bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study
Authors: Wang Yunlong , König Laura M. , Reiterer Harald 
Published: November 2020

Background
Prolonged sedentary behavior is related to a number of risk factors for chronic diseases. Given the high prevalence of sedentary behavior in daily life, simple yet practical solutions for behavior change are needed to avoid detrimental health effects.

Objective
The mobile app SedVis was developed based on the health action process approach. The app provides personal mobility pattern visualization (for both physical activity and sedentary behavior) and action planning for sedentary behavior change. The primary aim of the study is to investigate the effect of mobility pattern visualization on users’ action planning for changing their sedentary behavior. The secondary aim is to evaluate user engagement with the visualization and user experience of the app.

Methods
A 3-week user study was conducted with 16 participants who had the motivation to reduce their sedentary behavior. Participants were allocated to either an active control group (n=8) or an intervention group (n=8). In the 1-week baseline period, none of the participants had access to the functions in the app. In the following 2-week intervention period, only the intervention group was given access to the visualizations, whereas both groups were asked to make action plans every day and reduce their sedentary behavior. Participants’ sedentary behavior was estimated based on the sensor data of their smartphones, and their action plans and interaction with the app were also recorded by the app. Participants’ intention to change their sedentary behavior and user experience of the app were assessed using questionnaires.

Results
The data were analyzed using both traditional null hypothesis significance testing (NHST) and Bayesian statistics. The results suggested that the visualizations in SedVis had no effect on the participants’ action planning according to both the NHST and Bayesian statistics. The intervention involving visualizations and action planning in SedVis had a positive effect on reducing participants’ sedentary hours, with weak evidence according to Bayesian statistics (Bayes factor, BF+0=1.92; median 0.52; 95% CI 0.04-1.25), whereas no change in sedentary time was more likely in the active control condition (BF+0=0.28; median 0.18; 95% CI 0.01-0.64). Furthermore, Bayesian analysis weakly suggested that the more frequently the users checked the app, the more likely they were to reduce their sedentary behavior (BF−0=1.49; r=−0.50).

Conclusions
Using a smartphone app to collect data on users’ mobility patterns and provide real-time feedback using visualizations may be a promising method to induce changes in sedentary behavior and may be more effective than action planning alone. Replications with larger samples are needed to confirm these findings.
Effects and Implementation of a Mindfulness and Relaxation App for Patients With Cancer: Mixed Methods Feasibility Study
Authors: Mikolasek Michael , Witt Claudia Margitta , Barth Jürgen 
Published: November 2020

Background
Cancer diagnosis and cancer treatment can cause high levels of distress, which is often not sufficiently addressed in standard medical care. Therefore, a variety of supportive nonpharmacological treatments have been suggested to reduce distress in patients with cancer. However, not all patients use these interventions because of limited access or lack of awareness. To overcome these barriers, mobile health may be a promising way to deliver the respective supportive treatments.

Objective
The aim of this study is to evaluate the effects and implementation of a mindfulness and relaxation app intervention for patients with cancer as well as patients’ adherence to such an intervention.

Methods
In this observational feasibility study with a mixed methods approach, patients with cancer were recruited through the web and through hospitals in Switzerland. All enrolled patients received access to a mindfulness and relaxation app. Patients completed self-reported outcomes (general health, health-related quality of life, anxiety, depression, distress, mindfulness, and fear of progression) at baseline and at weeks 4, 10, and 20. The frequency of app exercise usage was gathered directly through the app to assess the adherence of patients. In addition, we conducted interviews with 5 health professionals for their thoughts on the implementation of the app intervention in standard medical care. We analyzed patients’ self-reported outcomes using linear mixed models (LMMs) and qualitative data with content analysis.

Results
A total of 100 patients with cancer (74 female) with a mean age of 53.2 years (SD 11.6) participated in the study, of which 25 patients used the app regularly until week 20. LMM analyses revealed improvements in anxiety (P=.04), distress (P<.001), fatigue (P=.01), sleep disturbance (P=.02), quality of life (P=.03), and mindfulness (P<.001) over the course of 20 weeks. Further LMM analyses revealed a larger improvement in distress (P<.001), a moderate improvement in anxiety (P=.001), and a larger improvement in depression (P=.03) in patients with high levels of symptoms at baseline in the respective domains. The interviews revealed that the health professionals perceived the app as a helpful addition to standard care. They also made suggestions for improvements, which could facilitate the implementation of and adherence to such an app.

Conclusions
This study indicates that a mindfulness and relaxation app for patients with cancer can be a feasible and effective way to deliver a self-care intervention, especially for highly distressed patients. Future studies should investigate if the appeal of the app can be increased with more content, and the effectiveness of such an intervention needs to be tested in a randomized controlled trial.
Mobile Clinical Decision Support System for the Management of Diabetic Patients With Kidney Complications in UK Primary Care Settings: Mixed Methods Feasibility Study
Authors: Alhodaib Hala Ibrahim , Antza Christina , Chandan Joht Singh , Hanif Wasim , Sankaranarayanan Sailesh , Paul Sunjay , Sutcliffe Paul , Nirantharakumar Krishnarajah 
Published: November 2020

Background
Attempts to utilize eHealth in diabetes mellitus (DM) management have shown promising outcomes, mostly targeted at patients; however, few solutions have been designed for health care providers.

Objective
The purpose of this study was to conduct a feasibility project developing and evaluating a mobile clinical decision support system (CDSS) tool exclusively for health care providers to manage chronic kidney disease (CKD) in patients with DM.

Methods
The design process was based on the 3 key stages of the user-centered design framework. First, an exploratory qualitative study collected the experiences and views of DM specialist nurses regarding the use of mobile apps in clinical practice. Second, a CDSS tool was developed for the management of patients with DM and CKD. Finally, a randomized controlled trial examined the acceptability and impact of the tool.

Results
We interviewed 15 DM specialist nurses. DM specialist nurses were not currently using eHealth solutions in their clinical practice, while most nurses were not even aware of existing medical apps. However, they appreciated the potential benefits that apps may bring to their clinical practice. Taking into consideration the needs and preferences of end users, a new mobile CDSS app, “Diabetes & CKD,” was developed based on guidelines. We recruited 39 junior foundation year 1 doctors (44% male) to evaluate the app. Of them, 44% (17/39) were allocated to the intervention group, and 56% (22/39) were allocated to the control group. There was no significant difference in scores (maximum score=13) assessing the management decisions between the app and paper-based version of the app’s algorithm (intervention group: mean 7.24 points, SD 2.46 points; control group: mean 7.39, SD 2.56; t37=–0.19, P=.85). However, 82% (14/17) of the participants were satisfied with using the app.

Conclusions
The findings will guide the design of future CDSS apps for the management of DM, aiming to help health care providers with a personalized approach depending on patients’ comorbidities, specifically CKD, in accordance with guidelines.
Exchanges in a Virtual Environment for Diabetes Self-Management Education and Support: Social Network Analysis
Authors: Pérez-Aldana Carlos A , Lewinski Allison A , Johnson Constance M , Vorderstrasse Allison A , Myneni Sahiti 
Published: November 2020

Background
Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes–specific virtual communities.

Objective
The objective of this study was to examine social support exchanges from a type 2 diabetes self-management education and support intervention that was delivered via a virtual environment.

Methods
Data comprised virtual environment–mediated synchronous interactions among participants and between participants and providers from an intervention for type 2 diabetes self-management education and support. Network data derived from such social interactions were used to create networks to analyze patterns of social support exchange with the lens of social network analysis. Additionally, network correlations were used to explore associations between social support networks.

Results
The findings revealed structural differences between support networks, as well as key network characteristics of supportive interactions facilitated by the intervention. Emotional and appraisal support networks are the larger, most centralized, and most active networks, suggesting that virtual communities can be good sources for these types of support. In addition, appraisal and instrumental support networks are more connected, suggesting that members of virtual communities are more likely to engage in larger group interactions where these types of support can be exchanged. Lastly, network correlations suggest that participants who exchange emotional support are likely to exchange appraisal or instrumental support, and participants who exchange appraisal support are likely to exchange instrumental support.

Conclusions
Social interaction patterns from disease-specific virtual environments can be studied using a social network analysis approach to better understand the exchange of social support. Network data can provide valuable insights into the design of novel and effective eHealth interventions given the unique opportunity virtual environments have facilitating realistic environments that are effective and sustainable, where social interactions can be leveraged to achieve diverse health goals.
Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database
Authors: Islam Md Mohaimenul , Yang Hsuan-Chia , Poly Tahmina Nasrin , Li Yu-Chuan Jack 
Published: November 2020

BackgroundLaboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice.ObjectiveThe objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs.MethodsA retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model.ResultsWe used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity.ConclusionsThe developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.
THE PRESENCE OF THE MODERN INFRASTRUCTURE IN THE MIDDLE OF THE TRADITIONAL COMMUNITIES IN EAST TIMOR
Authors: GODINHO CONSTANTINO , KAMEO DANIEL D , ANTONIUS BELE 
Published: November 2020

Objective: The aim of this study is to find negative impacts of the construction of the modern infrastructure of the toll road and find out a good solution in East Timor.
Methods: The research methodology used is descriptive qualitative research methodology, namely, collecting data, interviewing key persons, making data deduction, analyzing data, drawing conclusions, and expressing recommendations, especially for the contractors and for the government.
Results: The construction of the toll road infrastructure megaprojects in the new country of Timor-Leste has both positive and negative impacts. The negative impact is huge and this is not taken into account by the contractors and the government in the construction of the Suai-Beasu toll road that connects the city of Suai on the South coast and the town of Dili on the North coast of the Democratic Republic of Timor-Leste. Hence, the recommendations suggested for the contractors and the government are to implement the aspirations of the communities, especially in the fair and just payment for the area used for the projects and building new cross-traffic for the residents alongside the toll road.
Conclusion: The presence of the toll road had positive impacts but there are several negative impacts on the lives of the Suai people, therefore, it is better that if the government and the contractors pay serious attention to overcome the negative impacts.
An Analysis of Perceived and Actual Anticoagulant Knowledge among Independent Pharmacy Patients
Authors: Ware Kenric , Faile Marty , Lynch Carrie 
Published: November 2020

Background: Deficits in knowledge of parameters surrounding anticoagulant therapy administration carry substantial risks for patient harm. Patients prescribed anticoagulants may overestimate their knowledge of anticoagulant use principles. The purpose of this study was to analyze independent pharmacy patients’ perceived versus actual knowledge of their anticoagulation therapies.
Methods: A 15-item survey across a dual-site pharmacy location evaluated patients’ confidence in their understanding of anticoagulant prescribing parameters and their awareness of measures to take to safeguard anticoagulant therapy routines. A pharmacist reviewed patient responses to survey items referencing actual understanding of anticoagulant therapies at the conclusion of the survey. Data analyses occurred by gender, age, ethnicity, location, living situation, self-management of anticoagulants, patient perception of anticoagulant safety, and reported receipt of anticoagulant education. Linear regressions were used for statistical analyses, with significance set at p<.05.
Results: During the span of three months, 45 patients, 20 females and 25 males, completed the survey. Perceived anticoagulant knowledge did not differ significantly by demographics. Actual anticoagulant knowledge declined by over 20% between age groups 60-69 and 80-89 years old. African Americans displayed 10% lower actual anticoagulant knowledge than Caucasian Americans.
Conclusions: This study revealed gaps in knowledge of anticoagulants among patients at an independent pharmacy. An appeal remains for more strategies to assist with knowledge of anticoagulant regimens. Healthcare institutions should continue to incorporate programs geared towards ongoing anticoagulant education, such as question and answer sessions, along with peer support mediums that foster optimal outcomes.

Article Type: Original Research
Frameless radiosurgical third ventriculostomy: Technical report
Authors: Gutierrez-Aceves Guillermo Axayacalt , Rodriguez-Camacho Alejandro , Celis-Lopez Miguel Angel , Moreno-Jimenez Sergio , Herrera-Gonzalez Jose Alfredo 
Published: November 2020

Background:
We describe the technical report and results of the first image-guided, linear accelerator, frameless radiosurgical third ventriculostomy.

Methods:
We report a 20 years old man, with diplopia, balance disturbances, and limitation for gaze supraversion. Magnetic resonance imaging resonance imaging of the brain and cranial computed tomography showed showed a left thalamic-midbrain lesion that caused partial compression of the Silvio aqueduct and mild ventricular dilatation. The biopsy revealed the diagnosis of pleomorphic xanthoastrocytoma. Before radical treatment of the tumor with fractionated stereotactic radiotherapy, the patient underwent to frameless radiosurgical third ventriculostomy, on the TrueBeam STX® platform with the ExacTrac localization system. The target used was the one defined on the floor of the third ventricle, at the midpoint between the mammillary bodies and the infundibular recess. The prescription dose was 120 Gy, given using a monoisocentric technique of multiple noncoplanar circular arches. The geometric arrangement of the plan consisted of 15 arches, with a 4 mm cone, distributed over a 110° table.

Results:
There was symptomatic and image improvement two days after radiosurgery. On CT, a reduction in ventricular dilation was observed with a reduction in the Evans index from 0.39 (initial CT) to 0.29 (CT at 15 days). In 3.0T magnetic resonance image at 3 months, we showed the third ventriculostomy. There have been no treatment failures or complications.

Conclusion:
It is possible to effectively perform the frameless radiosurgical third ventriculostomy without associated morbidity in the short term.
A rare cystic lymphoplasmacyte-rich meningioma: A case report and review of the literature
Authors: Firdaus Muhammad , Gill Arwinder S. , Andriani Rini , Cahyanti Dian , Yunti Maria R. , Faried Ahmad 
Published: November 2020

Background:
Meningiomas are common central nervous system neoplasms, accounts for 30% of all primary intracranial neoplasms; the occurrence of meningiomas with cystic lesions is an exceptionally rare. Lymphoplasmacyte-rich meningioma (LPRM) is a rare pathological entity belong to the World Health Organization Grade I meningiomas. LPRM is characterized by abundant lymphoplasmacytic infiltrates which over-shadow the underlying meningothelial component.

Case Description:
A 42-year-old male was admitted to our hospital with a chronic headache for about 3 weeks prior to admission. His symptoms worsen, and subsequently, he experienced left extremities weakness about 1 week before admission. His brain magnetic resonance imaging revealed an irregular and heterogeneously enhancing solid lesion with intratumoral cystic changes at the temporal lobe. A gross total resection was performed; pathological examination revealed a cystic LPRM.

Conclusion:
This rare variant of meningioma is a benign tumor entity featured with massive inflammatory cell infiltration and often less proportion of meningothelial elements. Surgical resection remains the treatment of choice. This is the first report regarding cystic LPRM from Indonesia; we also summarized relevant literature upto-date, May 2020, reported LPRM cases.
Uji Efek Analgetika Ekstrak Etanol Buah Ceplukan (Physalis angulata L.) pada Mencit Putih Jantan
Authors: Indriani Indriani 
Published: November 2020

Salah satu tumbuhan obat yang digunakan dalam pengobatan tradisional adalah ceplukan (Physalis angulata L.). Penelitian ini bertujuan untuk mengetahui efek analgetik dan seberapa besar daya analgetik ekstrak etanol buah ceplukan dibanding parasetamol. Penelitian ini menggunakan metode geliat dengan rangsangan kimia asam asetat dari hewan uji selama 60 menit. Hewan uji yang digunakan adalah mencit putih jantan galur DDI, yang berumur 8-12 minggu, dengan berat badan 20-30 gram sebanyak 50 ekor yang dibagi menjadi 5 kelompok perlakuan. Kelompok I sebagai kontrol negatif diberi aquadest, kelompok II sebagai kontrol positif diberi parasetamol dosis 65 mg/kg BB, kelompok III, IV dan V diberi ekstrak etanol buah ceplukan (Physalis angulata L.) dengan dosis 0,03 g/kg BB; 0,1 g/kg BB dan 0,3 g/kg BB secara peroral. Setelah diberi perlakuan, hewan uji didiamkan selama 5 menit, setelah itu disuntik dengan asam asetat 0,5 % v/v dosis 50 mg/kg BB secara intraperitoneal sebagai rangsang nyeri. Kemudian diamati jumlah kumulatif geliat tiap 5 menit selama 1 jam. Daya analgetika dihitung dengan menggunakan persamaan Hendersoth and Forsaith. Data yang diperoleh kemudian dianalisis statistik menggunakan analisis varian satu jalan (ANAVA) dan dilanjutkan dengan uji-t taraf kepercayaan 95 %. Hasil penelitian menunjukkan bahwa ekstrak etanol buah ceplukan (Physalis angulata L.) mempunyai efek analgetika. Daya analgetika tiap kelompok perlakuan ekstrak etanol buah ceplukan (Physalis angulata L.) berturut-turut adalah kelompok III, IV dan V sebesar 22,20%; 47,84% dan 77,98%. Efek analgetika ekstrak etanol buah ceplukan (Physalis angulata L.) dosis 0,03 g/kg BB (22,20%) dan ekstrak etanol buah ceplukan (Physalis angulata L.) dosis 0,1 g/kg BB (47,84%) lebih kecil daripada parasetamol (66,42%). Ekstrak etanol buah ceplukan (Physalis angulata L.) dosis 0,3 g/kg BB (77,98%) mempunyai efek analgetika yang lebih besar daripada parasetamol (66,42%)