Browsing by Subject "Consumer health information technology"

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    Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure
    (Elsevier, 2017-12) Holden, Richard J.; Kulanthaivel, Anand; Purkayastha, Saptarshi; Kripalani, Sunil; BioHealth Informatics, School of Informatics and Computing
    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.
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    Systematic review of smartphone-based passive sensing for health and wellbeing
    (Elsevier, 2018-01) Cornet, Victor P.; Holden, Richard J.; BioHealth Informatics, School of Informatics and Computing
    OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.