The Six Pillars of Lifestyle Medicine in Managing Noncommunicable Diseases - The Gaps in Current Guidelines

BACKGROUND

Noncommunicable diseases (NCDs), also known as chronic diseases that are long-lasting, are considered the major cause of death and disability worldwide, and the six pillars of lifestyle medicine (nutrition, exercise, toxic control, stress management, restorative sleep, and social connection) play an important role in a holistic management of their prevention and treatment. In addition, medical guidelines are the most accepted documents with recommendations to manage NCDs.

OBJECTIVE

The present study aims to analyze the lack of lifestyle pillars concerning the major Brazilian medical guidelines for NCDs and identify evidence in the literature that could justify their inclusion in the documents.

METHOD

Brazilian guidelines were selected according to the most relevant causes of death in Brazil, given by the Mortality Information System, published by the Brazilian Ministry of Health in 2019. Journals were screened in the PUBMED library according to the disease and non-mentioned pillars of lifestyle.

RESULTS

Relevant causes of deaths in Brazil are acute myocardial infarction (AMI), diabetes mellitus (DM), and chronic obstructive pulmonary diseases (COPD). Six guidelines related to these NCDs were identified, and all address aspects of lifestyle, but only one, regarding cardiovascular prevention, highlights all six pillars. Despite this, a literature search involving over 50 articles showed that there is evidence that all the pillars can help control each of these NCDs.

CONCLUSION

Rarely are the six pillars of lifestyle contemplated in Brazilian guidelines for AMI, DM, and COPD. The literature review identified evidence of all lifestyle pillars to offer a holistic approach for the management and prevention of NCDs.

A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

Objective

Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs.

Materials and Methods

We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features.

Results

Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria.

Conclusion

Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

BACKGROUND

Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

OBJECTIVES

This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

METHODS

This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

RESULTS

Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

CONCLUSION

For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Background

Computerized decision support systems (CDSSs) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSSs, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes.

Objective

The purpose of this review was to analyze the relationship between CDSSs, practitioner performance, and patient medical outcomes. PubMed, CINAHL, Embase, Web of Science, and Cochrane databases were queried.

Methods

Articles were chosen based on year published (last 10 years), high quality, peer-reviewed sources, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet (Microsoft Corporation) to collect information on the relationship between CDSSs and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, outcomes, and study design (PICOS) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting.

Results

Themes were identified for both practitioner performance (n=38) and medical outcomes (n=36). A total of 66% (25/38) of articles had occurrences of positive practitioner performance, 13% (5/38) found no difference in practitioner performance, and 21% (8/38) did not report or discuss practitioner performance. Zero articles reported negative practitioner performance. A total of 61% (22/36) of articles had occurrences of positive patient medical outcomes, 8% (3/36) found no statistically significant difference in medical outcomes between intervention and control groups, and 31% (11/36) did not report or discuss medical outcomes. Zero articles found negative patient medical outcomes attributed to using CDSSs.

Conclusions

Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found a high level of reporting of positive effects on patient medical outcomes.

A pilot study of a smartphone-based monitoring intervention on head and neck cancer patients undergoing concurrent chemo-radiotherapy

BACKGROUND

Multidisciplinary treatment for head and neck carcinoma offers the best curative results but generates acute toxicities, which negatively affect both patients' quality of life and treatment compliance. Usually, the patient's clinical condition is recorded during scheduled, time-limited office visits and patients might forget to discuss symptoms occurred weeks before. They could also have difficulties contacting their clinicians outside of these limited encounters. Technology-based interventions for oncological patients have already been proved to encourage accurate symptoms report through regular inquiries of their clinical conditions.

OBJECTIVES

The aim of this work is to present the results of a pilot study about the assessment of a novel mobile application for reporting clinical parameters, quality of life, and symptoms of home patients affected by head and neck carcinoma, during chemo-radiotherapy and the subsequent follow-up period. Results will inform app designers about the necessary modifications to face a full-scale trial.

METHODS

Ten patients used the app for the foreseen period (up to 65 days, median 50.5), at the end of which they answered a paper questionnaire addressing user satisfaction with the app. The questionnaire included 8 questions and a free text comment field. Patients were followed by three clinicians, who also answered a similar paper questionnaire at the end of the pilot study. Questionnaires total score ranged 0-25 and a threshold of 16 was set in the study protocol to represent an overall positive outcome. However, to consider the individual constructs, questions about usability, perceived usefulness and user acceptance were also analyzed separately, and association among them was investigated. Finally, the feasibility of the intervention was analyzed in terms of the actual use of the app, i.e. dropout rates and compliance with the required data input. Statistics were only performed on patients' data, due to the small number of doctors involved in the study.

RESULTS

The median of the total score per patient was 18.5 (interquartile range 11.2-20.5), and per doctor was 16 (range 11-20), thus showing a positive overall satisfaction with the app. Concerning patients, only 4 out of a total of 80 answers (10 patients × 8 questions) expressed a definite negative feeling. Perceived usefulness was a critical issue for some patients. It was positively correlated with usability, and both aspects were independent predictors of acceptance. Feasibility was demonstrated by the low percentage of dropouts (9%) and noncompliance with assignments (10%). A significant (p = 0.007) negative correlation between the severity of reported symptoms and the EuroQoL questionnaire scores was found, supporting the consistency of the entered data. Free comments were reported by 6 Patients.

CONCLUSIONS

This study was meant to explore the context of outpatients' remote monitoring through the collection of patient-reported outcomes. The intervention for a proactive approach to symptoms monitoring in curatively treated head and neck cancer patients resulted feasible and acceptable by both patients and oncologists. The study revealed a criticality on the perceived usefulness, but, at the same time, the patients' comments suggested how to improve this aspect. Further actions will need to focus on measuring the impact of HeNeA on the process of care and on the health outcomes.

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

INTRODUCTION

 Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry.

OBJECTIVE

 Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017.

METHODS

 Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally.

RESULTS

 CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related "Big Data" sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of "app" ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions.

CONCLUSION

 Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.