Bryan Gibson

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using “ontologies.” In information science, an ontology is a systematic method for articulating a “controlled vocabulary” of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine’s Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.

Regular expression-based learning to extract bodyweight values from clinical notes

BACKGROUND Bodyweight related measures (weight, height, BMI, abdominal circumference) are extremely important for clinical care, research and quality improvement. These and other vitals signs data are frequently missing from structured tables of electronic health records. However they are often recorded as text within clinical notes. In this project we sought to develop and validate a learning algorithm that would extract bodyweight related measures from clinical notes in the Veterans Administration (VA) Electronic Health Record to complement the structured data used in clinical research. METHODS We developed the Regular Expression Discovery Extractor (REDEx), a supervised learning algorithm that generates regular expressions from a training set. The regular expressions generated by REDEx were then used to extract the numerical values of interest. To train the algorithm we created a corpus of 268 outpatient primary care notes that were annotated by two annotators. This annotation served to develop the annotation process and identify terms associated with bodyweight related measures for training the supervised learning algorithm. Snippets from an additional 300 outpatient primary care notes were subsequently annotated independently by two reviewers to complete the training set. Inter-annotator agreement was calculated. REDEx was applied to a separate test set of 3561 notes to generate a dataset of weights extracted from text. We estimated the number of unique individuals who would otherwise not have bodyweight related measures recorded in the CDW and the number of additional bodyweight related measures that would be additionally captured. RESULTS REDExs performance was: accuracy=98.3%, precision=98.8%, recall=98.3%, F=98.5%. In the dataset of weights from 3561 notes, 7.7% of notes contained bodyweight related measures that were not available as structured data. In addition 2 additional bodyweight related measures were identified per individual per year. CONCLUSION Bodyweight related measures are frequently stored as text in clinical notes. A supervised learning algorithm can be used to extract this data. Implications for clinical care, epidemiology, and quality improvement efforts are discussed.

Efficacy of a computerized simulation in promoting walking in individuals with diabetes

Background Regular walking is a recommended but underused self-management strategy for individuals with type 2 diabetes mellitus (T2DM). Objective To test the impact of a simulation-based intervention on the beliefs, intentions, knowledge, and walking behavior of individuals with T2DM. We compared two versions of a brief narrated simulation. The experimental manipulation included two components: the presentation of the expected effect of walking on the glucose curve; and the completion of an action plan for walking over the next week. Primary hypotheses were (1) intervention participants’ walking (minutes/week) would increase more than control participants’ walking, and (2) change in outcome expectancies (beliefs) would be a function of the discrepancy between prior beliefs and those presented in the simulation. Secondary hypotheses were that, overall, behavioral intentions to walk in the coming week and diabetes-related knowledge would increase in both groups. Methods Individuals were randomly assigned to condition. Preintervention measures included self-reported physical activity (International Physical Activity Questionnaire [IPAQ] 7-day), theory of planned behavior-related beliefs, and knowledge (Diabetes Knowledge Test). During the narrated simulation we measured individuals’ outcome expectancies regarding the effect of exercise on glucose with a novel drawing task. Postsimulation measures included theory of planned behavior beliefs, knowledge, and qualitative impressions of the narrated simulation. The IPAQ 7-day was readministered by phone 1 week later. We used a linear model that accounted for baseline walking to test the main hypothesis regarding walking. Discrepancy scores were calculated between the presented outcome and individuals’ prior expectations (measured by the drawing task). A linear model with an interaction between intervention status and the discrepancy score was used to test the hypothesis regarding change in outcome expectancy. Pre–post changes in intention and knowledge were tested using paired t tests. Results Of 65 participants, 33 were in the intervention group and 32 in the control group. We excluded 2 participants from analysis due to being extreme outliers in baseline walking. After adjustment for baseline difference in age and intentions between groups, intervention participants increased walking by 61.0 minutes/week (SE 30.5, t 58 = 1.9, P = .05) more than controls. The proposed interaction between the presented outcome and the individual’s prior beliefs was supported: after adjustment for baseline differences in age and intentions between groups, the coefficient for the interaction was –.25, (SE 0.07, t 57 = –3.2, P < .01). On average participants in both groups improved significantly from baseline in intentions (mean difference 0.66, t 62 = 4.5, P < .001) and knowledge (mean difference 0.38, t 62 = 2.4, P = .02). Conclusions This study suggests that a brief, Internet-ready, simulation-based intervention can improve knowledge, beliefs, intentions, and short-term behavior in individuals with T2DM.

High Prevalence of Medication Discrepancies Between Home Health Referrals and Centers for Medicare and Medicaid Services Home Health Certification and Plan of Care and Their Potential to Affect Safety of Vulnerable Elderly Adults

To describe the prevalence of discrepancies between medication lists that referring providers and home healthcare (HH) nurses create.

A qualitative evaluation of the crucial attributes of contextual Information necessary in EHR design to support patient-centered medical home care

BackgroundEffective implementation of a Primary Care Medical Home model of care (PCMH) requires integration of patients’ contextual information (physical, mental, social and financial status) into an easily retrievable information source for the healthcare team and clinical decision-making.This project explored clinicians’ perceptions about important attributes of contextual information for clinical decision-making, how contextual information is expressed in CPRS clinical documentation as well as how clinicians in a highly computerized environment manage information flow related to these areas.MethodsA qualitative design using Cognitive Task Analyses and a modified Critical Incident Technique were used. The study was conducted in a large VA with a fully implemented EHR located in the western United States. Seventeen providers working in a PCMH model of care in Primary Care, Home Based Care and Geriatrics reported on a recent difficult transition requiring contextual information for decision-making. The transcribed interviews were qualitatively analyzed for thematic development related to contextual information using an iterative process and multiple reviewers with ATLAS@ti software.ResultsSix overarching themes emerged as attributes of contextual information: Informativeness, goal language, temporality, source attribution, retrieval effort, and information quality.ConclusionsThese results indicate that specific attributes are needed to in order for contextual information to fully support clinical decision-making in a Medical Home care delivery environment. Improved EHR designs are needed for ease of contextual information access, displaying linkages across time and settings, and explicit linkages to both clinician and patient goals. Implications relevant to providers’ information needs, team functioning and EHR design are discussed.

Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes

BackgroundOur purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.Design and methodsData from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals’ attributes with model error.ResultsMinutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).ConclusionsThe preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.

Understanding cancer survivors’ information needs and information-seeking behaviors for complementary and alternative medicine from short- to long-term survival: a mixed-methods study

Objective The research examined complementary and alternative medicine (CAM) information-seeking behaviors and preferences from short- to long-term cancer survival, including goals, motivations, and information sources. Methods A mixed-methods approach was used with cancer survivors from the “Assessment of Patients’ Experience with Cancer Care” 2004 cohort. Data collection included a mail survey and phone interviews using the critical incident technique (CIT). Results Seventy survivors from the 2004 study responded to the survey, and eight participated in the CIT interviews. Quantitative results showed that CAM usage did not change significantly between 2004 and 2015. The following themes emerged from the CIT: families’ and friends’ provision of the initial introduction to a CAM, use of CAM to manage the emotional and psychological impact of cancer, utilization of trained CAM practitioners, and online resources as a prominent source for CAM information. The majority of participants expressed an interest in an online information-sharing portal for CAM. Conclusion Patients continue to use CAM well into long-term cancer survivorship. Finding trustworthy sources for information on CAM presents many challenges such as reliability of source, conflicting information on efficacy, and unknown interactions with conventional medications. Study participants expressed interest in an online portal to meet these needs through patient testimonials and linkage of claims to the scientific literature. Such a portal could also aid medical librarians and clinicians in locating and evaluating CAM information on behalf of patients.

Evaluation of an Electronic Module for Reconciling Medications in Home Health Plans of Care

OBJECTIVES Transitions in patient care pose an increased risk to patient safety. One way to reduce this risk is to ensure accurate medication reconciliation during the transition. Here we present an evaluation of an electronic medication reconciliation module we developed to reduce the transition risk in patients referred for home healthcare. METHODS Nineteen physicians with experience in managing home health referrals were recruited to participate in this within-subjects experiment. Participants completed medication reconciliation for three clinical cases in each of two conditions. The first condition (paper-based) simulated current practice - reconciling medication discrepancies between a paper plan of care (CMS 485) and a simulated Electronic Health Record (EHR). For the second condition (electronic) participants used our medication reconciliation module, which we integrated into the simulated EHR. To evaluate the effectiveness of our medication reconciliation module, we employed repeated measures ANOVA to test the hypotheses that the module will: 1) Improve accuracy by reducing the number of unaddressed medication discrepancies, 2) Improve efficiency by reducing the reconciliation time, 3) have good perceived usability. RESULTS The improved accuracy hypothesis is supported. Participants left more discrepancies unaddressed in the paper-based condition than the electronic condition, F (1,1) = 22.3, p < 0.0001 (Paper Mean = 1.55, SD = 1.20; Electronic Mean = 0.45, SD = 0.65). However, contrary to our efficiency hypothesis, participants took the same amount of time to complete cases in the two conditions, F (1, 1) =0.007, p = 0.93 (Paper Mean = 258.7 seconds, SD = 124.4; Electronic Mean = 260.4 seconds, SD = 158.9). The usability hypothesis is supported by a composite mean ability and confidence score of 6.41 on a 7-point scale, 17 of 19 participants preferring the electronic system and an SUS rating of 86.5. CONCLUSION We present the evaluation of an electronic medication reconciliation module that increases detection and resolution of medication discrepancies compared to a paper-based process. Further work to integrate medication reconciliation within an electronic medical record is warranted.

Maximizing clinical cohort size using free text queries

BACKGROUND Cohort identification is important in both population health management and research. In this project we sought to assess the use of text queries for cohort identification. Specifically we sought to determine the incremental value of unstructured data queries when added to structured queries for the purpose of patient cohort identification. METHODS Three cohort identification tasks were evaluated: identification of individuals taking gingko biloba and warfarin simultaneously (Gingko/Warfarin), individuals who were overweight, and individuals with uncontrolled diabetes (UCD). We assessed the increase in cohort size when unstructured data queries were added to structured data queries. The positive predictive value of unstructured data queries was assessed by manual chart review of a random sample of 500 patients. RESULTS For Gingko/Warfarin, text query increased the cohort size from 9 to 28,924 over the cohort identified by query of pharmacy data only. For the weight-related tasks, text search increased the cohort by 5-29% compared to the cohort identified by query of the vitals table. For the UCD task, text query increased the cohort size by 2-43% compared to the cohort identified by query of laboratory results or ICD codes. The positive predictive values for text searches were 52% for Gingko/Warfarin, 19-94% for the weight cohort and 44% for UCD. DISCUSSION This project demonstrates the value and limitation of free text queries in patient cohort identification from large data sets. The clinical domain and prevalence of the inclusion and exclusion criteria in the patient population influence the utility and yield of this approach.

An Interactive Simulation to Change Outcome Expectancies and Intentions in Adults With Type 2 Diabetes: Within-Subjects Experiment

Background Computerized simulations are underutilized to educate or motivate patients with chronic disease. Objective The aim of this study was to test the efficacy of an interactive, personalized simulation that demonstrates the acute effect of physical activity on blood glucose. Our goal was to test its effects on physical activity-related outcome expectancies and behavioral intentions among adults with type 2 diabetes mellitus (T2DM). Methods In this within-subjects experiment, potential participants were emailed a link to the study website and directed through 7 tasks: (1) consent; (2) demographics, baseline intentions, and self-reported walking; (3) orientation to the diurnal glucose curve; (4) baseline outcome expectancy, measured by a novel drawing task in which participants use their mouse to draw the expected difference in the diurnal glucose curve if they had walked; (5) interactive simulation; (6) postsimulation outcome expectancy measured by a second drawing task; and (7) final measures of intentions and impressions of the website. To test our primary hypothesis that participants’ outcome expectancies regarding walking would shift toward the outcome presented in the interactive simulation, we used a paired t test to compare the difference of differences between the change in area under the curve in the simulation and participants’ two drawings. To test whether intentions to walk increased, we used paired t tests. To assess the intervention’s usability, we collected both quantitative and qualitative data on participants’ perceptions of the drawing tasks and simulation. Results A total of 2019 individuals visited the website and 1335 (566 males, 765 females, and 4 others) provided complete data. Participants were largely late middle-aged (mean=59.8 years; standard deviation=10.5), female 56.55% (755/1335), Caucasian 77.45% (1034/1335), lower income 64.04% (855/1335) t1334=3.4, P ≤.001). Our second hypothesis, that participants’ intentions to walk in the coming week would increase, was also supported; general intention (mean difference=0.31/7, t1001=10.8, P<.001) and minutes of walking last week versus planned for coming week (mean difference=33.5 min, t1334=13.2, P<.001) both increased. Finally, an examination of qualitative feedback and drawing task data suggested that some participants had difficulty understanding the website. This led to a post-hoc subset analysis. In this analysis, effects for our hypothesis regarding outcome expectancies were markedly stronger, suggesting that further work is needed to determine moderators of the efficacy of this simulation. Conclusions A novel interactive simulation is efficacious in changing the outcome expectancies and behavioral intentions of adults with T2DM. We discuss applications of our results to the design of mobile health (mHealth) interventions.