bluuebunny/crossref_metadata_2025_split huggingface dataset:
Continuous 7-Month Internet of Things–Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study
Authors: Saarikko Johanna , Niela-Vilen Hannakaisa , Ekholm Eeva , Hamari Lotta , Azimi Iman , Liljeberg Pasi , Rahmani Amir M , Löyttyniemi Eliisa , Axelin Anna 
Published: May 2020

Background
Monitoring during pregnancy is vital to ensure the mother’s and infant’s health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments.

Objective
This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)–based monitoring system.

Methods
This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period.

Results
Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (P<.001). The decrease continued by a mean of 1339 (95% CI 474-2205) steps to the postpartum period (P=.004). Sleep during pregnancy also decreased from the second trimester to the third trimester by a mean of 20 minutes (95% CI –0.7 to 42 minutes; P=.06) and sleep time shortened an additional 1 hour (95% CI 39 minutes to 1.5 hours) after delivery (P<.001). The mean resting heart rate increased toward the third trimester and returned to the early pregnancy level during the postpartum period.

Conclusions
The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up.
Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques
Authors: Wang Max , Ge Wenbo , Apthorp Deborah , Suominen Hanna 
Published: May 2020

Background
Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications.

Objective
This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set.

Methods
We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold.

Results
We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%.

Conclusions
The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.
Efficiency of Text Message Contact on Medical Safety in Outpatient Surgery: Retrospective Study
Authors: Peuchot Jeremy , Allard Etienne , Dureuil Bertrand , Veber Benoit , Compère Vincent 
Published: May 2020

Background
Establishing pre- and postoperative contact with patients is part of successful medical management in outpatient surgery. In France, this is mostly done via telephone. Automated information with short message service (SMS) reminders might be an interesting alternative to increase the rate of compliance with preoperative instructions, but no study has shown the safety of this approach.

Objective
The objective of this study was to evaluate the impact of pre- and postoperative automated information with SMS reminders on medical safety in outpatient surgery.

Methods
We conducted a retrospective, single-center, nonrandomized, controlled study with a before-after design. All adult patients who had outpatient surgery between September 2016 and December 2017 in our university hospital center were included. Before April 2017, patients were contacted by telephone by an outpatient surgery nurse. After April 2017, patients were contacted by SMS reminder. All patients were contacted the day before and the day after surgery. Patients contacted by SMS reminder were also contacted on day 7 after surgery. The primary end point was the conversion rate to full-time hospitalization. Secondary end points were hospitalization causes (anesthetic, surgical, organizational) and hospitalization costs.

Results
A total of 4388 patients were included, 2160 before and 2228 after the introduction of SMS reminders. The conversion rate to full-time hospitalization was 34/4388 (0.77%) with a difference between SMS group (8/2228, 0.36%) and telephone group (26/2160, 1.20%). The cost of SMS reminders was estimated as half that of telephone calls.

Conclusions
In this work, we report a decrease in the rate of conversion to full-time hospitalization with the use of pre- and postoperative SMS reminders. This new approach could represent a safe and cost-effective method in an outpatient surgery setting.
Monitoring Occupational Sitting, Standing, and Stepping in Office Employees With the W@W-App and the MetaWearC Sensor: Validation Study
Authors: Bort-Roig Judit , Chirveches-Pérez Emilia , Garcia-Cuyàs Francesc , Dowd Kieran P , Puig-Ribera Anna 
Published: May 2020

Background
Replacing occupational sitting time with active tasks has several proposed health benefits for office employees. Mobile phones and motion sensors can provide objective information in real time on occupational sitting behavior. However, the validity and feasibility of using mobile health (mHealth) devices to quantify and modify occupational sedentary time is unclear.

Objective
The aim of this study is to validate the new Walk@Work-Application (W@W-App)—including an external motion sensor (MetaWearC) attached to the thigh—for measuring occupational sitting, standing, and stepping in free-living conditions against the activPAL3M, the current gold-standard, device-based measure for postural behaviors.

Methods
In total, 20 office workers (16 [80%] females; mean age 39.5, SD 8.1 years) downloaded the W@W-App to their mobile phones, wore a MetaWearC sensor attached to their thigh using a tailored band, and wore the activPAL3M for 3-8 consecutive working hours. Differences between both measures were examined using paired-samples t tests and Wilcoxon signed-rank tests. Agreement between measures was examined using concordance correlation coefficients (CCCs), 95% CIs, Bland-Altman plots (mean bias, 95% limits of agreement [LoA]), and equivalence testing techniques.

Results
The median recording time for the W@W-App+MetaWearC and the activPAL3M was 237.5 (SD 132.8) minutes and 240.0 (SD 127.5) minutes, respectively (P<.001). No significant differences between sitting (P=.53), standing (P=.12), and stepping times (P=.61) were identified. The CCC identified substantial agreement between both measures for sitting (CCC=0.98, 95% CI 0.96-0.99), moderate agreement for standing (CCC=0.93, 95% CI 0.81-0.97), and poor agreement for stepping (CCC=0.74, 95% CI 0.47-0.88). Bland-Altman plots indicated that sitting time (mean bias –1.66 minutes, 95% LoA –30.37 to 20.05) and standing time (mean bias –4.85 minutes, 95% LoA –31.31 to 21.62) were underreported. For stepping time, a positive mean bias of 1.15 minutes (95% LoA –15.11 to 17.41) was identified. Equivalence testing demonstrated that the estimates obtained from the W@W-App+MetaWearC and the activPAL3M were considered equivalent for all variables excluding stepping time.

Conclusions
The W@W-App+MetaWearC is a low-cost tool with acceptable levels of accuracy that can objectively quantify occupational sitting, standing, stationary, and upright times in real time. Due to the availability of real-time feedback for users, this tool can positively influence occupational sitting behaviors in future interventions.

Trial Registration
ClinicalTrials.gov NCT04092738; https://clinicaltrials.gov/ct2/show/NCT04092738
Effectiveness of Wearable Trackers on Physical Activity in Healthy Adults: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Tang Matilda Swee Sun , Moore Katherine , McGavigan Andrew , Clark Robyn A , Ganesan Anand N 
Published: May 2020

Background
Wearable trackers are an increasingly popular tool among healthy adults and are used to facilitate self-monitoring of physical activity.

Objective
We aimed to systematically review the effectiveness of wearable trackers for improving physical activity and weight reduction among healthy adults.

Methods
This review used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and reporting criteria. English-language randomized controlled trials with more than 20 participants from MEDLINE, CINAHL, Cochrane Library, Web of Science, PubMed, and Scopus (2000-2017) were identified. Studies were eligible for inclusion if they reported an intervention group using wearable trackers, reporting steps per day, total moderate-to-vigorous physical activity, activity, physical activity, energy expenditure, and weight reduction.

Results
Twelve eligible studies with a total of 1693 participants met the inclusion criteria. The weighted average age was 40.7 years (95% CI 31.1-50.3), with 64.4% women. The mean intervention duration was 21.4 weeks (95% CI 6.1-36.7). The usage of wearable trackers was associated with increased physical activity (standardized mean difference 0.449, 95% CI 0.10-0.80; P=.01). In the subgroup analyses, however, wearable trackers demonstrated no clear benefit for physical activity or weight reduction.

Conclusions
These data suggest that the use of wearable trackers in healthy adults may be associated with modest short-term increases in physical activity. Further data are required to determine if a sustained benefit is associated with wearable tracker usage.
Combination of 3-Dimensional Virtual Reality and Hands-On Aromatherapy in Improving Institutionalized Older Adults’ Psychological Health: Quasi-Experimental Study
Authors: Cheng Vivian Ya-Wen , Huang Chiu-Mieh , Liao Jung-Yu , Hsu Hsiao-Pei , Wang Shih-Wen , Huang Su-Fei , Guo Jong-Long 
Published: May 2020

Background
In Taiwan, which has one of the most rapidly aging populations in the world, it is becoming increasingly critical to promote successful aging strategies that are effective, easily usable, and acceptable to institutionalized older adults. Although many practitioners and professionals have explored aromatherapy and identified its psychological benefits, the effectiveness of combining 3-dimensional (3D) virtual reality and hands-on aromatherapy remains unknown.

Objective
A quasi-experimental trial was designed to evaluate the effectiveness of this combination in lowering perceived stress and promoting happiness, sleep quality, meditation experience, and life satisfaction among institutionalized older adults in Taiwan.

Methods
A total of 60 institutionalized elderly participants either received the combined intervention or were in a control group. Weekly 2-hour sessions were implemented over 9 weeks. The outcome variables were happiness, perceived stress, sleep quality, meditation experience, and life satisfaction, which were assessed at baseline and after the intervention.

Results
Generalized estimating equation (GEE) analyses indicated that the experimental group showed significant post-intervention improvements in terms of scores for happiness, perceived stress, sleep quality, meditation experience, and life satisfaction (n=48; all P<.001). Another GEE analysis showed that the significant improvements in the 5 outcome variables persisted in participants aged 80 years and older (n=35; all P<.001).

Conclusions
This is the first trial to explore the effectiveness of a combination of 3D virtual reality and hands-on aromatherapy in improving older adults’ psychological health. The results are promising for the promotion of psychological health in institutionalized older adults.

Trial Registration
ClinicalTrials.gov NCT04324216; https://clinicaltrials.gov/ct2/show/NCT04324216.
Ecological Momentary Assessment Within a Digital Health Intervention for Reminiscence in Persons With Dementia and Caregivers: User Engagement Study
Authors: Potts Courtney , Bond Raymond , Ryan Assumpta , Mulvenna Maurice , McCauley Claire , Laird Elizabeth , Goode Deborah 
Published: May 2020

BackgroundUser-interaction event logs provide rich and large data sets that can provide valuable insights into how people engage with technology. Approaches such as ecological momentary assessment (EMA) can be used to gather accurate real-time data in an individual’s natural environment by asking questions at any given instant.ObjectiveThe purpose of this study was to evaluate user engagement and responses to EMA questions using InspireD, an app used for reminiscence by persons with dementia and their caregivers. Research findings can be used to inform EMA use within digital health interventions.MethodsA feasibility trial was conducted in which participants (n=56) used the InspireD app over a 12-week period. Participants were a mean age of 73 (SD 13) and were either persons with dementia (n=28) or their caregivers (n=28). Questions, which they could either answer or choose to dismiss, were presented to participants at various instants after reminiscence with personal or generic photos, videos, and music. Presentation and dismissal rates for questions were compared by hour of the day and by trial week to investigate user engagement.ResultsOverall engagement was high, with 69.1% of questions answered when presented. Questions that were presented in the evening had the lowest dismissal rate; the dismissal rate for questions presented at 9 PM was significantly lower than the dismissal rate for questions presented at 11 AM (9 PM: 10%; 11 AM: 50%; χ21=21.4, P<.001). Questions asked following reminiscence with personal media, especially those asked after personal photos, were less likely to be answered compared to those asked after other media. In contrast, questions asked after the user had listened to generic media, in particular those asked after generic music, were much more likely to be answered.ConclusionsThe main limitation of our study was the lack of generalizability of results to a larger population given the quasi-experimental design and older demographic where half of participants were persons with dementia; however, this study shows that older people are willing to participate and engage in EMA. Based on this study, we propose a series of recommendations for app design to increase user engagement with EMA. These include presenting questions no more than once per day, after 8 PM in the evening, and only if the user is not trying to complete a task within the app.
Managing Illicit Online Pharmacies: Web Analytics and Predictive Models Study
Authors: Zhao Hui , Muthupandi Sowmyasri , Kumara Soundar 
Published: May 2020

Background
Online pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily).

Objective
This study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs.

Methods
We first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor.

Results
We found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM).

Conclusions
Our prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.
Wikipedia in Vascular Surgery Medical Education: Comparative Study
Authors: Yacob Michael , Lotfi Shamim , Tang Shannon , Jetty Prasad 
Published: May 2020

Background
Medical students commonly refer to Wikipedia as their preferred online resource for medical information. The quality and readability of articles about common vascular disorders on Wikipedia has not been evaluated or compared against a standard textbook of surgery.

Objective
The aims of this study were to (1) compare the quality of Wikipedia articles to that of equivalent chapters in a standard undergraduate medical textbook of surgery, (2) identify any errors of omission in either resource, and (3) compare the readability of both resources using validated ease-of-reading and grade-level tools.

Methods
Using the Medical Council of Canada Objectives for the Qualifying Examination, 8 fundamental topics of vascular surgery were chosen. The articles were found on Wikipedia using Wikipedia’s native search engine. The equivalent chapters were identified in Schwartz Principles of Surgery (ninth edition). Medical learners (n=2) assessed each of the texts on their original platforms to independently evaluate readability, quality, and errors of omission. Readability was evaluated with Flesch Reading Ease scores and 5 grade-level scores (Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure of Gobbledygook Index, and Automated Readability Index), quality was evaluated using the DISCERN instrument, and errors of omission were evaluated using a standardized scoring system that was designed by the authors.

Results
Flesch Reading Ease scores suggested that Wikipedia (mean 30.5; SD 8.4) was significantly easier to read (P=.03) than Schwartz (mean 20.2; SD 9.0). The mean grade level (calculated using all grade-level indices) of the Wikipedia articles (mean 14.2; SD 1.3) was significantly different (P=.02) than the mean grade level of Schwartz (mean 15.9; SD 1.4). The quality of the text was also assessed using the DISCERN instrument and suggested that Schwartz (mean 71.4; SD 3.1) had a significantly higher quality (P=.002) compared to that of Wikipedia (mean 52.9; SD 11.4). Finally, the Wikipedia error of omission rate (mean 12.5; SD 6.8) was higher than that of Schwartz (mean 21.3; SD 1.9) indicating that there were significantly fewer errors of omission in the surgical textbook (P=.008).

Conclusions
Online resources are increasingly easier to access but can vary in quality. Based on this comparison, the authors of this study recommend the use of vascular surgery textbooks as a primary source of learning material because the information within is more consistent in quality and has fewer errors of omission. Wikipedia can be a useful resource for quick reference, particularly because of its ease of reading, but its vascular surgery articles require further development.