Background: Recently there has been dramatic increase in the use of mobile technologies for health (m-Health) in both high and low- and middle-income countries (LMICs). However, little is known whether m-Health interventions in LMICs are based on relevant theories critical for effective implementation of such interventions. This review aimed to systematically identify m-Health studies on health behavioral changes in LMICs and to examine how each study applied behavior change theories.Materials and Methods: A systematic review was conducted using the standard method from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. By searching electronic databases (MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials [CENTRAL]), we identified eligible studies published in English from inception to June 30, 2017. For the identified m-Health studies in LMICs, we examined their theoretical bases, use of behavior change techniques (BCTs), and modes of delivery.Results: A total of 14 m-Health studies on behavioral changes were identified and, among them, only 5 studies adopted behavior change theory. The most frequently cited theory was the health belief model, which was adopted in three studies. Likewise, studies have applied only a limited number of BCTs. Among the seven BCTs identified, the most frequently used one was the social support (practical) technique for medication reminder and medical appointment. m-Health studies in LMICs most commonly used short messaging services and phone calls as modes of delivery for behavior change interventions.Conclusions: m-Health studies in LMICs are suboptimally based on behavior change theory yet. To maximize effectiveness of m-Health, rigorous delivery methods as well as theory-based intervention designs will be needed.

BackgroundMobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade.MethodsAs a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth Database, the World Bank Projects & Operations Database, and the USAID mHealth Database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Moran’s I and Local Indicator of Spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities.ResultsFrom our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription rates per 100 population, the northern area shows extensive low-low association.ConclusionsThis study aimed to shed some light on the potential for strategic regional collaboration for scale-up of mHealth and mobile penetration. Firstly, countries in the eastern area with much experience can take the lead role in pursuing regional collaboration for mHealth programs in sub-Saharan Africa. Secondly, collective effort in improving mobile penetration rates for the northern area is recommended.Electronic supplementary materialThe online version of this article (doi:10.1186/s12992-017-0286-9) contains supplementary material, which is available to authorized users.

ObjectivesPopulation aging has increased the burden of chronic diseases globally. mHealth is often cited as a viable solution to enhance the management of chronic conditions. In this study, we conducted a systematic review of mHealth interventions for the self-management of chronic diseases in Korea, a highly-connected country with a high chronic care burden.MethodsFive databases were searched for relevant empirical studies that employed randomized controlled trial (RCT) or quasi-experimental methods published in English or Korean from the years 2008 to 2018. The selected studies were reviewed according to the PRISMA guidelines. The selected studies were classified using the Individual and Family Self-Management Theory conceptual framework.ResultsSixteen studies met the inclusion criteria, 9 of which were targeted towards diabetes management, and 7 of which were RCTs. Other target diseases included hypertension, stroke, asthma, and others. mHealth interventions were primarily delivered through smartphone applications, mobile phones connected to a monitoring device, and short message services (SMS). Various self-management processes were applied, including providing social influence and support, and facilitating self-monitoring and goal setting. Eleven studies showed mHealth interventions to be effective in improving self-management behaviors, biomarkers, or patient-reported outcome measures associated with chronic diseases.ConclusionsWhile the number of identified studies was not large, none reported negative impacts of mHealth on selected outcomes. Future studies on mHealth should design interventions with a greater variety of targeted functions and should adopt more rigorous methodologies to strengthen the evidence for its effectiveness in chronic disease management.

Objectives: During the coronavirus disease (COVID-19) pandemic, crude incidence and mortality rates have been widely reported; however, age-standardized rates are more suitable for comparison. In this study, we estimated and compared the age-standardized incidence, mortality, and case fatality rates among countries and investigated the relationship between these rates and factors associated with healthcare resources: gross domestic product per capita, number of hospital beds per population, and number of doctors per population. Methods:The incidence, mortality, and case fatality rates of 79 countries were age-standardized using the WHO standard population. The rates for persons 60 years or older were also calculated. The relationships among the rates were analysed using trend lines and coefficients of determination (R 2 ). The Pearson's correlation coefficients between the rates and the healthcare resource-related factors were calculated. Results:The countries with the highest age-standardized incidence, mortality, and case fatality rates were Czechia (14,253 cases/100,000), Mexico (182 deaths/100,000), and Mexico (6.7%), respectively. The R 2 between the incidence and mortality rates was 0.8520 for all ages and 0.9452 for those 60 years or older. The healthcare resources-related factors were associated positively with incidence rates, and negatively with case fatality rates: the correlations were weaker among the elderly.Conclusions: Compared to age-standardized rates, crude rates showed greater variation between countries. Medical resources may be important in preventing COVID-19-related deaths; however, considering the small variation in fatality among the elderly, prevention such as vaccination is more important especially for the elderly population to minimize the mortality rates in the elderly population.

The risk of neurodevelopmental disorders in low birth weight (LBW) infants has gained recognition but remains debatable. We investigated the risk of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in school-aged children according to their birth weight. We conducted a retrospective cohort study using the Korean National Health Insurance claims data of 2,143,652 children who were born between 2008 and 2012. Gestational age of infants was not available; thus, outcomes were not adjusted with it. Not only infants with birth weights of < 1.5 kg, but also 2.0–2.4 kg and 1.5–1.9 kg were associated with having ADHD; odds ratio (OR), 1.41 (95% confidence interval [CI] 1.33–1.50), and 1.49 (95% CI 1.33–1.66), respectively. The OR in infants with birth weights of 2.0–2.4 kg and 1.5–1.9 kg was 1.91 (95% CI 1.79–2.05) and 3.25 (95% CI 2.95–3.59), respectively, indicating increased odds of having ASD. Subgroup analysis for children without perinatal diseases showed similar results. In this national cohort, infants with birth weights of < 2.5 kg were associated with ADHD and ASD, regardless of perinatal history. Children born with LBW need detailed clinical follow-up.