book.bib

@misc{What_are_clinical_trials_2023,
  title={What Are Clinical Trials and Studies?},
  url={https://www.nia.nih.gov/health/clinical-trials-and-studies/what-are-clinical-trials-and-studies},
  abstractNote={Interested in clinical research? Learn about the phases of clinical trials, why older and diverse participants are needed, and what to ask before participating.},
  journal={National Institute on Aging},
  year={2023},
  month=mar,
  language={en}
}

@article{psihogios_ethical_2024,
	title = {Ethical considerations in using sensors to remotely assess pediatric health behaviors.},
	volume = {79},
	copyright = {http://www.apa.org/pubs/journals/resources/open-access.aspx},
	issn = {1935-990X, 0003-066X},
	url = {https://doi.apa.org/doi/10.1037/amp0001196},
	doi = {10.1037/amp0001196},
	abstract = {Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.},
	language = {en},
	number = {1},
	urldate = {2025-04-22},
	journal = {American Psychologist},
	author = {Psihogios, Alexandra M. and King-Dowling, Sara and Mitchell, Jonathan A. and McGrady, Meghan E. and Williamson, Ariel A.},
	month = jan,
	year = {2024},
	pages = {39--51}
}
	
@misc{NIHClinicalTrialsProspectively,
  title={NIH Clinical Trials},
  journal={Division of Research at Brown University},
  url={https://division-research.brown.edu/research-cycle/proposal/agency-specific/nih/clinical-trials},
  abstractNote={The National Institutes of Health (NIH) requires that all funding applications involving one or more clinical trials be submitted through a funding opportunity announcement (FOA) or request for proposal (RFP) specifically designed for clinical trials.},
  language={en}
}

@misc{CTDefinition,
  title={NIH's Definition of a Clinical Trial},
  url={https://grants.nih.gov/policy-and-compliance/policy-topics/clinical-trials/definition}
}

@book{Drummond_Sculpher_Claxton_Stoddart_Torrance_2015, 
  address={Oxford},
  title={Methods for the Economic Evaluation of Health Care Programmes},
  ISBN={978-0-19-966588-4},
  publisher={Oxford University Press},
  author={Drummond, M.F. and Sculpher, M.J. and Claxton, K. and Stoddart, G.L. and Torrance, G.W.},
  year={2015},
  month=sep
}



@misc{AHRQ,
  title = {AHRQ Quality Indicator Measures}, 
  journal = {Agency for Healthcare Research and Quality (Department of Health and Human Services)},
  url={https://qualityindicators.ahrq.gov/measures/qi_resources} 
}

@article{Bodaghi_Fattahi_Ramazani_2023,
 title={Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases},
 volume={9},
 ISSN={2405-8440},
 url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884646/},
 DOI={10.1016/j.heliyon.2023.e13323},
 abstractNote={The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.},
 number={2},
 journal={Heliyon},
 author={Bodaghi, Ali and Fattahi, Nadia and Ramazani, Ali},
 year={2023},
 month=jan,
 pages={e13323}
}

@article{BoydCCT2023,
  title={Equity and bias in electronic health records data},
  volume={130},
  ISSN={1551-7144},
  url={https://www.sciencedirect.com/science/article/pii/S1551714423001611},
  DOI={10.1016/j.cct.2023.107238},
  abstractNote={Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.},
  journal={Contemporary Clinical Trials},
  author={Boyd, Andrew D. and Gonzalez-Guarda, Rosa and Lawrence, Katharine and Patil, Crystal L. and Ezenwa, Miriam O. and O’Brien, Emily C. and Paek, Hyung and Braciszewski, Jordan M. and Adeyemi, Oluwaseun and Cuthel, Allison M. and Darby, Juanita E. and Zigler, Christina K. and Ho, P. Michael and Faurot, Keturah R. and Staman, Karen and Leigh, Jonathan W. and Dailey, Dana L. and Cheville, Andrea and Del Fiol, Guilherme and Knisely, Mitchell R. and Marsolo, Keith and Richesson, Rachel L. and Schlaeger, Judith M.},
  year={2023},
  month=jul,
  pages={107238}
}

@article{BoydJAMIA2023,
  title={Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory},
  volume={30},
  ISSN={1527-974X},
  url={https://doi.org/10.1093/jamia/ocad115},
  DOI={10.1093/jamia/ocad115},
  abstractNote={Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.},
  number={9},
  journal={Journal of the American Medical Informatics Association},
  author={Boyd, Andrew D and Gonzalez-Guarda, Rosa and Lawrence, Katharine and Patil, Crystal L and Ezenwa, Miriam O and O’Brien, Emily C and Paek, Hyung and Braciszewski, Jordan M and Adeyemi, Oluwaseun and Cuthel, Allison M and Darby, Juanita E and Zigler, Christina K and Ho, P Michael and Faurot, Keturah R and Staman, Karen L and Leigh, Jonathan W and Dailey, Dana L and Cheville, Andrea and Del Fiol, Guilherme and Knisely, Mitchell R and Grudzen, Corita R and Marsolo, Keith and Richesson, Rachel L and Schlaeger, Judith M},
  year={2023},
  month=sep,
  pages={1561–1566}
}

@article{Bradshaw2022,
  title={GARDE: a standards-based clinical decision support platform for identifying population health management cohorts},
  volume={29},
  ISSN={1067-5027},
  url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006693/},
  DOI={10.1093/jamia/ocac028},
  abstractNote={Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. Objective: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. Materials and Methods: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. Results: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. Discussion: GARDE’s component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.}, number={5}, journal={Journal of the American Medical Informatics Association : JAMIA},
  author={Bradshaw, Richard L and Kawamoto, Kensaku and Kaphingst, Kimberly A and Kohlmann, Wendy K and Hess, Rachel and Flynn, Michael C and Nanjo, Claude J and Warner, Phillip B and Shi, Jianlin and Morgan, Keaton and Kimball, Kadyn and Ranade-Kharkar, Pallavi and Ginsburg, Ophira and Goodman, Melody and Chambers, Rachelle and Mann, Devin and Narus, Scott P and Gonzalez, Javier and Loomis, Shane and Chan, Priscilla and Monahan, Rachel and Borsato, Emerson P and Shields, David E and Martin, Douglas K and Kessler, Cecilia M and Del Fiol, Guilherme},
  year={2022},
  month=feb,
  pages={928–936}
}


@article{Dhir2008,
  title={A multidisciplinary approach to honest broker services for tissue banks and clinical data},
  volume={113},
  url={https://acsjournals-onlinelibrary-wiley-com.fhcrc.idm.oclc.org/doi/10.1002/cncr.23768},
  DOI={10.1002/cncr.23768},
  issue={7},
  journal={Cancer},
  author={Rajiv Dhir and Ashok A Patel and Sharon Winters and Michelle Bisceglia and Dennis Swanson and Roger Aamodt and Michael J Becich},
  year={2008},
  month=aug,
  pages={1705-1715}
}

@article{Gupta2020,
  title={Mailed Fecal Immunochemical Test Outreach for Colorectal Cancer Screening: Summary of a Centers for Disease Control-Sponsored Summit},
  volume={70},
  ISSN={0007-9235},
  url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523556/},
  DOI={10.3322/caac.21615},
  abstractNote={Uptake of colorectal cancer (CRC) screening remains suboptimal. Mailed fecal immunochemical testing (FIT) offers promise for increasing screening rates, but optimal strategies for implementation have not been well synthesized. In June 2019, the Centers for Disease Control and Prevention convened a meeting of subject matter experts and stakeholders to answer key questions regarding mailed FIT implementation in the US. Points of agreement included: 1) Primers such as texts, phone calls, and printed mailings prior to mailed FIT appear to contribute to effectiveness; 2) Invitation letters should be brief, easy to read, and signatory should be tailored based on setting; 3) Instructions for FIT completion should be simple and address challenges that may lead to failed lab processing, such as notation of collection date; 4) Reminders delivered to initial non-completers should be utilized to increase FIT return; 5) Data infrastructure should identify eligible patients and track each step in the outreach process, from primer delivery through abnormal FIT follow up; 6) Protocols and procedures such as navigation should be in place to promote colonoscopy after abnormal FIT; 7) A high-quality, 1 sample FIT should be used; 8) Sustainability requires a program champion and organizational support for the work, including sufficient funding, and external policies (such as quality reporting requirements) to drive commitment to program investment; and 9) Cost effectiveness of mailed FIT has been established. Participants concluded that Mailed FIT is an effective and efficient strategy with great potential for increasing CRC screening in diverse healthcare settings if more widely implemented.},
  number={4},
  journal={CA: a cancer journal for clinicians},
  author={Gupta, Samir and Coronado, Gloria D. and Argenbright, Keith and Brenner, Alison T. and Castañeda, Sheila F. and Dominitz, Jason A. and Green, Beverly and Issaka, Rachel B. and Levin, Theodore R. and Reuland, Daniel S. and Richardson, Lisa C. and Robertson, Douglas J. and Singal, Amit G. and Pignone, Michael},
  year={2020},
  month=jul,
  pages={283–298}
}


@article{Kaphingst2024,
  title={Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial},
  volume={7},
  ISSN={2574-3805},
  url={https://doi.org/10.1001/jamanetworkopen.2024.32143},
  DOI={10.1001/jamanetworkopen.2024.32143},
  abstractNote={Increasing numbers of unaffected individuals could benefit from genetic evaluation for inherited cancer susceptibility. Automated conversational agents (ie, chatbots) are being developed for cancer genetics contexts; however, randomized comparisons with standard of care (SOC) are needed.To examine whether chatbot and SOC approaches are equivalent in completion of pretest cancer genetic services and genetic testing.This equivalence trial (Broadening the Reach, Impact, and Delivery of Genetic Services [BRIDGE] randomized clinical trial) was conducted between August 15, 2020, and August 31, 2023, at 2 US health care systems (University of Utah Health and NYU Langone Health). Participants were aged 25 to 60 years, had had a primary care visit in the previous 3 years, were eligible for cancer genetic evaluation, were English or Spanish speaking, had no prior cancer diagnosis other than nonmelanoma skin cancer, had no prior cancer genetic counseling or testing, and had an electronic patient portal account.Participants were randomized 1:1 at the patient level to the study groups at each site. In the chatbot intervention group, patients were invited in a patient portal outreach message to complete a pretest genetics education chat. In the enhanced SOC control group, patients were invited to complete an SOC pretest appointment with a certified genetic counselor.Primary outcomes were completion of pretest cancer genetic services (ie, pretest genetics education chat or pretest genetic counseling appointment) and completion of genetic testing. Equivalence hypothesis testing was used to compare the study groups.This study included 3073 patients (1554 in the chatbot group and 1519 in the enhanced SOC control group). Their mean (SD) age at outreach was 43.8 (9.9) years, and most (2233 of 3063 [72.9%]) were women. A total of 204 patients (7.3%) were Black, 317 (11.4%) were Latinx, and 2094 (75.0%) were White. The estimated percentage point difference for completion of pretest cancer genetic services between groups was 2.0 (95% CI, −1.1 to 5.0). The estimated percentage point difference for completion of genetic testing was −1.3 (95% CI, −3.7 to 1.1). Analyses suggested equivalence in the primary outcomes.The findings of the BRIDGE equivalence trial support the use of chatbot approaches to offer cancer genetic services. Chatbot tools can be a key component of sustainable and scalable population health management strategies to enhance access to cancer genetic services.ClinicalTrials.gov Identifier: NCT03985852},
  number={9},
  journal={JAMA Network Open},
  author={Kaphingst, Kimberly A. and Kohlmann, Wendy K. and Lorenz Chambers, Rachelle and Bather, Jemar R. and Goodman, Melody S. and Bradshaw, Richard L. and Chavez-Yenter, Daniel and Colonna, Sarah V. and Espinel, Whitney F. and Everett, Jessica N. and Flynn, Michael and Gammon, Amanda and Harris, Adrian and Hess, Rachel and Kaiser-Jackson, Lauren and Lee, Sang and Monahan, Rachel and Schiffman, Joshua D. and Volkmar, Molly and Wetter, David W. and Zhong, Lingzi and Mann, Devin M. and Ginsburg, Ophira and Sigireddi, Meenakshi and Kawamoto, Kensaku and Del Fiol, Guilherme and Buys, Saundra S.},
  year={2024},
  month=sep,
  pages={e2432143}
}

@misc{ncdj_style_guide,
  title = {Disability Language Style Guide},
  journal = {National Center on Disability and Journalism},
  url={https://ncdj.org/style-guide/},
  language={en-US},
  year={2021}
}

@article{Osheroff_2007,
  title={A Roadmap for National Action on Clinical Decision Support},
  volume={14},
  ISSN={1067-5027},
  url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2213467/},
  DOI={10.1197/jamia.M2334},
  abstractNote={This document comprises an AMIA Board of Directors approved White Paper that presents a roadmap for national action on clinical decision support. It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder.},
  number={2},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  author={Osheroff, Jerome A. and Teich, Jonathan M. and Middleton, Blackford and Steen, Elaine B. and Wright, Adam and Detmer, Don E.},
  year={2007},
  pages={141–145}
}


@Manual{rmarkdown2021,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2021},
  note = {R package version 2.10},
  url = {https://github.com/rstudio/rmarkdown},
}

@article{Swarthout_Bishop_2017,
  title={Population health management: Review of concepts and definitions},
  volume={74},
  ISSN={1079-2082},
  url={https://doi.org/10.2146/ajhp170025},
  DOI={10.2146/ajhp170025},
  abstractNote={The terms population health, population health improvement, and population health management are discussed.A key concept in defining population health activities is clearly delineating the population(s) of focus. The Institute for Healthcare Improvement’s (IHI’s) Triple Aim Initiative uses the term population health management to describe the work by healthcare organizations to improve outcomes for individual patients to maximize population health. The National Academy of Medicine favors the term population health improvement and uses this term to describe work to identify and improve aspects of or contributors to population health, expanding the focus beyond traditional healthcare delivery systems. As organizations like IHI and the National Academy of Medicine continue to focus on population health, the terms and definitions used to describe these activities will continue to evolve.The use of consistent, clear definitions for population health activities is critical to the practice of pharmacy and healthcare delivery.},
  number={18},
  journal={American Journal of Health-System Pharmacy},
  author={Swarthout, Meghan and Bishop, Martin A.},
  year={2017},
  month=sep,
  pages={1405–1411}
}

@Book{Xie2018,
  title = {R Markdown: The Definitive Guide},
  author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2018},
  note = {ISBN 9781138359338},
  url = {https://bookdown.org/yihui/rmarkdown},
}

@Book{Xie2020,
  title = {R Markdown Cookbook},
  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2020},
  note = {ISBN 9780367563837},
  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}


@article{psihogios_ethical_2024,
	title = {Ethical considerations in using sensors to remotely assess pediatric health behaviors.},
	volume = {79},
	copyright = {http://www.apa.org/pubs/journals/resources/open-access.aspx},
	issn = {1935-990X, 0003-066X},
	url = {https://doi.apa.org/doi/10.1037/amp0001196},
	doi = {10.1037/amp0001196},
	abstract = {Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.},
	language = {en},
	number = {1},
	urldate = {2025-04-22},
	journal = {American Psychologist},
	author = {Psihogios, Alexandra M. and King-Dowling, Sara and Mitchell, Jonathan A. and McGrady, Meghan E. and Williamson, Ariel A.},
	month = jan,
	year = {2024},
	pages = {39--51}
	}

@article{vrijens_new_2012,
	title = {A new taxonomy for describing and defining adherence to medications},
	volume = {73},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0306-5251, 1365-2125},
	url = {https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/j.1365-2125.2012.04167.x},
	doi = {10.1111/j.1365-2125.2012.04167.x},
	abstract = {Interest in patient adherence has increased in recent years, with a growing literature that shows the pervasiveness of poor adherence to appropriately prescribed medications. However, four decades of adherence research has not resulted in uniformity in the terminology used to describe deviations from prescribed therapies. The aim of this review was to propose a new taxonomy, in which adherence to medications is conceptualized, based on behavioural and pharmacological science, and which will support quantifiable parameters. A systematic literature review was performed using MEDLINE, EMBASE, CINAHL, the Cochrane Library and PsycINFO from database inception to 1 April 2009. The objective was to identify the different conceptual approaches to adherence research. Definitions were analyzed according to time and methodological perspectives. A taxonomic approach was subsequently derived, evaluated and discussed with international experts. More than 10 different terms describing medication‐taking behaviour were identified through the literature review, often with differing meanings. The conceptual foundation for a new, transparent taxonomy relies on three elements, which make a clear distinction between processes that describe actions through established routines (‘Adherence to medications’, ‘Management of adherence’) and the discipline that studies those processes (‘Adherence‐related sciences’). ‘Adherence to medications’ is the process by which patients take their medication as prescribed, further divided into three quantifiable phases: ‘Initiation’, ‘Implementation’ and ‘Discontinuation’. In response to the proliferation of ambiguous or unquantifiable terms in the literature on medication adherence, this research has resulted in a new conceptual foundation for a transparent taxonomy. The terms and definitions are focused on promoting consistency and quantification in terminology and methods to aid in the conduct, analysis and interpretation of scientific studies of medication adherence.},
	language = {en},
	number = {5},
	urldate = {2025-04-22},
	journal = {British Journal of Clinical Pharmacology},
	author = {Vrijens, Bernard and De Geest, Sabina and Hughes, Dyfrig A. and Przemyslaw, Kardas and Demonceau, Jenny and Ruppar, Todd and Dobbels, Fabienne and Fargher, Emily and Morrison, Valerie and Lewek, Pawel and Matyjaszczyk, Michal and Mshelia, Comfort and Clyne, Wendy and Aronson, Jeffrey K. and Urquhart, J. and {for the ABC Project Team}},
	month = may,
	year = {2012},
	pages = {691--705}
	}
	
	@article{modi_pediatric_2012,
	title = {Pediatric {Self}-management: {A} {Framework} for {Research}, {Practice}, and {Policy}},
	volume = {129},
	issn = {0031-4005, 1098-4275},
	shorttitle = {Pediatric {Self}-management},
	url = {https://publications.aap.org/pediatrics/article/129/2/e473/32549/Pediatric-Self-management-A-Framework-for-Research},
	doi = {10.1542/peds.2011-1635},
	abstract = {Self-management of chronic pediatric conditions is a formidable challenge for patients, families, and clinicians, with research demonstrating a high prevalence of poor self-management and nonadherence across pediatric conditions. Nevertheless, effective self-management is necessary to maximize treatment efficacy and clinical outcomes and to reduce unnecessary health care utilization and costs. However, this complex behavior is poorly understood as a result of insufficient definitions, reliance on condition-specific and/or adult models of self-management, failure to consider the multitude of factors that influence patient self-management behavior, and lack of synthesis of research, clinical practice, and policy implications. To address this need, we present a comprehensive conceptual model of pediatric self-management that articulates the individual, family, community, and health care system level influences that impact self-management behavior through cognitive, emotional, and social processes. This model further describes the relationship among self-management, adherence, and outcomes at both the patient and system level. Implications for research, clinical practice, and health care policy concerning pediatric chronic care are emphasized with a particular focus on modifiable influences, evidence-based targets for intervention, and the role of clinicians in the provision of self-management support. We anticipate that this unified conceptual approach will equip stakeholders in pediatric health care to (1) develop evidence-based interventions to improve self-management, (2) design programs aimed at preventing the development of poor self-management behaviors, and (3) inform health care policy that will ultimately improve the health and psychosocial outcomes of children with chronic conditions.},
	language = {en},
	number = {2},
	urldate = {2025-04-22},
	journal = {Pediatrics},
	author = {Modi, Avani C. and Pai, Ahna L. and Hommel, Kevin A. and Hood, Korey K. and Cortina, Sandra and Hilliard, Marisa E. and Guilfoyle, Shanna M. and Gray, Wendy N. and Drotar, Dennis},
	month = feb,
	year = {2012},
	pages = {e473--e485}
	}