Good or bad, ups and downs, and getting better: Use of personal health data for temporal reflection in chronic illness

Barbarin, Andrea; Klasnja, Predrag; Veinot, Tiffany C.

doi:10.1016/j.ijmedinf.2016.06.011

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…Support measures, in general, exhibited more change than measures of network cohesion overall. This is in line with prior work showing that chronic illness management, and associated information and other support needs, evolve over time (Barbarin et al, 2016;Maes et al, 1996). However, more work is needed to understand what drives these changes.…”

Section: Discussionsupporting

confidence: 90%

“…As mentioned, personal and family networks can be a vital source of informational, emotional, and tangible support for chronic illness (Barbarin, Klasnja, & Veinot, 2016; Barbarin, Veinot, & Klasnja, 2015; Veinot, 2009). However, these networks may change in different ways than the aforementioned research on general personal network change since prior research has included voluntary (e.g., friends or neighbors) connections.…”

Section: Background Literaturementioning

confidence: 99%

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All in the family: A descriptive analysis of family network change in families managing chronic illness

Brown

Veinot

2020

Proc Assoc Inf Sci Technol

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Background Social networks can be a source of support, including informational support, in chronic illness management, but can change over time in response to health crises. However, little is known about how families leverage their support networks to manage chronic illness over time—and how these networks may change. Methods For 28 families managing either diabetes or HIV, we gathered survey‐based social network data, including network size, exchanges of support, and tie strength, up to 5 times over 2 years. We then used descriptive analysis to examine changes in network size, structure and function (e.g., informational support). Findings Although family networks remained stable in terms of network size and transitivity, these networks experienced regular fluctuations in both tie‐level variables (i.e., tie strength) and density of informational support exchanges. Discussion Observed changes in these measures indicate that even family support networks are susceptible to change over time, particularly at the tie‐level, indicating a need to expand the way we think of network change beyond whole network measures when looking at small, family networks, especially examining how information exchanges fluctuate over time. Future research should explore tie‐level measures and support exchange networks to understand why networks change over time.

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Section: Discussionsupporting

confidence: 90%

Section: Background Literaturementioning

confidence: 99%

All in the family: A descriptive analysis of family network change in families managing chronic illness

Brown

Veinot

2020

Proc Assoc Inf Sci Technol

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“…While patients with chronic illnesses are motivated to use temporal health information from personal data sources to identify trends and optimal timing for interventions, 38 they may also feel that self-tracking is burdensome, 39 and mHealth interventions may not keep patients engaged in the long term. 40 At this stage of research, our vision for the self-tracking module of the iDECIDE app does not entail longterm use by patients in clinical settings, but rather we envision it being used for short periods of time (e.g., 3-4 weeks) during clinical studies or in clinical settings to gather data for measuring baseline or changes of SMBs by populating the proposed SMB report template.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy

et al. 2020

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Objective It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices. Methods This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted. Results Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38–100% range), and frequency of changing the bolus waveform, median 11 (7–95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control. Conclusion The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current “one size fits all” approach.

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“…Charmaz (1991) further reports that people, particularly those with intrusive illnesses, respond by adjusting their schedules and expectations to accommodate the illness and its demands. This work has been influential in making visible narratives that were seldom acknowledged, highlighting the impact of socioeconomic variables on illness experience, making the case for affordable health services, and informing the design of patient-centered health-tracking devices (Barbarin et al, 2015;Barbarin et al, 2016;Charmaz, 2007;Chen, 2010Chen, , 2011Pierret, 2003). Overall, however, sociological literature tends to align with the medical model in that chronic illness is viewed as a primarily negative influence on individuals and their social relations, with narratives of suffering and overcoming chronic illness running through the literature (Charmaz and Rosenfeld, 2009).…”

Section: Related Workmentioning

confidence: 99%

Seizure aesthetics: Temporal regimes and medical technology in epilepsy diagnosis

Marathe

2020

Time & Society

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Epilepsy is a neurological condition defined by time; it is characterized by a lifelong tendency for recurrent, unpredictable, and unprovoked seizures, during which people lose control over parts of body-mind function. Diagnosing seizures involves using electroencephalograms to represent and classify brain waves in relation to clock time. Drawing upon ethnographic fieldwork in a North American teaching hospital, this paper shows that as neurologists learn to diagnose seizures, they internalize clock time norms for normal and abnormal brain waves. The paper demonstrates how these temporal norms work to assign a set of aesthetics to brain waves: patterns that conform to clock-time norms are beautiful, whereas hard-to-classify patterns are ugly. These aesthetic judgments follow diagnostically complex patients in future hospital visits, who become known, for instance, as “the patient with the ugly EEG.” The paper critiques this ascription of labels to patients and situates the role of the electroencephalogram's clock time in this predicament. It concludes with a speculative design project that reorients the relationship between temporality and embodiment by using the heartbeat as a situated and co-produced alternative to the standardized and invariant clock. Ultimately, the paper argues that the aesthetics of medical technology are fundamental to clinical care, thereby opening up new directions for research at the intersection of critical time studies and disability studies.

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Good or bad, ups and downs, and getting better: Use of personal health data for temporal reflection in chronic illness

Cited by 15 publications

References 21 publications

All in the family: A descriptive analysis of family network change in families managing chronic illness

All in the family: A descriptive analysis of family network change in families managing chronic illness

Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy

Seizure aesthetics: Temporal regimes and medical technology in epilepsy diagnosis

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