BackgroundRemote measurement technology refers to the use of mobile health technology to track and measure change in health status in real time as part of a person’s everyday life. With accurate measurement, remote measurement technology offers the opportunity to augment health care by providing personalized, precise, and preemptive interventions that support insight into patterns of health-related behavior and self-management. However, for successful implementation, users need to be engaged in its use.ObjectiveOur objective was to systematically review the literature to update and extend the understanding of the key barriers to and facilitators of engagement with and use of remote measurement technology, to guide the development of future remote measurement technology resources.MethodsWe conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines involving original studies dating back to the last systematic review published in 2014. We included studies if they met the following entry criteria: population (people using remote measurement technology approaches to aid management of health), intervention (remote measurement technology system), comparison group (no comparison group specified), outcomes (qualitative or quantitative evaluation of the barriers to and facilitators of engagement with this system), and study design (randomized controlled trials, feasibility studies, and observational studies). We searched 5 databases (MEDLINE, IEEE Xplore, EMBASE, Web of Science, and the Cochrane Library) for articles published from January 2014 to May 2017. Articles were independently screened by 2 researchers. We extracted study characteristics and conducted a content analysis to define emerging themes to synthesize findings. Formal quality assessments were performed to address risk of bias.ResultsA total of 33 studies met inclusion criteria, employing quantitative, qualitative, or mixed-methods designs. Studies were conducted in 10 countries, included male and female participants, with ages ranging from 8 to 95 years, and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes, and reporting of usage statistics was inconsistent. Acceptability of remote measurement technology according to the average percentage of time used (64%-86.5%) and dropout rates (0%-44%) was variable. The barriers and facilitators from the content analysis related to health status, perceived utility and value, motivation, convenience and accessibility, and usability.ConclusionsThe results of this review highlight gaps in the design of studies trialing remote measurement technology, including the use of quantitative assessment of usage and acceptability. Several processes that could facilitate engagement with this technology have been identified and may drive the development of more person-focused remote measurement technology. However, these factors need further t...

Background There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. Methods RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). Discussion This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed. Electronic supplementary material The online version of this article (10.1186/s12888-019-2049-z) contains supplementary material, which is available to authorized users.

Background Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts. Objective This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. Methods Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. Results Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience, and ease of use was noted across all countries. Conclusions Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.

Familial amyloid polyneuropathy (FAP) is characterized by deposition of mutated transthyretin (TTR) in the peripheral nervous system. Prior to amyloid fibrils, nonfibrillar TTR aggregates are deposited inducing oxidative stress with increased nitration (3-NT). As the major source of TTR is the liver, liver transplantation (LT) is used to halt FAP. Given the shortage of liver donors, domino LT (DLT) using FAP livers is performed. The correlation between TTR deposition in the skin and nerve was tested in biopsies from normal individuals, asymptomatic carriers (FAP 0) and FAP patients; in FAP 0, nonfibrillar TTR was observed both in the skin and nerve in the same individuals; in patients, amyloid was detected in both tissues. The occurrence of amyloidosis in recipients of FAP livers was evaluated 1-7 years after DLT: TTR deposition occurred in the skin 3 years after transplantation either as amyloid or aggregates; in one of the recipients, fibrillar TTR was present in the epineurium 6 years after DLT. Deposits were scarce and 3-NT immunostaining was irrelevant. Nerve biopsies from DLT recipients had no FAP-related neuropathy. Our findings suggest that TTR amyloid formation occurs faster than predicted and that TTR of liver origin can cross the blood-nerve barrier. Recipients of FAP livers should be under surveillance for TTR deposition and tissue damage.

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Influenza epidemics are a serious global public health and economic problem. The IFITM3 allele (rs12252-C) was suggested as a population-based genetic risk factor for severe influenza virus infection by A(H1N1)pdm09. We analyzed the population genetics of IFITM3 variants in the Portuguese general population (n = 200) and Central Africans (largely Angolan) (n = 148) as well as its association to influenza severity in Portuguese patients (n = 41). Seven SNPs, within the 352 bp IFITM3 amplicon around rs12252, were identified. SNP distributions in the Portuguese appeared at an intermediate level between the Africans and other Europeans. According to HapMap, rs34481144 belongs to the same linkage disequilibrium (LD) block as rs12252 and is in strong LD with rs6421983. A negative association with severe relative to mild disease was observed for allele rs34481144-A, indicating a protective effect under the dominant model. Moreover, haplotype Hap4 with rs34481144-A, not including rs12252-C, was significantly associated to mild influenza. Conversely, although with borderline significance, haplotype Hap1 with rs34481144-G, not including rs12252-C, was associated to severe disease. Moreover, in comparison to the general Portuguese population, statistical significant differences in the frequencies of the protective allele rs34481144-A in the severe disease group, the deleterious Hap1 in the mild disease group, and the protective Hap4 in the severe disease group were observed. The population attributable risk (PAR) for the targeted rs34481144 allele or genotype was of 55.91 and 64.44% in the general population and the mildly infected individuals, respectively. Implication of these variants in disease phenotype needs further validation, namely through functional analysis as is discussed.

This study proposes a methodology to support coding professionals in assigning ICD-9-CM codes to inpatient episodes. This subject has been predominantly addressed through the use of natural language processing methods, which show limited generalizability. To surpass this issue, this paper proposes a methodology entailing an adaptive data processing method based on structured electronic health record data, whereby raw clinical data is mapped into a feature set, and based on which supervised learning algorithms are trained. After applying a filter method for feature selection, support vector machine (SVM) classifiers are trained to obtain predictions for assigning codes to each episode. This approach is tested using a dataset of inpatient episodes from a department of Internal Medicine. Classifiers exhibited F1-measure values around 52%. Recall was generally higher than precision, which is considered valuable for coding support purposes. Analyzing results on an individual code basis sheds light on some key-issues regarding the use of structured electronic health record data in supporting clinical coding.