Developing mobile health applications for neglected tropical disease research.

Mobile applications (apps) can bring health research and its potential downstream benefits closer to underserved populations. Drawing on experience developing an app for detecting and referring cases of cutaneous leishmaniasis in Colombia, called Guaral/app, we review key steps in creating such mobile health (mHealth) tools. These require consideration of the sociotechnical context using methods such as systems analysis and human-centered design (HCD), predicated on engagement and iteration with all stakeholders. We emphasize usability and technical concerns and describe the interdependency of technical and human considerations for mHealth systems in rural communities

Just in Time Research: Rural & Remote Access to Justice: Intake Platform Research

This memo provides a wide scan of tools and platforms that either are, or ostensibly could be, used to conduct intake assessment and document storage in a clinic context. Our findings include comprehensive intake platforms that are designed exclusively for intake purposes, as well as a suite of tools that have broader application but lend themselves to application in an intake environment. We looked at tools marketed to professionals, institutions, businesses, and consumers

Promoting Health for Chronic Conditions: a Novel Approach that integrates Clinical and Personal Decision Support

Direct and indirect economic costs related to chronic diseases are increasing in Europe due to the aging of population. One of the most challenging goals is to improve the quality of life of patients affected by chronic conditions, and enhance their self-management. In this paper, we propose a novel architecture of a scalable solution, based on mobile tools, aimed to keep patients with chronic diseases away from acute episodes, to improve their quality of life and, consequently, to reduce their economic impact. Our solution aims to provide patients with a personalized tool for improving self-management, and it supports both patients and clinicians in decision-making through the implementation of two different Decision Support Systems. Moreover, the proposed architecture takes into account the interoperability and, particularly, the compliance with data transfer protocols (e.g., BT4/LE, ANT+, ISO/IEEE 11073) to ensure integration with existing devices, and with the semantic web approaches and standards related to the content and structure of the information (e.g., HL7, ICD-10 and openEHR) to ensure correct sharing of information with hospital information systems, and classification of patient behaviors (Coelition). The solution will be implemented and validated in future study

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

Personalized Pain Study Platform Using Evidence-Based Continuous Learning Tool

With the increased accessibility to mobile technologies, research utilizing mobile technologies in medical and public health area has also increased. The efficiency and effectiveness of healthcare services are also improved by introduction of mobile technologies. Effective pain treatment requires regular and continuous pain assessment of the patients. Mobile Health or mHealth has been an active interdisciplinary research area for more than a decade to research pain assessment through different software research tools. Different mHealth support systems are developed to assess pain level of patient using different techniques. Close attention to participant’s self- reported pain along with data mining based pain level detection could help the healthcare industry and researchers to deliver effective health services in pain treatment. Pain expression recognition can be a good way for data mining based approach though pain expression recognition itself may utilize different approach based on the research study scope. Most of the pain research tools are study or disease specific. Some of the tools are pain specific (lumber pain, cancer pain etc) and some are patient group specific (neonatal, adult, woman etc). This results in recurrent but potentially avoidable costs such as time, money, and workforce to develop similar service or software research tools for each research study. Based on the pain study research characteristics, it is possible to design and implement a customizable and extensible generic pain research tool. In this thesis, we have proposed, designed, and implemented a customizable personalized pain study platform tool following a micro service architecture. It has most of the common software research modules that are needed for a pain research study. These include real-time data collection, research participant management, role based access control, research data anonymization etc. This software research tool is also used to investigate pain level detection accuracy using evidence-based continuous learning from facial expression which yielded about 71% classification accuracy. This tool is also HIPAA compliant and platform independent which makes it device independent, privacy-aware, and security-aware

Impact of a Web-Based Exercise and Nutritional Education Intervention in Patients Who Are Obese With Hypertension: Randomized Wait-List Controlled Trial

Background: Internet-based interventions are a promising strategy for promoting healthy lifestyle behaviors. These have a tremendous potential for delivering electronic health interventions in scalable and cost-effective ways. There is strong evidence that the use of these programs can lead to weight loss and can lower patients’ average blood pressure (BP) levels. So far, few studies have investigated the effects of internet-based programs on patients who are obese with hypertension (HTN). Objective: The aim of this study is to investigate the short- and long-term efficacy, in terms of body composition and BP parameters, of a self-administered internet-based intervention involving different modules and learning techniques aimed at promoting lifestyle changes (both physical activity and healthy eating) in patients who are obese with HTN. Methods: A randomized wait-list controlled trial design was used. We recruited 105 adults with HTN who were overweight or obese and randomly assigned them to either a 3-month internet-based intervention group (n=55) or the wait-list control group (n=50). We assessed BMI (primary outcome), body fat mass (BFM), systolic (S)BP and diastolic (D)BP, blood glucose and insulin levels, physical activity levels, and functional capacity for aerobic exercise at Time 0 (preintervention) and Time 1 (postintervention). All the patients in the wait-list control group subsequently received the intervention, and a secondary within-group analysis, which also included these participants, was conducted at Time 2 (12-month follow-up). Results: A 2-way mixed analysis of covariance showed a significant decrease in BMI, BFM, and blood glucose at 3 months in the internet-based intervention group; the effect size for the BMI and BFM parameters was moderate to large, and there was also a borderline significant trend for DBP and insulin. These results were either maintained or improved upon at Time 2 and showed significant changes for BMI (mean difference −0.4, 95% CI −0.1 to −0.6; P=.005), BFM (mean difference −2.4, 95% CI −1.1 to −3.6; P<.001), DBP (mean difference −1.8, 95% CI −0.2 to −3.3; P=.03), and blood glucose (mean difference −2, 95% CI 0 to −4; P=.04). Conclusions: Implementation of our self-administered internet-based intervention, which involved different learning techniques aimed to promote lifestyle changes, resulted in positive short- and long-term health benefits in patients who are obese with HTN

Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field

Lifeline: Tech innovations for maternal and child health - Part 2

At 16% and 27%, India contributes the highest global share of maternal and new-born deaths. Most of these are preventable through simple, proven and low-cost solutions. With close to a billion mobile phones and over a million broadband connections, Information and Communication Technologies (ICTs) can address the key informational and process challenges to RMNCH+A in India.Dasra's report, Life Line, lays out the key challenges and solutions, alongside the work of scalable and impactful social organizations for funders' consideration

An Interactive, Mobile-Based Tool for Personal Social Network Data Collection and Visualization Among a Geographically Isolated and Socioeconomically Disadvantaged Population: Early-Stage Feasibility Study with Qualitative User Feedback

Background: Personal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky. Objective: We explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky. Methods: A total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio. Results: OpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher\u27s perspective, and hindering practicality in the field. Conclusions: OpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations

A novel patient engagement platform using accessible text messages and calls (Epharmix): Feasibility study

BACKGROUND: Patient noncompliance with therapy, treatments, and appointments represents a significant barrier to improving health care delivery and reducing the cost of care. One method to improve therapeutic adherence is to improve feedback loops in getting clinically acute events and issues to the relevant clinical providers as necessary (ranging from detecting hypoglycemic events for patients with diabetes to notifying the provider when patients are out of medications). Patients often don\u27t know which information should prompt a call to their physician and proactive checks by the clinics themselves can be very resource intensive. We hypothesized that a two-way SMS system combined with a platform web service for providers would enable both high patient engagement but also the ability to detect relevant clinical alerts. OBJECTIVE: The objectives of this study are to develop a feasible two-way automated SMS/phone call + web service platform for patient-provider communication, and then study the feasibility and acceptability of the Epharmix platform. First, we report utilization rates over the course of the first 18 months of operation including total identified clinically significant events, and second, review results of patient user-satisfaction surveys for interventions for patients with diabetes, COPD, congestive heart failure, hypertension, surgical site infections, and breastfeeding difficulties. METHODS: To test this question, we developed a web service + SMS/phone infrastructure ( Epharmix ). Utilization results were measured based on the total number of text messages or calls sent and received, with percentage engagement defined as a patient responding to a text message at least once in a given week, including the number of clinically significant alerts generated. User satisfaction surveys were sent once per month over the 18 months to measure satisfaction with the system, frequency and degree of communication. Descriptive statistics were used to describe the above information. RESULTS: In total, 28,386 text messages and 24,017 calls were sent to 929 patients over 9 months. Patients responded to 80% to 90% of messages allowing the system to detect 1164 clinically significant events. Patients reported increased satisfaction and communication with their provider. Epharmix increased the number of patient-provider interactions to over 10 on average in any given month for patients with diabetes, COPD, congestive heart failure, hypertension, surgical site infections, and breastfeeding difficulties. CONCLUSIONS: Engaging high-risk patients remains a difficult process that may be improved through novel, digital health interventions. The Epharmix platform enables increased patient engagement with very low risk to improve clinical outcomes. We demonstrated that engagement among high-risk populations is possible when health care comes conveniently to where they are