scholarly journals Hormonal Health: Period Tracking Apps, Wellness, and Self-Management in the Era of Surveillance Capitalism

2021 ◽  
Vol 7 (1) ◽  
pp. 48-66
Author(s):  
Andrea Ford ◽  
Giulia De Togni ◽  
Livia Miller
Keyword(s):  
Social Interactions ◽  
Data Privacy ◽  
The Self ◽  
Self Management ◽  
Health And Wellness ◽  
Embodied Experience ◽  
Medical Expertise ◽  
Menstrual Cycles ◽  
Management Project ◽  
Mental And Physical Health

Period tracking is an increasingly widespread practice, and its emphasis is changing from monitoring fertility to encompassing a more broad-based picture of users’ health. Delving into the data of one’s menstrual cycle, and the hormones that are presumed to be intimately linked with it, is a practice that is reshaping ideas about health and wellness, while also shaping subjects and subjectivities that succeed under conditions of surveillance capitalism. Through close examination of six extended interviews, this article elaborates a version of period tracking that sidesteps fertility and, in doing so, participates in the “queering” of menstrual technologies. Apps can facilitate the integration of institutional medical expertise and quotidian embodied experience within a broader approach to the self as a management project. We introduce the concept of “hormonal health” to describe a way of caring for, and knowing about, bodies, one that weaves together mental and physical health, correlates subjective and objective information, and calls into question the boundary between illness and wellness. For those we spoke with, menstrual cycles are understood to affect selfhood across any simplistic body-mind division or reproductive imperative, engendering complex techniques of self-management, including monitoring, hypothesizing, intervening in medical appointments, adjusting schedules, and interpreting social interactions. Such techniques empower their proponents, but not within conditions of their choosing. In addition to problems with data privacy and profit, these techniques perpetuate individualized solutions and the internalization of pressures in a gender-stratified, neoliberal context, facilitating success within flawed structures.

Identifying the Predictors of Self-Management Behaviors in Patients with Diabetes Based on Ecological Approach: A Systematic Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahnaz Davari ◽  
Hamed Rezakhani Moghaddam ◽  
Aghil Habibi Soola
Keyword(s):  
Scientific Information ◽  
The Self ◽  
Self Management ◽  
Ecological Approach ◽  
Selection Strategy ◽  
Diabetic Patients ◽  
Personal Factors ◽  
Management Interventions ◽  
Number Of Patients ◽  
Management Behaviors

Background: Recognizing and promoting the factors that affect the self-management behaviors of diabetes leads to a reduction in the number of patients and an improvement in the quality of care. The ecological approach focuses on the nature of people's interactions with their physical and socio-cultural environments. Objective: The purpose of this study was to identify the predictors of self-management behaviors with a comprehensive approach in these patients. Methods: The Keywords were investigated in the relevant national and international databases, including PubMed, Google Scholar, Science Direct, Scopus, and Scientific Information Database, Magiran, and Iran Medex to obtain the articles published from 2009 to 2019. The search and article selection strategy was developed based on the Prisma checklist and was carried out in three steps. Results: Most studies have shown that personal factors had the highest prediction power for the self-management of diabetes. Then, the interpersonal factors, society and policy-making factors, and group and organization factors were most frequently reported predictors of self-management behaviors in diabetic patients. Conclusion: Self-management of diabetes is necessary for controlling it because 95% of care is done by the patient. When designing self-management interventions, factors based on the individual level that increasing self-management behaviors should be taken into account.

scholarly journals Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

2020 ◽  
Vol 11 (05) ◽  
pp. 769-784
Author(s):  
Ipek Ensari ◽  
Adrienne Pichon ◽  
Sharon Lipsky-Gorman ◽  
Suzanne Bakken ◽  
Noémie Elhadad
Keyword(s):  
Additional Data ◽  
Intraclass Correlation ◽  
Management Strategies ◽  
The Self ◽  
Self Management ◽  
Cross Sectional ◽  
Two Samples ◽  
Data Source ◽  
Daily Function ◽  
Over Time

Abstract Background Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement. Objectives This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR. Methods This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking. Results Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%). Conclusion For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

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