Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Chronic pain is one of the major causes of disability in the general population. Even though there are effective treatment options available for reducing symptoms, these treatments often do not have consistent lasting effects. As the usage of mobile devices has increased enormously during the last few years, mobile application-based treatment options are widespread. Such app-based programs are not yet empirically proven but might enable patients to become more independent in their pain management in order to prevent relapse. The aim of this meta-analysis was to summarize the literature on mobile application-based interventions for chronic pain patients. Therefore, three electronic bibliographic databases, PubMed, PsycINFO, and Web of Science, were searched for studies that investigated the effectiveness of mobile application-based intervention for chronic pain on pain intensity. The final sample comprised twenty-two studies, with a total of 4679 individuals. Twelve of these twenty-two studies used a randomized control trial (RCT) design, while ten studies only used an observational design. For all twenty-two studies, a small but significant effect (d = −0.40) was found when compared to baseline measures or control groups. The results suggest that apps-based treatment can be helpful in reducing pain, especially in the long-term.

Background: The use of assistive technologies (ATs) to support older people has been fueled by the demographic change and technological progress in many countries. These devices are designed to assist seniors, enable independent living at home or in residential facilities, and improve quality of life by addressing age-related difficulties. Objective: We aimed to evaluate the effectiveness of ATs on relevant outcomes with a focus on frail older adults. Methods: A systematic literature review of randomized controlled trials evaluating ATs was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Ovid Medline, PsycINFO, SocIndex, CINAHL (Cumulative Index to Nursing and Allied Health Literature), CENTRAL (Cochrane Central Register of Controlled Trials), and IEEEXplore databases were searched from January 1, 2009, to March 15, 2019. ATs were included when aiming to support the domains autonomy, communication, or safety of older people with a mean age ≥65 years. Trials performed within a laboratory setting were excluded. Studies were retrospectively categorized according to the physical frailty status of participants. Results: A total of 19 trials with a high level of heterogeneity were included in the analysis. Six device categories were identified: mobility, personal disease management, medication, mental support, hearing, and vision. Eight trials showed significant effectiveness in all or some of the primary outcome measures. Personal disease management devices seem to be the most effective, with four out of five studies showing significant improvement of disease-related outcomes. Frailty could only be assessed for seven trials. Studies including participants with significant or severe impairment showed no effectiveness. Conclusions: Different ATs show some promising results in well-functioning but not in frail older adults, suggesting that the evaluated ATs might not (yet) be suitable for this subgroup. The uncertainty of the effectiveness of ATs and the lack of high-quality research for many promising supportive devices were confirmed in this systematic review. Large studies, also including frail older adults, and clear standards are needed in the future to guide professionals, older users, and their relatives. Trial Registration: PROSPERO CRD42019130249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130249

Background: In recent years, the fast-paced adoption of digital health (DH) technologies has transformed health care delivery. However, this rapid evolution has also led to challenges such as uncoordinated development and information silos, impeding effective health care integration. Recognizing these challenges, nations have developed digital health strategies (DHSs), aligning with their national health priorities and guidance from global frameworks. The World Health Organization (WHO)’s Global Strategy on Digital Health 2020-2025 (GSDH) guides national DHSs. Objective: This study analyzes the DHSs of Tanzania and Germany as case studies and assesses their alignment with the GSDH and identifies strengths, shortcomings, and areas for improvement. Methods: A comparative policy analysis was conducted, focusing on the DHSs of Tanzania and Germany as case studies, selected for their contrasting health care systems and cooperative history. The analysis involved a three-step process: (1) assessing consistency with the GSDH, (2) comparing similarities and differences, and (3) evaluating the incorporation of emergent technologies. Primary data sources included national eHealth policy documents and related legislation. Results: Both Germany’s and Tanzania’s DHSs align significantly with the WHO’s GSDH, incorporating most of its 35 elements, but each missing 5 distinct elements. Specifically, Tanzania’s DHS lacks in areas such as knowledge management and capacity building for leaders, while Germany’s strategy falls short in engaging health care service providers and beneficiaries in development phases and promoting health equity. Both countries, however, excel in other aspects like collaboration, knowledge transfer, and advancing national DHSs, reflecting their commitment to enhancing DH infrastructures. The high ratings of both countries on the Global Digital Health Monitor underscore their substantial progress in DH, although challenges persist in adopting the rapidly advancing technologies and in the need for more inclusive and comprehensive strategies. Conclusions: This study reveals that both Tanzania and Germany have made significant strides in aligning their DHSs with the WHO’s GSDH. However, the rapid evolution of technologies like artificial intelligence and machine learning presents challenges in keeping strategies up-to-date. This study recommends the development of more comprehensive, inclusive strategies and regular revisions to align with emerging technologies and needs. The research underscores the importance of context-specific adaptations in DHSs and highlights the need for broader, strategic guidelines to direct the future development of the DH ecosystem. The WHO’s GSDH serves as a crucial blueprint for national DHSs. This comparative analysis demonstrates the value and challenges of aligning national strategies with global guidelines. Both Tanzania and Germany offer valuable insights into developing and implementing effective DHSs, highlighting the importance of continuous adaptation and context-specific considerations. Future policy assessments require in-depth knowledge of the country’s health care needs and structure, supplemented by stakeholder input for a comprehensive evaluation.

Climate change critically impacts global pediatric health, presenting unique and escalating challenges due to children’s inherent vulnerabilities and ongoing physiological development. This scoping review intricately intertwines the spheres of climate change, pediatric health, and Artificial Intelligence (AI), with a goal to elucidate the potential of AI and digital health in mitigating the adverse child health outcomes induced by environmental alterations, especially in Low- and Middle-Income Countries (LMICs). A notable gap is uncovered: literature directly correlating AI interventions with climate change-impacted pediatric health is scant, even though substantial research exists at the confluence of AI and health, and health and climate change respectively. We present three case studies about AI’s promise in addressing pediatric health issues exacerbated by climate change. The review spotlights substantial obstacles, including technical, ethical, equitable, privacy, and data security challenges in AI applications for pediatric health, necessitating in-depth, future-focused research. Engaging with the intricate nexus of climate change, pediatric health, and AI, this work underpins future explorations into leveraging AI to navigate and neutralize the burgeoning impact of climate change on pediatric health outcomes. Impact: Our scoping review highlights the scarcity of literature directly correlating AI interventions with climate change-impacted pediatric health that disproportionately affects vulnerable populations, even though substantial research exists at the confluence of AI and health, and health and climate change respectively. We present three case studies about AI’s promise in addressing pediatric health issues exacerbated by climate change. The review spotlights substantial obstacles, including technical, ethical, equitable, privacy, and data security challenges in AI applications for pediatric health, necessitating in-depth, future-focused research.

Mobile Health (mHealth) refers to using mobile devices to support health. This study aimed to identify specific methodological challenges in systematic reviews (SRs) of mHealth interventions and to develop guidance for addressing selected challenges. Two-phase participatory research project. First, we sent an online survey to corresponding authors of SRs of mHealth interventions. On a five-category scale, survey respondents rated how challenging they found 24 methodological aspects in SRs of mHealth interventions compared to non-mHealth intervention SRs. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address the most challenging methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and subsequent interaction via email with the workshop participants and two external mHealth SR authors. We contacted 953 corresponding authors of mHealth intervention SRs, of whom 50 (5 %) completed the survey. All the respondents identified at least one methodological aspect as more or much more challenging in mHealth intervention SRs than in non-mHealth SRs. A median of 11 (IQR 7.25-15) out of 24 aspects (46 %) were rated as more or much more challenging. Those most frequently reported were: defining intervention intensity and components (85 %), extracting mHealth intervention details (71 %), dealing with dynamic research with evolving interventions (70 %), assessing intervention integrity (69 %), defining the intervention (66 %) and maintaining an updated review (65 %). Eleven survey respondents participated in the workshop (five had authored more than three mHealth SRs). Eighteen consensus-based recommendations were developed to address issues related to mHealth intervention integrity and to keep mHealth SRs up to date. mHealth SRs present specific methodological challenges compared to non-mHealth interventions, particularly related to intervention integrity and keeping SRs current. Our recommendations for addressing these challenges can improve mHealth SRs.