A framework for self-experimentation in personalized health

Karkar, Ravi; Zia, Jasmine; Vilardaga, Roger; Mishra, Sonali R.; Fogarty, James; Munson, Sean A.; Kientz, Julie A.

doi:10.1093/jamia/ocv150

Cited by 97 publications

(95 citation statements)

References 38 publications

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“…The framework presented here further extends previously proposed frameworks, particularly those outlined by Li et al [28], Mamykina et al [30], and Karkar et al [29]. …”

Section: Connection With Other Frameworksupporting

confidence: 78%

“…In recent years, there emerged several frameworks that examine how individuals engage with personal data; these include Personal Informatics framework [28], Self-Experimentation framework [29], and Sensemaking framework [30]. These frameworks often incorporate reflection and discovery as an essential step in individuals’ engagement with their personal data and suggest including visual representations of collected data for facilitating discovery.…”

Section: Background and Significancementioning

confidence: 99%

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Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

Mamykina

Heitkemper

Smaldone

et al. 2017

Journal of Biomedical Informatics

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Objective To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. Materials and methods We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). Results The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals’ activities and changes in their blood glucose levels that the participants referred to as “cause and effect”. This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). Discussion The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes. Conclusions Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions.

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“…The framework presented here further extends previously proposed frameworks, particularly those outlined by Li et al [28], Mamykina et al [30], and Karkar et al [29]. …”

Section: Connection With Other Frameworksupporting

confidence: 78%

Section: Background and Significancementioning

confidence: 99%

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

Mamykina

Heitkemper

Smaldone

et al. 2017

Journal of Biomedical Informatics

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“…The different viewpoints between clinicians and student patients may be due to the fact that clinicians have professional knowledge so that they are aware of behavioral goals facilitating depression management, whereas student patients focus on self-experimenting [28,29] to learn management skills from personal experience. Both views have merits.…”

Section: Individual Vs Integrated Behavioral Target Datamentioning

confidence: 99%

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Meng¹,

Hussain²,

Mohr³

et al. 2018

Preprint

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Background: Depression is the leading health issue on college campuses in the U.S. The increasing depression prevalence has resulted in record numbers of students seeking mental health services in colleges and universities across the country. Unfortunately, as the primary mental health service provider, university counseling centers are limited in their capacity and efficiency due to time constraints and reliance on student patients' selfreports. We propose to develop a mobile behavioral sensing platform, named iSee, to enhance the efficiency, accessibility, and scalability of university counseling services.

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“…A body of work also exists on tracking and predicting mood [1,7] and promoting general wellbeing [12]. Kakar et al [11] have produced a generic framework for self-experimentation for self-diagnosis, applying this to areas such as Irritable Bowel Syndrome. There is also a growing body of relevant research on management of chronic conditions including HIV [5] and diabetes [15], although this work tends to focus on monitoring established metrics.…”

Section: Background: Self Management Of Rosaceamentioning

confidence: 99%

Technology for Self-Management of Rosacea

Buls

Rooksby

2017

Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

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Rosacea is a chronic skin condition with multiple signs and symptoms, but typically associated with reddening of the face. Sufferers usually want to avoid and reduce flare-ups of the condition, and so will try to identify and limit possible triggers. In this paper we report an investigation into technology for managing rosacea, particularly for identifying and tracking triggers. Our study has included: a survey of the existing technology; a survey of the attitudes and opinions of people with rosacea on technology; and finally, the production and evaluation of a prototype for identifying triggers. This work is investigatory and so our formative evaluation and discussion focus on understanding the problem space and establishing directions for future development work.

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A framework for self-experimentation in personalized health

Abstract: Using mobile technology to guide people through self-experimentation to investigate health questions is a feasible and promising approach to advancing personalized health.

Cited by 97 publications

References 38 publications

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Technology for Self-Management of Rosacea

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