Abstract Major depressive disorder (MDD) is prevalent in clinical weight-loss settings and predicts poor weight-loss outcomes. It is unknown whether the severity of depressive symptoms among those ...with MDD is associated with diet quality or physical activity levels. This knowledge is important for improving weight-loss treatment for these patients. It was hypothesized that more severe depression is associated with poorer diet quality and lower physical activity levels among individuals with obesity and MDD. Participants were 161 women with current MDD and obesity enrolled in the baseline phase of a weight-loss trial between 2007 and 2010. Depression severity was measured with the Beck Depression Inventory II. The Alternate Healthy Eating Index was applied to data from three 24-hour diet recalls to capture overall diet quality. Daily metabolic equivalents expended per day were calculated from three 24-hour physical activity recalls. Greater depression severity was associated with poorer overall diet quality (estimate=−0.26, standard error 0.11; P =0.02), but not with physical activity (estimate=0.07, standard error 0.05; P =0.18), in linear regression models controlling for income, education, depression-related appetite change, binge eating disorder, and other potential confounds. Associations with diet quality were primarily driven by greater intake of sugar ( r =0.20; P <0.01), saturated fat ( r =0.21; P <0.01), and sodium ( r =0.22; P <0.01). More severe depression was associated with poorer overall diet quality, but not physical activity, among treatment-seeking women with MDD and obesity. Future studies should identify mechanisms linking depression to diet quality and determine whether diet quality improves with depression treatment.
Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent ...results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools.
This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care.
A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior.
To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images.
These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS.
Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.
The seminal Dietary Approaches to Stopping Hypertension (DASH) study demonstrated the effectiveness of diet to control hypertension; however, the effective implementation and dissemination of its ...principles have been limited.
This study aimed to determine the feasibility and effectiveness of a DASH mobile health intervention. We hypothesized that combining Bluetooth-enabled data collection, social networks, and a human coach with a smartphone DASH app (DASH Mobile) would be an effective medium for the delivery of the DASH program.
We conducted a single-arm pilot study from August 2015 through August 2016, using a pre-post evaluation design to evaluate the feasibility and preliminary effectiveness of a smartphone version of DASH that incorporated a human health coach. Participants were recruited both online and offline.
A total of 17 patients participated in this study; they had a mean age of 59 years (SD 6) and 10 (60%) were women. Participants were engaged with the app; in the 120 days of the study, the mean number of logged blood pressure measurements was 63 (SD 46), the mean number of recorded weight measurements was 52 (SD 45), and participants recorded a mean of 55 step counts (SD 36). Coaching phone calls had a high completion rate (74/102, 73%). The mean number of servings documented per patient for the dietary assessment was 709 (SD 541), and patients set a mean number of 5 (SD 2) goals. Mean systolic and diastolic blood pressure, heart rate, weight, body mass index, and step count did not significantly change over time (P>.10 for all parameters).
In this pilot study, we found that participants were engaged with an interactive mobile app that promoted healthy behaviors to treat hypertension. We did not find a difference in the physiological outcomes, but were underpowered to identify such changes.
Design thinking and human-centered design approaches have become increasingly common in health care literature, particularly in relation to health information technology (HIT), as a pathway toward ...the development of usable, diffusible tools and processes. There is a need in academic medical centers tasked with digital innovation for a comprehensive process model to guide development that incorporates current industry trends, including design thinking and lean and agile approaches to digital development.
This study aims to describe the foundations and phases of our model for user-centered HIT development.
Based on our experience, we established an integrated approach and rigorous process for HIT development that leverages design thinking and lean and agile strategies in a pragmatic way while preserving methodological integrity in support of academic research goals.
A four-phased pragmatic process model was developed for user-centered digital development in HIT.
The model for user-centered HIT development that we developed is the culmination of diverse innovation projects and represents a multiphased, high-fidelity process for making more creative, flexible, efficient, and effective tools. This model is a critical step in building a rigorous approach to HIT design that incorporates a multidisciplinary, pragmatic perspective combined with academic research practices and state-of-the-art approaches to digital product development to meet the unique needs of health care.
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, ...there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
Abstract Objective To assess the impact of a decision aid on perceived risk of heart attacks and medication adherence among urban primary care patients with diabetes. Methods We randomly allocated ...150 patients with diabetes to participate in a usual primary care visit either with or without the Statin Choice tool. Participants completed a questionnaire at baseline and telephone follow-up at 3 and 6 months. Results Intervention patients were more likely to accurately perceive their underlying risk for a heart attack without taking a statin (OR: 1.9, CI: 1.0–3.8) and with taking a statin (OR: 1.4, CI: 0.7–2.8); a decline in risk overestimation among patients receiving the decision aid accounts for this finding. There was no difference in statin adherence at 3 or 6 months. Conclusion A decision aid about using statins to reduce coronary risk among patients with diabetes improved risk communication, beliefs, and decisional conflict, but did not improve adherence to statins. Practice implications Decision aid enhanced communication about the risks and benefits of statins improved patient risk perceptions but did not alter adherence among patients with diabetes.
Diabetes mellitus is a common medical complication of pregnancy, and its treatment is complex. Recent years have seen an increase in the application of mobile health tools and advanced technologies, ...such as remote patient monitoring, with the aim of improving care for diabetes mellitus in pregnancy. Previous studies of these technologies for the treatment of diabetes in pregnancy have been small and have not clearly shown clinical benefit with implementation.
Remote patient monitoring allows clinicians to monitor patients’ health data (such as glucose values) in near real-time, between office visits, to make timely adjustments to care. Our objective was to determine if using remote patient monitoring for the management of diabetes in pregnancy leads to an improvement in maternal and neonatal outcomes.
This was a retrospective cohort study of pregnant patients with diabetes mellitus managed by the maternal-fetal medicine practice at one academic institution between October 2019 and April 2021. This practice transitioned from paper-based blood glucose logs to remote patient monitoring in February 2020. Remote patient monitoring options included (1) device integration with Bluetooth glucometers that automatically uploaded measured glucose values to the patient’s Epic MyChart application or (2) manual entry in which patients manually logged their glucose readings into their MyChart application. Values in the MyChart application directly transferred to the patient’s electronic health record for review and management by clinicians. In total, 533 patients were studied. We compared 173 patients managed with paper logs to 360 patients managed with remote patient monitoring (176 device integration and 184 manual entry). Our primary outcomes were composite maternal morbidity (which included third- and fourth-degree lacerations, chorioamnionitis, postpartum hemorrhage requiring transfusion, postpartum hysterectomy, wound infection or separation, venous thromboembolism, and maternal admission to the intensive care unit) and composite neonatal morbidity (which included umbilical cord pH <7.00, 5 minute Apgar score <7, respiratory morbidity, hyperbilirubinemia, meconium aspiration, intraventricular hemorrhage, necrotizing enterocolitis, sepsis, pneumonia, seizures, hypoxic ischemic encephalopathy, shoulder dystocia, trauma, brain or body cooling, and neonatal intensive care unit admission). Secondary outcomes were measures of glycemic control and the individual components of the primary composite outcomes. We also performed a secondary analysis in which the patients who used the two different remote patient monitoring options (device integration vs manual entry) were compared. Chi-square, Fisher’s exact, 2-sample t, and Mann-Whitney tests were used to compare the groups. A result was considered statistically significant at P<.05.
Maternal baseline characteristics were not significantly different between the remote patient monitoring and paper groups aside from a slightly higher baseline rate of chronic hypertension in the remote patient monitoring group (6.1% vs 1.2%; P=.011). The primary outcomes of composite maternal and composite neonatal morbidity were not significantly different between the groups. However, remote patient monitoring patients submitted more glucose values (177 vs 146; P=.008), were more likely to achieve glycemic control in target range (79.2% vs 52.0%; P<.0001), and achieved the target range sooner (median, 3.3 vs 4.1 weeks; P=.025) than patients managed with paper logs. This was achieved without increasing in-person visits. Remote patient monitoring patients had lower rates of preeclampsia (5.8% vs 15.0%; P=.0006) and their infants had lower rates of neonatal hypoglycemia in the first 24 hours of life (29.8% vs 51.7%; P<.0001).
Remote patient monitoring for the management of diabetes mellitus in pregnancy is superior to a traditional paper-based approach in achieving glycemic control and is associated with improved maternal and neonatal outcomes.
A validated tool to assess adherence with inhaled corticosteroids (ICS) could help physicians and researchers determine whether poor asthma control is due to poor adherence or severe intrinsic ...asthma.
To assess the performance of the Medication Adherence Report Scale for Asthma (MARS-A), a 10-item, self-reported measure of adherence with ICS.
We interviewed 318 asthmatic adults receiving care at 2 inner-city clinics. Self-reported adherence with ICS was measured by MARS-A at baseline and 1 and 3 months. ICS adherence was measured electronically in 53 patients. Electronic adherence was the percentage of days patients used ICS. Patients with a mean MARS-A score of 4.5 or higher or with electronic adherence of more than 70% were defined as good adherers. We assessed internal validity (Cronbach alpha, test-retest correlations), criterion validity (associations between self-reported adherence and electronic adherence), and construct validity (correlating self-reported adherence with ICS beliefs).
The mean patient age was 47 years; 40% of patients were Hispanic, 40% were black, and 18% were white; 53% had prior asthma hospitalizations; and 70% had prior oral steroid use. Electronic substudy patients were similar to the rest of the cohort in age, sex, race, and asthma severity. MARS-A had good interitem correlation in English and Spanish (Cronbach alpha = 0.85 and 0.86, respectively) and good test-retest reliability (r = 0.65, P < .001). According to electronic measurements, patients used ICS 52% of days. Continuous MARS-A scores correlated with continuous electronic adherence (r = 0.42, P<.001), and dichotomized high self-reported adherence predicted high electronic adherence (odds ratio, 10.6; 95% confidence interval, 2.5-44.5; P < .001). Construct validity was good, with self-reported adherence higher in those saying daily ICS use was important and ICS were controller medications (P = .04).
MARS-A demonstrated good psychometric performance as a self-reported measure of adherence with ICS among English- and Spanish-speaking, low-income, minority patients with asthma.
Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a ...shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited.
Our primary aim is to assess user interactions with a conversational agent for pretest genetics education.
We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses.
We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question.
The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.
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