Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure

BACKGROUND Personas are a canonical user-centered design method increasingly used in health informatics research. Personas-empirically-derived user archetypes-can be used by eHealth designers to gain a robust understanding of their target end users such as patients. OBJECTIVE To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data. METHOD Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n=30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables. RESULTS Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers. DISCUSSION Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.

[1]  Yuanyuan Ding,et al.  Robust clustering in high dimensional data using statistical depths , 2007, BMC Bioinformatics.

[2]  E. Coleman,et al.  Assessing the Quality of Preparation for Posthospital Care from the Patient's Perspective: The Care Transitions Measure , 2005, Medical care.

[3]  B. Löwe,et al.  The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. , 2010, General hospital psychiatry.

[4]  Leanne M Currie,et al.  Personas to Guide Understanding Traditions of Gay Men Living With HIV Who Smoke , 2016, Qualitative health research.

[5]  Jobke Wentzel,et al.  Designing eHealth that Matters via a Multidisciplinary Requirements Development Approach , 2013, JMIR research protocols.

[6]  Michael B. Twidale,et al.  Resolving Incommensurable Debates: A Preliminary Identification of Persona Kinds, Attributes, and Characteristics , 2008 .

[7]  Richard J. Holden,et al.  Self-care Barriers Reported by Emergency Department Patients With Acute Heart Failure: A Sociotechnical Systems-Based Approach. , 2015, Annals of emergency medicine.

[8]  David Coyle,et al.  Empirically derived user attributes for the design of home healthcare technologies , 2015, Personal and Ubiquitous Computing.

[9]  Renee F Wilson,et al.  Enabling patient-centered care through health information technology. , 2012, Evidence report/technology assessment.

[10]  Richard J Holden,et al.  The patient work system: an analysis of self-care performance barriers among elderly heart failure patients and their informal caregivers. , 2015, Applied ergonomics.

[11]  C. A. Smith,et al.  The development and validation of the Perceived Health Competence Scale. , 1995, Health education research.

[12]  Andrew T. Perrin.  Social Media Usage: 2005-2015 , 2015 .

[13]  Charlotte A. Weaver,et al.  Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[14]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[15]  Blaine Reeder,et al.  Scenarios, personas and user stories: User-centered evidence-based design representations of communicable disease investigations , 2013, J. Biomed. Informatics.

[16]  Pernille Bertelsen,et al.  Citizen Personas: Exploring Challenges of Citizen-Centric eHealth , 2015, MIE.

[17]  Ben Shneiderman,et al.  Usability and accessibility in consumer health informatics current trends and future challenges. , 2011, American journal of preventive medicine.

[18]  Board on Human-Systems Integration.  Health Care Comes Home: The Human Factors , 2011 .

[19]  Alan Cooper,et al.  The Inmates are Running the Asylum , 1999, Software-Ergonomie.

[20]  L. Hall,et al.  The Cancer Experience Map: An Approach to Including the Patient Voice in Supportive Care Solutions , 2015, Journal of medical Internet research.

[21]  F. Camacho,et al.  Measuring Patients’ Trust in their Primary Care Providers , 2002, Medical care research and review : MCRR.

[22]  P. Shekelle,et al.  The Vulnerable Elders Survey: A Tool for Identifying Vulnerable Older People in the Community , 2001, Journal of the American Geriatrics Society.

[23]  L. Appel,et al.  State of the science: promoting self-care in persons with heart failure: a scientific statement from the American Heart Association. , 2009, Circulation.

[24]  Amanda H. Salanitro,et al.  Determinants of health after hospital discharge: rationale and design of the Vanderbilt Inpatient Cohort Study (VICS) , 2014, BMC Health Services Research.

[25]  Nalini Kotamraju,et al.  Data-driven persona development , 2008, CHI.

[26]  Rupa S. Valdez,et al.  Macroergonomic factors in the patient work system: examining the context of patients with chronic illness , 2017, Ergonomics.

[27]  P. Burkhart,et al.  Adherence to long-term therapies: evidence for action. , 2003, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.

[28]  Haiyan Wang,et al.  Partition clustering of high dimensional low sample size data based on p-values , 2009, Comput. Stat. Data Anal..

[29]  Cynthia LeRouge,et al.  User profiles and personas in the design and development of consumer health technologies , 2013, Int. J. Medical Informatics.

[30]  Luc P de Witte,et al.  Experiences of Multidisciplinary Development Team Members During User-Centered Design of Telecare Products and Services: A Qualitative Study , 2014, Journal of medical Internet research.

[31]  Xiang Zhang,et al.  Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry , 2016, CHI.

[32]  Margot Brereton,et al.  A Collaborative Rapid Persona-Building Workshop: Creating Design Personas with Health Researchers , 2014, Int. J. Sociotechnology Knowl. Dev..

[33]  Hamed Abedtash,et al.  Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs) , 2017, J. Am. Medical Informatics Assoc..

[34]  Monique W. M. Jaspers,et al.  Using personas to tailor educational messages to the preferences of coronary heart disease patients , 2015, J. Biomed. Informatics.

[35]  Tiffany C. Veinot,et al.  Transforming consumer health informatics through a patient work framework: connecting patients to context , 2015, J. Am. Medical Informatics Assoc..

[36]  Piotr Ponikowski,et al.  Heart failure: preventing disease and death worldwide , 2014, ESC heart failure.

[37]  S. Kripalani,et al.  Development and evaluation of the Adherence to Refills and Medications Scale (ARMS) among low-literacy patients with chronic disease. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[38]  P. Ubel,et al.  Measuring Numeracy without a Math Test: Development of the Subjective Numeracy Scale , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[39]  E. Boyko,et al.  Brief questions to identify patients with inadequate health literacy. , 2004, Family medicine.

[40]  Michael Seid,et al.  Using Goal-Directed Design to Create a Novel System for Improving Chronic Illness Care , 2013, JMIR research protocols.

[41]  R. Deber,et al.  What role do patients wish to play in treatment decision making? , 1996, Archives of internal medicine.

[42]  Richard J. Holden,et al.  Consumer Health Informatics: Empowering Healthy-Lifestyle-Seekers Through mHealth , 2016 .

[43]  Michele Freeman,et al.  Mobile Applications and Internet-based Approaches for Supporting Non-professional Caregivers: A Systematic Review , 2012 .

[44]  Richard J. Holden,et al.  Human Factors Analysis, Design, and Evaluation of Engage, a Consumer Health IT Application for Geriatric Heart Failure Self-Care , 2017, Int. J. Hum. Comput. Interact..

[45]  Martin Steinert,et al.  USING SECONDARY VIDEO MATERIAL FOR USER OBSERVATION IN THE NEEDFINDING PROCESS FOR NEW PRODUCT DEVELOPMENT AND DESIGN , 2016 .

[46]  P. Carayon,et al.  SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients , 2013, Ergonomics.

[47]  Richard J. Holden,et al.  Human Factors Engineering and Human–Computer Interaction: Supporting User Performance and Experience , 2016 .

[48]  Jenna L. Marquard,et al.  Commercial off-the-shelf consumer health informatics interventions: recommendations for their design, evaluation and redesign , 2012, J. Am. Medical Informatics Assoc..

[49]  Erin Friess,et al.  Personas and decision making in the design process: an ethnographic case study , 2012, CHI.

[50]  Marie Sjölinder,et al.  Participatory Design With Seniors: Design of Future Services and Iterative Refinements of Interactive eHealth Services for Old Citizens , 2013, Medicine 2.0.

[51]  Tomasz Miaskiewicz,et al.  Personas and user-centered design: How can personas benefit product design processes? , 2011 .

[52]  Wendy A. Rogers,et al.  Self-Management of Wellness and Illness in an Aging Population , 2013 .

[53]  Alan Cooper,et al.  About Face 3: the essentials of interaction design , 1995 .

[54]  Anthony Faiola,et al.  Consumer Health Informatics: Empowering Healthy-Living-Seekers Through mHealth. , 2017, Progress in cardiovascular diseases.

[55]  Steve Whittaker,et al.  How do designers and user experience professionals actually perceive and use personas? , 2012, CHI.

[56]  A. Nobel,et al.  Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data , 2008 .

[57]  Ann Blandford,et al.  The challenges of delivering validated personas for medical equipment design. , 2014, Applied ergonomics.

[58]  Russell E Glasgow,et al.  Starting the conversation performance of a brief dietary assessment and intervention tool for health professionals. , 2011, American journal of preventive medicine.

[59]  Lene Nielsen,et al.  Personas - User Focused Design , 2012, Human–Computer Interaction Series.

[60]  Timo Jokela,et al.  The standard of user-centered design and the standard definition of usability: analyzing ISO 13407 against ISO 9241-11 , 2003, CLIHC '03.

[61]  A. Ash,et al.  Measuring patients' desire for autonomy: decision making and information-seeking preferences among medical patients. , 1989, Journal of general internal medicine.

[62]  Chris Chapman,et al.  The Personas' New Clothes: Methodological and Practical Arguments against a Popular Method , 2006 .

[63]  M G Marmot,et al.  Contribution of psychosocial factors to socioeconomic differences in health. , 1998, The Milbank quarterly.

[64]  K. Wallston,et al.  The Development and Psychometric Evaluation of the Brief Resilient Coping Scale , 2004, Assessment.

[65]  Jonathan Grudin,et al.  Personas: practice and theory , 2003, DUX '03.

[66]  Teresa Zayas-Cabán,et al.  Considerations for the design of safe and effective consumer health IT applications in the home , 2010, Quality and Safety in Health Care.

[67]  L. Radloff.  The CES-D Scale , 1977 .

[68]  Ronald C. Serlin,et al.  QUANTITATIVE METHODS IN PSYCHOLOGY Comparison of ANOVA Alternatives Under Variance Heterogeneity and Specific Noncentrality Structures , 1986 .

[69]  Katrina Armstrong,et al.  Development of a Revised Health Care System Distrust Scale , 2008, Journal of General Internal Medicine.

[70]  David G. Stork,et al.  Pattern Classification , 1973 .

[71]  E. Rackow.  Rehospitalizations among patients in the Medicare fee-for-service program. , 2009, The New England journal of medicine.

[72]  Andre Kushniruk,et al.  Enhancing the Effectiveness of Consumer-Focused Health Information Technology Systems Through eHealth Literacy: A Framework for Understanding Users' Needs , 2015, JMIR human factors.

[73]  S. De Geest,et al.  Adherence to Long-Term Therapies: Evidence for Action , 2003, European journal of cardiovascular nursing : journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology.

[74]  Gail Ironson,et al.  A short social support measure for patients recovering from myocardial infarction: the ENRICHD Social Support Inventory. , 2003, Journal of cardiopulmonary rehabilitation.

[75]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[76]  Ali H. Mokdad,et al.  Behavioral risk factor surveillance system. , 1989, Iowa medicine : journal of the Iowa Medical Society.

[77]  Janet Reis,et al.  The Effect of Online Chronic Disease Personas on Activation: Within-Subjects and Between-Groups Analyses , 2015, JMIR research protocols.

[78]  Stefanie Butz,et al.  Health Care Comes Home: The Human Factors , 2013, International Journal of Integrated Care.

[79]  John S. Pruitt,et al.  The Persona Lifecycle: Keeping People in Mind Throughout Product Design , 2006 .

[80]  Victor R. Preedy,et al.  Behavioral Risk Factor Surveillance System , 2010 .