Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes

Objective: Patient self-management support may be augmented by using home-based technologies that generate data points that providers can potentially use to make more timely changes in the patients' care. The purpose of this study was to evaluate the effectiveness of short-term targeted use of remote data transmission on treatment outcomes in patients with diabetes who had either out-of-range hemoglobin A1c (A1c) and/or blood pressure (BP) measurements. Materials and Methods: A single-center randomized controlled clinical trial design compared in-home monitoring (n=55) and usual care (n=53) in patients with type 2 diabetes and hypertension being treated in primary care clinics. Primary outcomes were A1c and systolic BP after a 12-week intervention. Results: There were no significant differences between the intervention and control groups on either A1c or systolic BP following the intervention. Conclusions: The addition of technology alone is unlikely to lead to improvements in outcomes. Practices need to be selective in their use of telemonitoring with patients, limiting it to patients who have motivation or a significant change in care, such as starting insulin. Attention to the need for effective and responsive clinic processes to optimize the use of the additional data is also important when implementing these types of technology

Transfer of Information from Personal Health Records: A Survey of Veterans Using My HealtheVet

Abstract Objective: Personal health records provide patients with ownership of their health information and allow them to share information with multiple healthcare providers. However, the usefulness of these records relies on patients understanding and using their records appropriately. My HealtheVet is a Web-based patient portal containing a personal health record administered by the Veterans Health Administration. The goal of this study was to explore veterans' interest and use of My HealtheVet to transfer and share information as well as to identify opportunities to increase veteran use of the My HealtheVet functions. Materials and Methods: Two waves of data were collected in 2010 through an American Customer Satisfaction Index Web-based survey. A random sample of veterans using My HealtheVet was invited to participate in the survey conducted on the My HealtheVet portal through a Web-based pop-up browser window. Results: Wave One results (n=25,898) found that 41% of veterans reported printing information, 21% reported saving information electronically, and only 4% ever sent information from My HealtheVet to another person. In Wave Two (n=18,471), 30% reported self-entering medication information, with 18% sharing this information with their Veterans Affairs (VA) provider and 9.6% sharing with their non-VA provider. Conclusion: Although veterans are transferring important medical information from their personal health records, increased education and awareness are needed to increase use. Personal health records have the potential to improve continuity of care. However, more research is needed on both the barriers to adoption as well as the actual impact on patient health outcomes and well-being.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98490/1/tmj%2E2011%2E0109.pd

Evidence and recommendations on the use of telemedicine for the management of arterial hypertension:an international expert position paper

Telemedicine allows the remote exchange of medical data between patients and healthcare professionals. It is used to increase patients’ access to care and provide effective healthcare services at a distance. During the recent coronavirus disease 2019 (COVID-19) pandemic, telemedicine has thrived and emerged worldwide as an indispensable resource to improve the management of isolated patients due to lockdown or shielding, including those with hypertension. The best proposed healthcare model for telemedicine in hypertension management should include remote monitoring and transmission of vital signs (notably blood pressure) and medication adherence plus education on lifestyle and risk factors, with video consultation as an option. The use of mixed automated feedback services with supervision of a multidisciplinary clinical team (physician, nurse, or pharmacist) is the ideal approach. The indications include screening for suspected hypertension, management of older adults, medically underserved people, high-risk hypertensive patients, patients with multiple diseases, and those isolated due to pandemics or national emergencies

Individual patient data meta-analysis of self-monitoring of blood pressure (BP-SMART): a protocol.

INTRODUCTION: Self-monitoring of blood pressure is effective in reducing blood pressure in hypertension. However previous meta-analyses have shown a considerable amount of heterogeneity between studies, only part of which can be accounted for by meta-regression. This may be due to differences in design, recruited populations, intervention components or results among patient subgroups. To further investigate these differences, an individual patient data (IPD) meta-analysis of self-monitoring of blood pressure will be performed. METHODS AND ANALYSIS: We will identify randomised trials that have compared patients with hypertension who are self-monitoring blood pressure with those who are not and invite trialists to provide IPD including clinic and/or ambulatory systolic and diastolic blood pressure at baseline and all follow-up points where both intervention and control groups were measured. Other data requested will include measurement methodology, length of follow-up, cointerventions, baseline demographic (age, gender) and psychosocial factors (deprivation, quality of life), setting, intensity of self-monitoring, self-monitored blood pressure, comorbidities, lifestyle factors (weight, smoking) and presence or not of antihypertensive treatment. Data on all available patients will be included in order to take an intention-to-treat approach. A two-stage procedure for IPD meta-analysis, stratified by trial and taking into account age, sex, diabetes and baseline systolic BP will be used. Exploratory subgroup analyses will further investigate non-linear relationships between the prespecified variables. Sensitivity analyses will assess the impact of trials which have and have not provided IPD. ETHICS AND DISSEMINATION: This study does not include identifiable data. Results will be disseminated in a peer-reviewed publication and by international conference presentations. CONCLUSIONS: IPD analysis should help the understanding of which self-monitoring interventions for which patient groups are most effective in the control of blood pressure

Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers.

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions, finding major pathways contributing to risk, with some loci shared across immune disorders. To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new T1D-associated regions (P < 5 × 10(-8)). A comparative analysis with 15 immune diseases showed that T1D is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new T1D risk loci. Using a Bayesian approach, we defined credible sets for the T1D-associated SNPs. The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34(+) stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.This research uses resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the National Institute of Allergy and Infectious Diseases (NIAID), the National Human Genome Research Institute (NHGRI), the National Institute of Child Health and Human Development (NICHD) and JDRF and supported by grant U01 DK062418 from the US National Institutes of Health. Further support was provided by grants from the NIDDK (DK046635 and DK085678) to P.C. and by a joint JDRF and Wellcome Trust grant (WT061858/09115) to the Diabetes and Inflammation Laboratory at Cambridge University, which also received support from the NIHR Cambridge Biomedical Research Centre. ImmunoBase receives support from Eli Lilly and Company. C.W. and H.G. are funded by the Wellcome Trust (089989). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). We gratefully acknowledge the following groups and individuals who provided biological samples or data for this study. We obtained DNA samples from the British 1958 Birth Cohort collection, funded by the UK Medical Research Council and the Wellcome Trust. We acknowledge use of DNA samples from the NIHR Cambridge BioResource. We thank volunteers for their support and participation in the Cambridge BioResource and members of the Cambridge BioResource Scientific Advisory Board (SAB) and Management Committee for their support of our study. We acknowledge the NIHR Cambridge Biomedical Research Centre for funding. Access to Cambridge BioResource volunteers and to their data and samples are governed by the Cambridge BioResource SAB. Documents describing access arrangements and contact details are available at http://www.cambridgebioresource.org.uk/. We thank the Avon Longitudinal Study of Parents and Children laboratory in Bristol, UK, and the British 1958 Birth Cohort team, including S. Ring, R. Jones, M. Pembrey, W. McArdle, D. Strachan and P. Burton, for preparing and providing the control DNA samples. This study makes use of data generated by the Wellcome Trust Case Control Consortium, funded by Wellcome Trust award 076113; a full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk/.This is the author accepted manuscript. The final version is available via NPG at http://www.nature.com/ng/journal/v47/n4/full/ng.3245.html

Clinical symptoms, signs and tests for identification of impending and current water-loss dehydration in older people (Review)

BackgroundThere is evidence that water-loss dehydration is common in older people and associated with many causes of morbidity and mortality.However, it is unclear what clinical symptoms, signs and tests may be used to identify early dehydration in older people, so that support can be mobilised to improve hydration before health and well-being are compromised.ObjectivesTo determine the diagnostic accuracy of state (one time), minimally invasive clinical symptoms, signs and tests to be used as screeningtests for detecting water-loss dehydration in older people by systematically reviewing studies that have measured a reference standard and at least one index test in people aged 65 years and over. Water-loss dehydration was defined primarily as including everyone with either impending or current water-loss dehydration (including all those with serum osmolality ≥ 295 mOsm/kg as being dehydrated).Search methodsStructured search strategies were developed for MEDLINE (OvidSP), EMBASE (OvidSP), CINAHL, LILACS, DARE and HTAdatabases (The Cochrane Library), and the International Clinical Trials Registry Platform (ICTRP). Reference lists of included studiesand identified relevant reviews were checked. Authors of included studies were contacted for details of further studies.Selection criteriaTitles and abstracts were scanned and all potentially relevant studies obtained in full text. Inclusion of full text studies was assessed independently in duplicate, and disagreements resolved by a third author. We wrote to authors of all studies that appeared to have collected data on at least one reference standard and at least one index test, and in at least 10 people aged ≥ 65 years, even where no comparative analysis has been published, requesting original dataset so we could create 2 x 2 tables.Data collection and analysis.Diagnostic accuracy of each test was assessed against the best available reference standard for water-loss dehydration (serum or plasma osmolality cut-off≥295mOsm/kg, serumosmolarity or weight change) within each study. For each index test study data were presented in forest plots of sensitivity and specificity. The primary target condition was water-loss dehydration (including either impending or current water-loss dehydration). Secondary target conditions were intended as current (> 300 mOsm/kg) and impending (295 to 300 mOsm/kg) water-loss dehydration, but restricted to current dehydration in the final review.We conducted bivariate random-effects meta-analyses (Stata/IC, StataCorp) for index tests where there were at least four studies and study datasets could be pooled to construct sensitivity and specificity summary estimates. We assigned the same approach for index tests with continuous outcome data for each of three pre-specified cut-off points investigated.Pre-set minimum sensitivity of a useful test was 60%, minimum specificity 75%. As pre-specifying three cut-offs for each continuoustest may have led to missing a cut-off with useful sensitivity and specificity, we conducted post-hoc exploratory analyses to createreceiver operating characteristic (ROC) curves where there appeared some possibility of a useful cut-off missed by the original three.These analyses enabled assessment of which tests may be worth assessing in further research. A further exploratory analysis assessed the value of combining the best two index tests where each had some individual predictive ability.Main resultsThere were few published studies of the diagnostic accuracy of state (one time), minimally invasive clinical symptoms, signs or tests tobe used as screening tests for detecting water-loss dehydration in older people. Therefore, to complete this review we sought, analysed and included raw datasets that included a reference standard and an index test in people aged ≥ 65 years.We included three studies with published diagnostic accuracy data and a further 21 studies provided datasets that we analysed. Weassessed 67 tests (at three cut-offs for each continuous outcome) for diagnostic accuracy of water-loss dehydration (primary targetcondition) and of current dehydration (secondary target condition).Only three tests showed any ability to diagnose water-loss dehydration (including both impending and current water-loss dehydration) as stand-alone tests: expressing fatigue (sensitivity 0.71 (95% CI 0.29 to 0.96), specificity 0.75 (95% CI 0.63 to 0.85), in one study with 71 participants, but two additional studies had lower sensitivity); missing drinks between meals (sensitivity 1.00 (95% CI 0.59 to 1.00), specificity 0.77 (95% CI 0.64 to 0.86), in one study with 71 participants) and BIA resistance at 50 kHz (sensitivities 1.00 (95% CI 0.48 to 1.00) and 0.71 (95% CI 0.44 to 0.90) and specificities of 1.00 (95% CI 0.69 to 1.00) and 0.80 (95% CI 0.28 to 0.99) in 15 and 22 people respectively for two studies, but with sensitivities of 0.54 (95% CI 0.25 to 0.81) and 0.69 (95% CI 0.56 to 0.79) and specificities of 0.50 (95% CI 0.16 to 0.84) and 0.19 (95% CI 0.17 to 0.21) in 21 and 1947 people respectively in two other studies). In post-hoc ROC plots drinks intake, urine osmolality and axillial moisture also showed limited diagnostic accuracy. No test was consistently useful in more than one study.Combining two tests so that an individual both missed some drinks between meals and expressed fatigue was sensitive at 0.71 (95%CI 0.29 to 0.96) and specific at 0.92 (95% CI 0.83 to 0.97).There was sufficient evidence to suggest that several stand-alone tests often used to assess dehydration in older people (including fluid intake, urine specific gravity, urine colour, urine volume, heart rate, dry mouth, feeling thirsty and BIA assessment of intracellular water or extracellular water) are not useful, and should not be relied on individually as ways of assessing presence or absence of dehydration in older people.No tests were found consistently useful in diagnosing current water-loss dehydration.Authors’ conclusionsThere is limited evidence of the diagnostic utility of any individual clinical symptom, sign or test or combination of tests to indicatewater-loss dehydration in older people. Individual tests should not be used in this population to indicate dehydration; they miss a highproportion of people with dehydration, and wrongly label those who are adequately hydrated.Promising tests identified by this review need to be further assessed, as do new methods in development. Combining several tests may improve diagnostic accuracy

Bottom trawl fishing footprints on the world’s continental shelves

Publication history: Accepted - 23 August 2018; Published online - 8 October 2018.Bottom trawlers land around 19 million tons of fish and invertebrates annually, almost one-quarter of wild marine landings. The extent of bottom trawling footprint (seabed area trawled at least once in a specified region and time period) is often contested but poorly described. We quantify footprints using high-resolution satellite vessel monitoring system (VMS) and logbook data on 24 continental shelves and slopes to 1,000-m depth over at least 2 years. Trawling footprint varied markedly among regions: from <10% of seabed area in Australian and New Zealand waters, the Aleutian Islands, East Bering Sea, South Chile, and Gulf of Alaska to >50% in some European seas. Overall, 14% of the 7.8 million-km2 study area was trawled, and 86% was not trawled. Trawling activity was aggregated; the most intensively trawled areas accounting for 90% of activity comprised 77% of footprint on average. Regional swept area ratio (SAR; ratio of total swept area trawled annually to total area of region, a metric of trawling intensity) and footprint area were related, providing an approach to estimate regional trawling footprints when highresolution spatial data are unavailable. If SAR was ≤0.1, as in 8 of 24 regions, therewas >95% probability that >90%of seabed was not trawled. If SAR was 7.9, equal to the highest SAR recorded, there was >95% probability that >70% of seabed was trawled. Footprints were smaller and SAR was ≤0.25 in regions where fishing rates consistently met international sustainability benchmarks for fish stocks, implying collateral environmental benefits from sustainable fishing.Funding for meetings of the study group and salary support for R.O.A. were provided by the following: David and Lucile Packard Foundation; the Walton Family Foundation; the Alaska Seafood Cooperative; American Seafoods Group US; Blumar Seafoods Denmark; Clearwater Seafoods Inc.; Espersen Group; Glacier Fish Company LLC US; Gortons Seafood; Independent Fisheries Limited N.Z.; Nippon Suisan (USA), Inc.; Pesca Chile S.A.; Pacific Andes International Holdings, Ltd.; San Arawa, S.A.; Sanford Ltd. N.Z.; Sealord Group Ltd. N.Z.; South African Trawling Association; Trident Seafoods; and the Food and Agriculture Organisation of the United Nations. Additional funding to individual authors was provided by European Union Project BENTHIS EU-FP7 312088 (to A.D.R., O.R.E., F.B., N.T.H., L.B.-M., R.C., H.O.F., H.G., J.G.H., P.J., S.K., M.L., G.G.-M., N.P., P.E.P., T.R., A.S., B.V., and M.J.K.); the Instituto Português do Mar e da Atmosfera, Portugal (C.S.); the International Council for the Exploration of the Sea Science Fund (R.O.A. and K.M.H.); the Commonwealth Scientific and Industrial Research Organisation (C.R.P. and T.M.); the National Oceanic and Atmospheric Administration (R.A.M.); New Zealand Ministry for Primary Industries Projects BEN2012/01 and DAE2010/ 04D (to S.J.B. and R.F.); the Institute for Marine and Antarctic Studies, University of Tasmania and the Department of Primary Industries, Parks, Water and Environment, Tasmania, Australia (J.M.S.); and UK Department of Environment, Food and Rural Affairs Project MF1225 (to S.J.)