An addiction-based mobile health weight loss intervention: protocol of a randomized controlled trial
Contemporary Clinical Trials
Alaina P Vidmar, Sarah J Salvy, Robert Pretlow, Steven D Mittelman, Choo Phei Wee, Cassandra Fink, D Steven Fox, Jennifer K Raymond
2019
This multi-center randomized control trial (RCT) is designed to test the effectiveness of a mobile health (mHealth) weight-loss intervention based on addiction principles, such as withdrawal and tolerance, in a sample of 180 adolescents (ages 14–18) recruited from four pediatric weight management clinics in Southern California. Akin to a Multiphase Optimization Strategy (MOST) design evaluating multicomponent behavioral interventions, we will compare the combination of an app + phone coaching (App+Coach) to app alone (App) and in-clinic multi-disciplinary (Clinic) intervention arms.
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An addiction model‐based mobile health weight loss intervention in adolescents with obesity
Pediatric Obesity
AP Vidmar, R Pretlow, C Borzutzky, CP Wee, DS Fox, C Fink, SD Mittelman
2019
Adolescents with obesity were recruited from an multidisciplinary weight management clinic (EMPOWER). Adolescents without significant obesity comorbidities, who exhibited signs of addictive eating, based on the Yale Food Addiction Scale, were enrolled in a pilot study of an interactive, addiction‐based, weight loss smartphone app with coaching (http://clinicaltrials.gov: NCT02689154). The app was designed to help subjects omit problem foods, avoid snacking and reduce meal size. A contemporary cohort of adolescents who completed the EMPOWER program were evaluated. Feasibility of recruitment, adherence, retention rates, BMI change and cost of intervention were examined.
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Comparative treatment effectiveness of direct acting antiviral regimens for hepatitis C: data from the Veterans Administration
Journal of Gastroenterology and Hepatology
D Steven Fox, Justin J McGinnis, Ivy Q Tonnu‐Mihara, Jeffrey S McCombs
2017
A retrospective cohort study was conducted using patients who terminated treatment by July 1, 2015. Data were retrieved from the Veterans Health Administration electronic medical records system. Patients were included if sufficient viral load laboratory data were available to determine sustained virologic response. Applying an intention to treat approach and logistic regression analysis, the sustained virologic response rates achieved were compared across drug regimens.
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The impact of delayed hepatitis C viral load suppression on patient risk: historical evidence from the veterans administration
Forum for Health Economics and Policy
Tara Matsuda, Jeffrey S McCombs, Ivy Tonnu-Mihara, Justin McGinnis, D Steven Fox
2017
187,860 patients were selected from the Veterans Administration’s (VA) clinical registry (CCR), a longitudinal compilation of electronic medical records (EMR) data for 1999–2010. Inclusion criteria required at least 6 months of CCR/EMR data prior to their HCV diagnosis and sufficient data post-diagnosis to calculate one or more FIB-4 scores. Primary outcome measures were time-to-death and time-to-a composite of liver-related clinical events. Cox proportional hazards models were estimated separately using three critical FIB-4 levels to define early and late viral response.
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Optimizing HCV treatment–Moving beyond the cost conundrum
Journal of Hepatology
D Steven Fox, Jeffrey S McCombs
2016
When is it appropriate to delay a potentially lifesaving treatment? Should treatment decisions be based purely on economic grounds? In the United States, these questions confront payers and government programs covering patients with hepatitis C virus (HCV) infection. New, highly effective oral treatments for this serious infection have recently received food and drug administration (FDA) approval in the U.S. but their list prices make immediate treatment of all infected patients infeasible. In response, many payers have instituted coverage policies that authorize treatment only for the sickest patients, putting off therapy for less severely ill patients. However, new data suggests that this approach may constitute a suboptimal policy, if not carefully executed. While not all patients require immediate treatment, an optimal strategy should treat patients before they progress too far towards end-stage disease; beyond the point when even highly effective treatments can confer only diminished benefit. Minimally invasive clinical markers of disease progression should be monitored to help guide when treatment should be initiated.
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