BACKGROUND: Globally, the use of mobile health (mHealth) apps or interventions has increased. Robust synthesis of existing systematic reviews on mHealth apps may offer useful insights to guide maternal health clinicians and policy makers.
OBJECTIVE: This systematic review aims to assess the effectiveness or impact of mHealth apps on maternal health care delivery globally.
METHODS: We systematically searched Scopus, Web of Science (Core Collection), MEDLINE or PubMed, CINAHL, and Cochrane Database of Systematic Reviews using a predeveloped search strategy. The quality of the reviews was independently assessed by 3 reviewers, while study selection was done by 2 independent raters. We presented a narrative synthesis of the findings, highlighting the specific mHealth apps, where they are implemented, and their effectiveness or outcomes toward various maternal conditions.
RESULTS: A total of 2527 documents were retrieved, out of which 16 documents were included in the review. Most mHealth apps were implemented by sending SMS text messages with mobile phones. mHealth interventions were most effective in 5 areas: maternal anxiety and depression, diabetes in pregnancy, gestational weight management, maternal health care use, behavioral modification toward smoking cessation, and controlling substance use during pregnancy. We noted that mHealth interventions for maternal health care are skewed toward high-income countries (13/16, 81%).
CONCLUSIONS: The effectiveness of mHealth apps for maternity health care has drawn attention in research and practice recently. The study showed that research on mHealth apps and their use dominate in high-income countries. As a result, it is imperative that low- and middle-income countries intensify their commitment to these apps for maternal health care, in terms of use and research.
BACKGROUND: PROSPERO CRD42022365179; https://tinyurl.com/e5yxyx77.

Health care is ever more important with the aging population and with the increased awareness of the importance of the medical systems due to the corona crisis that showed the capacity of the health care infrastructure, especially in terms of numbers of health care personnel such as doctors, was not sufficient. Assuming that the number of doctors per patient is one of the determinants of patient satisfaction, optimal investments in new doctors, specialist doctors and foreign doctors are analyzed. Optimal Control Theory is employed to determine the optimal investment strategy for new doctors (new graduates), specialists and foreign doctors to maximize the net (of costs) patient satisfaction over a fixed time horizon. It is found that a nation with an insufficient number of total doctors and specialist doctors at the beginning of the planning horizon should increase the investment in new doctors as a quadratic function of time, increase the local specialist doctors linearly, while employing foreign doctors as to equate their cost to the marginal satisfaction of patients.

Developments in the nanotechnology area occur ensuring compliance with regulatory requirements, not only in terms of safety requirements, but also to meet sustainability goals. Hence, safer and sustainable-by-design (SSbD) materials are also aimed for during developmental process. Similar to with any new materials their safety must be assessed. Nanobiomaterials can offer large advantages in the biomedical field, in areas such as tissue repair and regeneration, cancer therapy, etc. For example, although hydroxyapatite-based nanomaterials (nHA) are among the most studied biomaterials, its ecotoxicological effects are mostly unknown. In the present study we investigated the toxicity of seven nHA-based materials, covering both different biomedical applications, e.g., iron-doped hydroxyapatite designed for theragnostic applications), hybrid collagen/hydroxyapatite composites, designed for bone tissue regeneration, and SSbD alternative materials such as titanium-doped hydroxyapatite/alginate composite, designed as sunscreen. The effects were assessed using the soil model Enchytraeus crypticus (Oligochaeta) in the natural standard LUFA 2.2 soil. The assessed endpoints included the 2, 3 and 4 days avoidance behavior (short-term), 28 days survival, size and reproduction (long term based on the OECD standard reproduction test), and 56 days survival and reproduction (longer-term OECD extension). Although overall results showed little to no toxicity among the tested nHA, there was a significant decrease in animals\' size for Ti-containing nHA. Moreover, there was a tendency for higher toxicity at the lowest concentrations (i.e., 100 mg/kg). This requires further investigation to ensure safety.

There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health Information System (HIS). In addition, these research studies contribute greatly to the development of HIS in alignment with major stakeholders such as health practitioners and recipients of health care. Conversely, there has been trepidation about HIS\' sustainability and resilience for healthcare applications in the era of digitalization and globalization. Hence, this paper investigates research on HIS with a primary focus on health care applications to ascertain its sustainability and resilience amidst the transformation of the global healthcare space. Therefore, using a bibliometric approach, this paper measures the performance of health information systems and healthcare for health care applications using bibliometric data from the web of science database. The findings reveal solid evidence of the constructive transformation of health information systems and health care applications in the healthcare arena, providing ample evidence of the adaptation of HIS and health care applications within the healthcare arena to the fourth industrial revolution and, additionally, revealing the resilient alignment of health care applications and health information systems.

BACKGROUND: Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.
OBJECTIVE: The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future.
METHODS: This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model\'s predictive ability was evaluated based on self-reports from an independent set of patients.
RESULTS: Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds.
CONCLUSIONS: Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.