bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Geocaching to Promote Active Aging: Qualitative Study
Authors: Fornasini Silvia , Dianti Marco , Bacchiega Alessandro , Forti Stefano , Conforti Diego
Published: May 2020

Background

Over the past few years, the development of technologies supporting active aging has been increasing. Among the activities that promote physical exercise by using technologies is geocaching—a treasure hunt of sorts in which participants use a receiver GPS to hide or find real or virtual objects. Although this activity is particularly suited to the promotion of healthy lifestyles in older people, geocaching remains to be unexplored in this area.

Objective

This study aimed to investigate the effectiveness of activities combining geocaching and self-tracking technologies to promote active aging, evaluate the usability of technologies, and explore the ways in which technologies have been integrated in the organizational context under examination to determine the user experience of participants.

Methods

A group of individuals aged 65 years and older (N=14) attending a senior center managed by a social cooperative was involved in the study. Some of them created the geocaching contents, and others, split into 2 teams, participated in the game. Each participant was given a pedometer bracelet and the geocaching app. The steps taken by individual participants along with the number of caches found by each group translated into team scores.

Results

The main results of the study were as follows: (1) activities in favor of active aging that involve the use of new technologies can foster the participation of elderly people; in particular, adding gamification to self-tracking can be a valid strategy to promote physical exercise among the elderly; (2) for this to happen, involvement of older people firsthand is crucial, and there must be a focus on their active involvement and empowerment in every phase of the project; and (3) the mediation of conflicts and competition that arise from the gamification could only take place because of the strong support of the organization in the form of social workers.

Conclusions

The results show that promoting active aging through technologies requires more effort than simply using these tools; it requires a wider process that involves an articulated organizational network with heterogeneous actors, technologies, and relations.
Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
Authors: Fernandes Chrystinne , Miles Simon , Lucena Carlos José Pereira
Published: May 2020

Background

Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue.

Objective

This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next.

Methods

We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue.

Results

The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue.

Conclusions

Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.
Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort
Authors: Sufriyana Herdiantri , Wu Yu-Wei , Su Emily Chia-Yu
Published: May 2020

Background

Preeclampsia and intrauterine growth restriction are placental dysfunction–related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from datasets with highly imbalanced classes.

Objective

In this study, we developed a predictive model of PDDs by machine learning that uses features at 24-37 weeks’ gestation, including maternal characteristics, uterine artery (UtA) Doppler measures, soluble fms-like tyrosine kinase receptor-1 (sFlt-1), and placental growth factor (PlGF).

Methods

A public dataset was taken from a prospective cohort study that included pregnant women with PDDs (66/95, 69%) and a control group (29/95, 31%). Preliminary selection of features was based on a statistical analysis using SAS 9.4 (SAS Institute). We used Weka (Waikato Environment for Knowledge Analysis) 3.8.3 (The University of Waikato, Hamilton, NZ) to automatically select the best model using its optimization algorithm. We also manually selected the best of 23 white-box models. Models, including those from recent studies, were also compared by interval estimation of evaluation metrics. We used the Matthew correlation coefficient (MCC) as the main metric. It is not overoptimistic to evaluate the performance of a prediction model developed from a dataset with a class imbalance. Repeated 10-fold cross-validation was applied.

Results

The classification via regression model was chosen as the best model. Our model had a robust MCC (.93, 95% CI .87-1.00, vs .64, 95% CI .57-.71) and specificity (100%, 95% CI 100-100, vs 90%, 95% CI 90-90) compared to each metric of the best models from recent studies. The sensitivity of this model was not inferior (95%, 95% CI 91-100, vs 100%, 95% CI 92-100). The area under the receiver operating characteristic curve was also competitive (0.970, 95% CI 0.966-0.974, vs 0.987, 95% CI 0.980-0.994). Features in the best model were maternal weight, BMI, pulsatility index of the UtA, sFlt-1, and PlGF. The most important feature was the sFlt-1/PlGF ratio. This model used an M5P algorithm consisting of a decision tree and four linear models with different thresholds. Our study was also better than the best ones among recent studies in terms of the class balance and the size of the case class (66/95, 69%, vs 27/239, 11.3%).

Conclusions

Our model had a robust predictive performance. It was also developed to deal with the problem of a class imbalance. In the context of clinical management, this model may improve maternal mortality and neonatal morbidity and reduce health care costs.
Implementation of the Operating Room Black Box Research Program at the Ottawa Hospital Through Patient, Clinical, and Organizational Engagement: Case Study
Authors: Boet Sylvain , Etherington Cole , Lam Sandy , Lê Maxime , Proulx Laurie , Britton Meghan , Kenna Julie , Przybylak-Brouillard Antoine , Grimshaw Jeremy , Grantcharov Teodor , Singh Sukhbir
Published: May 2020

Background

A large proportion of surgical patient harm is preventable; yet, our ability to systematically learn from these incidents and improve clinical practice remains limited. The Operating Room Black Box was developed to address the need for comprehensive assessments of clinical performance in the operating room. It captures synchronized audio, video, patient, and environmental clinical data in real time, which are subsequently analyzed by a combination of expert raters and software-based algorithms. Despite its significant potential to facilitate research and practice improvement, there are many potential implementation challenges at the institutional, clinician, and patient level. This paper summarizes our approach to implementation of the Operating Room Black Box at a large academic Canadian center.

Objective

We aimed to contribute to the development of evidence-based best practices for implementing innovative technology in the operating room for direct observation of the clinical performance by using the case of the Operating Room Black Box. Specifically, we outline the systematic approach to the Operating Room Black Box implementation undertaken at our center.

Methods

Our implementation approach included seeking support from hospital leadership; building frontline support and a team of champions among patients, nurses, anesthesiologists, and surgeons; accounting for stakeholder perceptions using theory-informed qualitative interviews; engaging patients; and documenting the implementation process, including barriers and facilitators, using the consolidated framework for implementation research.

Results

During the 12-month implementation period, we conducted 23 stakeholder engagement activities with over 200 participants. We recruited 10 clinician champions representing nursing, anesthesia, and surgery. We formally interviewed 15 patients and 17 perioperative clinicians and identified key themes to include in an information campaign run as part of the implementation process. Two patient partners were engaged and advised on communications as well as grant and protocol development. Many anticipated and unanticipated challenges were encountered at all levels. Implementation was ultimately successful, with the Operating Room Black Box installed in August 2018, and data collection beginning shortly thereafter.

Conclusions

This paper represents the first step toward evidence-guided implementation of technologies for direct observation of performance for research and quality improvement in surgery. With technology increasingly being used in health care settings, the health care community should aim to optimize implementation processes in the best interest of health care professionals and patients.
Exploring the Relationship Between Internet Use and Mental Health Among Older Adults in England: Longitudinal Observational Study
Authors: Lam Sabrina Sze Man , Jivraj Stephen , Scholes Shaun
Published: May 2020

Background

There is uncertainty about the impact of internet use on mental health in older adults. Moreover, there is very little known specifically about the impact of particular purposes of internet use.

Objective

This study aims to investigate the longitudinal relationship between two distinct concepts of mental health with the frequency of internet use among older adults: the moderating role of socioeconomic position (SEP) and the association between specific purposes of internet use.

Methods

Longitudinal fixed and random effects (27,507 person-years) models were fitted using waves 6-8 of the English Longitudinal Study of Ageing to examine the relationship between different aspects of internet use (frequency and purpose) and two mental health outcomes (depression and life satisfaction). The potential moderating effect of SEP on these associations was tested using interaction terms.

Results

Infrequent internet use (monthly or less vs daily) was predictive of deteriorating life satisfaction (β=−0.512; P=.02) but not depression. Education and occupational class had a moderating effect on the association between frequency of internet use and mental health. The associations were stronger in the highest educational group in both depression (P=.09) and life satisfaction (P=.02), and in the highest occupational group in life satisfaction (P=.05) only. Using the internet for communication was associated with lower depression (β=−0.24; P=.002) and better life satisfaction (β=.97; P<.001), whereas those using the internet for information access had worse life satisfaction (β=−0.86; P<.001) compared with those who did not.

Conclusions

Policies to improve mental health in older adults should encourage internet use, especially as a tool to aid communication.
Determining Factors Affecting Nurses’ Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data
Authors: Ho Kuei-Fang , Chang Pi-Chen , Kurniasari Maria Dyah , Susanty Sri , Chung Min-Huey
Published: May 2020

Background

Health information technology is used in nursing practice worldwide, and holistic patient care planning can serve as a guide for nursing practice to ensure quality in patient-centered care. However, few studies have thoroughly analyzed users’ acceptance of care plan systems to establish individual plans.

Objective

Based on the technology acceptance model 3 (TAM3), a user technology acceptance model was established to explore what determines the acceptance of care plan systems by users in clinical settings.

Methods

Cross-sectional quantitative data were obtained from 222 nurses at eight hospitals affiliated with public organizations in Taiwan. Using the modified TAM3, the collected data were employed to analyze the determinants of user acceptance of a care plan system through structural equation modeling (SEM). We also employed moderated multiple regression analysis and partial least squares–SEM to test the moderating effects.

Results

We verified all significant effects from the use of a care plan system among bivariate patterns in the modified TAM3, except for moderating effects. Our results revealed that the determinants of perceived usefulness and perceived ease of use significantly influenced perceived usefulness and perceived ease of use, respectively. The results also indicated that nurses’ perceptions of subjective norm (path coefficient=.25, P<.001), perceived ease of use (path coefficient=.32, P<.001), and perceived usefulness (path coefficient=.31, P<.001) had significantly positive effects on their behavioral intention to use the care plan system, accounting for 69% of the total explained variance.

Conclusions

By exploring nurses’ acceptance of a care plan system, this study revealed relationships among the variables in TAM3. Our results confirm that the modified TAM3 is an innovative assessment instrument that can help managers understand nurses’ acceptance of health information technology in nursing practice to enhance the adoption of health information technology.
Economic Advantages of Telehealth and Virtual Health Practitioners: Return on Investment Analysis
Authors: Snoswell Centaine L , North John B , Caffery Liam J
Published: May 2020

Background

Telehealth is a disruptive modality that challenges the traditional model of having a clinician or patient physically present for an appointment. The benefit is that it offers the opportunity to redesign the way services are offered. For instance, a virtual health practitioner can provide videoconference consultations while being located anywhere in the world that has internet. A virtual health practitioner also obviates the issues of attracting a specialist medical workforce to rural areas, and allows the rural health service to control the specialist services that they offer.

Objective

The aim of this research was to evaluate the economic effects of 3 different models of care on rural and metropolitan hospital sites. The models of care examined were patient travel, telehealth using videoconferencing, and employment of a virtual health practitioner by a rural site.

Methods

Using retrospective activity data for 3 years, a return on investment (ROI) analysis was undertaken from the perspective of a rural site and metropolitan partner site using a telehealth orthopedic fracture clinic as an example. Further analysis was conducted to calculate the number of patients that would be required to attend the clinic in each model of care for the sites to break even.

Results

The only service model that resulted in a positive ROI for the rural site over the 3-year period was the virtual health practitioner model. The breakeven analysis demonstrated that the rural site required the lowest number of patients to recoup costs in the virtual health practitioner model of care. The rural site was unable to recoup its costs within the travel model due to the lack of opportunity for reimbursement for services and the requirement to cover the cost of travel for patients.

Conclusions

Our model demonstrated that rural health care providers can increase their ROI by employing a virtual health practitioner.
Evaluation of the Tobbstop Mobile App for Smoking Cessation: Cluster Randomized Controlled Clinical Trial
Authors: Pallejà-Millán Meritxell , Rey-Reñones Cristina , Barrera Uriarte Maria Luisa , Granado-Font Esther , Basora Josep , Flores-Mateo Gemma , Duch Jordi
Published: May 2020

Background

Mobile apps provide an accessible way to test new health-related methodologies. Tobacco is still the primary preventable cause of death in industrialized countries, constituting an important public health issue. New technologies provide novel opportunities that are effective in the cessation of smoking tobacco.

Objective

This paper aims to evaluate the efficacy and usage of a mobile app for assisting adult smokers to quit smoking.

Methods

We conducted a cluster randomized clinical trial. We included smokers older than 18 years who were motivated to stop smoking and used a mobile phone compatible with our mobile app. We carried out follow-up visits at 15, 30, and 45 days, and at 2, 3, 6, and 12 months. Participants of the intervention group had access to the Tobbstop mobile app designed by the research team. The primary outcomes were continuous smoking abstinence at 3 and 12 months.

Results

A total of 773 participants were included in the trial, of which 602 (77.9%) began the study on their D-Day. Of participants in the intervention group, 34.15% (97/284) did not use the app. The continuous abstention level was significantly larger in the intervention group participants who used the app than in those who did not use the app at both 3 months (72/187, 38.5% vs 13/97, 13.4%; P<.001) and 12 months (39/187, 20.9% vs 8/97, 8.25%; P=.01). Participants in the intervention group who used the app regularly and correctly had a higher probability of not being smokers at 12 months (OR 7.20, 95% CI 2.14-24.20; P=.001) than the participants of the CG.

Conclusions

Regular use of an app for smoking cessation is effective in comparison with standard clinical practice.

Trial Registration

Clinicaltrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421
Characteristics of Neuropsychiatric Mobile Health Trials: Cross-Sectional Analysis of Studies Registered on ClinicalTrials.gov
Authors: Minen Mia Tova , Reichel Julia Frederica , Pemmireddy Pallavi , Loder Elizabeth , Torous John
Published: May 2020

Background

The development of mobile health (mHealth) technologies is progressing at a faster pace than that of the science to evaluate their validity and efficacy. Under the International Committee of Journal Medical Editors (ICMJE) guidelines, clinical trials that prospectively assign people to interventions should be registered with a database before the initiation of the study.

Objective

The aim of this study was to better understand the smartphone mHealth trials for high-burden neuropsychiatric conditions registered on ClinicalTrials.gov through November 2018, including the number, types, and characteristics of the studies being conducted; the frequency and timing of any outcome changes; and the reporting of results.

Methods

We conducted a systematic search of ClinicalTrials.gov for the top 10 most disabling neuropsychiatric conditions and prespecified terms related to mHealth. According to the 2016 World Health Organization Global Burden of Disease Study, the top 10 most disabling neuropsychiatric conditions are (1) stroke, (2) migraine, (3) major depressive disorder, (4) Alzheimer disease and other dementias, (5) anxiety disorders, (6) alcohol use disorders, (7) opioid use disorders, (8) epilepsy, (9) schizophrenia, and (10) other mental and substance use disorders. There were no date, location, or status restrictions.

Results

Our search identified 135 studies. A total of 28.9% (39/135) of studies evaluated interventions for major depressive disorder, 14.1% (19/135) of studies evaluated interventions for alcohol use disorders, 12.6% (17/135) of studies evaluated interventions for stroke, 11.1% (15/135) of studies evaluated interventions for schizophrenia, 8.1% (11/135) of studies evaluated interventions for anxiety disorders, 8.1% (11/135) of studies evaluated interventions for other mental and substance use disorders, 7.4% (10/135) of studies evaluated interventions for opioid use disorders, 3.7% (5/135) of studies evaluated interventions for Alzheimer disease or other dementias, 3.0% (4/135) of studies evaluated interventions for epilepsy, and 3.0% (4/135) of studies evaluated interventions for migraine. The studies were first registered in 2008; more than half of the studies were registered from 2016 to 2018. A total of 18.5% (25/135) of trials had results reported in some publicly accessible location. Across all the studies, the mean estimated enrollment (reported by the study) was 1078, although the median was only 100. In addition, across all the studies, the actual reported enrollment was lower, with a mean of 249 and a median of 80. Only about a quarter of the studies (35/135, 25.9%) were funded by the National Institutes of Health.

Conclusions

Despite the increasing use of health-based technologies, this analysis of ClinicalTrials.gov suggests that only a few apps for high-burden neuropsychiatric conditions are being clinically evaluated in trials.
Perceptions of Home Telemonitoring Use Among Patients With Chronic Obstructive Pulmonary Disease: Qualitative Study
Authors: Lundell Sara , Modig Mari , Holmner Åsa , Wadell Karin
Published: May 2020

Background

Chronic obstructive pulmonary disease (COPD) is a major health problem and an economic burden globally. There is growing interest in how electronic health (eHealth) can be used to provide efficient health care. Telemonitoring, where the patient’s health-related data is transmitted to a health care provider, can be used to detect early signs of exacerbations. A successful implementation of telemonitoring systems into clinical practice requires in-depth knowledge of the users’ preferences.

Objective

The aim of this study was to explore perceptions of the use of a home telemonitoring system among patients with COPD.

Methods

Semistructured individual interviews were carried out with 8 women and 5 men who were participants in a project aimed at developing and evaluating a telemonitoring system. The web-based telemonitoring system measured pulmonary function, subjective symptoms, and oxygen saturation. Participants were interviewed after having used the system for 2-4 months. Interview transcripts were analyzed with qualitative content analysis.

Results

The analysis resulted in the theme A transition toward increased control and security and four categories: using with (in)security, affecting technical concern or confidence, providing easy access to health care, and increasing control over the disease. The participants reported various perceptions of using the telemonitoring system. They expressed initial feelings of insecurity, both in terms of operating the system and in terms of their disease. However, the practical management of the telemonitoring system became easier with time; the participants gradually gained confidence and improved their self-management. New technology was perceived as an important complement to existing health care, but the importance of maintaining a human contact in real life or through the telemonitoring system was emphasized.

Conclusions

This study captured a transition among the participants from being insecure and experiencing technical concerns to acquiring technical confidence and improving disease management. Telemonitoring can be a valuable complement to health care, leading to increased self-knowledge, a sense of security, and improved self-management. Suggestions to improve the further development and implementation of telemonitoring systems include better patient education and the involvement of end users in the technical development process. Additional research is needed, particularly in the design of user-friendly systems, as well as in developing tools to predict which patients are most likely to find the equipment useful, as this may result in increased empowerment, improved quality of life, reduced costs, and a contribution to equity in health.
Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping
Authors: Horne Elsie , Tibble Holly , Sheikh Aziz , Tsanas Athanasios
Published: May 2020

Background

In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective

This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Methods

We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.

Results

Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.

Conclusions

This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.
A Retrospective Analysis of Provider-to-Patient Secure Messages: How Much Are They Increasing, Who Is Doing the Work, and Is the Work Happening After Hours?
Authors: North Frederick , Luhman Kristine E , Mallmann Eric A , Mallmann Toby J , Tulledge-Scheitel Sidna M , North Emily J , Pecina Jennifer L
Published: May 2020

Background

Patient portal registration and the use of secure messaging are increasing. However, little is known about how the work of responding to and initiating patient messages is distributed among care team members and how these messages may affect work after hours.

Objective

This study aimed to examine the growth of secure messages and determine how the work of provider responses to patient-initiated secure messages and provider-initiated secure messages is distributed across care teams and across work and after-work hours.

Methods

We collected secure messages sent from providers from January 1, 2013, to March 15, 2018, at Mayo Clinic, Rochester, Minnesota, both in response to patient secure messages and provider-initiated secure messages. We examined counts of messages over time, how the work of responding to messages and initiating messages was distributed among health care workers, messages sent per provider, messages per unique patient, and when the work was completed (proportion of messages sent after standard work hours).

Results

Portal registration for patients having clinic visits increased from 33% to 62%, and increasingly more patients and providers were engaged in messaging. Provider message responses to individual patients increased significantly in both primary care and specialty practices. Message responses per specialty physician provider increased from 15 responses per provider per year to 53 responses per provider per year from 2013 to 2018, resulting in a 253% increase. Primary care physician message responses increased from 153 per provider per year to 322 from 2013 to 2018, resulting in a 110% increase. Physicians, nurse practitioners, physician assistants, and registered nurses, all contributed to the substantial increases in the number of messages sent.

Conclusions

Provider-sent secure messages at a large health care institution have increased substantially since implementation of secure messaging between patients and providers. The effort of responding to and initiating messages to patients was distributed across multiple provider categories. The percentage of message responses occurring after hours showed little substantial change over time compared with the overall increase in message volume.
Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies
Authors: Tan Choon Han , Kyaw Bhone Myint , Smith Helen , Tan Colin S , Tudor Car Lorainne
Published: May 2020

Background

Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods.

Objective

This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.

Methods

We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines.

Results

In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225.

Conclusions

We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.
Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study
Authors: Rawtaer Iris , Mahendran Rathi , Kua Ee Heok , Tan Hwee Pink , Tan Hwee Xian , Lee Tih-Shih , Ng Tze Pin
Published: May 2020

Background

Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.

Objective

The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.

Methods

We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered.

Results

Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system.

Conclusions

We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.
Expression of Genes Related to Lipid Handling and the Obesity Paradox in Melanoma: Database Analysis
Authors: Giampietri Claudia , Tomaipitinca Luana , Scatozza Francesca , Facchiano Antonio
Published: May 2020

Background

Publicly available genomic and transcriptomic data in searchable databases allow researchers to investigate specific medical issues in thousands of patients. Many studies have highlighted the role lipids play in cancer initiation and progression and reported nutritional interventions aimed at improving prognosis and survival. Therefore, there is an increasing interest in the role that fat intake may play in cancer. It is known that there is a relationship between BMI and survival in patients with cancer, and that there is an association between a high-fat diet and increased cancer risk. In some cancers, such as colorectal cancer, obesity and high fat intake are known to increase the risk of cancer initiation and progression. On the contrary, in patients undergoing treatment for melanoma, a higher BMI unexpectedly acts as a protective factor rather than a risk factor; this phenomenon is known as the obesity paradox.

Objective

We aimed to identify the molecular mechanism underlying the obesity paradox, with the expectation that this could indicate new effective strategies to reduce risk factors and improve protective approaches.

Methods

In order to determine the genes potentially involved in this process, we investigated the expression values of lipid-related genes in patients with melanoma or colorectal cancer. We used available data from 2990 patients from 3 public databases (IST [In Silico Transcriptomics] Online, GEO [Gene Expression Omnibus], and Oncomine) in an analysis that involved 3 consecutive validation steps. Of this group, data from 1410 individuals were analyzed in the IST Online database (208 patients with melanoma and 147 healthy controls, as well as 991 patients with colorectal cancer and 64 healthy controls). In addition, 45 melanoma, 18 nevi, and 7 healthy skin biopsies were analyzed in another database, GEO, to validate the IST Online data. Finally, using the Oncomine database, 318 patients with melanoma (312 controls) and 435 patients with colorectal cancer (445 controls) were analyzed.

Results

In the first and second database investigated (IST Online and GEO, respectively), patients with melanoma consistently showed significantly (P<.001) lower expression levels of 4 genes compared to healthy controls: CD36, MARCO, FABP4, and FABP7. This strong reduction was not observed in patients with colorectal cancer. An additional analysis was carried out on a DNA-TCGA data set from the Oncomine database, further validating CD36 and FABP4.

Conclusions

The observed lower expression of genes such as CD36 and FABP4 in melanoma may reduce the cellular internalization of fat and therefore make patients with melanoma less sensitive to a high dietary fat intake, explaining in part the obesity paradox observed in patients with melanoma.
Mobile App for Monitoring 3-Month Postoperative Functional Outcome After Hip Fracture: Usability Study
Authors: Geerds Merle A J , Nijmeijer Wieke S , Hegeman J H , Vollenbroek-Hutten Miriam M R
Published: May 2020

Background

As a result of an aging population, there has been an increasing incidence of hip fractures worldwide. In the Netherlands, in order to improve the quality of care for elderly patients with hip fractures, the multidisciplinary Centre for Geriatric Traumatology was established in 2008 at the Department of Trauma Surgery at Ziekenhuisgroep Twente hospital (located in Almelo and Hengelo in the Netherlands).

Objective

Though the Dutch Hip Fracture audit is used to monitor the quality of care for patients with fractures of the hip, only 30.7% of patients complete registration in the 3-month follow-up period. Mobile apps offer an opportunity for improvement in this area. The aim of this study was to investigate the usability and acceptance of a mobile app for gathering indicators of quality of care in a 3-month follow-up period after postoperative treatment of hip fracture.

Methods

From July 2017 to December 2017, patients who underwent surgical treatment for hip fracture were recruited. Patients and caregivers, who were collectively considered the participant cohort, were asked to download the app and answer a questionnaire. Participants were divided into two groups—those who downloaded the app and those who did not download the app. A telephone interview that was based upon the Unified Theory of Acceptance and Use of Technology was conducted with a subset of participants from each group (1:1 ratio). This study was designated as not being subject to the Dutch Medical Research Involving Human Subjects Act according to the appropriate medical research ethics committees.

Results

Of the patients and caregivers who participated, 26.4% (29/110) downloaded the app, whereas 73.6% (81/110) did not. Telephone interviews with the subset of participants (n=24 per group) revealed that 54.0% (13/24) of the group of participants who did not download the app had forgotten the study. Among the group who downloaded the app, 95.8% (23/24) had the intention of completing the questionnaire, but only 4.2% (1/24) did so. The reasons for not completing the questionnaire included technical problems, cognitive disorders, or patient dependency on caregivers. Most participants in the group who downloaded the app self-reported a high level of expertise in using a smartphone (22/24, 91.7%), and sufficient facilitating conditions for using a smartphone were self-reported in both groups (downloaded the app: 23/24, 95.8%; did not download the app: 21/24, 87.5%), suggesting that these factors were not barriers to completion.

Conclusions

Despite self-reported intention to use the app, smartphone expertise, and sufficient facilitating conditions for smartphone use, implementation of the mobile app was infeasible for daily practice. This was due to a combination of technical problems, factors related to the implementation process, and the population of interest having cognitive disorders or a dependency on caregivers for mobile technology.
An Algorithm (LaD) for Monitoring Childbirth in Settings Where Tracking All Parameters in the World Health Organization Partograph Is Not Feasible: Design and Expert Validation
Authors: Balikuddembe Michael S , Wakholi Peter K , Tumwesigye Nazarius M , Tylleskar Thorkild
Published: May 2020

Background

After determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts’ suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts.

Objective

The objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making.

Methods

The LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts’ selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm.

Results

The mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91.

Conclusions

The LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.
The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study
Authors: Nguyen Thu T , Adams Nikki , Huang Dina , Glymour M Maria , Allen Amani M , Nguyen Quynh C
Published: May 2020

Background

In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level.

Objective

This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes.

Methods

We utilized Twitter’s Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ≤2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models.

Results

The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities.

Conclusions

A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people.