Primary Care Clinic Approaches to Facilitating Patient Health Behavior Change in Alabama.

Background  Non-communicable chronic diseases (NCCDs), such as cardiovascular disease, diabetes, and cancer, are the leading cause of death and disability and the leading driver of healthcare costs in the U.S. It is estimated that 80% of chronic diseases and premature deaths are attributable to modifiable lifestyle factors related to smoking and alcohol intake, poor eating patterns, and physical inactivity. Inadequate sleep also plays a significant role. Among other directives, primary care providers (PCPs) have the opportunity to contribute to preventing and treating NCCD in their patients. Comprehensive, evidence-based behavioral counseling interventions are recommended to PCPs as a first-line approach to improving outcomes. However, presumably due to a lack of PCP time, training or resources, most patients report not receiving such services. Currently, the extent to which PCPs in Alabama offer or refer patients to health behavior change (HBC) services is unknown.  Objectives  This study aims to assess the following: (1) Alabama PCPs' current approaches in facilitating patient HBC in the domains of eating patterns, physical activity, sleep, and stress and (2) the likelihood of the Alabama PCPs referring patients to virtual HBC programs, once developed by an osteopathic medical school in the state.  Methods  Data were collected from clinic personnel who were knowledgeable regarding the clinic's approach to facilitating patient HBC via scripted telephone interviews and online surveys sent via email. The clinic list utilized for the study was derived from a list of VCOM-Auburn clinical preceptors. Primary care and specialty clinics were included. Data were analyzed descriptively to determine the number of clinics that (1) provide, recommend, or refer programs, services, or resources to patients to facilitate HBC related to eating patterns, physical activity, sleep, and stress management and (2) are likely to refer patients to free virtual HBC programs, once developed by an osteopathic medical school in the state. Results  Of the 198 clinics that were contacted, 75 were excluded, 46 were "no response," 53 agreed to participate, and 50 completed the survey. Of the 50 clinics that completed the survey, 33 indicated offering resources or referrals for diet, 29 stated they offered resources or referral services for physical activity, 33 indicated offering resources or referrals for sleep, and 28 indicated offering or recommending resources for stress management to patients. Most of the clinics (29/50) felt that their patients would benefit most from a program that facilitates improvement in eating patterns, and 41/50 clinics said that they are either "somewhat" or "extremely" likely to refer patients to a free VCOM-Auburn HBC program, once available.  Conclusions Findings indicate that a significant percentage of PCP clinics are not offering HBC resources to patients and that most PCP clinics would consider referring patients to free VCOM-Auburn HBC programs, once available. Phone data were significantly different from email data. The primary limitations were a low response rate and potential response bias.

Tiny Habits® for Gratitude-Implications for Healthcare Education Stakeholders.

The COVID-19 pandemic has led to diminished sleep and increased stress, anxiety, and burnout for many health professionals and health professions students. One simple approach that may be effective for bolstering personal well-being is consciously cultivating gratitude. Gratitude is positively associated with physical health, psychological health, hope, sleep, and health behavior engagement; and randomized studies indicate that gratitude interventions can improve psychological well-being and sleep. The primary aim of this study was to assess the impact of practicing Tiny Habits® on self-reported gratitude, as measured by the 6-Item Gratitude Questionnaire (GQ-6). In January 2021, 154 adult participants with GQ-6 <35/42 were randomized to one of 3 groups: Tiny Habits for Gratitude (n = 50), Tiny Habits Control (n = 52), and Inactive Control (n = 52). Both Tiny Habits groups chose 3 Tiny Habits Recipes to practice daily and participated in the free, email-based 5-Day Program with automated daily check-in emails and personalized feedback from a Certified Tiny Habits Coach. The Recipes for the Tiny Habits for Gratitude group focused on cultivating gratitude, while those for the Tiny Habits Control group did not. Post-intervention, the mean change in GQ-6 scores in the Tiny Habits for Gratitude (Δ = ↑6.9 ± 5.6; n = 37/50, 74%; p< 0.001, Cohen's d = 0.85) and Tiny Habits Control (Δ = ↑5.6 ± 4.1; n = 31/52, 60%; p = 0.009, Cohen's d = 0.71) groups were greater than that of the Inactive Control group (Δ = ↑2.5 ± 4.4; N = 42/52, 81%). At 1 month, the mean change in GQ-6 scores in the Tiny Habits for Gratitude group (Δ = ↑7.0 ± 5.3; N = 28/50, 56%) was greater than that of the Inactive Control group (Δ = ↑2.9 ± 5.4; N = 39/52, 72%; p = 0.002, Cohen's d = 0.78). These findings suggest that practicing Tiny Habits Recipes and participating in the 5-Day Program can significantly increase gratitude in the short term and focusing specifically on gratitude during this process can sustain the increase in gratitude for up to 1 month. Implementation is quick, simple, and free. This has significant implications for healthcare education stakeholders.

Evaluation of the impact of enhanced virtual forms and gamification on intervention identification in a pharmacist-led ambulatory care clinic.

Background: Adoption of healthcare technology in the ambulatory care setting is nearly universal. Clinical decision support system (CDSS) technologies improve patient care through the identification of additional care opportunities. With the movement from paper-based to electronic clinical intake forms, the opportunity to improve identification of gaps in care utilizing CDSS in the ambulatory care setting exists. Objective: To evaluate the impact of CDSS-enhanced digital intake forms, with- and without aspects of gamification, on the identification of intervention opportunities in an ambulatory care pharmacy setting. Methods: Patients were invited to complete visit intake paperwork via virtual forms as part of a CDSS-enhanced mobile application designed to identify potential interventions based on patient age, sex, disease state(s), and user-provided information. Patients were randomized to receive optional patient-specific health questions 1) with or 2) without elements of gamification. Gamification elements included trivia questions, fun facts, and the chance to win a prize. A retrospective review was used to assess interventions identified for a random sample of patients seen within the same time frame who did not utilize the mobile application. Interventions were compared across groups utilizing ANOVA. t-tests were used for a subgroup analysis. Results: From January to May 2019, 353 potential interventions were identified for 220 study participants. 0.44 (±0.82), 1.8 (±2.0) and 2.1 (±1.8) interventions per participant were identified for the control, virtual forms, and virtual forms + gamification groups, respectively. Significant differences in intervention identification across groups were found using a one-way ANOVA (F = 17.46, p < .001). Post hoc analysis demonstrated a significant difference in interventions identified for those completing 50-100% (n = 32) and those completing less than 50% (n = 18; p < .001) of the optional health questions in the virtual forms + gamification group. Conclusions: Utilization of CDSS-enhanced clinical intake forms increased identification of potential interventions, though gamification did not significantly impact this identification.

Protocol for Minute Calisthenics: a randomized controlled study of a daily, habit-based, bodyweight resistance training program.

BACKGROUND: Resistance-training (RT) provides significant health benefits. However, roughly 3/4 of adults in the United States do not meet current Physical Activity Guidelines in this regard. There has been a call for research examining the effectiveness of interventions to increase participation in physical activity and to better understand the dose response relationship upon health outcomes. Studies are needed that assess the effectiveness of RT programs that are time-efficient and simple to perform. This fully-powered, randomized controlled study will assess a habit-based RT program consisting of one set of push-ups, angled-rows, and bodyweight-squats performed every weekday for 12-24 weeks in untrained individuals. METHODS: Forty-60 untrained osteopathic medical students and college/university employees who work in an office setting will be recruited and randomized (1:1) to an intervention or waitlist control group. After 12-week follow-up assessment, the intervention group will continue the program and the control group will initiate the program for 12 weeks. In addition to the equipment and training needed to safely perform the exercises, all participants will receive training in the Tiny Habits® Method (THM) and digital coaching for the duration of the study. Participants will complete weekly assessments regarding the program during their initial 12-week intervention phase. The primary outcome is the change from baseline to 12 weeks in the intervention group versus the control group, in the combined number of repetitions performed in one set of each of the three exercises (composite repetitions) under a standardized protocol. Secondary outcomes include adherence to and satisfaction with the program, and change from baseline to 12- and 24-week follow-up in blood pressure, fasting lipid panel, hemoglobin A1c, body mass index, anthropometry, body composition, mid-thigh muscle thickness, and habit strength. DISCUSSION: This study will evaluate a simple, habit-based RT intervention in untrained individuals. The approach is unique in that it utilizes brief but frequent bodyweight exercises and, via the THM, focuses on consistency and habit formation first, with effort being increased as participants are motivated and able. If effective, the program can be easily scaled for wider adoption. TRIAL REGISTRATION: This study was prospectively registered at ClinicalTrials.gov, identifier NCT04207567 , on December 23rd, 2019.

Implementation of a Medicare Plan Selection Assistance Program Through a Community Partnership.

Objective. To describe the implementation and outcomes of a sustainable Medicare Plan Selection Assistance Program conducted through a partnership between Auburn University Harrison School of Pharmacy (AU) and the Alabama State Health Insurance and Assistance Program (SHIP) since 2013. Methods. The program's goal is to assist Medicare beneficiaries in Medicare Part D plan selection. Reported outcomes included Medicare beneficiaries' plan cost savings and satisfaction, and pharmacy students' self-reported changes in knowledge and attitudes. Results. Each year, more than 80 pharmacy students assist more than 120 beneficiaries; at least 10 events are held covering 6-10 Alabama counties. On average, Medicare beneficiaries had a projected savings of $278.71 (2013), $1,081.66 (2014), $842.84 (2015), and $1,382.90 (2016) after enrolling in a new plan, and most students reported perceived increased ability to help beneficiaries select the most appropriate Medicare Part D plan. Conclusion. The program produced positive outcomes for both beneficiaries and students. Other pharmacy schools may consider partnering with their State Health Insurance and Assistance Program to deliver a similar program to benefit their students and Medicare beneficiaries.

Development and delivery of an interdisciplinary course in mobile health (mHealth).

BACKGROUND: Interdisciplinary practice is a primary focus within the United States health care system. Despite the existence of interdisciplinary educational efforts in pharmacy for many years, the practice only recently entered mainstream health care education. Informatics offers interdisciplinary educational opportunities. Mobile health (mHealth), an informatics sub-discipline, is the use of mobile devices for health and wellness activities. We used the mHealth domain as the context for an interdisciplinary learning experience for pharmacy and computer science and software engineering (CSSE) students. EDUCATIONAL ACTIVITY: Educational activities focused on creating an mHealth course sequence and an mHealth application (app). Students worked in teams to complete various assignments, including developing and presenting a course proposal, building a purchase plan for mHealth equipment, developing an mHealth app prototype, delivering a disease state presentation (pharmacy students only), developing use case scenarios (CSSE students only), and completing peer evaluations. CRITICAL ANALYSIS OF THE EDUCATIONAL ACTIVITY: Course evaluations were only available from pharmacy students. They indicated an overall favorable rating (mean 3.27-3.55; 4=strongly agree). Survey data collected after students entered the workforce indicated that students felt better prepared than their colleagues to work with individuals from other disciplines (mean 4.82; 5=strongly agree). Students also indicated using the knowledge from course proposal development in their careers (mean 4.27; 5=strongly agree). Through team interactions, students learned from each other while overcoming challenges related to terminology and content areas. Skills learned through team interactions reflect real-world processes and are expected to support students' future professional responsibilities.

Expert panel assessment of acute liver injury identification in observational data.

BACKGROUND: Observational data are useful for studying drug safety; however, to be effective, accurate outcome measurement is paramount. OBJECTIVES: This study compared alternative outcome definitions for acute liver injury (ALI) and explored opportunities for improving ALI identification in observational data. METHODS: The Truven MarketScan® Lab Database (MSLR) was used to identify patients meeting at least 1 of 4 ALI definitions, including definitions based on diagnosis codes, laboratory measures, or combinations of diagnoses, procedures, and/or laboratory measures. Expert panelists reviewed patient data using a Web dashboard. Panelists determined whether they believed the patient had ALI and identified factors influencing their decision. Logistic regression models explored which factors were influential in case determination. RESULTS: Overall, only 37 of 208 reviewed patients (17.8%) were classified as cases. The diagnosis-based definition yielded no positive cases and the laboratory-based definition yielded the most positive cases (31 of 60). The most influential factors in case classification were occurrence of procedures after the index date (OR = 13.2, 95% CI = 5.3-32.9), no occurrence of drug treatments before the index date (OR = 4.6; 95% CI = 1.6-13.2), occurrence of drug treatments before the index date (OR = 0.3; 95% CI = 0.1-0.6), and no drug treatments after the index date (OR = 0.2; 95% CI = 0.0-0.5). CONCLUSIONS: Comparing ALI definitions illustrated tradeoffs between the number of plausible cases identified and the likelihood of cases being classified as positive. Future research should refine ALI case definitions, considering the import of laboratory results, procedures, and drugs in defining a case.

How well do various health outcome definitions identify appropriate cases in observational studies?

BACKGROUND: Observational data can be useful for drug safety research, but accurate measurement of adverse health outcomes is paramount. Best practices for identifying important health outcomes of interest (HOI) are needed. OBJECTIVES: To evaluate the extent to which health outcome definitions commonly used in observational database studies identify cases that are consistent with expert panel assessment of the underlying data. METHODS: Competing HOI definitions were used to identify potential cases of acute liver injury (ALI; n = 208), acute kidney injury (AKI; n = 200), and myocardial infarction (MI; n = 204) in the Truven MarketScan Lab Database (MSLR). Panelists reviewed patient-level data and answered questions about whether they believed the case actually reflected the HOI and their certainty of case classification on a 10-point scale (1 = unlikely to 10 = likely). Each patient was reviewed independently by two panelists. Case disagreements were resolved through consensus meetings. Positive predictive value (PPV) was calculated as the number of cases deemed to be true over the total number of sampled cases. Kappa statistics assessed inter-rater agreement. RESULTS: PPV ranged from 0 to 52 % across ALI definitions, 12 to 82 % across AKI definitions, and 1 to 56 % across MI definitions. Certainty scores on the 10-point scale paralleled the PPV, with a range of mean values from 1.7 to 4.8 across ALI definitions, 3.1 to 6.0 across AKI definitions, and 2.8 to 5.7 across MI definitions. Inter-rater agreement was low to moderate (Kappa range 0.0-0.6). IMPLICATIONS/CONCLUSIONS: Existing HOI definitions had relatively low PPV based on expert panel review. Experts commonly disagreed on case classification. Additional work is needed to refine HOI case definitions if observational data are to be reliably used for health outcome assessment.

Developing an expert panel process to refine health outcome definitions in observational data.

OBJECTIVES: Drug safety surveillance using observational data requires valid adverse event, or health outcome of interest (HOI) measurement. The objectives of this study were to develop a method to review HOI definitions in claims databases using (1) web-based digital tools to present de-identified patient data, (2) a systematic expert panel review process, and (3) a data collection process enabling analysis of concepts-of-interest that influence panelists' determination of HOI. METHODS: De-identified patient data were presented via an interactive web-based dashboard to enable case review and determine if specific HOIs were present or absent. Criteria for determining HOIs and their severity were provided to each panelist. Using a modified Delphi method, six panelist pairs independently reviewed approximately 200 cases across each of three HOIs (acute liver injury, acute kidney injury, and acute myocardial infarction) such that panelist pairs independently reviewed the same cases. Panelists completed an assessment within the dashboard for each case that included their assessment of the presence or absence of the HOI, HOI severity (if present), and data contributing to their decision. Discrepancies within panelist pairs were resolved during a consensus process. RESULTS: Dashboard development was iterative, focusing on data presentation and recording panelists' assessments. Panelists reported quickly learning how to use the dashboard. The assessment module was used consistently. The dashboard was reliable, enabling an efficient review process for panelists. Modifications were made to the dashboard and review process when necessary to facilitate case review. Our methods should be applied to other health outcomes of interest to further refine the dashboard and case review process. CONCLUSION: The expert review process was effective and was supported by the web-based dashboard. Our methods for case review and classification can be applied to future methods for case identification in observational data sources.