Interventions and contextual factors to improve retention in care for patients with hypertension in primary care: Hermeneutic systematic review.

BACKGROUND: Regular engagement over time in hypertension care, or retention, is a crucial but understudied step in optimizing patient outcomes. This systematic review leverages a hermeneutic methodology to identify, evaluate, and quantify the effects of interventions and contextual factors for improving retention for patients with hypertension. METHODS: We searched for articles that were published between 2000 and 2022 from multiple electronic databases, including MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, clinicaltrials.gov, and WHO International Trials Registry. We followed the latest version of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guideline to report the findings for this review. We also synthesized the findings using a hermeneutic methodology for systematic reviews, which used an iterative process to review, integrate, analyze, and interpret evidence. RESULTS: From 4686 screened titles and abstracts, 18 unique studies from 9 countries were identified, including 10 (56%) randomized controlled trials (RCTs), 3 (17%) cluster RCTs, and 5 (28%) non-RCT studies. The number of participants ranged from 76 to 1562. The overall mean age range was 41-67 years, and the proportion of female participants ranged from 0% to 100%. Most (n = 17, 94%) studies used non-physician personnel to implement the proposed interventions. Fourteen studies (78%) implemented multilevel combinations of interventions. Education and training, team-based care, consultation, and Short Message Service reminders were the most common interventions tested. CONCLUSIONS: This review presents the most comprehensive findings on retention in hypertension care to date and fills the gaps in the literature, including the effectiveness of interventions, their components, and contextual factors. Adaptation of and implementing HIV care models, such differentiated service delivery, may be more effective and merit further study. REGISTRATION: CRD42021291368. PROTOCOL REGISTRATION: PROSPERO 2021 CRD42021291368. Available at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=291368.

Social Networking Service, Patient-Generated Health Data, and Population Health Informatics: National Cross-sectional Study of Patterns and Implications of Leveraging Digital Technologies to Support Mental Health and Well-being.

BACKGROUND: The emerging health technologies and digital services provide effective ways of collecting health information and gathering patient-generated health data (PGHD), which provide a more holistic view of a patient's health and quality of life over time, increase visibility into a patient's adherence to a treatment plan or study protocol, and enable timely intervention before a costly care episode. OBJECTIVE: Through a national cross-sectional survey in the United States, we aimed to describe and compare the characteristics of populations with and without mental health issues (depression or anxiety disorders), including physical health, sleep, and alcohol use. We also examined the patterns of social networking service use, PGHD, and attitudes toward health information sharing and activities among the participants, which provided nationally representative estimates. METHODS: We drew data from the 2019 Health Information National Trends Survey of the National Cancer Institute. The participants were divided into 2 groups according to mental health status. Then, we described and compared the characteristics of the social determinants of health, health status, sleeping and drinking behaviors, and patterns of social networking service use and health information data sharing between the 2 groups. Multivariable logistic regression models were applied to assess the predictors of mental health. All the analyses were weighted to provide nationally representative estimates. RESULTS: Participants with mental health issues were significantly more likely to be younger, White, female, and lower-income; have a history of chronic diseases; and be less capable of taking care of their own health. Regarding behavioral health, they slept <6 hours on average, had worse sleep quality, and consumed more alcohol. In addition, they were more likely to visit and share health information on social networking sites, write online diary blogs, participate in online forums or support groups, and watch health-related videos. CONCLUSIONS: This study illustrates that individuals with mental health issues have inequitable social determinants of health, poor physical health, and poor behavioral health. However, they are more likely to use social networking platforms and services, share their health information, and actively engage with PGHD. Leveraging these digital technologies and services could be beneficial for developing tailored and effective strategies for self-monitoring and self-management.

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.

BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. METHODS: We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. RESULTS: The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. CONCLUSION: UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

Label-Free Method Using a Weighted-Phase Algorithm To Quantitate Nanoscale Interactions between Molecules on DNA Microarrays.

White light interference is used as a label-free method to detect nanoscale changes on surfaces. However, the signal-to-noise ratio of the white light interference method is very low, thus resulting in inaccurate results. In this paper, we report a corrected label-free method based on hyperspectral interferometry to overcome the shortcoming of the white light interference method. A platform based on hyperspectral interferometry was established, and a DNA hybridization microarray was constructed to quantitate thickness variation of molecules on a solid surface. We used fluorescence resonance energy transfer (FRET) to validate the results of our method. Compared to conventional fluorescence-labeled method like FRET, our method has advantages because it does not require a fluorescent label and has a detection limit of 1.78 nm, a high accuracy, and wide detection range (5-64 bp).

Transforming and facilitating health care delivery through social networking platforms: evidences and implications from WeChat.

Objectives: Telehealth or remote care has been widely leveraged to provide health care support and has achieved tremendous developments and positive results, including in low- and middle-income countries (LMICs). Social networking platform, as an easy-to-use tool, has provided users with simplified means to collect data outside of the traditional clinical environment. WeChat, one of the most popular social networking platforms in many countries, has been leveraged to conduct telehealth and hosted a vast amount of patient-generated health data (PGHD), including text, voices, images, and videos. Its characteristics of convenience, promptness, and cross-platform support enrich and simplify health care delivery and communication, addressing some weaknesses of traditional clinical care during the pandemic. This study aims to systematically summarize how WeChat platform has been leveraged to facilitate health care delivery and how it improves the access to health care. Materials and Methods: Utilizing Levesque's health care accessibility model, the study explores WeChat's impact across 5 domains: Approachability, Acceptability, Availability and accommodation, Affordability, and Appropriateness. Results: The findings highlight WeChat's diverse functionalities, ranging from telehealth consultations and remote patient monitoring to seamless PGHD exchange. WeChat's integration with health tracking apps, support for telehealth consultations, and survey capabilities contribute significantly to disease management during the pandemic. Discussion and Conclusion: The practices and implications from WeChat may provide experiences to utilize social networking platforms to facilitate health care delivery. The utilization of WeChat PGHD opens avenues for shared decision-making, prompting the need for further research to establish reporting guidelines and policies addressing privacy and ethical concerns associated with social networking platforms in health research.

Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.

This study aims to propose a novel approach for enhancing clinical prediction models by combining structured and unstructured data with multimodal data fusion. We presented a comprehensive framework that integrated multimodal data sources, including textual clinical notes, structured electronic health records (EHRs), and relevant clinical data from National Electronic Injury Surveillance System (NEISS) datasets. We proposed a novel hybrid fusion method, which incorporated state-of-the-art pre-trained language model, to integrate unstructured clinical text with structured EHR data and other multimodal sources, thereby capturing a more comprehensive representation of patient information. The experimental results demonstrated that the hybrid fusion approach significantly improved the performance of clinical prediction models compared to traditional fusion frameworks and unimodal models that rely solely on structured data or text information alone. The proposed hybrid fusion system with RoBERTa language encoder achieved the best prediction of the Top 1 injury with an accuracy of 75.00% and Top 3 injuries with an accuracy of 93.54%. Our study highlights the potential of integrating natural language processing (NLP) techniques with multimodal data fusion for enhancing clinical prediction models' performances. By leveraging the rich information present in clinical text and combining it with structured EHR data, the proposed approach can improve the accuracy and robustness of predictive models. The approach has the potential to advance clinical decision support systems, enable personalized medicine, and facilitate evidence-based health care practices. Future research can further explore the application of this hybrid fusion approach in real-world clinical settings and investigate its impact on improving patient outcomes.

The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.

This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.

Fixed-dose combination therapy for the prevention of atherosclerotic cardiovascular disease.

Fixed-dose combination (FDC) therapy, also known as polypill therapy, targets risk factors for atherosclerotic cardiovascular disease (ASCVD) and has been proposed as a strategy to reduce global ASCVD burden. Here we conducted a systematic search for relevant studies from 2016-2022 to assess the effects of FDC therapy for prevention of ASCVD. The studies selected include randomized trials evaluating FDC therapy with at least one blood pressure-lowering drug and one lipid-lowering drug. The study data were independently extracted, the quality of evidence was appraised by multiple reviewers and effect estimates were pooled using a fixed-effect meta-analysis when statistical heterogeneity was low to moderate. The main outcomes of the analysis were all-cause mortality, fatal and nonfatal ASCVD events, adverse events, systolic blood pressure, low-density lipoprotein cholesterol and adherence. Among 26 trials (n = 27,317 participants, 43.2% female and mean age range 52.9-76.0), FDC therapy was associated with lower low-density lipoprotein cholesterol and systolic blood pressure, with higher rates of adherence and adverse events in both primary and mixed secondary prevention populations. For studies with a mostly primary prevention population, FDC therapy was associated with lower risk of all-cause mortality by 11% (5.6% versus 6.3%; relative risk (risk ratio) of 0.89; 95% confidence interval 0.78 to 1.00; I2 = 0%; four trials and 16,278 participants) and risk of fatal and nonfatal ASCVD events by 29% (6.1% versus 8.4%; relative risk (risk ratio) of 0.71; 95% confidence interval 0.63 to 0.79; I2 = 0%; five trials and 15,503 participants). One adequately powered trial in an exclusively secondary prevention population showed that FDC therapy reduced the risk of major adverse cardiovascular events by 24%. These findings support adoption and implementation of polypills to lower risk for all-cause mortality and ASCVD.

Patient Safety of Perioperative Medication Through the Lens of Digital Health and Artificial Intelligence.

Perioperative medication has made significant contributions to enhancing patient safety. Nevertheless, administering medication during this period still poses considerable safety concerns, with many errors being detected only after causing significant physiological disturbances. The intricacy of medication administration in the perioperative setting poses specific challenges to patient safety. To address these challenges, implementing potential strategies and interventions is critical. One such strategy is raising awareness and revising educational curricula regarding drug safety in the operating room. Another crucial strategy is recognizing the importance of redundancy and multiple checks in the operating room as a hallmark of medication safety, which is not a common practice. Digital health technologies and artificial intelligence (AI) also offer the potential to improve perioperative medication safety. Computerized physician order entry systems, electronic medication administration records, and barcode medication administration systems have been proven to reduce medication errors and improve patient safety. By implementing these strategies and interventions, health care professionals can enhance the safety of perioperative medication administration and improve patient outcomes.

Implications for implementation and adoption of telehealth in developing countries: a systematic review of China's practices and experiences.

The rapid advancement of telehealth technologies has the potential to revolutionize healthcare delivery, especially in developing countries and resource-limited settings. Telehealth played a vital role during the COVID-19 pandemic, supporting numerous healthcare services. We conducted a systematic review to gain insights into the characteristics, barriers, and successful experiences in implementing telehealth during the COVID-19 pandemic in China, a representative of the developing countries. We also provide insights for other developing countries that face similar challenges to developing and using telehealth during or after the pandemic. This systematic review was conducted through searching five prominent databases including PubMed/MEDLINE, Embase, Scopus, Cochrane Library, and Web of Science. We included studies clearly defining any use of telehealth services in all aspects of health care during the COVID-19 pandemic in China. We mapped the barriers, successful experiences, and recommendations based on the Consolidated Framework for Implementation Research (CFIR). A total of 32 studies met the inclusion criteria. Successfully implementing and adopting telehealth in China during the pandemic necessitates strategic planning across aspects at society level (increasing public awareness and devising appropriate insurance policies), organizational level (training health care professionals, improving workflows, and decentralizing tasks), and technological level (strategic technological infrastructure development and designing inclusive telehealth systems). WeChat, a widely used social networking platform, was the most common platform used for telehealth services. China's practices in addressing the barriers may provide implications and evidence for other developing countries or low-and middle- income countries (LMICs) to implement and adopt telehealth systems.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.

Objective: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination. Methods: We collected Tweet posts by the residents in the United States after the dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially. Results: A total of 3 198 686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2 358 783 Tweets were identified to contain clear opinions, among which 824 755 (35.0%) expressed negative opinions towards vaccination while 1 534 028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity. Conclusion: We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.

The Roles of Electronic Health Records for Clinical Trials in Low- and Middle-Income Countries: Scoping Review.

BACKGROUND: Clinical trials are a crucial element in advancing medical knowledge and developing new treatments by establishing the evidence base for safety and therapeutic efficacy. However, the success of these trials depends on various factors, including trial design, project planning, research staff training, and adequate sample size. It is also crucial to recruit participants efficiently and retain them throughout the trial to ensure timely completion. OBJECTIVE: There is an increasing interest in using electronic health records (EHRs)-a widely adopted tool in clinical practice-for clinical trials. This scoping review aims to understand the use of EHR in supporting the conduct of clinical trials in low- and middle-income countries (LMICs) and to identify its strengths and limitations. METHODS: A comprehensive search was performed using 5 databases: MEDLINE, Embase, Scopus, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature. We followed the latest version of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline to conduct this review. We included clinical trials that used EHR at any step, conducted a narrative synthesis of the included studies, and mapped the roles of EHRs into the life cycle of a clinical trial. RESULTS: A total of 30 studies met the inclusion criteria: 13 were randomized controlled trials, 3 were cluster randomized controlled trials, 12 were quasi-experimental studies, and 2 were feasibility pilot studies. Most of the studies addressed infectious diseases (15/30, 50%), with 80% (12/15) of them about HIV or AIDS and another 40% (12/30) focused on noncommunicable diseases. Our synthesis divided the roles of EHRs into 7 major categories: participant identification and recruitment (12/30, 40%), baseline information collection (6/30, 20%), intervention (8/30, 27%), fidelity assessment (2/30, 7%), primary outcome assessment (24/30, 80%), nonprimary outcome assessment (13/30, 43%), and extended follow-up (2/30, 7%). None of the studies used EHR for participant consent and randomization. CONCLUSIONS: Despite the enormous potential of EHRs to increase the effectiveness and efficiency of conducting clinical trials in LMICs, challenges remain. Continued exploration of the appropriate uses of EHRs by navigating their strengths and limitations to ensure fitness for use is necessary to better understand the most optimal uses of EHRs for conducting clinical trials in LMICs.

Fixed-dose combination therapy-based protocol compared with free pill combination protocol: Results of a cluster randomized trial.

Fixed-dose combination (FDC) therapy is recommended for hypertension management in Nigeria based on randomized trials at the individual level. This cluster-randomized trial evaluates effectiveness and safety of a treatment protocol that used two-drug FDC therapy as the second and third steps for hypertension control compared with a protocol that used free pill combinations. From January 2021 to June 2021, 60 primary healthcare centers in the Federal Capital Territory of Nigeria were randomized to a protocol using FDC therapy as second and third steps compared with a protocol that used the same medications in free pill combination therapy for these steps. Eligible patients were adults (≥18 years) with hypertension. The primary outcome was the odds of a patient being controlled at their last visit between baseline to 6-month follow-up in the FDC group compared to the free pill group. 4427 patients (mean [SD] age: 49.0 [12.4] years, 70.5% female) were registered with mean (SD) baseline systolic/diastolic blood pressure 155 (20.6)/96 (13.1) mm Hg. Baseline characteristics of groups were similar. After 6-months, hypertension control rate improved in the two treatment protocols, but there were no differences between the groups after adjustment (FDC = 53.9% versus free pill combination = 47.9%, cluster-adjusted p = .29). Adverse events were similarly low (<1%) in both groups. Both protocols improved hypertension control rates at 6-months in comparison to baseline, though no differences were observed between groups. Further work is needed to determine if upfront FDC therapy is more effective and efficient to improve hypertension control rates.

Examining the impact of sex differences and the COVID-19 pandemic on health and health care: findings from a national cross-sectional study.

Objective: To examine the association of the coronavirus disease 2019 (COVID-19) pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. Materials and Methods: We drew data from the National Cancer Institute's 2020 Health Information National Trends Survey. We described and compared the characteristics of social determinants of health, physical activity, mental health, alcohol use, patterns of social networking service use, and health information data sharing. Analyses were weighted to provide nationally representative estimates. Multivariate models (multiple linear regression, multiple logistic regression, and multinomial logistic model) were used to assess the sole and joint association with sex and pandemic. In addition, we applied the Bonferroni correction to adjust P values to decrease the risks of type I errors when making multiple statistical tests. Results: Females were more likely to use mobile health and health communication technologies than males, and the difference increased after the pandemic. The association between sex and mental health was significant after the COVID-19 pandemic. Females were more likely to experience depression or anxiety disorders. Both males and females had a slight decrease in terms of the quantity and intensity of physical activity and females were less likely to perform moderate exercise and strength training regularly. Males were likely to drink more alcohol than females. Conclusion: The COVID-19 pandemic amplifies the differences between males and females in health communication, physical activity, mental health, and behavioral health. Intersectional analyses of sex are integral to addressing issues that arise and mitigating the exacerbation of inequities. Responses to the pandemic should consider diverse perspectives, including sex and gender.

Identifying Contextual Factors and Strategies for Practice Facilitation in Primary Care Quality Improvement Using an Informatics-Driven Model: Framework Development and Mixed Methods Case Study.

BACKGROUND: The past decade has seen increasing opportunities and efforts to integrate quality improvement into health care. Practice facilitation is a proven strategy to support redesign and improvement in primary care practices that focuses on building organizational capacity for continuous improvement. Practice leadership, staff, and practice facilitators all play important roles in supporting quality improvement in primary care. However, little is known about their perspectives on the context, enablers, barriers, and strategies that impact quality improvement initiatives. OBJECTIVE: This study aimed to develop a framework to enable assessment of contextual factors, challenges, and strategies that impact practice facilitation, clinical measure performance, and the implementation of quality improvement interventions. We also illustrated the application of the framework using a real-world case study. METHODS: We developed the TITO (task, individual, technology, and organization) framework by conducting participatory stakeholder workshops and incorporating their perspectives to identify enablers and barriers to quality improvement and practice facilitation. We conducted a case study using a mixed methods approach to demonstrate the use of the framework and describe practice facilitation and factors that impact quality improvement in a primary care practice that participated in the Healthy Hearts in the Heartland study. RESULTS: The proposed framework was used to organize and analyze different stakeholders' perspectives and key factors based on framework domains. The case study showed that practice leaders, staff, and practice facilitators all influenced the success of the quality improvement program. However, these participants faced different challenges and used different strategies. The framework showed that barriers stemmed from patients' social determinants of health, a lack of staff and time, and unsystematic facilitation resources, while enablers included practice culture, staff buy-in, implementation of effective practice facilitation strategies, practice capacity for change, and shared complementary resources from similar, ongoing programs. CONCLUSIONS: Our framework provided a useful and generalizable structure to guide and support assessment of future practice facilitation projects, quality improvement initiatives, and health care intervention implementation studies. The practice leader, staff, and practice facilitator all saw value in the quality improvement program and practice facilitation. Practice facilitators are key liaisons to help the quality improvement program; they help all stakeholders work toward a shared target and leverage tailored strategies. Taking advantage of resources from competing, yet complementary, programs as additional support may accelerate the effective achievement of quality improvement goals. Practice facilitation-supported quality improvement programs may be opportunities to assist primary care practices in achieving improved quality of care through focused and targeted efforts. The case study demonstrated how our framework can support a better understanding of contextual factors for practice facilitation, which could enable well-prepared and more successful quality improvement programs for primary care practices. Combining implementation science and informatics thinking, our TITO framework may facilitate interdisciplinary research in both fields.

Metadata Correction: Identifying Contextual Factors and Strategies for Practice Facilitation in Primary Care Quality Improvement Using an Informatics-Driven Model: Framework Development and Mixed Methods Case Study.

[This corrects the article DOI: 10.2196/32174.].

A New Taxonomy for Technology-Enabled Diabetes Self-Management Interventions: Results of an Umbrella Review.

BACKGROUND: A 2017 umbrella review defined the technology-enabled self-management (TES) feedback loop associated with a significant reduction in A1C. The purpose of this 2021 review was to develop a taxonomy of intervention attributes in technology-enabled interventions; review recent, high-quality systematic reviews and meta-analyses to determine if the TES framework was described and if elements contribute to improved diabetes outcomes; and to identify gaps in the literature. METHODS: We identified key technology attributes needed to describe the active ingredients of TES interventions. We searched multiple databases for English language reviews published between April 2017 and April 2020, focused on PwD (population) receiving diabetes care and education (intervention) using technology-enabled self-management (comparator) in a randomized controlled trial, that impact glycemic, behavioral/psychosocial, and other diabetes self-management outcomes. AMSTAR-2 guidelines were used to assess 50 studies for methodological quality including risk of bias. RESULTS: The TES Taxonomy was developed to standardize the description of technology-enabled interventions; and ensure research uses the taxonomy for replication and evaluation. Of the 26 included reviews, most evaluated smartphones, mobile applications, texting, internet, and telehealth. Twenty-one meta-analyses with the TES feedback loop significantly lowered A1C. CONCLUSIONS: Technology-enabled diabetes self-management interventions continue to be associated with improved clinical outcomes. The ongoing rapid adoption and engagement of technology makes it important to focus on uniform measures for behavioral/psychosocial outcomes to highlight healthy coping. Using the TES Taxonomy as a standard approach to describe technology-enabled interventions will support understanding of the impact technology has on diabetes outcomes.

Characteristics, treatment, and control of hypertension in public primary healthcare centers in Nigeria: baseline results from the Hypertension Treatment in Nigeria Program.

BACKGROUND: There are limited data on large-scale, multilevel implementation research studies to improve hypertension diagnosis, treatment, and control rates at the primary healthcare (PHC) level in Africa. We describe the characteristics, treatment, and control rates of patients with hypertension in public PHC centers in the Hypertension Treatment in Nigeria Program. METHODS: Data were collected from adults at least 18 years at 60 public PHC centers between January 2020 and November 2020. Hypertension treatment rates were calculated at registration and upon completion of the initial visit. Hypertension control rates were calculated based on SBP and DBPs less than 140/90 mmHg. Regression models were created to evaluate factors associated with hypertension treatment and control status. RESULTS: Four thousand, nine hundred and twenty-seven individuals [66.7% women, mean (SD) age = 48.2 (12.9) years] were included. Mean (SD) SBP was higher in men compared with women [152.9 (20.0) mmHg versus 150.8 (21) mmHg, P = 0.001]. Most (58.3%) patients were on treatment at the time of registration, and by the end of the baseline visit, 89.2% of patients were on treatment. The baseline hypertension control rate was 13.1%, and control was more common among patients who were older [adjusted OR (95% CI) 1.01 [1.01 -1.02)], women [adjusted OR (95% CI) 1.30 (1.05- 1.62)], who used fixed dose combination therapy [adjusted OR (95% CI) 1.83 (1.49 -2.26)], and had higher education levels. CONCLUSION: This baseline report of the largest facility-based hypertension study in Africa demonstrates high hypertension treatment rates but low control rates.

Hypertension Treatment in Nigeria (HTN) Program: rationale and design for a type 2 hybrid, effectiveness, and implementation interrupted time series trial.

BACKGROUND: Hypertension is the most common cardiovascular disease in Nigeria and contributes to a large non-communicable disease burden. Our aim was to implement and evaluate a large-scale hypertension treatment and control program, adapted from the Kaiser Permanent Northern California and World Health Organization HEARTS models, within public primary healthcare centers in the Federal Capital Territory, Nigeria. METHODS: A type 2 hybrid, interrupted time series design was used to generate novel information on large-scale implementation and effectiveness of a multi-level hypertension control program within 60 primary healthcare centers in the Federal Capital Territory, Nigeria. During the formative phase, baseline qualitative assessments were held with patients, health workers, and administrators to inform implementation package adaptation. The package includes a hypertension patient registry with empanelment, performance and quality reporting, simplified treatment guideline emphasizing fixed-dose combination therapy, reliable access to quality essential medicines and technology, team-based care, and health coaching and home blood pressure monitoring. Strategies to implement and adapt the package were identified based on barriers and facilitators mapped in the formative phase, previous implementation experience, mid-term qualitative evaluation, and ongoing stakeholder and site feedback. The control phase included 11 months of sequential registration of hypertensive patients at participating primary healthcare centers, followed by implementation of the remainder of the package components and evaluation over 37 subsequent, consecutive months of the intervention phase. The formative phase was completed between April 2019 and August 2019, followed by initiation of the control phase in January 2020. The control phase included 11 months (January 2020 to November 2020) of sequential registration and empanelment of hypertensive patients at participating primary healthcare centers. After completion of the control phase in November 2020, the intervention phase commenced in December 2020 and will be completed in December 2023. DISCUSSION: This trial will provide robust evidence for implementation and effectiveness of a multi-level implementation package more broadly throughout the Federal Capital Territory, which may inform hypertension systems of care throughout Nigeria and in other low- and middle-income countries. Implementation outcome results will be important to understand what system-, site-, personnel-, and patient-level factors are necessary for successful implementation of this intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT04158154 . The trial was prospectively registered on November 8, 2019.

Characteristics and Patterns of Retention in Hypertension Care in Primary Care Settings From the Hypertension Treatment in Nigeria Program.

Background: More than 1.2 billion adults worldwide have hypertension. High retention in clinical care is essential for long-term management of hypertension, but 1-year retention rates are less than 50% in many resource-limited settings. Objective: To evaluate short-term retention rates and associated factors among patients with hypertension in primary health care centers in the Federal Capital Territory of Nigeria. Design, Setting, and Participants: In this cohort study, data were collected by trained study staff from adults aged 18 years or older at 60 public, primary health care centers in Nigeria between January 2020 and July 2021 as part of the Hypertension Treatment in Nigeria (HTN) Program. Patients with hypertension were registered. Exposures: Follow-up visit for hypertension care within 37 days of the registration visit. Main Outcomes and Measures: The main outcome was the 3-month rolling average 37-day retention rate in hypertension care, calculated by dividing the number of patients who had a follow-up visit within 37 days of their first (ie, registration) visit in the program by the total number of registered patients with hypertension during multiple consecutive 3-month periods. Interrupted time series analyses evaluated trends in retention rates before and after the intervention phase of the HTN Program. Mixed-effects, multivariable regression models evaluated associations between patient-, site-, and area council-level factors, hypertension treatment and control status, and 37-day retention rate. Results: In total, 10 686 patients (68.3% female; mean [SD] age, 48.8 [12.7] years) were included in the analysis. During the study period, the 3-month rolling average 37-day retention rate was 41% (95% CI, 37%-46%), with wide variability among sites. The retention rate was higher among patients who were older (adjusted odds ratio [aOR], 1.01 per year; 95% CI, 1.01-1.02 per year), were female (aOR, 1.11; 95% CI, 1.01-1.23), had a higher body mass index (aOR, 1.01; 95% CI, 1.00-1.02), were in the Kuje vs the Abaji area council (aOR, 2.25; 95% CI, 1.25-4.04), received hypertension treatment at the registration visit (aOR, 1.27; 95% CI, 1.07-1.50), and were registered during the postintervention period (aOR, 1.16; 95% CI, 1.06-1.26). Conclusions and Relevance: The findings suggest that retention in hypertension care is suboptimal in primary health care centers in Nigeria, although large variability among sites was found. Potentially modifiable and nonmodifiable factors associated with retention were identified and may inform multilevel, contextualized implementation strategies to improve retention.