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Background A lack of physical activity and overconsumption of energy dense food is associated with overweight and obesity. The neighbourhood environment may stimulate or hinder the development and/or maintenance of a healthy lifestyle. To improve research on the obesogenicity of neighbourhood environments, reliable, valid and convenient assessment methods of potential obesogenic characteristics of neighbourhood environments are needed. This study examines the reliability and validity of the SPOTLIGHT-Virtual Audit Tool (S-VAT), which uses remote sensing techniques (Street View feature in Google Earth) for desk-based assessment of environmental obesogenicity. Methods A total of 128 street segments in four Dutch urban neighbourhoods – heterogeneous in socio-economic status and residential density – were assessed using the S-VAT. Environmental characteristics were categorised as walking related items, cycling related items, public transport, aesthetics, land use-mix, grocery stores, food outlets and physical activity facilities. To assess concordance of inter- and intra-observer reliability of the Street View feature in Google Earth, and validity scores with real life audits, percentage agreement and Cohen's Kappa (k) were calculated. Results Intra-observer reliability was high and ranged from 91.7% agreement (k = 0.654) to 100% agreement (k = 1.000) with an overall agreement of 96.4% (k = 0.848). Inter-observer reliability results ranged from substantial agreement 78.6% (k = 0.440) to high agreement, 99.2% (k = 0.579), with an overall agreement of 91.5% (k = 0.595). Criterion validity was substantial to high for most of the categories ranging from 87.3% agreement (k = 0.539) to 99.9% agreement (k = 0.887) with an overall score of 95.6% agreement (k = 0.747). Conclusion These study results suggest that the S-VAT is a highly reliable and valid remote sensing tool to assess potential obesogenic environmental characteristics.

Background Observational data from the retrospective, non-randomized P regnancy RE mote MO nitoring I (PREMOM I) study showed that remote monitoring (RM) may be beneficial for prenatal observation of women at risk for gestational hypertensive disorders (GHD) in terms of clinical outcomes, health economics, and stakeholder perceptions. PREMOM II is a prospective, randomized, multicenter follow-up study that was performed to explore these promising results. Methods After providing written consent, 3922 pregnant women aged ≥18 years who are at increased risk of developing GHD will be randomized (1:1:1 ratio) to (a) conventional care (control group), (b) a patient self-monitoring group, and (c) a midwife-assisted RM group. The women in each group will be further divided (1:1 ratio) to evaluate the outcomes of targeted or non-targeted (conventional) antihypertensive medication. Women will be recruited in five hospitals in Flanders, Belgium: Ziekenhuis Oost-Limburg, Universitaire Ziekenhuis Antwerpen, Universitaire Ziekenhuis Leuven, AZ Sint Jan Brugge–Oostende, and AZ Sint Lucas Brugge. The primary outcomes are: (1) numbers and types of prenatal visits; (2) maternal outcomes; (3) neonatal outcomes; (4) the applicability and performance of RM; and (5) compliance with RM and self-monitoring. The secondary outcomes are: (1) cost-effectiveness and willingness to pay; (2) patient-reported outcome measures (PROMS) questionnaires on the experiences of the participants; and (3) the maternal and perinatal outcomes according to the type of antihypertensive medication. Demographic, and maternal and neonatal outcomes are collected from the patients’ electronic records. Blood pressure and compliance rate will be obtained from an online digital coordination platform for remote data handling. Information about the healthcare-related costs will be obtained from the National Coordination Committee of Belgian Health Insurance Companies (Intermutualistisch Agentschap). PROMS will be assessed using validated questionnaires. Discussion To our knowledge, this is the first randomized trial comparing midwife-assisted RM and self-monitoring of prenatal blood pressure versus conventional management among women at increased risk of GHD. Positive results of this study may lead to a practical framework for caregivers, hospital management, and payers to introduce RM into the prenatal care programs of high-risk pregnancies. Trial registration This study was registered on clinicaltrials.gov , identification number NCT04031430. Registered 24 July 2019, https://clinicaltrials.gov/ct2/show/NCT04031430?cond=premom+ii&draw=2&rank=1 .

Researchers in Nova Scotia and Ontario, Canada, implemented a passive remote monitoring (PRM) model of home care unique to their health system contexts. Each PRM model integrated tailored PRM devices (eg, motion sensors, cameras, and door alarms) into home care patients' residences with the aim of linking patients, family and friend caregivers, and health care providers to support older adults' aging in place. The purpose of this study was to examine the use of PRM technologies in the home to support older adults' safe aging in place and avoidance or delay of higher levels of care. This multiprovincial pragmatic randomized controlled trial examined how PRM technologies support older adults to safely remain in their home and avoid or delay admission to higher levels of care. Pairs of home care patients and their family and friend caregivers were recruited in Ontario and Nova Scotia. Participant pairs were randomly assigned to one of two conditions: (1) standard home care (ie, control) or (2) standard home care plus study-provided PRM (ie, intervention). Participants provided their provincial health insurance numbers to link with provincial health administrative databases and identify if patients were admitted to higher levels of care after 1 year. Cox proportional hazards models were used to evaluate the primary outcome in each province. In total, 313 patient-caregiver pairs were recruited: 174 pairs in Ontario (intervention: n=60; control: n=114) and 139 pairs in Nova Scotia (intervention: n=45; control: n=94). Results indicate PRM was associated with a nonsignificant 30% reduction in risk of patients being admitted to higher levels of care in Ontario (hazard ratio 0.7, 95% CI 0.3-1.4) and no reduction in risk in Nova Scotia (hazard ratio 1.1, 95% CI 0.3-3.7). Adjusting for patient sex had no impact on model estimates for either province. Limitations related, in part, to the impact of the COVID-19 pandemic may have contributed to the effectiveness of the intervention. While our study did not yield statistically significant results (P=.30 and P=.90) regarding the effectiveness of the PRM model in prolonging home stays, the observed trends suggest that technology-assisted aging in place may be a valuable goal for older adults. Further study is required to understand if longer follow-up time allows more effects of PRM on patients' avoidance of higher levels of care to be detected. ISRCTN ISRCTN79884651; https://www.isrctn.com/ISRCTN79884651. RR2-10.2196/15027.

IntroductionCardiac implantable electronic devices (CIED) can transfer data to the healthcare team, remotely. National and international cardiac organisations recommend all patients use this technology, however patient engagement is suboptimal. Previously, in cardiovascular patients, SMS messaging services have shown improvements in patient engagement and subsequent health outcomes. This paper describes the protocol and intervention of a randomised controlled trial (RCT) to assess the feasibility of a novel CIED remote monitoring SMS patient support programme on self-efficacy in managing CIED and cardiovascular health following CIED implantation.Methods/analysisA two-arm RCT will be conducted of 100 participants with 1:1 allocation to intervention or control. Participants awaiting-CIED or immediately post-CIED implantation from sites throughout Australia will be invited to partake. The intervention group will receive regular SMS communication with a range of educational and self-efficacy resources, in conjunction with engagement initiatives following CIED clinical issue detection. The control group will receive CIED remote monitoring education and clinical issue management as per standard practice at their respective sites. The primary outcome will assess the patient’s capacity to manage their CIED as measured by the ‘Self-Efficacy Expectations After Implantable Cardioverter Defibrillator Implantation Scale’. Secondary outcomes will assess participant’s ability to manage their cardiovascular condition, CIED remote monitoring, quality of life, impact on health service utilisation, cardiovascular behavioural risk factor change and motivation to improve cardiovascular health. A sample size of 100 will have a 90% power to detect a minimum difference of 1.07 in the ‘Self-Efficacy Expectations After Implantable Cardioverter Defibrillator Implantation Scale’ between the intervention and control group with an alpha value of 0.05.Ethics and disseminationEthics approval for this study has been obtained from the Western Sydney Local Health District Human Research Ethics Committee. The project results will be published in peer-reviewed journals, at scientific meetings and in the media.Trial registration numberACTRN12623000702617

Background The cardioprotective effect of remote ischemic preconditioning (RIPC) in cardiovascular surgery is controversial. This study investigated whether RIPC combined with remote ischemic postconditioning (RIPostC) reduces myocardial injury to donor hearts in patients undergoing heart transplantation. Methods One hundred and twenty patients scheduled for orthotopic heart transplantation were enrolled and randomly assigned to an RIPC+RIPostC group ( n  = 60) or a control (n = 60) group. In the RIPC+RIPostC group, after anesthesia induction, four cycles of 5-min of ischemia and 5-min of reperfusion were applied to the right upper limb by a cuff inflated to 200 mmHg (RIPC) and 20 min after aortic declamping (RIPostC). Serum cardiac troponin I (cTnI) levels were determined preoperatively and at 3, 6, 12, and 24 h after aortic declamping. Postoperative clinical outcomes were recorded. The primary endpoint was a comparison of serum cTnI levels at 6 h after aortic declamping. Results Compared with the preoperative baseline, in both groups, serum cTnI levels peaked at 6 h after aortic declamping. Compared with the control group, RIPC+RIPostC significantly reduced serum cTnI levels at 6 h after aortic declamping (38.87 ± 31.81 vs 69.30 ± 34.13 ng/ml, P  = 0.02). There were no significant differences in in-hospital morbidity and mortality between the two groups. Conclusion In patients undergoing orthotopic heart transplantation, RIPC combined with RIPostC reduced myocardial injury at 6 h after aortic declamping, while we found no evidence of this function provided by RIPC+RIPostC could improve clinical outcomes. Trial registration Trial Registration Number: chictr.org.cn . no. ChiCTR-INR-16010234 (prospectively registered). The initial registration date was 9/1/2017.

IntroductionThe proliferation of unmanned aerial vehicles (UAV) has the potential to change the situational awareness of incident commanders allowing greater scene safety. The aim of this study was to compare UAV technology to standard practice (SP) in hazard identification during a simulated multi-vehicle motor collision (MVC) in terms of time to identification, accuracy and the order of hazard identification. A prospective observational cohort study was conducted with 21 students randomized into UAV or SP group, based on a MVC with 7 hazards. The UAV group remained at the UAV ground station while the SP group approached the scene. After identifying hazards the time and order was recorded. The mean time (SD, range) to identify the hazards were 3 minutes 41 seconds (1 minute 37 seconds, 1 minute 48 seconds-6 minutes 51 seconds) and 2 minutes 43 seconds (55 seconds, 1 minute 43 seconds-4 minutes 38 seconds) in UAV and SP groups corresponding to a mean difference of 58 seconds (P=0.11). A non-parametric permutation test showed a significant (P=0.04) difference in identification order. Both groups had 100% accuracy in hazard identification with no statistical difference in time for hazard identification. A difference was found in the identification order of hazards. (Disaster Med Public Health Preparedness. 2018;12:631-634).

Patient acceptance is one of the major barriers toward widespread use of mHealth systems. The aim of this study was to assess system operability and whole trial feasibility, including patients’ experience with their use of COMMODITY12 mHealth system under. Secondary study aims included assessment of several metabolic parameters as well as patient adherence to the treatment. This was a prospective parallel-arm randomized controlled trial in outpatients diagnosed with DM2, being treated in the primary care settings in Lodz region, Poland, with 6 weeks period of follow-up. Patients opinions were collected with 7-item questionnaire, assessing different aspects of system use, as well as EuroQol-5D-5 L questionnaire, assessing health-related quality of life. Sixty patients (female, 24, male, 36, mean age +/− SD 59.5 +/− 6.8) completed study. All four layers of the COMMODITY12 system proved to work smooth under real-life conditions, without major problems. All dimensions of experience with system use were assessed well, with maximum values for clearness of instructions, and ease of use (4.80, and 4.63, respectively). Health related quality of life, as assessed with cumulative utility measure, improved significantly in COMMODITY12 system users ( P  < 0.05). mHealth system modestly improved glycaemic and blood pressure control, assuring high level of patient adherence with overall adherence reaching 92.9 %. Study proved that the COMODITY12 system is well accepted by type 2 diabetes patients taking part in clinical trial, leading to several clinical benefits, and improved quality of life. Nevertheless, before future commercialisation of the system, several minor problems identified during the study need to be addressed.

Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

Light Detection and Ranging (LiDAR) technology has been widely used in forestry surveys in the form of airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS). The acquisition of important basic tree parameters (e.g., diameter at breast height and tree position) in forest inventory did not solve the problem of low measurement efficiency or weak GNSS signal under the canopy. A personal laser scanning (PLS) device combined with SLAM technology provides an effective solution for forest inventory under complex conditions with its light weight and flexible mobility. This study proposes a new method for calculating the volume of a cylinder using point cloud data obtained by a PLS device by fitting to a polygonal cylinder to calculate the diameter of the trunk. The point cloud data of tree trunks of different thickness were modeled using different fitting methods. The rate of correct tree trunk detection was 93.3% and the total deviation of the estimations of tree diameter at breast height (DBH) was -1.26 cm. The root mean square errors (RMSEs) of the estimations of the extracted DBH and the tree position were 1.58 cm and 26 cm, respectively. The survey efficiency of the personal laser scanning (PLS) device was 30m2/min for each investigator, compared with 0.91m2/min for the field survey. The test demonstrated that the PLS device combined with the SLAM algorithm provides an efficient and convenient solution for forest inventory.

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).