Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 29.03.19 in Vol 3, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/11300, first published Jun 14, 2018.

This paper is in the following e-collection/theme issue:

    Original Paper

    Challenges in the Development of e-Quit worRx: An iPad App for Smoking Cessation Counseling and Shared Decision Making in Primary Care

    1Department of Family and Community Medicine, University of Cincinnati, Cincinnati, OH, United States

    2School of Social Work, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States

    Corresponding Author:

    Charles R Doarn, MBA

    Department of Family and Community Medicine

    University of Cincinnati

    231 Albert Sabin Way

    ML0582

    Cincinnati, OH, 45267

    United States

    Phone: 1 5135586148

    Fax:1 5135585344

    Email: charles.doarn@uc.edu


    ABSTRACT

    Background: Smoking is the leading preventable cause of morbidity and mortality in the United States, killing more than 450,000 Americans. Primary care physicians (PCPs) have a unique opportunity to discuss smoking cessation evidence in a way that enhances patient-initiated change and quit attempts. Patients today are better equipped with technology such as mobile devices than ever before.

    Objective: The aim of this study was to evaluate the challenges in developing a tablet-based, evidence-based smoking cessation app to optimize interaction for shared decision making between PCPs and their patients who smoke.

    Methods: A group of interprofessional experts developed content and a graphical user interface for the decision aid and reviewed these with several focus groups to determine acceptability and usability in a small population.

    Results: Using a storyboard methodology and subject matter experts, a mobile app, e-Quit worRx, was developed through an iterative process. This iterative process helped finalize the content and ergonomics of the app and provided valuable feedback from both patients and provider teams. Once the app was made available, other technical and programmatic challenges arose.

    Conclusions: Subject matter experts, although generally amenable to one another’s disciplines, are often challenged with effective interactions, including language, scope, clinical understanding, technology awareness, and expectations. The successful development of this app and its evaluation in a clinical setting highlighted those challenges and reinforced the need for effective communications and team building.

    JMIR Form Res 2019;3(1):e11300

    doi:10.2196/11300

    KEYWORDS



    Introduction

    Background

    Smoking is the leading preventable cause of morbidity and mortality in the United States [1]. Each year, smoking kills nearly 450,000 people in the United States and costs almost US $100 billion in health care costs and productivity losses. An estimated 19% of adults in the United States smoke [1]. Although numerous interventions improve the likelihood of successful smoking cessation and the resulting health benefits [2], most smokers relapse or require several interventions and attempts before staying smoke-free [3]. Primary care physicians (PCPs) have an opportunity to discuss smoking cessation evidence in a way that enhances patient-initiated change [4] and quit attempts [5] using new approaches. Unfortunately, although current guidelines summarize the comparative effectiveness of available smoking cessation medications, counseling techniques, and other methods, including smoking cessation apps and social media tools [6-8], physicians discuss cessation with smokers infrequently and underutilize tobacco cessation medications [9,10]. A shared decision-making (SDM) tool can add value to the interchange between a PCP and patient.

    Methods that allow physicians to conduct more frequent, efficient tobacco counseling are necessary to disseminate smoking cessation evidence [11-14] and could have a substantial impact, as even brief counseling by a PCP can increase the likelihood of smoking cessation [4]. Decision aids are a method that can assist clinicians and patients in finding motivating, personally effective quit strategies that can be integrated into physician offices where patient-provider discussions about smoking cessation typically occur [15].

    Innovative Approaches

    The use of a hand-held electronic tool can increase physicians’ comfort with cessation counseling [16]. Furthermore, SDM has the potential to engage and inform patients and improve quality of care, especially when combined with decision aids and health information technology tools [17]. Integrating such tools can also enable better and timely interaction between a PCP and patient. Tudor-Sfetea et al evaluated mobile health (mHealth) apps in the context of smoking cessation [18]. As a result of using Quit Genius or Smokefree, which are 2 smoking cessation mobile apps in the United Kingdom, this study demonstrated positive preliminary changes in smoking behavior and it was demonstrated that these apps are feasible and potentially effective tools [18].

    With so many options for cessation support, it is important for clinicians to personalize evidence-based interventions that are both useful and appealing to patients. During primary care office visits with competing priorities [19], applying patient-centered outcomes research (PCOR) for any given problem can be challenging but can also benefit workflow in the clinic setting by increasing efficiency.

    To address these opportunities and challenges, we developed an iPad/tablet-based mHealth decision aid app (e-Quit worRx, University of Cincinnati) to assist PCPs in disseminating PCOR evidence about smoking cessation options and engage in SDM.

    The primary objective of this research effort was to develop an acceptable and easy-to-use smoking cessation decision aid that incorporated PCOR evidence into an mHealth tablet-based app called e-Quit worRx. We have discussed the challenges of developing the app through an iterative process.


    Methods

    Project Team

    An interdisciplinary team of subject matter experts was formed to complete this project. These experts included specialists in primary care, smoking cessation and social work, health information technology and mHealth, app development and computer programming, and qualitative and primary care practice–based research. Each brought a unique perspective to the overall design of the app both in functionality and utility.

    Conceptual Framework

    This project was guided by a conceptual framework grounded in SDM and behavioral theories of smoking cessation (eg, stages of change and the 5As—Ask, Advise, Assess, Assist, and Arrange; Figure 1) [20]. This framework includes key determinants, tools, and outcomes that lead to SDM and smoking cessation. We adapted frameworks developed for primary care for smoking cessation counseling [21] and for SDM such as those used in colorectal cancer screening [22] to create a conceptual framework to guide the study innovations, interventions, and outcomes. We aimed to combine theory-driven aspects of smoking cessation (eg, stages of change and self-efficacy) with iPad-based interactive, tailored delivery of PCOR evidence to smokers at the point-of-care (their PCP’s office). The overall goal of the decision aid was to provide evidence-based and patient-centered smoking risk and cessation information to patients. Once developed and acceptable to patients and physicians, the decision aid would then be introduced into a routine office visit while minimizing physician and office staff training and ongoing time commitment.

    Project Design

    The project was completed in 3 phases using a design process depicted in Figure 2: (1) development of a storyboard of app content and flow and initial app version; (2) evaluation of the app at various development stages with physicians, medical staff, and patients through an iterative process and app refinement; and (3) clinical pilot testing of the app with patients in the PCPs office. This process was used in the first 2 phases. The third phase will be reported in a separate study.

    Phase 1: Content Development, Initial Feedback, and Storyboarding

    Development of a new task or device is often conceptualized through the use of storyboarding [23]. Some processes of app development that are Web based have recently been patented [24]. Iqbal et al have laid out some of the requirements for engineering practices of mobile app development [25]. This process, although challenging, provides an excellent tool for development teams to understand what the final process or device should look like and how it works. The entire research team laid out a storyboard for how the app should flow from a physician and patient perspective.

    Figure 1. Conceptual framework for shared decision making using an app for evidence-based smoking cessation.
    View this figure
    Figure 2. Study flow and app design process.
    View this figure

    One of the steps in the storyboarding process is to provide input on how the user will interact with the app. Human factors and ergonomics play a role as well. To accomplish this, separate focus groups with patients and smoking cessation experts were held as well as individual interviews with PCPs and medical support staff. The goal of these initial sessions was to understand what stakeholders wanted and needed to be included in a clinical encounter for smoking cessation. These interviews addressed, as appropriate, previous smoking attempts, previous and desired communication about smoking, use and comfort with electronic media, and knowledge and comfort with evidence-based smoking cessation tools.

    Clinical evidence-based content for app development was obtained in a large part from the Smoking Cessation Guidelines for Clinicians [6] and Cochrane reviews [2]. In addition, content from the following were also obtained: the Centers for Disease Control and Prevention’s smokefree.gov website [26] and incorporation of feedback from focus group interviews as well as knowledge from the scientific literature in the following: (1) PCOR studies in the areas of primary care [4,27-29], (2) smoking cessation medications [30], (3) mHealth tools [31-34], and (4) decision aids [35].

    e-Quit worRx Coding and Design

    The team used an iPad platform (iPad 2, Apple), using iOS 7, as the user interface for the decision aid. Code for the app was written in Apple’s Xcode software on a MacMini using the Swift programming language (Apple). In total, 2 graduate students from the university’s computer science program worked with the team to write the code. Prototype app versions were tested on 3 iPad 2 devices.

    Phase 2: Iterative Usability Testing With Stakeholders and End Users

    Once a prototype app (version 1.0) was complete, a second round of key informant interviews was completed with patients, clinicians, and clinical support staff. These interviews focused on usability and included a modified System Usability Scale (SUS) as well as a semistructured questionnaire [36]. Interviews touched upon participants’ experiences using the app, recommendations for modifications, and evaluations of specific app components. Initial rounds of testing used concurrent think-aloud techniques to elicit real-time feedback and emotional responses. Later rounds of testing used retrospective think-aloud techniques to assess important metrics, such as accuracy and time, needed to complete tasks on the app.

    Participants

    This study was approved by the Institutional Review Board of the University of Cincinnati.

    For the first 2 phases described in this study, our team sought feedback from and recruited key stakeholders including patients and PCPs and primary care office staff (nurses, medical assistants, and office managers) ranging in their comfort and familiarity with technology. Table 1 summarizes the demographics and role of all participants. We also sought feedback from an interdisciplinary team of faculty and staff from the University of Cincinnati with expertise in addressing tobacco cessation.

    Table 1. Basic demographics and role in Phase 1 and Phase 2. The data reported here reflects those individuals who participated in year 1 in the following: (1) interviews (clinical personnel), (2) focus groups (experts and test patients), and (3) the testing with both clinical personnel and test patients.
    View this table

    Patient participants were recruited from the target practices for the eventual pilot trial and recruitment guidelines were in line with previous similar studies [27]. Nonpatient participants were recruited using the snowball technique, beginning with physicians and experts known to the research team, who were then asked to recommend others who could speak on the topic of interest and so on.

    A mixed-methods approach was incorporated for app design. The primary outcome was to determine usability. Data sources included qualitative feedback from semistructured interviews and focus groups with key stakeholders, SUS results, and feedback and discussions among our research team members. Interviews were conducted until saturation was achieved—no new ideas were being brought forward [37]. The stakeholders included 10 patients, 7 clinical support staff members (medical assistants and nurses), 8 primary care providers (physicians and advanced practice nurses), and 9 smoking cessation experts.


    Results

    Overview

    Stakeholder feedback was obtained iteratively before the first app version and with each of the 5 app versions (Table 2). During each increment, changes were made in app content, appearance, and flow based on detailed feedback from the focus groups with changes between versions ranging from relatively minor content revisions or additions to major changes to the graphical user interface. Figure 3 illustrates representative screenshots showing how the app content and appearance changed from version to version.

    Readability

    Testing of version 2.2 produced feedback that the literacy level was too high for the clinical populations served. A literacy evaluation revealed that the initial text averaged a seventh-grade reading level. Between app versions 2.2 and 2.3, text edits were made screen-by-screen and each focused on improving readability to a fifth-grade reading level (Figure 4 illustrates the decrease in the reading level and an increase in ease of reading; enabling a wider audience to understand the wording and phraseology of the app).

    Usability

    Usability, as assessed with the SUS, increased with each version for a final of 90/100, above 65 was considered usable (Table 3). After iterative usability testing, a final app version was ready for pilot testing in the clinical setting.

    Description of e-Quit worRx

    The app-based decision aid e-Quit worRx has several key components, including collecting (1) a comprehensive smoking history, (2) personal reasons for and against smoking, (3) barriers and facilitators to quitting, (4) describing treatment options, including their level of evidence, risks, and costs, and finally (5) summarizing content to aid in SDM. The graphical user interface was unidirectional but used branching logic based on user input. The app begins with a splash screen followed by a secure login screen so that user data were encrypted on the device. Before each participant used the device, the research assistant or principal investigator logged in for the user. The system then randomly assigned a number for each participant.

    The app was designed to personalize users’ examination of the positive and negative effects of smoking and increase their knowledge of smoking cessation treatment options.

    Treatment options included first-line medications, therapy including local cognitive behavioral therapy providers, and other treatments such as telephone quit lines and mHealth tools.

    A summary screen was saved, entirely customized to an individual’s input, to facilitate discussion with their PCP. The summary screen included personalized information derived from their responses. In addition to summarizing their personal considerations about the pros and cons of smoking, it summarized interest in the various cessation aids. The app included a provider input screen, where a plan was selected and an exit interview was to be completed by the research team after the clinical encounter.

    The app collects basic demographics, including race, sex, income, age, frequency of smoking, and desire to quit for control groups and intervention groups.

    Table 2. Themes from qualitative analysis of focus groups and usability testing.
    View this table
    Figure 3. Development of select e-Quit worRx screenshots from Grant Concept to Storyboard, through iterative app versions.
    View this figure
    Figure 4. Grade level (left scale) and Reading Ease score (right scale) for app version 2.2 (left) and 2.3 (right).
    View this figure

    User input, including audio capture from the exit interview, is temporarily stored on the device in an app-based database until the session is complete. Data are then uploaded wirelessly to a Health Insurance Portability and Accountability Act (HIPAA)–compliant Research Electronic Data Capture (REDCap) database.

    Overcoming challenges faced in the design process, a user friendly and acceptable iPad app-based decision aid for use in primary care offices was created. Challenges enumerated in Textbox 1 include navigating requests to our coders for repeated changes to both content and design, resolving conflicting feedback from our diverse group of stakeholders and even within our study group, realizing the time intensity of editing content and code, and integration into a clinical setting. We observed challenges between engineers and physicians that required management and interaction to remain on target.

    Limitations

    Our study had a few predicted and unforeseen limitations. Although we were able to upload content into the REDCap database, we were not, as we had foreseen, able to fully integrate the app into the electronic health record (EHR) so that patient selections and chosen interventions would populate into the medical record. Our Health Information Technology Department reported that both the timeframe and budget were far too small for this.

    Another limitation was that we were unable to create a generic app framework so that clinical content could be swapped out to create decision aid apps for other clinical scenarios, for example, diabetes medicine selection. This was an initial goal, but during the app design process, we made a decision to choose personalization for the patient over future generalizability.

    We also discovered that not only would much more work have to go into making our iPad app compatible with iPhones or even Android devices, but we had to choose landscape or portrait display on the iPad instead of allowing the user to decide to ensure the app displayed correctly on the screen. Making the display orientation neutral would have required more programming time than allowed for our study.

    Table 3. System Usability Scale across app versions.
    View this table

    Textbox 1. Examples of challenges by area.
    View this box

    We were able to integrate into the clinic sites in several ways. We gained access to the network and internet connection, allowing real-time secure data transfer to our database. We enabled automated HIPAA-compliant email messaging to patients at the end of the session summarizing the interventions chosen, and we built new matching templates (ie, SmartPhrases and a SmartSet order set) for our EHR so that providers could quickly copy over patient selections from the study. Finally, although the existing clinic printers could not be used to print from our app, we placed AirPrint-enabled printers at each site to allow printing summaries for patients and PCPs.


    Discussion

    mHealth is no longer a novel approach or tool for health care. Tools have been developed and tested for smoking cessation [15,27-30] and other clinical conditions. Today, patients are more engaged in the management of their health than ever before. SDM tools are important in the management of disease, and some health care is actually moving toward the patient-centered home [37-39]. Acceptance of computer-based tools in addressing patients’ needs whether in the home or exam room are more acceptable today as well [40-43].

    As demonstrated by this study, development of mHealth solutions is time-consuming and challenging. The life cycle of such devices is short-lived and must be upgradeable with changes in software versions, operating systems, and consumer needs. Nevertheless, health care will continue to integrate technologies such as e-Quit worRx into the management of a patient’s health.

    This research effort was focused on the development of an app for smoking cessation SDM using an iPad-based platform. A fully functional system was developed over several iterations. There were several challenges in the development phase. Insufficient funding limited the level of computer programming expertise. Although the students were adept at programming, the complexity of the software used to develop the app, concomitant with automatic Apple Operating System upgrades and the varying levels of communications, provided a significant challenge. Of the observations noted, 1 was the dichotomy in conversation between the graduate student programmers and clinicians. Multiple discussions and interaction with all of the research team provided resolution and a functional app, which was able to be used in clinical testing in year 2.

    Acknowledgments

    This study was funded by the Agency for Health Research and Quality #R21HS023994. The authors acknowledge the following individuals for their contributions in helping develop this research effort: Balaji Baskaran and Nandita Subramanian for their efforts in programming the app; Josh Magee helped during initial conceptualization of the project; Brett Harnett provided invaluable technical feedback and bridge building; Haley Boling and Kelsey Dirksing supported the research team, conducting literature searches and other tasks assigned by the principal investigator.

    Conflicts of Interest

    None declared.

    References

    1. Centers for Disease Control and Prevention. Fast Facts: Diseases and Death   URL: https://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/ [accessed 2019-02-25] [WebCite Cache]
    2. Hartmann-Boyce J, Stead L, Cahill K, Lancaster T. Efficacy of interventions to combat tobacco addiction: Cochrane update of 2013 reviews. Addiction 2014;109(9):1414-1425. [CrossRef] [Medline]
    3. Brandon TH, Tiffany ST, Baker TB. The process of smoking relapse. NIDA Res Monogr 1986;72:104-117. [Medline]
    4. Gorin SS, Heck JE. Meta-analysis of the efficacy of tobacco counseling by health care providers. Cancer Epidemiol Biomarkers Prev 2004 Dec;13(12):2012-2022 [FREE Full text] [Medline]
    5. Zwar NA, Richmond RL. Role of the general practitioner in smoking cessation. Drug Alcohol Rev 2006 Jan;25(1):21-26. [CrossRef] [Medline]
    6. Agency for Healthcare Research and Quality. Rockville; 2008. Treating Tobacco Use and Dependence: 2008 Update   URL: https://tinyurl.com/y3tpnce7 [accessed 2019-02-25] [WebCite Cache]
    7. Baskerville NB, Struik LL, Guindon GE, Norman CD, Whittaker R, Burns C, et al. Effect of a Mobile Phone Intervention on Quitting Smoking in a Young Adult Population of Smokers: Randomized Controlled Trial. JMIR Mhealth Uhealth 2018 Oct 23;6(10):e10893 [FREE Full text] [CrossRef] [Medline]
    8. McKelvey K, Ramo D. Conversation Within a Facebook Smoking Cessation Intervention Trial For Young Adults (Tobacco Status Project): Qualitative Analysis. JMIR Form Res 2018 Sep 04;2(2):e11138 [FREE Full text] [CrossRef] [Medline]
    9. Thorndike AN, Regan S, Rigotti NA. The treatment of smoking by US physicians during ambulatory visits: 1994 2003. Am J Public Health 2007 Oct;97(10):1878-1883. [CrossRef] [Medline]
    10. Goldstein MG, Niaura R, Willey-Lessne C, DePue J, Eaton C, Rakowski W, et al. Physicians counseling smokers. A population-based survey of patients' perceptions of health care provider-delivered smoking cessation interventions. Arch Intern Med 1997 Jun 23;157(12):1313-1319. [CrossRef] [Medline]
    11. Thorndike AN, Rigotti NA, Stafford RS, Singer DE. National patterns in the treatment of smokers by physicians. J Am Med Assoc 1998 Feb 25;279(8):604-608. [CrossRef] [Medline]
    12. Doescher MP, Saver BG. Physicians' advice to quit smoking. The glass remains half empty. J Fam Pract 2000 Jun;49(6):543-547. [Medline]
    13. Ellerbeck EF, Ahluwalia JS, Jolicoeur DG, Gladden J, Mosier MC. Direct observation of smoking cessation activities in primary care practice. J Fam Pract 2001 Aug;50(8):688-693. [Medline]
    14. Gilpin EA, Pierce JP, Johnson M, Bal D. Physician advice to quit smoking: results from the 1990 California Tobacco Survey. J Gen Intern Med 1993 Oct;8(10):549-553. [CrossRef] [Medline]
    15. Agarwal SD, Kerwin M, Meindertsma J, Wolf AM. A novel decision aid to encourage smoking cessation among patients at an urban safety net clinic. Prev Chronic Dis 2018 Dec 11;15:E124 [FREE Full text] [CrossRef] [Medline]
    16. Strayer SM, Heim SW, Rollins LK, Bovbjerg ML, Nadkarni M, Waters DB, et al. Improving smoking cessation counseling using a point-of-care health intervention tool (IT): from the Virginia Practice Support and Research Network (VaPSRN). J Am Board Fam Med 2013;26(2):116-125 [FREE Full text] [CrossRef] [Medline]
    17. Fowler FJ, Levin CA, Sepucha KR. Informing and involving patients to improve the quality of medical decisions. Health Aff (Millwood) 2011 Apr;30(4):699-706. [CrossRef] [Medline]
    18. Tudor-Sfetea C, Rabee R, Najim M, Amin N, Chadha M, Jain M, et al. Evaluation of two mobile health apps in the context of smoking cessation: qualitative study of cognitive behavioral therapy (CBT) versus non-CBT-based digital solutions. JMIR Mhealth Uhealth 2018 Apr 18;6(4):e98 [FREE Full text] [CrossRef] [Medline]
    19. Jaén CR, Stange KC, Nutting PA. Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract 1994 Feb;38(2):166-171. [Medline]
    20. Vidrine JI, Shete S, Cao Y, Greisinger A, Harmonson P, Sharp B, et al. Ask-Advise-Connect: a new approach to smoking treatment delivery in health care settings. JAMA Intern Med 2013 Mar 25;173(6):458-464 [FREE Full text] [CrossRef] [Medline]
    21. Strayer SM, Martindale JR, Pelletier SL, Rais S, Powell J, Schorling JB. Development and evaluation of an instrument for assessing brief behavioral change interventions. Patient Educ Couns 2011 Apr;83(1):99-105. [CrossRef] [Medline]
    22. Christy SM, Rawl SM. Shared decision-making about colorectal cancer screening: a conceptual framework to guide research. Patient Educ Couns 2013 Jun;91(3):310-317 [FREE Full text] [CrossRef] [Medline]
    23. Nosseir A, Flood D, Harrison R, Ibrahim O. IEEE Xplore Digital Library. Mobile development process spiral   URL: https://ieeexplore.ieee.org/document/6408529 [accessed 2019-02-25] [WebCite Cache]
    24. Brisebois M, Drummond B, Mehta A, Chene M, Flannigan M. Google Patents. 2017. Method and system for customizing a mobile application using a web-based interface   URL: https://patents.google.com/patent/US20100174974 [accessed 2019-02-25] [WebCite Cache]
    25. Iqbal J, Ahmad R, Nasir M, Khan M. Significant requirements engineering practices for outsourced mobile application development. J Inform Sci Eng 2017;33(6):1519-1530. [CrossRef]
    26. Smokefree.gov. 2018 Apr 19. Smokefree Apps   URL: https://smokefree.gov/tools-tips/apps [accessed 2019-02-25] [WebCite Cache]
    27. Unrod M, Smith M, Spring B, DePue J, Redd W, Winkel G. Randomized controlled trial of a computer- based, tailored intervention to increase smoking cessation counseling by primary care physicians. J Gen Intern Med 2007;22(4):478-484. [CrossRef] [Medline]
    28. Linder JA, Rigotti NA, Schneider LI, Kelley JH, Brawarsky P, Haas JS. An electronic health record-based intervention to improve tobacco treatment in primary care: a cluster-randomized controlled trial. Arch Intern Med 2009 Apr 27;169(8):781-787 [FREE Full text] [CrossRef] [Medline]
    29. Goldberg D, Hoffman A, Anel D. Understanding people who smoke and how they change: a foundation for smoking cessation in primary care, part 1. Dis Mon 2002;48(6):385-439. [Medline]
    30. Cornuz J, Gilbert A, Pinget C, McDonald P, Slama K, Salto E, et al. Cost-effectiveness of pharmacotherapies for nicotine dependence in primary care settings: a multinational comparison. Tob Control 2006 Jun;15(3):152-159 [FREE Full text] [CrossRef] [Medline]
    31. Marcy T, Skelly J, Shiffman R, Flynn B. Facilitating adherence to the tobacco use treatment guideline with computer-mediated decision support systems: physician and clinic office manager perspectives. Prev Med 2005;41(2):479-487. [CrossRef] [Medline]
    32. Chen Y, Madan J, Welton N, Yahaya I, Aveyard P, Bauld L, et al. Effectiveness and cost-effectiveness of computer and other electronic aids for smoking cessation: a systematic review and network meta-analysis. Health Technol Assess 2012;16(38):1-205, iii [FREE Full text] [CrossRef] [Medline]
    33. Michel G, Marcy T, Shiffman R. A wireless, handheld decision support system to promote smoking cessation in primary care. AMIA Annu Symp Proc 2005:530-534 [FREE Full text] [Medline]
    34. Marcy TW, Kaplan B, Connolly SW, Michel G, Shiffman RN, Flynn BS. Developing a decision support system for tobacco use counselling using primary care physicians. Inform Prim Care 2008;16(2):101-109 [FREE Full text] [Medline]
    35. Bakken S, Roberts W, Chen E, Dilone J, Lee N, Mendonca E, et al. PDA-based informatics strategies for tobacco use screening and smoking cessation management: a case study. Stud Health Technol Inform 2007;129(Pt 2):1447-1451. [CrossRef] [Medline]
    36. Ithnin M, Mohd Rani MD, Abd Latif Z, Kani P, Syaiful A, Nor Aripin KN, et al. Mobile App Design, Development, and Publication for Adverse Drug Reaction Assessments of Causality, Severity, and Preventability. JMIR Mhealth Uhealth 2017 May 30;5(5):e78 [FREE Full text] [CrossRef] [Medline]
    37. Sauro J. MeasuringU. 2011. Measuring usability with the system usability scale   URL: https://measuringu.com/sus/ [accessed 2019-02-25] [WebCite Cache]
    38. Willemsen M, Wiebing M, van Emst A, Zeeman G. Helping smokers to decide on the use of efficacious smoking cessation methods: a randomized controlled trial of a decision aid. Addiction 2006;101(3):441-449. [CrossRef] [Medline]
    39. Nutting P, Crabtree B, Miller W, Stange K, Stewart E, Jaen C. Transforming physician practices to patient-centered medical homes: lessons from the national demonstration project. Health Aff (Millwood) 2011;30(3):439-445. [CrossRef] [Medline]
    40. Barry MJ, Edgman-Levitan S. Shared decision making--pinnacle of patient-centered care. N Engl J Med 2012 Mar 1;366(9):780-781. [CrossRef] [Medline]
    41. Oshima LE, Emanuel EJ. Shared decision making to improve care and reduce costs. N Engl J Med 2013 Jan 3;368(1):6-8. [CrossRef] [Medline]
    42. Strayer SM, Semler MW, Kington ML, Tanabe KO. Patient attitudes toward physician use of tablet computers in the exam room. Fam Med 2010 Oct;42(9):643-647 [FREE Full text] [Medline]
    43. Sciamanna C, Marcus B, Goldstein M, Lawrence K, Schwartz S, Bock B, et al. Feasibility of incorporating computer-tailored health behaviour communications in primary care settings. Inform Prim Care 2004;12(1):40-48. [CrossRef] [Medline]


    Abbreviations

    EHR: electronic health record
    HIPAA: Health Insurance Portability and Accountability Act
    mHealth: mobile health
    PCOR: patient-centered outcomes research
    PCP: primary care physician
    REDCap: Research Electronic Data Capture
    SDM: shared decision making
    SUS: System Usability Scale


    Edited by G Eysenbach; submitted 14.06.18; peer-reviewed by D Alhuwail, M Derksen; comments to author 06.10.18; revised version received 09.01.19; accepted 27.01.19; published 29.03.19

    ©Charles R Doarn, Mary Beth Vonder Meulen, Harini Pallerla, Shauna P Acquavita, Saundra Regan, Nancy Elder, Matthew R Tubb. Originally published in JMIR Formative Research (http://formative.jmir.org), 29.03.2019.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included.