Objective To systematically review the existing evidence of mobile health (mHealth) tools for the treatment of eating disorders (ED). Method Electronic databases (Pubmed, PsycInfo, and SCOPUS) were searched, and PRISMA guidelines were followed. Selected studies were divided into three categories according to the intended purpose of the mHealth tools used: (a) sole means of support, (b) complementary to standard face-to-face treatment, and (c) for relapse prevention. Additionally, studies were assessed on efficacy, qualitative information, and methodological quality. Results Fifteen studies were identified. Most studies using mHealth as a sole means of intervention or adjunct to traditional therapy showed no effects, although an improvement at postassessment was present in vodcast, smartphone application, and text-messaging interventions. Between group effects were only found for a text-messaging intervention for relapse prevention. Qualitative analyses showed that most mHealth interventions were considered as acceptable, supporting, and motivating by patients and therapists, although different important problems were observed in individual studies. Conclusions Limited effects were found for mHealth interventions to reduce ED-related symptoms. A common evaluation framework for ED mHealth interventions should be proposed to assess the validity of interventions before implementing them on a larger scale in clinical practice.

A systematic review of mHealth interventions for the support of eating disorders.

The early diagnosis and monitoring of the progress of cancers are limited due to the lack of adequate screening tools. Ultrasensitive and precise point-of-care cancer diagnostic tools are required for the early detection and screening of cancer biomarkers at the bedside with accuracy and specificity of diagnosis. Recent advances in nano- and microfabrication based technologies integrated with different sensing platforms are accelerating the development of rapid, low-cost and reliable point-of-care cancer diagnostic devices. These technologies allow low volumes of reagents and samples, reduced analysis time, are disposable and portable, and provide ultrasensitive multiplexed analysis to improve diagnostic and prognostic evaluations. Networking for communicating and transferring the results through wireless networks provides additional opportunities for remote telemedicine-based diagnosis and planning of treatment regimens, even in semi-urban and rural areas. This review focuses on recent advances in point-of-care cancer diagnostics for efficient treatment along with the key challenges, opportunities and future scope of these technologies for clinical translation.

Advances in point-of-care diagnostic devices in cancers

PURPOSE Many who seek primary health care advice about mental health may be using mobile applications (apps) claiming to improve well-being or relieve symptoms We aimed to identify how prominent mental health apps frame mental health, including who has problems and how they should be managed METHODS We conducted a qualitative content analysis of advertising material for mental health apps found online in the United States, the United Kingdom, Canada, and Australia during late 2016 Apps were included if they explicitly referenced mental health diagnoses or symptoms and offered diagnosis and guidance, or made health claims Two independent coders analyzed app store descriptions and linked websites using a structured, open-ended instrument We conducted interpretive analysis to identify key themes and the range of messages RESULTS We identified 61 mental health apps: 34 addressed predominantly anxiety, panic, and stress (56%), 16 addressed mood disorders (26%), and 11 addressed well-being or other mental health issues (18%) Apps described mental health problems as being psychological symptoms, a risk state, or lack of life achievements Mental health problems were framed as present in everyone, but everyone was represented as employed, white, and in a family Explanations about mental health focused on abnormal responses to mild triggers, with minimal acknowledgment of external stressors Therapeutic strategies included relaxation, cognitive guidance, and self-monitoring Apps encouraged frequent use and promoted personal responsibility for improvement CONCLUSIONS Mental health apps may promote medicalization of normal mental states and imply individual responsibility for mental well-being Within the health care clinician-patient relationship, such messages should be challenged, where appropriate, to prevent overdiagnosis and ensure supportive health care where needed

https://www.annfammed.org/content/annalsfm/16/4/338.full.pdf

Mental Health Messages in Prominent Mental Health Apps.

The prevalence of diabetes is rising globally. Diabetes patients need continuous monitoring, and to achieve this objective, they have to be engaged in their healthcare management process. Mobile health (MH) is an information and communications technology trend to empower chronically ill patients in a smart environment. Discussing the current state of MH technologies is required in order to address their limitations. Existing review articles have evaluated the MH literature based on applicability and level of adoption by patients and healthcare providers. Most of these reviews asserted that MH apps and research have not reached a stable level yet. To the best of our knowledge, there is no clear description of solutions to these problems. In addition, no one has investigated and analyzed MH in its contextual environment in a detailed way. We conducted a comprehensive survey of MH research on diabetes management articles published between 2011 and September 27, 2017. In this survey, we discuss current challenges in MH, along with research gaps, opportunities, and trends. Our literature review searched three academic databases (ScienceDirect, IEEE Xplore, and SpringerLink). A total of 60 articles were analyzed, with 30% from ScienceDirect, 38% from IEEE Xplore, and 32% from SpringerLink. MH was analyzed in the context of the electronic health record (EHR) ecosystem. We consider dimensions such as clinical decision support systems, EHRs, cloud computing, semantic interoperability, wireless body area networks, and big data analytics. We propose specific metrics to analyze and evaluate MH from each of these dimensions. A comprehensive analysis of the literature from this viewpoint is valuable for both theoretical and developmental progress. This paper provides a critical analysis of challenges that have not been fully met and highlights directions for future research that could improve MH applicability.

/pdf/mobile-health-technologies-for-diabetes-mellitus-current-3sbuy9ljpy.pdf

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed “things” in the Internet of things (IoT) transforms our homes and industries. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks—SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff—in our case, a smart water bottle—dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user’s activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.

https://www.mdpi.com/1424-8220/19/19/4295/pdf

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

There has been tremendous increase in both the different types of Mobile Health (mHealth) applications and the number of applications being created for both the clinical and consumer healthcare space. The rapid proliferation of mHealth applications is creating confusion in the domain among both consumers and healthcare professionals due to uncertainty about reliability, security, regulation, and integration concerns. New applications are being developed faster than researchers, patients, and healthcare professionals can grasp the multiplicity of the mHealth applications and the various ways they can be used. This paper proposes a taxonomy of existing and emerging mHealth applications to help all users understand this domain.

Caley Shimskey

Papers

mHealth taxonomy: a literature survey of mobile health applications