory characteristics, it may lead to diagnostic confusion and erroneous treatment. We described a case of KFD, and suggested that this disease should be considered as a possible cause of fever of the unknown origin with lymphadenopathy.

Tuberculosis is an unusual infectious disease because of the latent period between the infection and the appearance of the disease may be prolonged for many weeks, months, or years as it is in case of the secondary tuberculosis. Tuberculosis in organs other than the lung has been observed for many years but has not always been recognized as tuberculosis, and it has been given many names. Extrapulmonary tuberculosis gained new importance, because it represented a progressively greater proportion of new cases. Multiple extrapulmonary sites were reported rarely except for one anatomical site, which was reported frequently. Extrapulmonary rates increase with age, so there are marked differences in age in specific rate patterns among the sites. Extrapulmonary tuberculosis occurred in respiratory organs other than lung, such as lymphatic, urogenital, and central nervous system, abdominal, osteoarticular, as well as tuberculosis of other organs such as skin, pericardium and endocrine glands. This case was reported to analyse clinical, morphologic and laboratory characteristics, method of diagnosis and the outcome in patients with multiorgan tuberculosis in order to explore the factors which might contribute to the decision making, concerning these forms of tuberculosis. Recent knowledge of pathogenesis was summarized as well as clinical presentation and the effects of cytokines produced by T lymphocytes and cellular population on antimycobacterial immune defences, and also susceptibility to tuberculosis. Mortality remains high and the treatment should start as soon as tuberculosis is suspected.

The first case of the confirmed necrotizing fasciitis caused by Group A Streptococcus in Yugoslavia was presented. Male patient, aged 28, in good health, suddenly developed symptoms and signs of severe infective syndrome and intensive pain in the axillary region. Parenteral antibiotic, substitution and supportive therapy was conducted along with the radical surgical excision of the necrotizing tissue. The patient did not develop streptococcal toxic shock syndrome thanks to the early established diagnosis and timely applied aggressive treatment. He was released from the hospital as completely cured two months after the admission.

The relation between HCV genotypes and the progression of chronic hepatitis is still unknown. Some studies implied more pathogenic effect of genotype 1b for the severity of liver inflammation. However, other studies did not show the association between HCV genotype 1b and the severe outcome of HCV infection. The aim of this study was to determine the most frequent genotypes in this environment and their influence on hepatitis C severity. The investigation included 34 patients with histologically confirmed chronic hepatitis C, aged 20-65 (mean 35.0 years). On the basis of patohistological findings, applying the modern classification, the disease activity was graded as: minimal (A1), moderate (A2) and severe (A3). The extent of fibrosis was marked as: absent (F0), mild (F1), moderate (F2) and severe (F3). Genotyping was performed by nested PCR with type-specific primers and LIPA test and verified by sequencing. The most prevailing genotype in our group of patients was 1b (44.1%), followed by genotype 3a (26.4%), genotype 1a (11.7%) and 2a (2.8%). Five patients had mixed genotypes (four 3a/1b, and one 1a/1b). The severity of liver cell necrosis, measured by alanintransferaze (ALT) levels in serum was not related to any of HCV genotypes. There was no statistically significant difference between histological disease activity in relation to HCV genotypes. Stage of the disease was not significantly related to the HCV genotypes. There was a strong association between the degree of fibrosis and the age of patients (p

Background/Aim. Ultrasonographic kidney changes might be a part of the clinical picture, or consequence of the various infections diseases. The aim of this study was to establish ultrasonographic findings obtained by portable devices, the frequency of abnormal findings of the kidneys in the non-selected group of the patients with infections diseases. Methods. Over a five-year period (January 1, 2000-December 31, 2004), the kidneys were examined by ultrasonography in 2718 patients, 1452 males and 1266 females, mean age 47.52±17 years, (16-92 years). The examination included the measurement of the size of the kidneys, evaluation of the condition of parenchyma and pyelo-calix, detection of simple cysts, calculi and tumor. The conventional portable ultrasonographic devices ALOKA SSD-500 and SSD-1000 (B-mode) with a convex 3.5 and 5 MHz sonde were used. Results. The size of kidneys was normal in 95% of the patients, while they were enlarged in 1.3% and reduced in 1.5% of the patients. A normal ultrasonographic recording was noted in 68.9% of the patients, double pelvis in 0.1%, while hydronephrosis was revealed in 0.9% of the patients. A reduced renal parenchyma was observed in 16.1% of the patients. Nephrolithiasis was found in 10.9% of the patients, and simple cysts of kidneys in 8.9% of the patients. The finding of polycystic kidneys was seen in 0.5% of the patients. An ultrasonographic recording of angiomyolipoma was noted in 0.4% of the patients, and the finding of other tumors in 0.1% of the patients. Adrenal tumors were found in 0.1% of the patients. Conclusion. Portable ultrasonographic units may be highly useful for the standard morphological diagnostics of renal changes during infections, as well as in clinical-epidemiological studies and screening of hereditary and the acquired diseases of this organ.

Objective: Recent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM., Design, Setting, and Participants: From a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria-accuracy, readability, helpfulness, empathy, and likelihood of harm-rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics., Results: Upon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability., Conclusion: Our study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation., Competing Interests: Declaration of competing interest Frederick Sundram is on the Clinical Advisory Board for Clearhead, a digital ecosystem for promoting mental wellbeing. All other authors declared no known competing financial interests and personal relationships with individuals or organizations that could inappropriately influence the reported work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Global pandemics like COVID-19 put a high amount of strain on health care systems and health workers worldwide. These crises generate a vast amount of news information published online across the globe. This extensive corpus of articles has the potential to provide valuable insights into the nature of ongoing events and guide interventions and policies. However, the sheer volume of information is beyond the capacity of human experts to process and analyze effectively., Objective: The aim of this study was to explore how natural language processing (NLP) can be leveraged to build a system that allows for quick analysis of a high volume of news articles. Along with this, the objective was to create a workflow comprising human-computer symbiosis to derive valuable insights to support health workforce strategic policy dialogue, advocacy, and decision-making., Methods: We conducted a review of open-source news coverage from January 2020 to June 2022 on COVID-19 and its impacts on the health workforce from the World Health Organization (WHO) Epidemic Intelligence from Open Sources (EIOS) by synergizing NLP models, including classification and extractive summarization, and human-generated analyses. Our DeepCovid system was trained on 2.8 million news articles in English from more than 3000 internet sources across hundreds of jurisdictions., Results: Rules-based classification with hand-designed rules narrowed the data set to 8508 articles with high relevancy confirmed in the human-led evaluation. DeepCovid's automated information targeting component reached a very strong binary classification performance of 98.98 for the area under the receiver operating characteristic curve (ROC-AUC) and 47.21 for the area under the precision recall curve (PR-AUC). Its information extraction component attained good performance in automatic extractive summarization with a mean Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 47.76. DeepCovid's final summaries were used by human experts to write reports on the COVID-19 pandemic., Conclusions: It is feasible to synergize high-performing NLP models and human-generated analyses to benefit open-source health workforce intelligence. The DeepCovid approach can contribute to an agile and timely global view, providing complementary information to scientific literature., (©Mathieu Ravaut, Ruochen Zhao, Duy Phung, Vicky Mengqi Qin, Dusan Milovanovic, Anita Pienkowska, Iva Bojic, Josip Car, Shafiq Joty. Originally published in JMIR AI (https://ai.jmir.org), 30.10.2024.)

Digital healthcare has rapidly evolved during and in the post-COVID pandemic era, expanding the roles and responsibilities of community pharmacists. Services like telepharmacy, e-prescriptions, remote medication therapy management, and digital monitoring of chronic conditions, have evolved into everyday routine pharmacy practices. Pharmacists are at the forefront and the most accessible healthcare professionals for patients and are increasingly pivotal in providing comprehensive patient care, including digital patient care services. To ensure that future generations of pharmacists are digitally competent, it is crucial that digital health education is provided to pharmacy students. Furthermore, fostering high-quality multidisciplinary research, particularly in collaboration with medicine and other health disciplines, is essential for advancing the digital health skills of the future pharmacy workforce. Despite the growing use of digital health technologies, there are significant between-country differences in digital health education, the clinical settings in which digital health technologies are used, and their implementation in day-to-day practice. This commentary summarizes key insights from the International Digital Health Workshop held at the University of Sydney in November 2023. To help ensure pharmacists are included as participants in future digital health research, recent advances in digital health education and interprofessional research projects across three universities from far-off world regions were presented. Participants discussed a possible collaborative, interprofessional, and international research project on chronic disease prevention using digital health technologies. The need for interdisciplinary digital health curricula was highlighted in the workshop discussions, specifically tailored to address the knowledge requirements of pharmacists and other healthcare professionals., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: To investigate the impacts of the COVID-19 pandemic on the health workforce, we aimed to develop a framework that synergizes natural language processing (NLP) techniques and human-generated analysis to reduce, organize, classify, and analyze a vast volume of publicly available news articles to complement scientific literature and support strategic policy dialogue, advocacy, and decision-making., Objective: This study aimed to explore the possibility of systematically scanning intelligence from media that are usually not captured or best gathered through structured academic channels and inform on the impacts of the COVID-19 pandemic on the health workforce, contributing factors to the pervasiveness of the impacts, and policy responses, as depicted in publicly available news articles. Our focus was to investigate the impacts of the COVID-19 pandemic and, concurrently, assess the feasibility of gathering health workforce insights from open sources rapidly., Methods: We conducted an NLP-assisted media content analysis of open-source news coverage on the COVID-19 pandemic published between January 2020 and June 2022. A data set of 3,299,158 English news articles on the COVID-19 pandemic was extracted from the World Health Organization Epidemic Intelligence through Open Sources (EIOS) system. The data preparation phase included developing rules-based classification, fine-tuning an NLP summarization model, and further data processing. Following relevancy evaluation, a deductive-inductive approach was used for the analysis of the summarizations. This included data extraction, inductive coding, and theme grouping., Results: After processing and classifying the initial data set comprising 3,299,158 news articles and reports, a data set of 5131 articles with 3,007,693 words was devised. The NLP summarization model allowed for a reduction in the length of each article resulting in 496,209 words that facilitated agile analysis performed by humans. Media content analysis yielded results in 3 sections: areas of COVID-19 impacts and their pervasiveness, contributing factors to COVID-19-related impacts, and responses to the impacts. The results suggest that insufficient remuneration and compensation packages have been key disruptors for the health workforce during the COVID-19 pandemic, leading to industrial actions and mental health burdens. Shortages of personal protective equipment and occupational risks have increased infection and death risks, particularly at the pandemic's onset. Workload and staff shortages became a growing disruption as the pandemic progressed., Conclusions: This study demonstrates the capacity of artificial intelligence-assisted media content analysis applied to open-source news articles and reports concerning the health workforce. Adequate remuneration packages and personal protective equipment supplies should be prioritized as preventive measures to reduce the initial impact of future pandemics on the health workforce. Interventions aimed at lessening the emotional toll and workload need to be formulated as a part of reactive measures, enhancing the efficiency and maintainability of health delivery during a pandemic., (©Anita Pienkowska, Mathieu Ravaut, Maleyka Mammadova, Chin-Siang Ang, Hanyu Wang, Qi Chwen Ong, Iva Bojic, Vicky Mengqi Qin, Dewan Md Sumsuzzman, Onyema Ajuebor, Mathieu Boniol, Juana Paola Bustamante, James Campbell, Giorgio Cometto, Siobhan Fitzpatrick, Catherine Kane, Shafiq Joty, Josip Car. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.06.2024.)

Objective: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality., Methods: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n  = 30) or the control conditions (QA system; n  = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention., Results: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95% CI: [-1.55-0.77], p  = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9-59.1], p  = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7-61.9], p  = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep., Conclusions: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Background: Diabetic foot ulcers (DFUs) cause significant morbidity affecting 19% to 34% of people living with diabetes mellitus. DFUs not only impair quality of life but may also result in limb loss and mortality. Patient education has been advocated to raise awareness of proper foot self-care and the necessity of seeking assistance when a foot wound occurs. Modern technologies, including mobile health (mHealth) interventions such as health apps, bring the potential for more cost-effective and scalable interventions., Objective: This study aims to examine the feasibility and usability of a newly developed mHealth app called Well Feet, which is a diabetes and foot care education app for individuals at risk of developing DFU., Methods: Well Feet was developed using an evidence-based and expert panel cocreation approach to deliver educational content available in 3 languages (ie, English, Chinese, and Malay) via animation videos and a range of additional features, including adaptive learning. A nonrandomized, single-arm feasibility study using a mixed methods approach with a series of validated questionnaires and focus group discussions will be conducted. In total, 40 patients and carers will be recruited from a tertiary hospital diabetes clinic to receive a 1-month mHealth intervention. The primary outcomes are the usability of the app and a qualitative perspective on user experience. Secondary outcomes include changes in foot care knowledge, self-management behaviors, and quality of life., Results: Patient recruitment began in July 2023, and the intervention and data collection will be completed by the end of September 2023. This study has been approved by National Healthcare Group Domain Specific Review Board (2022/00614) on February 10, 2023. The expected results will be published in spring 2024., Conclusions: Through this feasibility study, the Well Feet DFU education app will undergo a comprehensive quantitative and qualitative evaluation of its usability and acceptance for future improvement in its design. With local contextualization, cultural adaptation, and its multilingual functionality, the app addresses a critical aspect of DFU health education and self-management in a multiethnic population. Findings from this study will refine and enhance the features of the app based on user feedback and shape the procedural framework for a subsequent randomized controlled trial to assess the effectiveness of Well Feet., Trial Registration: ClinicalTrials.gov NCT05564728; https://clinicaltrials.gov/study/NCT05564728., International Registered Report Identifier (irrid): DERR1-10.2196/52036., (©Huiling Liew, Anita Pienkowska, Chin-Siang Ang, Muhammad Daniel Azlan Mahadzir, Kelley Fann Ing Goh, Nandika Lodh, Iva Bojic, Ashwini Lawate, Qi Chwen Ong, Kavita Venkataraman, Josip Car, Andy Hau Yan Ho. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.11.2023.)

Background: Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the "measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.", Objective: This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle., Methods: We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform., Results: We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners' interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively., Conclusions: We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course's run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed., (©Iva Bojic, Maleyka Mammadova, Chin-Siang Ang, Wei Lung Teo, Cristina Diordieva, Anita Pienkowska, Dragan Gašević, Josip Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2023.)

Over the last 10 years, ride-hailing companies (such as Uber and Grab) have proliferated in cities around the world. While generally beneficial from an economic viewpoint, having a plurality of operators that serve a given demand for point-to-point trips might induce traffic inefficiencies due to the lack of coordination between operators when serving trips. In fact, the efficiency of vehicle fleet management depends, among other things, density of the demand in the city, and in this sense having multiple operators in the market can be seen as a disadvantage. There is thus a tension between having a plurality of operators in the market, and the overall traffic efficiency. To this date, there is no systematic analysis of this trade-off, which is fundamental to design the best future urban mobility landscape. In this paper, we present the first systematic, data-driven characterization of the cost of non-coordination in urban on-demand mobility markets by proposing a simple, yet realistic, model. This model uses trip density and average traffic speed in a city as its input, and provides an accurate estimate of the additional number of vehicles that should circulate due to the lack of coordination between operators-the cost of non-coordination. We plot such cost across different cities-Singapore, New York (limited to the borough of Manhattan in this work), San Francisco, Vienna and Curitiba-and show that due to non-coordination, each additional operator in the market can increase the total number of circulating vehicles by up to 67%. Our findings could support city policy makers to make data supported decisions when regulating urban on-demand mobility markets in their cities. At the same time, our results outline the need of a more proactive government participation and the need for new, innovative solutions that would enable a better coordination of on-demand mobility operators., (© 2022. The Author(s).)

Background: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening., Objective: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population., Methods: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds., Results: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression., Conclusions: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk., (©Yuri Rykov, Thuan-Quoc Thach, Iva Bojic, George Christopoulos, Josip Car. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 25.10.2021.)

People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators.

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility - while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.

We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called "dilatation attractiveness index" and "dilatation index", to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there., Competing Interests: The authors have declared that no competing interests exist.