Dataset Information

Personalized Hypertension Management Using Patient-Generated Health Data Integrated With Electronic Health Records (EMPOWER-H): Six-Month Pre-Post Study.

ABSTRACT:

Background

EMPOWER-H (Engaging and Motivating Patients Online With Enhanced Resources-Hypertension) is a personalized-care model facilitating engagement in hypertension self-management utilizing an interactive Web-based disease management system integrated with the electronic health record. The model is designed to support timely patient-provider interaction by incorporating decision support technology to individualize care and provide personalized feedback for patients with chronic disease. Central to this process were patient-generated health data, including blood pressure (BP), weight, and lifestyle behaviors, which were uploaded using a smartphone.

Objective

The aim of this study was to evaluate the program among patients within primary care already under management for hypertension and with uncontrolled BP.

Methods

Using a 6-month pre-post design, outcome measures included office-measured and home-monitored BP, office-measured weight, intervention contacts, diet, physical activity, smoking, knowledge, and health-related quality of life.

Results

At 6 months, 55.9% of participants (N=149) achieved office BP goals (<140/90 mm Hg; P<.001) and 86.0% achieved clinically meaningful reduction in office BP (reduction in systolic BP [SBP] ?5 mm Hg or diastolic BP [DBP] ?3 mm Hg). At baseline, 25.2% of participants met home BP goals (<135/85 mm Hg), and this percentage significantly increased to 71.4% (P<.001) at 6 months. EMPOWER-H also significantly reduced both office and home SBP and DBP, decreased office-measured weight and consumption of high-salt and high-fat foods (all P<.005), and increased intake of fruit and vegetables, minutes of aerobic exercise, and hypertension knowledge (all P<.05). Patients with higher home BP upload frequencies had significantly higher odds of achieving home BP goals. Patients receiving more total intervention, behavioral, pharmaceutical contacts had significantly lower odds of achieving home BP goals but higher improvements in office BP (all P<.05).

Conclusions

EMPOWER-H significantly improved participants' office-measured and home-monitored BP, weight, and lifestyle behaviors, suggesting that technologically enabled BP home-monitoring, with structured use of patient-generated health data and a personalized care-plan facilitating patient engagement, can support effective clinical management. The experience gained in this study provides support for the feasibility and value of using carefully managed patient-generated health data in the day-to-day clinical management of patients with chronic conditions. A large-scale, real-world study to evaluate sustained effectiveness, cost-effectiveness, and scalability is warranted.

SUBMITTER: Lv N

PROVIDER: S-EPMC5627043 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Personalized Hypertension Management Using Patient-Generated Health Data Integrated With Electronic Health Records (EMPOWER-H): Six-Month Pre-Post Study.

Lv Nan N Xiao Lan L Simmons Martha L ML Rosas Lisa G LG Chan Albert A Entwistle Martin M

Journal of medical Internet research 20170919 9

<h4>Background</h4>EMPOWER-H (Engaging and Motivating Patients Online With Enhanced Resources-Hypertension) is a personalized-care model facilitating engagement in hypertension self-management utilizing an interactive Web-based disease management system integrated with the electronic health record. The model is designed to support timely patient-provider interaction by incorporating decision support technology to individualize care and provide personalized feedback for patients with chronic dise ...[more]

PMID: 28928111

Similar Datasets

Project description:Abstract Objective Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs. Materials and Methods We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A “train and validation”) using cross-validation, and then applied the models to a second dataset (dataset B “test”) to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance. Results With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the “train and validation” dataset A and 0.94 in the “test” dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension. Conclusion These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Dataset Information

Personalized Hypertension Management Using Patient-Generated Health Data Integrated With Electronic Health Records (EMPOWER-H): Six-Month Pre-Post Study.

Background

Objective

Methods

Results

Conclusions

Publications

Personalized Hypertension Management Using Patient-Generated Health Data Integrated With Electronic Health Records (EMPOWER-H): Six-Month Pre-Post Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets