Dataset Information

Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management.

ABSTRACT: Although self-tracking offers potential for a more complete, accurate, and longer-term understanding of personal health, many people struggle with or fail to achieve their goals for health-related self-tracking. This paper investigates how to address challenges that result from current self-tracking tools leaving a person's goals for their data unstated and lacking explicit support. We examine supporting people and health providers in expressing and pursuing their tracking-related goals via goal-directed self-tracking, a novel method to represent relationships between tracking goals and underlying data. Informed by a reanalysis of data from a prior study of migraine tracking goals, we created a paper prototype to explore whether and how goal-directed self-tracking could address current disconnects between the goals people have for data in their chronic condition management and the tools they use to support such goals. We examined this prototype in interviews with 14 people with migraine and 5 health providers. Our findings indicate the potential for scaffolding goal-directed self-tracking to: 1) elicit different types and hierarchies of management and tracking goals; 2) help people prepare for all stages of self-tracking towards a specific goal; and 3) contribute additional expertise in patient-provider collaboration. Based on our findings, we present implications for the design of tools that explicitly represent and support an individual's specific self-tracking goals.

SUBMITTER: Schroeder J

PROVIDER: S-EPMC7351123 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management.

Schroeder Jessica J Karkar Ravi R Murinova Natalia N Fogarty James J Munson Sean A SA

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 20191201 4

Although self-tracking offers potential for a more complete, accurate, and longer-term understanding of personal health, many people struggle with or fail to achieve their goals for health-related self-tracking. This paper investigates how to address challenges that result from current self-tracking tools leaving a person's goals for their data unstated and lacking explicit support. We examine supporting people and health providers in expressing and pursuing their tracking-related goals via goal ...[more]

PMID: 32656490

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Project description:BackgroundTo enable delivery of high quality patient-centered care, as well as to allow primary care health systems to allocate appropriate resources that align with patients' identified self-management problems (SM-Problems) and priorities (SM-Priorities), a practical, systematic method for assessing self-management needs and priorities is needed. In the current report, we present patient reported data generated from Connection to Health (CTH), to identify the frequency of patients' reported SM-Problems and SM-Priorities; and examine the degree of alignment between patient SM-Priorities and the ultimate Patient-Healthcare team member selected Behavioral Goal.MethodsCTH, an electronic self-management support system, was embedded into the flow of existing primary care visits in 25 primary care clinics and was used to assess patient-reported SM-Problems across 12 areas, patient identified SM-Priorities, and guide the selection of a Patient-Healthcare team member selected Behavioral Goal. SM-Problems included: BMI, diet (fruits and vegetables, salt, fat, sugar sweetened beverages), physical activity, missed medications, tobacco and alcohol use, health-related distress, general life stress, and depression symptoms. Descriptive analyses documented SM-Problems and SM-Priorities, and alignment between SM-Priorities and Goal Selection, followed by mixed models adjusting for clinic.Results446 participants with ≥ one chronic diseases (mean age 55.4 ± 12.6; 58.5% female) participated. On average, participants reported experiencing challenges in 7 out of the 12 SM-Problems areas; with the most frequent problems including: BMI, aspects of diet, and physical activity. Patient SM-Priorities were variable across the self-management areas. Patient- Healthcare team member Goal selection aligned well with patient SM-Priorities when patients prioritized weight loss or physical activity, but not in other self-management areas.ConclusionParticipants reported experiencing multiple SM-Problems. While patients show great variability in their SM-Priorities, the resulting action plan goals that patients create with their healthcare team member show a lack of diversity, with a disproportionate focus on weight loss and physical activity with missed opportunities for using goal setting to create targeted patient-centered plans focused in other SM-Priority areas. Aggregated results can assist with the identification of high frequency patient SM-Problems and SM-Priority areas, and in turn inform resource allocation to meet patient needs.Trial registrationClinicalTrials.gov ID: NCT01945918 .

Project description:Introduction: The Chronic Disease Self-Management Program (CDSMP) improves self-efficacy and health outcomes in people with chronic diseases. In the context of the EFFICHRONIC project, we evaluated the efficacy of CDSMP in relieving frailty, as assessed by the self-administered version of Multidimensional Prognostic Index (SELFY-MPI), identifying also potential predictors of better response over 6-month follow-up. Methods: The SELFY-MPI explores mobility, basal and instrumental activities of daily living (Barthel mobility, ADL, IADL), cognition (Test Your Memory-TYM Test), nutrition (Mini Nutritional Assessment-Short Form-MNA-SF), comorbidities, medications, and socio-economic conditions (social-familiar evaluation scale-SFES). Participants were stratified in three groups according to the 6-month change of SELFY-MPI: those who improved after CDSMP (Δ SELFY-MPI < 0), those who remained unchanged (Δ SELFY-MPI = 0), and those who worsened (Δ SELFY-MPI > 0). Multivariable logistic regression was modeled to identify predictors of SELFY-MPI improvement. Results: Among 270 participants (mean age = 61.45 years, range = 26–93 years; females = 78.1%) a benefit from CDSMP intervention, in terms of decrease in the SELFY-MPI score, was observed in 32.6% of subjects. SELFY-MPI improvement was found in participants with higher number of comorbidities (1–2 chronic diseases: adjusted odd ratio (aOR)=2.38, 95% confidence interval (CI) =1.01, 5.58; ⩾ 3 chronic diseases: aOR = 3.34, 95% CI = 1.25, 8.90 vs no chronic disease), poorer cognitive performance (TYM ⩽ 42: aOR = 2.41, 95% CI = 1.12, 5.19 vs TYM > 42) or higher risk of malnutrition (MNA-SF ⩽ 11: aOR = 6.11, 95% CI = 3.15, 11.83 vs MNA-SF > 11). Conclusion: These findings suggest that the CDSMP intervention contributes to decreasing the self-perceived severity of frailty (SELFY-MPI score) in more vulnerable participants with several chronic diseases and lower cognitive performance and nutritional status.

Project description:BACKGROUND:Hypertension is a lifestyle-induced chronic disease that threatens the lives of patients. Control of hypertension requires patients to follow self-management regimes; unfortunately, however, patient compliance with hypertension self-management is low, especially in developing countries. Improvement of patient compliance is premised on meeting patient needs. Mobile health apps are becoming increasingly popular for self-management of chronic diseases. However, few mobile apps have been designed to meet patient needs for hypertension self-management. OBJECTIVE:The goal of this study was to develop a mobile health app to improve patient compliance with hypertension self-management and evaluate the effectiveness of the app in terms of patient compliance. METHODS:The goal-directed design method was applied to guide study design. We divided the study into 4 stages. Stages 1 to 3 comprised the development process. To improve the applicability of the goal-directed design method to chronic disease management, we extracted elements of user models concerned with patient compliance and defined a concrete process for user modeling. In stage 1, personas of hypertensive patients were built using qualitative and quantitative methods. Clustering methods based on questionnaire responses were used to group patients. Qualitative interviews were conducted to identify the needs of different groups. In stage 2, several functional modules were designed to meet the needs of different groups based on the results from stage 1. In stage 3, prototypes of functional modules were designed and implemented as a real app. Stage 4 was the deployment process, in which we conducted a pilot study to investigate patient compliance after using the app. Patient compliance was calculated through the frequency with which they took blood pressure measurements. In addition, qualitative interviews were conducted to learn the underlying reasons for the compliance results. RESULTS:In stage 1, patients were divided into 3 groups based on 82 valid questionnaire responses. Eighteen patients from the different groups (7, 5, and 6 patients) were interviewed, and the needs of the groups were summarized as follows: improve self-management ability, enhance self-management motivation, and receive self-management support. In stages 2 and 3, 6 functional modules were designed and implemented based on specified needs, and the usability of the app was improved through usability tests. In stage 4, 143 patients were recruited to use different versions of the app for 2 months. Results show that patient compliance improved as functional modules were added (P<.001) and was maintained at a high level (rate of 0.73). Interview results from 32 patients show that the design of the app met different needs; thus, patients were more compliant with it. CONCLUSIONS:This study developed a mobile health app for hypertension self-management using the goal-directed design method. The app proved to be effective for improving patient compliance with hypertension self-management.

Dataset Information

Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management.

Publications

Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management.

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