mHealth Data Research Articles

Holistic Healthcare Demands Holistic Data Privacy Regulation

The U.S. and European Union (“E.U.”) share many of the same guiding principles, economic interests, and data privacy concerns regarding the rise of big data analytics and mHealth data. Yet the regulatory frameworks each region implemented to manage this data processing differs dramatically. Unlike the E.U., which implemented a comprehensive data privacy framework governing mHealth data processing, the U.S. maintains a fragmented approach that is far from holistic. As the U.S. continues to debate how to best regulate mHealth data, it may benefit from considering the regional similarities between the U.S. and E.U. and evaluating the effectiveness of the E.U.’s regulatory choices. This paper identifies those regional similarities and describes the different regulatory frameworks. It then evaluates the effectiveness of the E.U.’s General Data Protection Regulation (“GDPR”) and discusses whether there are any lessons the U.S. can glean from the E.U.’s experience. The resulting analysis indicates it is possible for mHealth data regulation to simultaneously encourage healthcare innovation and address data privacy concerns.

Queue Allocation Algorithm Based on Priority of Signals to Schedule M-Health Data Transfer

Queue Allocation Algorithm Based on Priority of Signals to Schedule M-Health Data Transfer

A Mobile Health Data Collection System for Remote Areas to Monitor Women Participating in a Cervical Cancer Screening Campaign.

Barriers to efficient cervical cancer screening in low- and medium-income countries include the lack of systematic monitoring of the participants' data. The aim of this study was to assess the feasibility of a mobile health (m-Health) data collection system to facilitate monitoring of women participating to cervical cancer screening campaign. Women aged 30-65 years, participating in a cervical cancer screening campaign in Ambanja, Madagascar, were invited to participate in the study. Cervical Cancer Prevention System, an m-Health application, allows the registration of clinical data, while women are undergoing cervical cancer screening. All data registered in the smartphone were transmitted onto a secure, Web-based platform through the use of an Internet connection. Healthcare providers had access to the central database and could use it for the follow-up visits. Quality of data was assessed by computing the percentage of key data missing. A total of 151 women were recruited in the study. Mean age of participants was 41.8 years. The percentage of missing data for the key variables was less than 0.02%, corresponding to one woman's medical history data, which was not sent to the central database. Technical problems, including transmission of photos, human papillomavirus test results, and pelvic examination data, have subsequently been solved through a system update. The quality of the data was satisfactory and allowed monitoring of cervical cancer screening data of participants. Larger studies evaluating the efficacy of the system for the women's follow-up are needed in order to confirm its efficiency on a long-term scale.

Tamper-Resistant Mobile Health Using Blockchain Technology.

BackgroundDigital health technologies, including telemedicine, mobile health (mHealth), and remote monitoring, are playing a greater role in medical practice. Safe and accurate management of medical information leads to the advancement of digital health, which in turn results in a number of beneficial effects. Furthermore, mHealth can help lower costs by facilitating the delivery of care and connecting people to their health care providers. Mobile apps help empower patients and health care providers to proactively address medical conditions through near real-time monitoring and treatment, regardless of the location of the patient or the health care provider. Additionally, mHealth data are stored in servers, and consequently, data management that prevents all forms of manipulation is crucial for both medical practice and clinical trials.ObjectiveThe aim of this study was to develop and evaluate a tamper-resistant mHealth system using blockchain technology, which enables trusted and auditable computing using a decentralized network.MethodsWe developed an mHealth system for cognitive behavioral therapy for insomnia using a smartphone app. The volunteer data collected with the app were stored in JavaScript Object Notation format and sent to the blockchain network. Thereafter, we evaluated the tamper resistance of the data against the inconsistencies caused by artificial faults.ResultsElectronic medical records collected using smartphones were successfully sent to a private Hyperledger Fabric blockchain network. We verified the data update process under conditions where all the validating peers were running normally. The mHealth data were successfully updated under network faults. We further ensured that any electronic health record registered to the blockchain network was resistant to tampering and revision. The mHealth data update was compatible with tamper resistance in the blockchain network.ConclusionsBlockchain serves as a tamperproof system for mHealth. Combining mHealth with blockchain technology may provide a novel solution that enables both accessibility and data transparency without a third party such as a contract research organization.

Risk factors for developing cardiovascular disease in women.

To evaluate the factors that predispose to the development of cardiovascular diseases in women who were attended at a Basic Health Unit in Londrina, Paraná, Brazil. The research was characterized as observational and quantitative. The population sample consisted of 60 women aged over 18 years, being chosen at random. For data collection, mHealth Data Collector (mHDC), a mobile device, was used with a questionnaire on daily habits, health status, and disease. Measurements were made of glucose and blood cholesterol, blood pressure and anthropometric measurements. Data collection took place in February 2014 to November 2014. Risk factors were overweight / obesity (63.8%) and sedentary lifestyle (65%), followed by family history of cardiovascular disease (43.3%), hypercholesterolemia (38.3%), and hypertension (13.3%). This population of women presented risk factors for the development of cardiovascular disease in all ages, being necessary the implementation of measures that stimulate changes in the daily habits and improve health conditions among women by the health team.

Analyzing mHealth Engagement: Joint Models for Intensively Collected User Engagement Data.

BackgroundEvaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention.ObjectiveThe objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia.MethodsEngagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement.ResultsThe joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model.ConclusionsNaïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.

Saúde Móvel: desafios globais à proteção de dados pessoais sob a perspectiva do direito da União Europeia

A saúde móvel ou mHealth – as práticas médicas e de saúde pública apoiadas por dispositivos móveis (telefones celulares, smartphones e tablets) – tem claro potencial para aumentar significativamente a qualidade e a eficiência dos cuidados de saúde. Contudo, constatam-se várias fragilidades na proteção dos dados pessoais utilizados nesse contexto. Este artigo tem como objetivo analisar os aspectos jurídicos mais importantes para que uma proteção efetiva dos dados pessoais na mHealth seja alcançada. Tendo este objetivo e, partindo das regras da União Europeia e das melhores práticas internacionais sobre a matéria, apresentamos uma série de pressupostos condensados em três eixos: maior responsabilidade de todos os atores envolvidos no tratamento de dados mHealth; maior transparência na forma como os dados são tratados, compartilhados e reutilizados; e maior controle dos usuários das tecnologias e da utilização de seus dados. O artigo procura contribuir com um panorama global a fim de levantar as reflexões para a criação e a validação de políticas locais.

Middleware for mobile medical data management with minimal latency

A promise of mHealth is its capacity to facilitate the consumption of Electronic Health Record (EHR) data using mobile devices, which is central to promoting remote healthcare delivery. Our ongoing project, called SOPHRA, in collaboration with the City Hospital in Saskatoon, Canada, focuses on supporting care providers (e.g., physicians) in the collection/recording of medical data from patients remotely. A major challenge that needs to be addressed is the potential loss of communication between the mobile-clients and the health information system (HIS) during the transfer of the electronic health record (EHR). Overcoming this challenge will foster soft-real time medical data exchanges. However, the issue of communication loss can be as a result of the over-reliance on wireless networks such as Wi-Fi, which can sometimes be unstable. Thus, the goal of this work is to propose a mobile health (mHealth) architectural environment that can exploit the limited available bandwidth during the mobile medical data transfer. A middleware is proposed with the capability of managing different states of the medical data as the physicians updates the EHR. The work details the employment of the Bernoulli model as a means of determining and controlling different updates of the mHealth data for effective propagation.

Potential for mobile health (mHealth) prevention of cardiovascular diseases in Kerala: A population-based survey

BackgroundIndia's southern state of Kerala stands at the forefront of India's epidemic of cardiovascular disease (CVD), among other non-communicable diseases (NCDs). Mobile phone use in healthcare (mHealth) has shown promise in India, including NCDs. However, suitability and acceptability of m-Health interventions is poorly researched, particularly in rural settings. Objectives(1)To explore mobile phone usage patterns in rural Kerala (Ernakulam).(2)To explore acceptability of mHealth delivery of health promotion and CVD prevention. MethodsA questionnaire regarding mobile phone usage and possible use in healthcare was verbally administered in five primary health centres and by home visits in five village councils (“panchayats”) of Ernakulam, Kerala. Adults who spoke Malayalam or English, with access to a mobile phone were recruited by convenience sampling in partnership with accredited social health activists (ASHAs). Quantitative data analysis was conducted using SPSS software. Results262 participants were recruited. 87% routinely used and 88% owned a mobile phone. 92% were willing to receive mHealth advice, and 94% favoured mobile medication reminders. 70.3% and 73% preferred voice calls over short messaging service (SMS) for delivering health information and medication reminders, respectively. 85.9% would send home recorded information on their blood pressure, weight, medication use and lifestyle to a doctor or ASHA. 75.2% trusted the confidentiality of mHealth data, while 77.1% had no concerns about the privacy of their information. ConclusionsThe majority of this population approve mHealth interventions. While further investigation of mHealth as a health education tool is warranted, SMS interventions may fail to maximise equity and penetration across all patient groups.

Using of Patient-Generated mHealth Data for Patient Care: a Comparison of Four Models

The rapid adoption of mobile applications for wellness and health tracking has resulted in vast amounts of patient-generated data. However, these data are often underutilized in the traditional patient care. In this paper, we explore how to use these patient-generated data to improve patient care. Based on a review of healthcare model and recommendations, we propose and compare four models with increasing integration with electronic health records. We also compared the freedom of choice of apps, as well as content validity and expected effectiveness. In the first model, patients have the full range of app choice, and full control over their data, in particular for sharing with healthcare providers. In the second model, patients use a selection of apps to export their data to a repository, which can be accessed by their providers (without integration into the EHR). In the third model, interoperability between the apps and the EHR allows full integration, but restricts app choice. Finally, the last model adds the notion of cost-effectiveness to the previous model. Although the EHR-integrated models limit app choice for patients, the app content is medically validated and patient-generated data is more easily accessed to improve patient care. However, these integrated models require decision support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care.

MHealth and Unregulated Data: Is this Farewell to Patient Privacy?

mHealth and Unregulated Data: Is this Farewell to Patient Privacy?

Design and rationale of the STRIVE trial to improve cardiometabolic health among children and families

BackgroundMany of the health behaviors known to contribute to cardiometabolic risk and disease (CMRD), including physical activity, diet, sleep, and screen time, begin during childhood. Given the population-wide burden of CMRD, novel ways of assessing risk and providing feedback to support behavior change are needed. PurposeThis paper describes the design and rationale for the Study for using Technology to Reach Individual Excellence (STRIVE), a randomized controlled trial testing the use of an integrated, closed-loop feedback system that incorporates longitudinal, patient-generated, mobile health technology (mHealth) data on health behaviors and provides clinical recommendations to help manage CMRD among at-risk families. MethodsSTRIVE is a 6-month trial among 68 children, ages 6–12year olds with a body mass index≥85th percentile from Massachusetts with at least one parent with CMRD. Data on several health behaviors will be collected daily over 6months. Children and parents will each wear wristbands that collect objective physical activity, sleep, and screen time data via accelerometry, noise, and infrared detection. Sugar sweetened beverage consumption will be assessed by self-report via a smartphone application. Weight will be collected using a wireless scale. Intervention group parents receive feedback on their child's health behaviors and personalized CMRD counseling via mobile messaging. Control parents receive standard of care recommendations and weekly health behavior reports for self-guided care. ConclusionThe STRIVE trial will test the use of mHealth and closed-loop feedback systems to improve health behaviors among families at-risk for or with established CMRD.

Rhetoric, Precarity, and mHealth Technologies

Wearable technologies in general and mHealth data in particular are championed frequently for ways they afford individual agency and empowerment and promote what the Robert Wood Johnson Foundation (RWJF) calls a “culture of health.” This article complicates such epideictic rhetorics based on results from a situational analysis of the RWJF’s Data for Health listening events, which incorporated panelists from the RWJF, JawBone, Inc., the Quantified Self, and other mHealth technology organizations as well as audience participants who work in community health. Given panelists’ and audiences’ diverging claims about how mHealth data either succeed or fail in creating a culture of health, I mobilize precarity as an analytic construct for critiquing the coexistence of technoscientific progress alongside the persistence of health disparities among vulnerable populations.

Mobile technology and the digitization of healthcare.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

Evaluation of a mHealth Data Quality Intervention to Improve Documentation of Pregnancy Outcomes by Health Surveillance Assistants in Malawi: A Cluster Randomized Trial.

BackgroundWhile community health workers are being recognized as an integral work force with growing responsibilities, increased demands can potentially affect motivation and performance. The ubiquity of mobile phones, even in hard-to-reach communities, has facilitated the pursuit of novel approaches to support community health workers beyond traditional modes of supervision, job aids, in-service training, and material compensation. We tested whether supportive short message services (SMS) could improve reporting of pregnancies and pregnancy outcomes among community health workers (Health Surveillance Assistants, or HSAs) in Malawi.Methods and FindingsWe designed a set of one-way SMS that were sent to HSAs on a regular basis during a 12-month period. We tested the effectiveness of the cluster-randomized intervention in improving the complete documentation of a pregnancy. We defined complete documentation as a pregnancy for which a specific outcome was recorded. HSAs in the treatment group received motivational and data quality SMS. HSAs in the control group received only motivational SMS. During baseline and intervention periods, we matched reported pregnancies to reported outcomes to determine if reporting of matched pregnancies differed between groups and by period. The trial is registered as ISCTRN24785657.ConclusionsStudy results show that the mHealth intervention improved the documentation of matched pregnancies in both the treatment (OR 1.31, 95% CI: 1.10–1.55, p<0.01) and control (OR 1.46, 95% CI: 1.11–1.91, p = 0.01) groups relative to the baseline period, despite differences in SMS content between groups. The results should be interpreted with caution given that the study was underpowered. We did not find a statistically significant difference in matched pregnancy documentation between groups during the intervention period (OR 0.94, 95% CI: 0.63–1.38, p = 0.74). mHealth applications have the potential to improve the tracking and data quality of pregnancies and pregnancy outcomes, particularly in low-resource settings.

Is mHealth Disrupting the Status Quo? Evidence from Implementations Highlighting Network vs. Hierarchical Institutional Logics

AbstractHealth Management Information Systems (HMIS) in developing countries have yet to live up to expectations because of the significant proportion of implementation failures. Mobile health (mHealth) technologies are being adopted increasingly in Ministries of Health (MoHs) to address aspects of HMIS implementation failure, such as the timeliness and ease of data collection from the lowest levels of healthcare. However, networked technologies such as mobile technologies used in mHealth data collection can introduce a network logic into the organization. This network logic, which often favors open, non‐hierarchical modes of communication, must work with the traditional hierarchical bureaucratic logic of the HMIS and the MoH in which they become embedded. This paper conceptualizes the interaction between these two logics. It draws on succinct, empirical vignettes from two action‐research projects involving the use of mHealth technology to improve data collection for the HMIS in Nigeria.Based on these findings and an institutional logics lens, this paper argues that the interaction between these logics potentially leads to a conflict where the logic embedded in networked technologies (such as mHealth) disrupts and challenges the existing hierarchical logic in the bureaucracy (such as in the MoH). It threatens not only to unsettle existing practices and norms in the organization, but also to restructure (flatten) the organization, due to resistance, loss, or changes in some pre‐existing roles. Practitioners and implementation researchers need to be sensitive to potential hierarchical and network‐centric forces that are involved in implementing mHealth in traditional hierarchical settings, particularly with regards to the unintended side effects that may arise in the process.

MHealth and Collecting Research Data From Patients With Mobile Tablet Apps.

mHealth and Collecting Research Data From Patients With Mobile Tablet Apps.

Mobile Phones in Research and Treatment: Ethical Guidelines and Future Directions.

Mobile phones and other remote monitoring devices, collectively referred to as "mHealth," promise to transform the treatment of a range of conditions, including movement disorders, such as Parkinson’s disease. In this viewpoint paper, we use Parkinson’s disease as an example, although most considerations discussed below are valid for a wide variety of conditions. The ability to easily collect vast arrays of personal data over long periods will give clinicians and researchers unique insights into disease treatment and progression. These capabilities also pose new ethical challenges that health care professionals will need to manage if this promise is to be realized with minimal risk of harm. These challenges include privacy protection when anonymity is not always possible, minimization of third-party uses of mHealth data, informing patients of complex risks when obtaining consent, managing data in ways that maximize benefit while minimizing the potential for disclosure to third parties, careful communication of clinically relevant information gleaned via mHealth technologies, and rigorous evaluation and regulation of mHealth products before widespread use. Given the complex array of symptoms and differences in comfort and literacy with technology, it is likely that these solutions will need to be individualized. It is therefore critical that developers of mHealth apps engage with patients throughout the development process to ensure that the technology meets their needs. These challenges will be best met through early and ongoing engagement with patients and other relevant stakeholders.

A new mHealth communication framework for use in wearable WBANs and mobile technologies.

Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for assessment in “real-world” environments producing more ecologically valid data. In the field of insomnia diagnosis, for example, it is now possible to offer patients wearable sleep monitoring systems which can be used in the comfort of their homes over long periods of time. The recorded data collected from body sensors can be sent to a remote clinical back-end system for analysis and assessment. Most of the research on sleep reported in the literature mainly looks into how to automate the analysis of the sleep data and does not address the problem of the efficient encoding and secure transmissions of the collected health data. This article reviews the key enabling communication technologies and research challenges for the design of efficient mHealth systems. An end-to-end mHealth system architecture enabling the remote assessment and monitoring of patient's sleep disorders is then proposed and described as a case study. Finally, various mHealth data serialization formats and machine-to-machine (M2M) communication protocols are evaluated and compared under realistic operating conditions.

Early detection of maternal deaths in Senegal through household-based death notification integrating verbal and social autopsy: a community-level case study

BackgroundReliable detection of maternal deaths is an essential prerequisite for successful diagnosis of barriers to care and formulation of relevant targeted interventions. In a community-level case study, the use of household-level surveillance in Senegal unveiled an apparent increase in maternal deaths, which triggered a rapid-cycle collaborative response to implement a multipronged set of quick-win and sustained interventions intended to improve quality care.MethodsPart of a multi-country effort, the Millennium Villages Project is implementing a routine community-level information system in Senegal, able to detect maternal deaths in real-time and uncover clinical and social factors contributing to mortality. Within this geographically demarcated area of approximately 32 000 inhabitants, with a well-structured health system with patient referral services, deaths were registered and notified by community health workers, followed by timely verbal and social autopsies. Using the Pathway to Survival conceptual framework, case analysis and mortality reviews were conducted for evaluation and quality improvement purposes.ResultsThe estimated maternal mortality rates rose from 67/100000 births in 2009 (1 death), to 202/100000 births in 2010 (3 deaths) and 392/100000 births (5 deaths) in 2011. Although absolute numbers of maternal deaths remained too small for robust statistical analysis, following verbal autopsy analyses in 2011, it became evident that an unexpectedly high proportion of maternal deaths were occurring at the referral hospital, mostly post-Caesarian section. Inadequate case management of post-partum haemorrhage at the referral hospital was the most frequently identified probable cause of death. A joint task team systematically identified several layers of inefficiencies, with a potential negative impact on a larger catchment area than the study community.ConclusionsIn this study, routine community-based surveillance identified inefficiencies at a tertiary level of care. Community-level surveillance systems that include pregnancy, birth and death tracking through household visits by community health workers , combined with verbal and social autopsy can identify barriers within the continuum of maternal care. Use of mHealth data collection tools sensitive enough to detect small changes in community-level mortality trends in real-time, can facilitate rapid-cycle quality improvement interventions, particularly when associated with social accountability structures of mortality reviews.