Author(s):
- Meghan Reading Turchioe
- Marissa Burgermaster
- Elliot G.Mitchell
- Pooja M.Desai
- Lena Mamykina
Abstract:
Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals’ engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants’ attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one’s life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual’s proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive—relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals’ engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.
Documentation:
https://doi.org/10.1016/j.jbi.2020.103572
References:
- E. Rich, A. MiahMobile, wearable and ingestible health technologies: towards a critical research agendaHealth Sociology Review, 26 (1) (2017), pp. 84-97
- [2]
X.F. Teng, et al.Wearable medical systems for p-HealthIEEE Rev. Biomed. Eng., 1 (2008), pp. 62-74- [3]
S.P. Bhavnani, J. Narula, P.P. SenguptaMobile technology and the digitization of healthcareEur. Heart J., 37 (18) (2016), pp. 1428-1438- [4]
- HealthIT.gov . Consumer eHealth: Patient-generated Health Data. 2016; Available from: https://www.healthit.gov/policy-researchers-implementers/patient-generated-health-data.
- [5]
A.E. Chung, E.M. BaschPotential and challenges of patient-generated health data for high-quality cancer careJ. Oncol. Pract., 11 (3) (2015), pp. 195-197- [6]
L. Howie, et al.Assessing the value of patient-generated data to comparative effectiveness researchHealth Aff. (Millwood), 33 (7) (2014), pp. 1220-1228- [7]
I. Lasorsa, et al.Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyleAppl. Clin. Inform., 7 (3) (2016), pp. 633-645- [8]
R.J. Shaw, et al.Mobile health technology for personalized primary care medicineAm. J. Med., 128 (6) (2015), pp. 555-557- [9]
S. HullPatient-generated health data foundation for personalized collaborative careComput. Inform. Nurs., 33 (5) (2015), pp. 177-180- [10]
- Epstein, D., et al., Taming data complexity in lifelogs: exploring visual cuts of personal informatics data, in Proceedings of the 2014 conference on Designing interactive systems. 2014, Association for Computing Machinery: Vancouver, BC, Canada. p. 667–676.
- [11]
R. Gulotta, et al.Fostering Engagement with Personal Informatics SystemsAssociation for Computing Machinery, Brisbane, QLD, Australia (2016), pp. 286-300- [12]
- Karanam, Y., et al., Motivational affordances and personality types in personal informatics, in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. 2014, Association for Computing Machinery: Seattle, Washington. p. 79–82.
- [13]
- Li , I., A. Dey, and J. Forlizzi, A stage-based model of personal informatics systems, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2010, Association for Computing Machinery: Atlanta, Georgia, USA. p. 557–566.
- [14]
D.A. Epstein, et al.A lived informatics model of personal informaticsAssociation for Computing Machinery, Osaka, Japan (2015), pp. 731-742- [15]
C.-F. ChungSupporting patient-provider communication and engagement with personal informatics dataAssociation for Computing Machinery: Maui, Hawaii (2017), pp. 335-338- [16]
- Quantified Self: self knowledge through numbers. 2015; Available from: http://quantifiedself.com/.
- [17]
- Braber, M.d., The Emergence of Quantified Self as a Data-driven Movement to Promote Health and Wellness, in Proceedings of the first Workshop on Lifelogging Tools and Applications. 2016, ACM: Amsterdam, The Netherlands. p. 1-1.
- [18]
- Lee, V.R., What’s happening in the“ Quantified Self” movement? ICLS 2014 Proceedings, 2014: p. 1032-1036.
- [19]
- Lee, V.R. and M. Briggs, Lessons learned from an initial effort to bring a quantified self “meetup” experience to a new demographic, in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. 2014, ACM: Seattle, Washington. p. 707-710.
- [20]
G.R. Oates, et al.Sociodemographic Patterns of Chronic Disease: How the Mid-South Region Compares to the Rest of the CountryAm. J. Preventive Med., 52 (1S1) (2017), pp. S31-S39- [21]
- Viego, V. and K. Temporelli, Socioeconomic Status and Self-Reported Chronic Diseases Among Argentina’s Adult Population: Results Based on Multivariate Probability Models. Journal of public health research, 2017. 6(1): p. 883-883.
- [22]
L.B. Dlugasch, D.N. UgarrizaSelf-monitoring of blood glucose experiences of adults with type 2 diabetesJ Am Assoc Nurse Pract, 26 (6) (2014), pp. 323-329- [23]
- Pew, Tracking for Health. 2013.
- [24]
F. Regnier, L. ChauvelDigital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factorsJMIR Mhealth Uhealth, 6 (4) (2018), Article e101- [25]
- ADA, Living with Diabetes. 2016.
- [26]
M.M. Funnell, et al.national standards for diabetes self-management educationDiabetes Care, 33 (Suppl 1) (2010), pp. S89-S96- [27]
A.B. Evert, et al.Nutrition therapy recommendations for the management of adults with diabetesDiabetes Care, 36 (11) (2013), pp. 3821-3842- [28]
D. Zeevi, et al.Personalized nutrition by prediction of glycemic responsesCell, 163 (5) (2015), pp. 1079-1094- [29]
S. Parsons, et al.Self-monitoring of blood glucose in non-insulin treated type 2 diabetes (the SMBG study): study protocol for a randomised controlled trialBMC Endocr Disord, 17 (1) (2017), p. 4- [30]
- Greenwood, D.A., et al., A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support. J Diabetes Sci Technol, 2017: p. 1932296817713506.
- [31]
P. Glasziou, L. Irwig, D. MantMonitoring in chronic disease: a rational approachBmj, 330 (7492) (2005), pp. 644-648- [32]
L.A. Young, et al.Glucose self-monitoring in non–insulin-treated patients with type 2 diabetes in primary care settings: a randomized trialJAMA Internal Medicine, 177 (7) (2017), pp. 920-929- [33]
- Reyes, J., et al., Factors Influencing Diabetes Self-Management Among Medically Underserved Patients With Type II Diabetes. Glob Qual Nurs Res, 2017. 4: p. 2333393617713097.
- [34]
- Yee, L.M., et al., Social and Environmental Barriers to Nutrition Therapy for Diabetes Management Among Underserved Pregnant Women: A Qualitative Analysis. J Nutr Educ Behav, 2016. 48(3): p. 170-80.e1.
- [35]
- Buttorff, C., Ruder, T., and Bauman, M. , Multiple Chronic Conditions in the United States. , R. Corporation, Editor. 2017: Santa Monica, CA.
- [36]
- CDC, Chronic Disease Overview. 2016.
- [37]
R.V. Milani, R.M. Bober, C.J. LavieThe role of technology in chronic disease careProg. Cardiovasc. Dis., 58 (6) (2016), pp. 579-583- [38]
A.M. Bauer, et al.Aligning health information technologies with effective service delivery models to improve chronic disease carePrev. Med., 66 (2014), pp. 167-172- [39]
S. Yoon, A.B. Wilcox, S. BakkenComparisons among health behavior surveys: implications for the design of informatics infrastructures that support comparative effectiveness researchEGEMS (Wash DC), 1 (1) (2013), p. 1021- [40]
- Patton, M.Q., How to Use Qualitative Methods in Evaluation. 1987, Newbury Park, CA: Sage.
- [41]
M.H. Wilde, S. GarvinA concept analysis of self-monitoringJ. Adv. Nurs., 57 (3) (2007), pp. 339-350- [42]
M. Vaismoradi, H. Turunen, T. BondasContent analysis and thematic analysis: implications for conducting a qualitative descriptive studyNurs. Health Sci., 15 (3) (2013), pp. 398-405- [43]
M.Q. PattonEnhancing the quality and credibility of qualitative analysisHealth Serv. Res., 34 (5 Pt 2) (1999), pp. 1189-1208- [44]
B.L. Rodgers, K.V. CowlesThe qualitative research audit trail: a complex collection of documentationRes Nurs Health, 16 (3) (1993), pp. 219-226- [45]
A. CooneyRigour and grounded theoryNurse Res, 18 (4) (2011), pp. 17-22- [46]
- USDA. What is My Plate? 2018; Available from: https://www.choosemyplate.gov/MyPlate.
- [47]
R.M. Ryan, E.L. DeciSelf-determination theory and the facilitation of intrinsic motivation, social development, and well-beingAm. Psychol., 55 (1) (2000), pp. 68-78- [48]
A. BanduraSelf-efficacy mechanism in human agencyAm. Psychologist, 37 (2) (1982), pp. 122-147- [49]
J.B. RotterGeneralized expectancies for internal versus external control of reinforcementPsychological monographs, 80 (1) (1966), pp. 1-28- [50]
I. AjzenPerceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior1J. Appl. Social Psychology, 32 (4) (2002), pp. 665-683- [51]
I. Lynam, et al.Autonomous regulation and locus of control as predictors of antiretroviral medication adherenceJ. Health Psychology, 14 (4) (2009), pp. 578-586- [52]
N. Weinstein, A.K. Przybylski, R.M. RyanThe index of autonomous functioning: development of a scale of human autonomyJ. Res. Personality, 46 (4) (2012), pp. 397-413- [53]
- Choe, E.K., Designing Self-Monitoring Technology to Promote Data Capture and Reflection, in The Information School. 2014, University of Washington.
- [54]
T. SharonSelf-tracking for health and the quantified self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcarePhilosophy Technol., 30 (1) (2017), pp. 93-121- [55]
M. Bishop, M.P. Frain, M.K. TschoppSelf-management, perceived control, and subjective quality of life in multiple sclerosis: an exploratory studyRehabilitation Counseling Bulletin, 52 (1) (2008), pp. 45-56- [56]
E.T. Kersten-van Dijk, et al.Personal informatics, self-insight, and behavior change: a critical review of current literatureHuman-Computer Interaction, 32 (5–6) (2017), pp. 268-296- [57]
L.M. Vizer, et al.“It’s not just technology, it’s people”: constructing a conceptual model of shared health informatics for tracking in chronic illness managementJ. Med. Internet Res., 21 (4) (2019), Article e10830- [58]
- Murnane, E.L., et al., Personal Informatics in Interpersonal Contexts: Towards the Design of Technology that Supports the Social Ecologies of Long-Term Mental Health Management. Proc. ACM Hum.-Comput. Interact., 2018. 2(CSCW): p. Article 127.
- [59]
F. Bentley, et al.Health mashups: presenting statistical patterns between wellbeing data and context in natural language to promote behavior changeACM Trans. Comput.-Hum. Interact., 20 (5) (2013), pp. 1-27- [60]
- Desai, P.M., et al., Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, ACM: Montreal QC, Canada. p. 1-13.
- [61]
W.K. Diprose, et al.Physician understanding, explainability, and trust in a hypothetical machine learning risk calculatorJ. Am. Medical Informatics Association, 27 (4) (2020), pp. 592-600- [62]
N.C. Benda, et al.“How did you get to this number?” Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative studyJ. Am. Medical Informatics Association, 27 (5) (2020), pp. 709-716- [63]
T. Hirsch, et al.Designing contestability: interaction design, machine learning, and mental healthDIS (Des. Interact Syst. Conf.), 2017 (2017), pp. 95-99- [64]
J.S. Ancker, et al.“You get reminded you’re a sick person”: personal data tracking and patients with multiple chronic conditionsJ. Med. Internet Res., 17 (8) (2015), Article e202- [65]
- Mamykina, L., et al., MAHI: investigation of social scaffolding for reflective thinking in diabetes management, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2008, Association for Computing Machinery: Florence, Italy. p. 477–486.
- [66]
M.R. Turchioe, et al.Designing for engagement with self-monitoring: A user-centered approach with low-income, latino adults with type 2 diabetesInt. J. Med. Inform., 130 (2019), Article 103941- [67]
K.W. CrenshawMapping the margins: intersectionality, identity politics, and violence against women of colorStanford Law Rev., 43 (6) (1991), pp. 1241-1299- [68]
P.H. CollinsBlack Feminist Thought: knowledge, consciousness, and the politics of empowermentRoutledge, New York (2000)- [69]
T.C. Veinot, et al.Leveling up: on the potential of upstream health informatics interventions to enhance health equityMedical Care, 57 (2019), pp. S108-S114- [70]
T.C. Veinot, H. Mitchell, J.S. AnckerGood intentions are not enough: how informatics interventions can worsen inequalityJ. Am. Med. Inform Assoc., 25 (8) (2018), pp. 1080-1088