Role of mHealth applications for improving antenatal and postnatal care in low and middle income countries: a systematic review

Background: From 1990 to 2015, the number of maternal deaths globally has dropped by 43%. Despite this, progress in attaining MDG 5 is not remarkable in LMICs. Only 52% of pregnant women in LMICs obtain WHO recommended minimum of four antenatal consultations and the coverage of postnatal care is relatively poor. In recent years, the increased cellphone penetration has brought the potential for mHealth to improve preventive maternal healthcare services. The objective of this review is to assess the effectiveness of mHealth solutions on a range of maternal health outcomes by categorizing the interventions according to the types of mHealth applications.Methods: Three international online electronic databases were searched between January 1, 2000 and January 25, 2016 to identify studies exploring the role of mHealth solutions in improving preventive maternal healthcare services. Of 1262 titles screened after duplication, 69 potentially relevant abstracts were obtained. Out of 69 abstracts, 42 abstracts were shortlisted. Full text of 42 articles was reviewed using data extraction sheet. A total of 14 full text studies were included in the final analysis.Results: The 14 final studies were categorized in to five mHealth applications defined in the conceptual framework. Based on our analysis, the most reported use of mHealth was for client education and behavior change communication, such as SMS and voice reminders [n = 9, 65%]. The categorization provided the understanding that much work have been done on client education and behavior change communication. Most of the studies showed that mHealth interventions have proven to be effective to improve antenatal care and postnatal care services, especially those that are aimed at changing behavior of pregnant women and women in postnatal period. However, little evidence exists on other type of mHealth applications.Conclusion: This review suggests that mHealth solutions targeted at pregnant women and women in postnatal period can improve preventive maternal healthcare services. However, there is a need to conduct more controlled-trials and quasi-experimental studies to strengthen the literature in this research area. The review recommends that mHealth researchers, sponsors, and publishers should prioritize the transparent reporting of interventions to allow effective interpretation of extracted data

Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

A Multilevel Mixed Methods Examination of Treatment Nonadherence Among Rural Cancer Survivors

While mortality rates have been decreasing over the last 40 years, cancer remains a leading cause of death in the United States. Over 1.7 million people were diagnosed with cancer in 2019, and there were more than 600,000 cancer deaths. Of the 15 million cancer survivors in the US, nearly 3 million reside in rural areas and experience 3% higher cancer incidence and 10% higher cancer mortality compared to their nonrural counterparts. During 2006-2015, the annual age-adjusted mortality rates for all cancer sites combined decreased at a slower pace in rural areas versus nonrural areas (-1.0% vs -1.6% per year, respectively), widening the disparity in mortality rates. Although the reasons for these disparities are not fully known, rural cancer survivors tend to be older, have additional comorbidities and poorer general health, and have a higher prevalence of lifestyle risk factors, such as smoking and lack of physical activity, that complicate survival and may contribute to the higher mortality rate. Nonadherence to cancer treatment is associated with poorer cancer outcomes, including higher rates of cancer recurrence or treatment failure and decreased survival. Reports of mortality have been up to four times as likely in nonadherent compared with adherent survivors. A growing set of studies have begun to document that cancer treatment adherence is poorer among rural populations, which may also partially explain the higher mortality rate observed in rural areas. This dissertation is comprised of three studies: 1) a systematic review of the role of digital health in rural oncology; 2) a data analysis of hospital and billing claims data examining geographic differences in sociodemographic and clinical factors associated with radiation treatment nonadherence; and 3) a multilevel, theory-driven examination of rural cancer treatment nonadherence utilizing survey and individual interview data

IMPACT OF TECHNOLOGY ON IMPROVING HIV AND TUBERCULOSIS HEALTH OUTCOMES AMONG AFRICAN COUNTRIES

This study used health analytics approach to evaluate the association between population health outcomes and Information and Communication Technology (ICT) infrastructures at a country level. This study used aggregate data obtained from the World Bank database, and the International Telecommunication Union (ITU) database for 53 African countries for the periods 2000 to 2016, and quantitatively explored the impact of ICT infrastructures’ diffusion on population health outcomes. ICT data was obtained from the ITU database. Similarly, population health attributes were retrieved from the World Bank database. ICT infrastructure variables used in this study include: internet access, mobile phone use, and fixed telephone subscriptions. However, population health outcome variables for this study include: HIV prevalence, access to antiretroviral therapy, Tuberculosis incidence, and mortality rates. Econometric study methodology involved a Dynamic Panel Model (DPM). Study findings showed that promoting ICT use among the public has opportunities for improving Tuberculosis (TB) and HIV health outcomes. However, the impact of each ICT infrastructures on improving TB and HIV health outcomes differed, which this study inferred to be as a result of different functionalities of the ICT infrastructures, as well as the peculiar features of the health outcomes studied. This study also did an Exploratory Spatial Data Analysis (ESDA) of TB treatment completion rates among health systems in Africa to help visualize trends and identify patterns, clusters and outliers. It evaluated spatial relationships between mobile phone use and TB treatment completion rates using differential local Moran’s I and bivariate Moran’s I techniques. Study result identified statistically significant positive autocorrelation values for the periods evaluated, as well as varying cluster patterns in TB treatment completion rates. The cluster patterns increased across the three-time periods among geographically referenced data evaluated in this study. This study also identified a direct relationship between mobile phone use and TB treatment completion rates among relevant African countries studied. Thereby, necessitating the need to strengthen national policies that promote TB and HIV medication adherence and completion using eHealth strategies among African health systems. Another important policy implication of this study for African governments is that investing in eHealth, including educating the masses on ICT use, could be an alternative policy to improve population health

Effectiveness of mobile telemonitoring applications in heart failure patients: systematic review of literature and meta-analysis

Q2Q1Pacientes con Insuficiencia cardiacaClose and frequent follow-up of heart failure (HF) patients improves clinical outcomes. Mobile telemonitoring applications are advantageous alternatives due to their wide availability, portability, low cost, computing power, and interconnectivity. This study aims to evaluate the impact of telemonitoring apps on mortality, hospitalization, and quality of life (QoL) in HF patients. We conducted a registered (PROSPERO CRD42022299516) systematic review of randomized clinical trials (RCTs) evaluating mobile-based telemonitoring strategies in patients with HF, published between January 2000 and December 2021 in 4 databases (PubMed, EMBASE, BVSalud/LILACS, Cochrane Reviews). We assessed the risk of bias using the RoB2 tool. The outcome of interest was the effect on mortality, hospitalization risk, and/or QoL. We performed meta-analysis when appropriate; heterogeneity and risk of publication bias were evaluated. Otherwise, descriptive analyses are offered. We screened 900 references and 19 RCTs were included for review. The risk of bias for mortality and hospitalization was mostly low, whereas for QoL was high. We observed a reduced risk of hospitalization due to HF with the use of mobile-based telemonitoring strategies (RR 0.77 [0.67; 0.89]; I2 7%). Non-statistically significant reduction in mortality risk was observed. The impact on QoL was variable between studies, with different scores and reporting measures used, thus limiting data pooling. The use of mobile-based telemonitoring strategies in patients with HF reduces risk of hospitalization due to HF. As smartphones and wirelessly connected devices are increasingly available, further research on this topic is warranted, particularly in the foundational therapy.https://orcid.org/0000-0002-4189-4317https://orcid.org/0000-0002-8244-2958https://orcid.org/0000-0001-5401-0018https://orcid.org/0000-0003-1490-1822https://orcid.org/0000-0002-3606-2102Revista Internacional - IndexadaA1N

Applying machine learning to predict future adherence to physical activity programs.

BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011

eRegistries: Electronic registries for maternal and child health

Background: The Global Roadmap for Health Measurement and Accountability sees integrated systems for health information as key to obtaining seamless, sustainable, and secure information exchanges at all levels of health systems. The Global Strategy for Women’s, Children’s and Adolescent’s Health aims to achieve a continuum of quality of care with effective coverage of interventions. The WHO and World Bank recommend that countries focus on intervention coverage to monitor programs and progress for universal health coverage. Electronic health registries - eRegistries - represent integrated systems that secure a triple return on investments: First, effective single data collection for health workers to seamlessly follow individuals along the continuum of care and across disconnected cadres of care providers. Second, real-time public health surveillance and monitoring of intervention coverage, and third, feedback of information to individuals, care providers and the public for transparent accountability. This series on eRegistries presents frameworks and tools to facilitate the development and secure operation of eRegistries for maternal and child health. Methods: In this first paper of the eRegistries Series we have used WHO frameworks and taxonomy to map how eRegistries can support commonly used electronic and mobile applications to alleviate health systems constraints in maternal and child health. A web-based survey of public health officials in 64 low- and middleincome countries, and a systematic search of literature from 2005–2015, aimed to assess country capacities by the current status, quality and use of data in reproductive health registries. Results: eRegistries can offer support for the 12 most commonly used electronic and mobile applications for health. Countries are implementing health registries in various forms, the majority in transition from paperbased data collection to electronic systems, but very few have eRegistries that can act as an integrating backbone for health information. More mature country capacity reflected by published health registry based research is emerging in settings reaching regional or national scale, increasingly with electronic solutions. 66 scientific publications were identified based on 32 registry systems in 23 countries over a period of 10 years; this reflects a challenging experience and capacity gap for delivering sustainable high quality registries. Conclusions: Registries are being developed and used in many high burden countries, but their potential benefits are far from realized as few countries have fully transitioned from paper-based health information to integrated electronic backbone systems. Free tools and frameworks exist to facilitate progress in health information for women and children

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762