1
|
MacNeill AL, MacNeill L, Yi S, Goudreau A, Luke A, Doucet S. Depiction of conversational agents as health professionals: a scoping review. JBI Evid Synth 2024; 22:831-855. [PMID: 38482610 DOI: 10.11124/jbies-23-00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
OBJECTIVE The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care. INTRODUCTION The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings. INCLUSION CRITERIA This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors. METHODS This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables. RESULTS A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents. CONCLUSIONS The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.
Collapse
Affiliation(s)
- A Luke MacNeill
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
| | - Lillian MacNeill
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
| | - Sungmin Yi
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- College of Pharmacy, Dalhousie University, Halifax, NS, Canada
| | - Alex Goudreau
- University of New Brunswick Libraries, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
| | - Alison Luke
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
| | - Shelley Doucet
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
| |
Collapse
|
2
|
Wutz M, Hermes M, Winter V, Köberlein-Neu J. Factors Influencing the Acceptability, Acceptance, and Adoption of Conversational Agents in Health Care: Integrative Review. J Med Internet Res 2023; 25:e46548. [PMID: 37751279 PMCID: PMC10565637 DOI: 10.2196/46548] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/10/2023] [Accepted: 07/10/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Conversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine based on natural language via an interface. The use of CAs offers new opportunities and various benefits for health care. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in health care has grown tremendously in recent years. OBJECTIVE This review aims to present a synthesis of the factors that facilitate or hinder the implementation of CAs from the perspectives of patients and health care professionals. Specifically, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success. METHODS We performed an integrative review. To identify relevant literature, a broad literature search was conducted in June 2021 with no date limits and using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the review current, another search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching reference lists and conducted a hand search. Factors influencing the acceptability, acceptance, and adoption of CAs in health care were coded through parallel deductive and inductive approaches, which were informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map. RESULTS Overall, 76 studies were included in this review. We identified influencing factors related to 4 core Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the health care context, we redefined the UTAUT2 factors social influence, habit, and price value. We identified 6 other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics. Overall, we identified 10 factors influencing acceptability, acceptance, and adoption among health care professionals (performance expectancy, effort expectancy, facilitating conditions, social influence, price value, perceived risk, trust, anthropomorphism, working alliance, and user characteristics) and 13 factors influencing acceptability, acceptance, and adoption among patients (additionally hedonic motivation, habit, and health issue). CONCLUSIONS This review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in health care. Knowledge of these factors is fundamental for implementation planning. Therefore, the findings of this review can serve as a basis for future studies to develop appropriate implementation strategies. Furthermore, this review provides an empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary. TRIAL REGISTRATION PROSPERO CRD42022343690; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343690.
Collapse
Affiliation(s)
- Maximilian Wutz
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Marius Hermes
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Vera Winter
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Juliane Köberlein-Neu
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| |
Collapse
|
3
|
Madujibeya I, Lennie TA, Pelzel J, Moser DK. Patients' Experiences Using a Mobile Health App for Self-Care of Heart Failure in a Real-World Setting: Qualitative Analysis. JMIR Form Res 2023; 7:e39525. [PMID: 37581912 PMCID: PMC10466157 DOI: 10.2196/39525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 02/28/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Publicly available patient-focused mobile health (mHealth) apps are being increasingly integrated into routine heart failure (HF)-related self-care. However, there is a dearth of research on patients' experiences using mHealth apps for self-care in real-world settings. OBJECTIVE The purpose of this study was to explore patients' experiences using a commercially available mHealth app, OnTrack to Health, for HF self-care in a real-world setting. METHODS Patient satisfaction, measured with a 5-point Likert scale, and an open-ended survey were used to gather data from 23 patients with HF who were provided the OnTrack to Health app as a part of routine HF management. A content analysis of patients' responses was conducted with the qualitative software Atlas.ti (version 8; ATLAS.ti Scientific Software Development GmbH). RESULTS Patients (median age 64, IQR 57-71 years; 17/23, 74% male) used OnTrack to Health for a median 164 (IQR 51-640) days before the survey. All patients reported excellent experiences related to app use and would recommend the app to other patients with HF. Five themes emerged from the responses to the open-ended questions: (1) features that enhanced self-care of HF (medication tracker, graphic performance feedback and automated alerts, secured messaging features, and HF self-care education); (2) perceived benefits (provided assurance of safety, improved HF self-care, and decreased hospitalization rates); (3) challenges with using apps for self-care (giving up previous self-care strategies); (4) facilitators (perceived ease of use and availability of technical support); and (5) suggested improvements (streamlining data entry, integration of apps with an electronic medical record, and personalization of app features). CONCLUSIONS Patients were satisfied with using OnTrack to Health for self-care. They perceived the features of the app as valuable tools for improving self-care ability and decreasing hospitalization rates. The development of apps in collaboration with end users is essential to ensure high-quality patient experiences related to app use for self-care.
Collapse
Affiliation(s)
- Ifeanyi Madujibeya
- Research and Interventions for Cardiovascular Health Heart Program, College of Nursing, University of Kentucky, Lexington, KY, United States
| | - Terry A Lennie
- Center for Nutritional Sciences, College of Nursing, University of Kentucky, Lexington, KY, United States
| | - Jamie Pelzel
- Heart and Vascular Center, CentraCare, St Cloud, MN, United States
| | - Debra K Moser
- Research and Interventions for Cardiovascular Health Heart Program, College of Nursing, University of Kentucky, Lexington, KY, United States
| |
Collapse
|
4
|
Pap IA, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. Int J Environ Res Public Health 2022; 19:11413. [PMID: 36141685 PMCID: PMC9517043 DOI: 10.3390/ijerph191811413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.
Collapse
Affiliation(s)
- Iuliu Alexandru Pap
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
| | - Stefan Oniga
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
- Department of IT Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
| |
Collapse
|
5
|
Madujibeya I, Lennie T, Aroh A, Chung ML, Moser D. Measures of Engagement With mHealth Interventions in Patients With Heart Failure: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e35657. [PMID: 35994345 PMCID: PMC9446141 DOI: 10.2196/35657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/04/2022] [Accepted: 06/10/2022] [Indexed: 11/23/2022] Open
Abstract
Background Despite the potential of mobile health (mHealth) interventions to facilitate the early detection of signs of heart failure (HF) decompensation and provide personalized management of symptoms, the outcomes of such interventions in patients with HF have been inconsistent. As engagement with mHealth is required for interventions to be effective, poor patient engagement with mHealth interventions may be associated with mixed evidence. It is crucial to understand how engagement with mHealth interventions is measured in patients with HF, and the effects of engagement on HF outcomes. Objective In this review, we aimed to describe measures of patient engagement with mHealth interventions and the effects of engagement on HF outcomes. Methods We conducted a systematic literature search in 7 databases for relevant studies published in the English language from 2009 to September 2021 and reported the descriptive characteristics of the studies. We used content analysis to identify themes that described patient engagement with mHealth interventions in the qualitative studies included in the review. Results We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (3239/4771, 67.88%, male), ranging from a sample of 7 to 1571 (median 53.3) patients, followed for a median duration of 90 (IQR 45-180) days. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data in 72% (23/32) of the studies, only qualitatively based on data from semistructured interviews and focus groups in 6% (2/32) of studies, and by a combination of both quantitative and qualitative data in 22% (7/32) of studies. System usage data were evaluated using 6 metrics of engagement: number of physiological parameters transmitted (19/30, 63% studies), number of HF questionnaires completed (2/30, 7% studies), number of log-ins (4/30, 13% studies), number of SMS text message responses (1/30, 3% studies), time spent (5/30, 17% studies), and the number of features accessed and screen viewed (4/30, 13% studies). There was a lack of consistency in how the system usage metrics were reported across studies. In total, 80% of the studies reported only descriptive characteristics of system usage data. The emotional, cognitive, and behavioral domains of patient engagement were identified through qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalization were inconclusive. Conclusions The measures of patient engagement with mHealth interventions in patients with HF are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. There is a need for a working group on mHealth that may consolidate the previous operational definitions of patient engagement into an optimal and standardized measure.
Collapse
Affiliation(s)
- Ifeanyi Madujibeya
- College of Nursing, University of Kentucky, Lexington, KY, United States
| | - Terry Lennie
- College of Nursing, University of Kentucky, Lexington, KY, United States
| | - Adaeze Aroh
- Department of Public Health, College of Health Professions, Slippery Rock University, Slippery Rock, PA, United States
| | - Misook L Chung
- College of Nursing, University of Kentucky, Lexington, KY, United States
| | - Debra Moser
- College of Nursing, University of Kentucky, Lexington, KY, United States
| |
Collapse
|
6
|
Shara N, Bjarnadottir MV, Falah N, Chou J, Alqutri HS, Asch FM, Anderson KM, Bennett SS, Kuhn A, Montalvo B, Sanchez O, Loveland A, Mohammed SF. Voice activated remote monitoring technology for heart failure patients: Study design, feasibility and observations from a pilot randomized control trial. PLoS One 2022; 17:e0267794. [PMID: 35522660 PMCID: PMC9075666 DOI: 10.1371/journal.pone.0267794] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 04/12/2022] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Heart failure (HF) is a serious health condition, associated with high health care costs, and poor outcomes. Patient empowerment and self-care are a key component of successful HF management. The emergence of telehealth may enable providers to remotely monitor patients' statuses, support adherence to medical guidelines, improve patient wellbeing, and promote daily awareness of overall patients' health. OBJECTIVE To assess the feasibility of a voice activated technology for monitoring of HF patients, and its impact on HF clinical outcomes and health care utilization. METHODS We conducted a randomized clinical trial; ambulatory HF patients were randomized to voice activated technology or standard of care (SOC) for 90 days. The system developed for this study monitored patient symptoms using a daily survey and alerted healthcare providers of pre-determined reported symptoms of worsening HF. We used summary statistics and descriptive visualizations to study the alerts generated by the technology and to healthcare utilization outcomes. RESULTS The average age of patients was 54 years, the majority were Black and 45% were women. Almost all participants had an annual income below $50,000. Baseline characteristics were not statistically significantly different between the two arms. The technical infrastructure was successfully set up and two thirds of the invited study participants interacted with the technology. Patients reported favorable perception and high comfort level with the use of voice activated technology. The responses from the participants varied widely and higher perceived symptom burden was not associated with hospitalization on qualitative assessment of the data visualization plot. Among patients randomized to the voice activated technology arm, there was one HF emergency department (ED) visit and 2 HF hospitalizations; there were no events in the SOC arm. CONCLUSIONS This study demonstrates the feasibility of remote symptom monitoring of HF patients using voice activated technology. The varying HF severity and the wide range of patient responses to the technology indicate that personalized technological approaches are needed to capture the full benefit of the technology. The differences in health care utilization between the two arms call for further study into the impact of remote monitoring on health care utilization and patients' wellbeing.
Collapse
Affiliation(s)
- Nawar Shara
- MedStar Health Research Institute, Hyattsville, MD, United States of America
- Georgetown University, Washington, DC, United States of America
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, United States of America
- * E-mail:
| | - Margret V. Bjarnadottir
- Center for Health Information and Decision Systems, University of Maryland, College Park, MD, United States of America
| | - Noor Falah
- MedStar Health Research Institute, Hyattsville, MD, United States of America
- Georgetown University, Washington, DC, United States of America
| | - Jiling Chou
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Hasan S. Alqutri
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Federico M. Asch
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | | | - Sonita S. Bennett
- MedStar Health Research Institute, Hyattsville, MD, United States of America
- MedStar Health National Center for Human Factors in Healthcare, MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Alexander Kuhn
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Becky Montalvo
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Osirelis Sanchez
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | - Amy Loveland
- MedStar Health Research Institute, Hyattsville, MD, United States of America
| | | |
Collapse
|
7
|
Nahar JK, Lopez-Jimenez F. Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care. Heart Fail Clin 2022; 18:311-323. [DOI: 10.1016/j.hfc.2021.11.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|
8
|
Nourse R, Lobo E, McVicar J, Kensing F, Islam SMS, Kayser L, Maddison R. Characteristics of smart health ecosystems that support self-care among people with heart failure: A scoping review (Preprint). JMIR Cardio 2022; 6:e36773. [DOI: 10.2196/36773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 07/22/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
|
9
|
Wei C, Finkelstein J. Comparison of Alexa Voice and Audio Video Interfaces for Home-Based Physical Telerehabilitation. AMIA Annu Symp Proc 2022; 2022:496-503. [PMID: 35854718 PMCID: PMC9285164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
The goal of this pilot study was to compare Alexa voice and video interfaces for home-based telerehabilitation dialog by conducting cognitive walkthrough testing. All task performance scores were higher in video interface as compared to the audio interface. The overall task score was significantly higher for video interface (42.4±4.6) as compared to the audio score (41.3±5.9). Comparative usability survey demonstrated higher preference of the video interface as compared to the audio interface. Based in the comparative survey, 85.7% stated they definitely prefer video interface, 85.7% felt that video introduction was simpler to understand, 71.4% felt that exercise instructions were simpler to understand with the video interface, and 78.6% felt that overall navigation was easier with the video interface. The overall time to accomplish all three tasks was significantly shorter (p<0.05) for the video interface (170.5±12.2 seconds) as compared to the audio interface (194.2±10.3 seconds). This is the first study systematically comparing two major Alexa interfaces in a telerehabilitation system. These results are instrumental for future development of Alexa-based telerehabilitation systems.
Collapse
Affiliation(s)
- Chenhao Wei
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | |
Collapse
|
10
|
Hyams T, Golden B, Sammarco J, Sultan S, King-Marshall E, Wang MQ, Curbow B. Evaluating preferences for colorectal cancer screening in individuals under age 50 using the Analytic Hierarchy Process. BMC Health Serv Res 2021; 21:754. [PMID: 34325701 PMCID: PMC8320058 DOI: 10.1186/s12913-021-06705-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 06/28/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND In 2021, the United States Preventive Services Task Force updated their recommendation, stating that individuals ages 45-49 should initiate screening for colorectal cancer. Since several screening strategies are recommended, making a shared decision involves including an individual's preferences. Few studies have included individuals under age 50. In this study, we use a multicriteria decision analysis technique called the Analytic Hierarchy Process to explore preferences for screening strategies and evaluate whether preferences vary by age. METHODS Participants evaluated a hierarchy with 3 decision alternatives (colonoscopy, fecal immunochemical test, and computed tomography colonography), 3 criteria (test effectiveness, the screening plan, and features of the test) and 7 sub-criteria. We used the linear fit method to calculate consistency ratios and the eigenvector method for group preferences. We conducted sensitivity analysis to assess whether results are robust to change and tested differences in preferences by participant variables using chi-square and analysis of variance. RESULTS Of the 579 individuals surveyed, 556 (96%) provided complete responses to the AHP portion of the survey. Of these, 247 participants gave responses consistent enough (CR < 0.18) to be included in the final analysis. Participants that were either white or have lower health literacy were more likely to be excluded due to inconsistency. Colonoscopy was the preferred strategy in those < 50 and fecal immunochemical test was preferred by those over age 50 (p = 0.002). These results were consistent when we restricted analysis to individuals ages 45-55 (p = 0.011). Participants rated test effectiveness as the most important criteria for making their decision (weight = 0.555). Sensitivity analysis showed our results were robust to shifts in criteria and sub-criteria weights. CONCLUSIONS We reveal potential differences in preferences for screening strategies by age that could influence the adoption of screening programs to include individuals under age 50. Researchers and practitioners should consider at-home interventions using the Analytic Hierarchy Process to assist with the formulation of preferences that are key to shared decision-making. The costs associated with different preferences for screening strategies should be explored further if limited resources must be allocated to screen individuals ages 45-49.
Collapse
Affiliation(s)
- Travis Hyams
- Department of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, USA. .,Division of Cancer Control and Population Sciences, Office of the Director, National Cancer Institute, Bethesda, USA.
| | - Bruce Golden
- Department of Decision, Operations, and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, USA
| | - John Sammarco
- Definitive Business Solutions, Inc., 11921 Freedom Drive, Suite 550, Reston, VA, 20190, USA
| | - Shahnaz Sultan
- Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, University of Minnesota, Minneapolis, USA
| | - Evelyn King-Marshall
- Department of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, USA
| | - Min Qi Wang
- Department of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, USA
| | - Barbara Curbow
- Department of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, USA
| |
Collapse
|