Treatment Effect Estimates From Pilot Trials Are Unreliable.

CONTEXT: The CONSORT guideline defines a pilot trial as a small-scale version of a desired future efficacy trial that is intended to answer the key questions of whether and how a larger study should be done. For example, a pilot trial might evaluate different approaches to data collection or outcome measurement. However, pilot trials are unreliable for assessing treatment efficacy due to the statistical phenomenon called sampling variability. OBJECTIVES: In this tutorial we use computer simulation to demonstrate the influence of sampling variability on efficacy estimates from pilot trials, illustrating why pilot trial designs should not be used to evaluate whether a treatment is promising or not. METHODS: We simulate a 2-arm parallel group trial (N=20 per group) with a survival outcome as an example. Simulations are done under two scenarios: 1) the treatment is efficacious at the level of a hypothetical minimum clinically important difference (hazard ratio [HR] = 0.75); and 2) the treatment is not efficacious (HR=1). RESULTS: As expected, in both simulated scenarios the range of observed results is distributed around the true treatment effect, HR=0.75 or HR=1. Importantly, ∼20% of trials simulated under scenario 1 incorrectly suggest the treatment may be harmful (HR > 1). Under scenario 2, half of the simulated studies incorrectly suggest the treatment is beneficial. CONCLUSION: Treatment effect estimates from pilot trials should not be used to make future development decisions regarding a novel therapy because of the high risk of misleading conclusions.

Advancing workforce diversity by leveraging the Clinical and Translational Science Awards (CTSA) program.

Clinical trials continue to disproportionately underrepresent people of color. Increasing representation of diverse backgrounds among clinical research personnel has the potential to yield greater representation in clinical trials and more efficacious medical interventions by addressing medical mistrust. In 2019, North Carolina Central University (NCCU), a Historically Black College and University with a more than 80% underrepresented student population, established the Clinical Research Sciences Program with support from the Clinical and Translational Science Awards (CTSA) program at neighboring Duke University. This program was designed to increase exposure of students from diverse educational, racial, and ethnic backgrounds to the field of clinical research, with a special focus on health equity education. In the first year, the program graduated 11 students from the two-semester certificate program, eight of whom now hold positions as clinical research professionals. This article describes how leveraging the CTSA program helped NCCU build a framework for producing a highly trained, competent, and diverse workforce in clinical research responsive to the call for increased diversity in clinical trial participation.

A Visual Tool to Help Develop a Statistical Analysis Plan for Randomized Trials in Palliative Care.

Collaboration with a statistician about the design of a statistical analysis plan can be enhanced by illustrating how statisticians conceptualize their task. This conceptualization can be represented by a directed acyclic graph (DAG), which illustrates the statistician's approach and also provides an actionable tool to assist in the development of the plan.

Secondary Prevention Risk Interventions via Telemedicine and Tailored Patient Education (SPRITE): A randomized trial to improve post myocardial infarction management.

OBJECTIVE: We evaluated the impact of a low intensity web-based and intensive nurse-administered intervention to reduce systolic blood pressure (SBP) among patients with prior MI. METHODS: Secondary Prevention Risk Interventions via Telemedicine and Tailored Patient Education (SPRITE) was a three-arm trial. Patients were randomized to 1) post-MI education-only; 2) nurse-administered telephone program; or 3) web-based interactive tool. The study was conducted 2009-2013. RESULTS: Participants (n = 415) had a mean age of 61 years (standard deviation [SD], 11). Relative to the education-only group, the 12-month differential improvement in SBP was - 3.97 and - 3.27 mmHg for nurse-administered telephone and web-based groups, respectively. Neither were statistically significant. Post hoc exploratory subgroup analyses found participants who received a higher dose (>12 encounters) in the nurse-administered telephone intervention (n = 60; 46%) had an 8.8 mmHg (95% CI, 0.69, 16.89; p = 0.03) differential SBP improvement versus low dose (<11 encounters; n = 71; 54%). For the web-based intervention, those who had higher dose (n = 73; 53%; >1 web encounter) experienced a 2.3 mmHg (95% CI, -10.74, 6.14; p = 0.59) differential SBP improvement versus low dose (n = 65; 47%). CONCLUSIONS: The main effects were not statistically significant. PRACTICAL IMPLICATIONS: Completing the full dose of the intervention may be essential to experience the intervention effect. CLINICAL TRIAL REGISTRATION: The unique identifier is NCT00901277 (http://www. CLINICALTRIALS: gov/ct2/show/NCT00901277?term=NCT00901277&rank=1).

Experiential Learning Methods for Biostatistics Students: A Model for Embedding Student Interns in Academic Health Centers.

This manuscript describes an experiential learning program for future collaborative biostatisticians (CBs) developed within an academic medical center. The program is a collaborative effort between the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the Master of Biostatistics (MB) program, both housed in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and supported in partnership with the Duke Clinical and Translational Science Institute. To date, the BERD Core Training and Internship Program (BCTIP) has formally trained over 80 students to work on collaborative teams that are integrated throughout the Duke School of Medicine. This manuscript focuses on the setting for the training program, the experiential learning model on which it is based, the structure of the program, and lessons learned to date.

Impact of lifestyle Intervention on branched-chain amino acid catabolism and insulin sensitivity in adolescents with obesity.

Insulin resistance in adolescents with obesity associates with a sex-dependent metabolic 'signature' comprising branched-chain amino acids (BCAAs), glutamate and C3/C5 acylcarnitines (C3/C5), implicating altered flux through BCAA catabolic pathways. Here, we investigated the effects of lifestyle intervention on BCAA catabolism and insulin sensitivity. We hypothesized (1) weight reduction and improved insulin sensitivity associate with enhanced BCAA catabolism; (2) baseline BCAAs and their metabolic by-products predict changes in weight and insulin sensitivity during lifestyle intervention. METHODS: A 33 adolescents with obesity were studied before and after 6 months of lifestyle intervention. Principal component analysis and multiple linear regression models were used to correlate changes in metabolic factors with changes in weight and insulin sensitivity assessed by HOMA-IR, adiponectin and ratio of triglyceride (TG) to HDL. Baseline metabolic factors were used as explanatory variables in prediction models. RESULTS: Weight reduction was associated with reductions in BCAA, glutamate, and C3/C5 (p = .002) and increases in urea cycle AA (p = .029), suggesting an increase in BCAA catabolism. Increases in urea cycle AA during weight reduction were associated with increases in adiponectin, a marker of insulin sensitivity. Markers of insulin resistance (high BCAA, glutamate, and C3/C5 and low urea cycle AA) at baseline predicted increases in metrics of insulin sensitivity (decreased TG/HDL and increased adiponectin) during lifestyle intervention. CONCLUSIONS: Weight reduction in adolescents is associated with increases in BCAA catabolism and improvements in insulin sensitivity. Our study underscores the therapeutic potential of manipulating BCAA catabolism to treat obesity-associated insulin resistance in adolescents and prevent progression to T2D.

Methods for training collaborative biostatisticians.

The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles. The training strategy also incorporates a "train the trainer" approach to enable CBs who have successfully completed training to train new staff or faculty.

A multimodal international collaborative clinical research training program in China.

Background: There is a strong need to include training of research methods in training programs for physicians. International clinical research training programs (CRTP) that comprehensively introduce the methodology of clinical research and combined with practice should be a priority. However, few studies have reported a multimodal international CRTP that provides clinicians with an introduction to the quantitative and methodological principles of clinical research. Objective: This manuscript is intended to comprehensively describe the development process and the structure of this multimodal training program. Methods: The CRTP was comprised of three distinct, sequential learning components: part 1 - a six-week online eLearning self-study; part 2 - a series of three weekly interactive synchronous webinars conducted between Durham, North Carolina, USA and Beijing, China; and part 3 - a five-day in-person workshop held at Beijing Friendship Hospital, Capital Medical University (BFH-CMU). Self-assessment quiz scores and participation rates were used to evaluate effectiveness of the training program. Participants' demographic characteristics, research experience, satisfaction and feedback on the program were collected using questionnaires. Results: A total of 50 participants joined the CRTP. Forty-four participants (88%) completed the program satisfaction questionnaires. The average quiz score of the six eLearning units varied from 31% to 73%. Among the three components of the program, the online eLearning self-study was felt to be the most challenging. Thirty-nine (89%) of the surveyed respondents were satisfied with all components of the training program. Among the respondents, 43 (98%) felt the training was helpful in preparing them for future clinical research projects and expressed willingness to recommend the program to other colleagues. Conclusions: We established a multimodal international collaborative training program. The program demonstrated acceptable participation rates and high satisfaction among Chinese clinicians. It provides a model that may be used by others developing similar international clinical research training programs for physicians.

Epidemiologic Trends in Clostridioides difficile Infections in a Regional Community Hospital Network.

Importance: Clostridioides difficile infection (CDI) remains a leading cause of health care facility-associated infection. A greater understanding of the regional epidemiologic profile of CDI could inform targeted prevention strategies. Objectives: To assess trends in incidence of health care facility-associated and community-acquired CDI among hospitalized patients over time and to conduct a subanalysis of trends in the NAP1 strain of CDI over time. Design, Setting, and Participants: This long-term multicenter cohort study reviewed records of patients (N = 2 025 678) admitted to a network of 43 regional community hospitals primarily in the southeastern United States from January 1, 2013, through December 31, 2017. Generalized linear mixed-effects models were used to adjust for potential clustering within facilities and changing test method (nucleic acid amplification testing or toxin enzyme immunoassay) over time. Main Outcomes and Measures: Clostridioides difficile infection incidence rates were counted as cases per 1000 admissions for community-acquired and total CDI cases or cases per 10 000 patient-days for health care facility-associated CDI. Long-term trends in the proportion of cases acquired in the community and in NAP1 strain incidence were also evaluated. Results: A total of 2 025 678 admissions and 21 254 CDI cases were included (12 678 [59.6%] female; median [interquartile range] age, 69 [55-80] years). Median (interquartile range) total CDI incidence increased slightly from 7.9 (3.5-12.4) cases per 1000 admissions in 2013 to 9.3 (4.9-13.7) cases per 1000 admissions in 2017. After adjustment, the overall incidence of health care facility-associated CDI declined (incidence rate ratio [IRR], 0.995; 95% CI, 0.990-0.999; P = .03), whereas insufficient evidence was found for either an increase or a decrease in community-acquired CDI (IRR, 1.004; 95% CI, 0.999-1.009; P = .14). The proportion of cases classified as community acquired increased over time from a mean (SD) of 0.49 (0.28) in 2013 to 0.61 (0.26) in 2017 (odds ratio, 1.010 per month; 95% CI, 1.006-1.015; P < .001). Rates of the NAP1 strain of CDI varied widely between facilities, with no statistically significant change in NAP1 strain incidence over time in the community setting (IRR, 1.007; 95% CI, 0.994-1.021) or health care facility setting (IRR, 1.011; 95% CI, 0.990-1.032). Conclusions and Relevance: The findings suggest that, despite the modest improvement in health care facility-associated CDI rates, a better understanding of community-acquired CDI incidence is needed for future infection prevention efforts.

A New Stress Test for Knee Joint Cartilage.

Cartilage metabolism-both the synthesis and breakdown of cartilage constituents and architecture-is influenced by its mechanical loading. Therefore, physical activity is often recommended to maintain cartilage health and to treat or slow the progression of osteoarthritis, a debilitating joint disease causing cartilage degeneration. However, the appropriate exercise frequency, intensity, and duration cannot be prescribed because direct in vivo evaluation of cartilage following exercise has not yet been performed. To address this gap in knowledge, we developed a cartilage stress test to measure the in vivo strain response of healthy human subjects' tibial cartilage to walking exercise. We varied both walk duration and speed in a dose-dependent manner to quantify how these variables affect cartilage strain. We found a nonlinear relationship between walk duration and in vivo compressive strain, with compressive strain initially increasing with increasing duration, then leveling off with longer durations. This work provides innovative measurements of cartilage creep behavior (which has been well-documented in vitro but not in vivo) during walking. This study showed that compressive strain increased with increasing walking speed for the speeds tested in this study (0.9-2.0 m/s). Furthermore, our data provide novel measurements of the in vivo strain response of tibial cartilage to various doses of walking as a mechanical stimulus, with maximal strains of 5.0% observed after 60 minutes of walking. These data describe physiological benchmarks for healthy articular cartilage behavior during walking and provide a much-needed baseline for studies investigating the effect of exercise on cartilage health.

The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial.

BACKGROUND: Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies. OBJECTIVE: This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement. METHODS: The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%). RESULTS: Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=-.213; personal coaching: r=-.319), number of apps use per day (cell phone: r=-.264; personal coaching: r=-.308), and percentage of days self-weighed (cell phone: r=-.297; personal coaching: r=-.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement. CONCLUSIONS: Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X).

Indications for and outcomes of therapeutic plasma exchange after cardiac transplantation: A single center retrospective study.

INTRODUCTION: Limited data are available describing indications for and outcomes of therapeutic plasma exchange (TPE) in cardiac transplantation. METHODS: In a retrospective study of patients who underwent cardiac transplantation at Duke University Medical Center from 2010 to 2014, we reviewed 3 TPE treatment patterns: a Single TPE procedure within 24 h of transplant; Multiple TPE procedures initiated within 24 h of transplant; and 1 or more TPE procedures beginning >24 h post-transplant. Primary and secondary outcomes were overall survival (OS) and TPE survival (TS), respectively. RESULTS: Of 313 patients meeting study criteria, 109 (35%) underwent TPE. TPE was initiated in 82 patients within 24 h, 40 (37%) receiving a single procedure (Single TPE), and 42 (38%) multiple procedures (Multiple TPE). Twenty-seven (25%) began TPE >24 h after transplant (Delayed TPE). The most common TPE indication was elevated/positive panel reactive or human leukocyte antigen antibodies (32%). With a median follow-up of 49 months, the non-TPE treated and Single TPE cohorts had similar OS (HR 1.08 [CI, 0.54, 2.14], P = .84), while the Multiple and Delayed TPE cohorts had worse OS (HR 2.62 [CI, 1.53, 4.49] and HR 1.98 [CI, 1.02, 3.83], respectively). The Multiple and Delayed TPE cohorts also had worse TS (HR 2.59 [CI, 1.31, 5.14] and HR 3.18 [CI, 1.56, 6.50], respectively). Infection rates did not differ between groups but was independently associated with OS (HR 2.31 [CI, 1.50, 3.54]). CONCLUSIONS: TPE is an important therapeutic modality in cardiac transplant patients. Prospective studies are needed to better define TPE's different roles in this patient population.

Outcome by Gender in the Veterans Health Administration Motivating Overweight/Obese Veterans Everywhere Weight Management Program.

BACKGROUND: Few evaluations of the Veterans Health Administration Motivating Overweight/Obese Veterans Everywhere (MOVE!) weight management program have assessed 6-month weight change or factors associated with weight change by gender. MATERIALS AND METHODS: Analysis of administrative data from a national sample of veterans in the VA MOVE! RESULTS: A total of 62,882 participants were included, 14.6% were women. Compared with men, women were younger (49.6 years [standard deviation, SD, 10.8] vs. 59.3 years [SD, 9.8], p < 0.0001), less likely to be married (34.1% vs. 56.0%, p < 0.0001), and had higher rates of post-traumatic stress disorder (26.0% vs. 22.4%, p < 0.0001) and depression (49.3% vs. 32.9%, p < 0.001). The mean number of MOVE! visits attended by women was lower than men (5.6 [SD, 5.3] vs. 6.0 [SD, 5.9], p < 0.0001). Women, compared with men, reported lower rates of being able to rely on family or friends (35.7% vs. 40.8%, p < 0.0001). Observed mean percent change in weight for women was -1.5% (SD, 5.2) and for men was -1.9% (SD, 4.8, p < 0.0001). The odds of ≥5% weight loss were no different for women (body-mass index [BMI] >25 kg/m2) compared with men (BMI >25 kg/m2; odds ratio, 1.05 [95% confidence interval, 0.99-1.11; p = 0.13]). CONCLUSIONS: Women veterans lost less weight overall compared with men. There was no difference in the odds of achieving clinically significant weight loss by gender. The majority of women and men enrolled lost <5% weight despite being enrolled in a lifestyle intervention. Future studies should focus on identifying program- and participant-level barriers to weight loss.

Statistical competencies for medical research learners: What is fundamental?

INTRODUCTION: It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from 'fundamental' (necessary for all) to 'specialized' (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized. METHODS: We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by 'fundamental' and 'specialized.' RESULTS: There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one's own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%). CONCLUSION: We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.

The Symptom Experience in Rectal Cancer Survivors.

CONTEXT: As the number of rectal cancer survivors grows, it is important to understand the symptom experience after treatment. Although data show that rectal cancer survivors experience a variety of symptoms after diagnosis, little has been done to study the way these symptoms are grouped and associated. OBJECTIVES: To determine symptom prevalence and intensity in rectal cancer survivors and if clusters of survivors exist, who share similar symptom-defined survivor subgroups that may vary based on antecedent variables. METHODS: A secondary analysis of the Cancer Care and Outcomes Research and Surveillance database was undertaken. Cluster analysis was performed on 15-month postdiagnosis data to form post-treatment survivor subgroups, and these were examined for differences in demographic and clinical characteristics. Data were analyzed using cluster analysis, chi-square, and analysis of variance. RESULTS: A total of 275 rectal cancer survivors were included who had undergone chemotherapy, radiation therapy, and surgery. Most frequently reported symptoms included feeling "worn out" (87%), feeling "tired" (85%), and "trouble sleeping" (66%). Four symptom-defined survivor subgroups (minimally symptomatic n = 40, tired and trouble sleeping n = 138, moderate symptoms n = 42, and highly symptomatic n = 55) were identified with symptom differences existing among each subgroup. Age and being married/partnered were the only two antecedents found to differ across subgroups. CONCLUSION: This study documents differences in the symptom experience after treatment. The identification of survivor subgroups allows researchers to further investigate tailored, supportive care strategies to minimize ongoing symptoms in those with the greatest symptom burden.

Mobile health devices: will patients actually use them?

Although mobile health (mHealth) devices offer a unique opportunity to capture patient health data remotely, it is unclear whether patients will consistently use multiple devices simultaneously and/or if chronic disease affects adherence. Three healthy and three chronically ill participants were recruited to provide data on 11 health indicators via four devices and a diet app. The healthy participants averaged overall weekly use of 76%, compared to 16% for those with chronic illnesses. Device adherence declined across all participants during the study. Patients with chronic illnesses, with arguably the most to benefit from advanced (or increased) monitoring, may be less likely to adopt and use these devices compared to healthy individuals. Results suggest device fatigue may be a significant problem. Use of mobile technologies may have the potential to transform care delivery across populations and within individuals over time. However, devices may need to be tailored to meet the specific patient needs.

Cell phone intervention for you (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology.

OBJECTIVE: To determine the effect on weight of two mobile technology-based (mHealth) behavioral weight loss interventions in young adults. METHODS: Randomized, controlled comparative effectiveness trial in 18- to 35-year-olds with BMI ≥ 25 kg/m(2) (overweight/obese), with participants randomized to 24 months of mHealth intervention delivered by interactive smartphone application on a cell phone (CP); personal coaching enhanced by smartphone self-monitoring (PC); or Control. RESULTS: The 365 randomized participants had mean baseline BMI of 35 kg/m(2) . Final weight was measured in 86% of participants. CP was not superior to Control at any measurement point. PC participants lost significantly more weight than Controls at 6 months (net effect -1.92 kg [CI -3.17, -0.67], P = 0.003), but not at 12 and 24 months. CONCLUSIONS: Despite high intervention engagement and study retention, the inclusion of behavioral principles and tools in both interventions, and weight loss in all treatment groups, CP did not lead to weight loss, and PC did not lead to sustained weight loss relative to Control. Although mHealth solutions offer broad dissemination and scalability, the CITY results sound a cautionary note concerning intervention delivery by mobile applications. Effective intervention may require the efficiency of mobile technology, the social support and human interaction of personal coaching, and an adaptive approach to intervention design.

Adaptive intervention design in mobile health: Intervention design and development in the Cell Phone Intervention for You trial.

BACKGROUND/AIMS: The obesity epidemic has spread to young adults, and obesity is a significant risk factor for cardiovascular disease. The prominence and increasing functionality of mobile phones may provide an opportunity to deliver longitudinal and scalable weight management interventions in young adults. The aim of this article is to describe the design and development of the intervention tested in the Cell Phone Intervention for You study and to highlight the importance of adaptive intervention design that made it possible. The Cell Phone Intervention for You study was a National Heart, Lung, and Blood Institute-sponsored, controlled, 24-month randomized clinical trial comparing two active interventions to a usual-care control group. Participants were 365 overweight or obese (body mass index≥25 kg/m2) young adults. METHODS: Both active interventions were designed based on social cognitive theory and incorporated techniques for behavioral self-management and motivational enhancement. Initial intervention development occurred during a 1-year formative phase utilizing focus groups and iterative, participatory design. During the intervention testing, adaptive intervention design, where an intervention is updated or extended throughout a trial while assuring the delivery of exactly the same intervention to each cohort, was employed. The adaptive intervention design strategy distributed technical work and allowed introduction of novel components in phases intended to help promote and sustain participant engagement. Adaptive intervention design was made possible by exploiting the mobile phone's remote data capabilities so that adoption of particular application components could be continuously monitored and components subsequently added or updated remotely. RESULTS: The cell phone intervention was delivered almost entirely via cell phone and was always-present, proactive, and interactive-providing passive and active reminders, frequent opportunities for knowledge dissemination, and multiple tools for self-tracking and receiving tailored feedback. The intervention changed over 2 years to promote and sustain engagement. The personal coaching intervention, alternatively, was primarily personal coaching with trained coaches based on a proven intervention, enhanced with a mobile application, but where all interactions with the technology were participant-initiated. CONCLUSION: The complexity and length of the technology-based randomized clinical trial created challenges in engagement and technology adaptation, which were generally discovered using novel remote monitoring technology and addressed using the adaptive intervention design. Investigators should plan to develop tools and procedures that explicitly support continuous remote monitoring of interventions to support adaptive intervention design in long-term, technology-based studies, as well as developing the interventions themselves.

Physician Non-adherence to Colonoscopy Interval Guidelines in the Veterans Affairs Healthcare System.

BACKGROUND & AIMS: Colonoscopy can decrease colorectal cancer (CRC) mortality, although performing this procedure more frequently than recommended could increase costs and risks to patients. We aimed to determine rates and correlates of physician non-adherence to guidelines for repeat colonoscopy screening and polyp surveillance intervals. METHODS: We performed a multi-center, retrospective, observational study using administrative claims, physician databases, and electronic medical records (EMR) from 1455 patients (50-64 y old) who underwent colonoscopy in the Veterans Affairs healthcare system in fiscal year 2008. Patients had no prior diagnosis of CRC or inflammatory bowel disease, and had not undergone colonoscopy examinations in the previous 10 years. We compared EMR-documented, endoscopist-recommended intervals for colonoscopies with intervals recommended by the 2008 Multi-Society Task Force guidelines. RESULTS: The overall rate of non-adherence to guideline recommendations was 36% and ranged from 3% to 80% among facilities. Non-adherence was 28% for patients who underwent normal colonoscopies, but 45%-52% after colonoscopies that identified hyperplastic or adenomatous polyps. Most of all recommendations that were not followed recommended a shorter surveillance interval. In adjusted analyses, non-adherence was significantly higher for patients whose colonoscopies identified hyperplastic (odds ratio [OR] = 3.1; 95% CI, 1.7-5.5) or high-risk adenomatous polyps (OR = 3.0; 95% CI, 1.2-8.0), compared to patients with normal colonoscopy examinations, but not for patients with low-risk adenomatous polyps (OR = 1.8; 95% CI, 0.9-3.7). Nonadherence was also associated with bowel preparation quality, geographic region, Charlson comorbidity score, and colonoscopy indication. CONCLUSIONS: In a managed care setting with salaried physicians, endoscopists recommend repeat colonoscopy sooner than guidelines for more than one third of patients. Factors associated with non-adherence to guideline recommendations were colonoscopy findings, quality of bowel preparation, and geographic region. Targeting endoscopist about non-adherence to colonoscopy guidelines could reduce overuse of colonoscopy and associated healthcare costs.