BackgroundIn low-resource settings, there are numerous socioeconomic challenges such as poverty, inadequate facilities, shortage of skilled health workers, illiteracy and cultural barriers that contribute to high maternal and newborn deaths. To address these challenges, there are several mHealth projects particularly in Sub-Sahara Africa seeking to exploit opportunities provided by over 90% rate of mobile penetration. However, most of these interventions have failed to justify their value proposition to inspire utilization in low-resource settings.MethodsThis study proposes a theoretical model named Technology, Individual, Process-Fit (TIPFit) suitable for user-centred evaluation of intervention designs to predict utilization of mHealth products in low-resource settings. To investigate the predictive power of TIPFit model, we operationalized its latent constructs into variables used to predict utilization of an mHealth prototype called mamacare. The study employed single-group repeated measures quasi-experiment in which a random sample of 79 antenatal and postnatal patients were recruited from a rural hospital. During the study conducted between May and October 2014, the treatment involved sending and receiving SMS alerts on vital signs, appointments, safe delivery, danger signs, nutrition, preventive care and adherence to medication.ResultsMeasurements taken during the study were cleaned and coded for analysis using statistical models like Partial Least Squares (PLS), Repeated Measures Analysis of Variance (RM-ANOVA), and Bonferroni tests. After analyzing 73 pretest responses, the model predicted 80.2% fit, and 63.9% likelihood of utilization. However, results obtained from initial post-test taken after three months demonstrated 69.1% fit, and utilization of 50.5%. The variation between prediction and the actual outcome necessitated improvement of mamacare based on feedback obtained from users. Three months later, we conducted the second post-test that recorded further drop in fit from 69.1 to 60.3% but utilization marginally improved from 50.5 to 53.7%.ConclusionsDespite variations between the pretest and post-test outcomes, the study demonstrates that predictive approach to user-centred design offers greater flexibility in aligning design attributes of an mHealth intervention to fulfill user needs and expectations. These findings provide a unique contribution for decision makers because it is possible to prioritize investments among competing digital health projects.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0649-z) contains supplementary material, which is available to authorized users.

Though mHealth is still at its formative stages, it is undeniably the next big thing in addressing healthcare challenges being experienced in developing countries. However, the complexity of implementing mHealth to address numerous health challenges is evident in many failed attempts to integrate it within healthcare system. We argue that this is due to complexity of migrating to virtual environment most healthcare processes; such as diagnosis and treatment that require more of physical interactions between patients and caregivers. To provide a concrete model to scale-up deployment of mHealth, this paper presents a conceptual framework combining constructs from Process Virtualization Theory, Theory of Planned Behaviour and Task-Technology Fit. The framework is a flexible schema for deriving concrete models that would be used as a blueprint for effective deployment and evaluation of mHealth applications' suitability to the intended use. To demonstrate the adaptability of the framework, we discuss its use regarding an mHealth application for maternal and child care in underserved rural and urban areas in Kenya.

In the recent years, the demand for data processing has been on the rise prompting researchers to investigate new ways of managing data. Our research delves into the emerging trends of data management methods, one of which is the agent based techniques and the active disk technology and also the use of Map-reduce functions in unstructured data management. Motivated by this new trend, our architecture employs mobile agents technology to develop an open source framework called SPADE to implement a simulation platform called SABSA. The architecture in this research compares the performance of four network storage architectures: Store and forward processes(SAF), Object Storage Devices(OSD), Mobile agent with a Domain Controller (DMC) enhanced with map-reduce function and Mobile agent with a Domain Controller and child DMC enhanced with Map-reduce (ABMR): both handling sorted and unsorted metadata. In order to accurately establish the performance improvements in the new hybrid agent based models and map-reduce functions, an analytic simulation model on which experiments based on the identified storage architectures were performed was developed and then analytical data and graphs were generated. The results indicated that all the agents based storage architectures minimize latencies by up to 45 % and reduce access time by up to 21% compared to SAF and OSD.

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With increase in computing and networking technologies, many organizations have managed to place their services online with the aim of achieving efficiency in customer service as well as reach more potential customers, also with communicable diseases such as COVID-19 and need for social distancing, many people are encouraged to work from home, including shopping. To meet this objective in areas with poor Internet connectivity, the government of Kenya recently announced partnership with Google Inc for use of Google Loon. This has come up with challenges which include information overload on the side of the end consumer as well as security loopholes such as dishonest vendors preying on unsuspecting consumers. Recommender systems have been used to alleviate these two challenges by helping online users select the best item for their case. However, most recommender systems, especially common filtering recommendation algorithm (CFRA) based systems still rely on presenting output based on selections of nearest neighbors (most similar users – birds of the same feathers flock together). This leaves room for manipulation of the output by mimicking the features of their target and then picking malicious item such that when the recommender system runs, it will output the same malicious item to the target – a trust issue. Data to construct trust is equally a challenge. In this research, we propose to address this issue by creating a trust adjustment factor (TAF) for recommender systems for online services.