An Evaluation of the Diabetes Conversation Map™ Program—Are Program Factors Associated with Health Outcomes and Health Care Utilization at 12 Months Post-Enrollment?

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 666-P
Author(s):  
EINAV SRULOVICI ◽  
MAYA LEVENTER-ROBERTS ◽  
BRADLEY CURTIS ◽  
XUANYAO HE ◽  
AMICHAY AKRIV ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Health Outcomes ◽  
Care Utilization ◽  
Factors Associated ◽  
Conversation Map

An Evaluation of the Diabetes Conversation Map Program—Health Outcomes and Health Care Utilization during a Four-Year Follow-Up

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 665-P
Author(s):  
EINAV SRULOVICI ◽  
MAYA LEVENTER-ROBERTS ◽  
BRADLEY CURTIS ◽  
XUANYAO HE ◽  
MOSHE B. HOSHEN ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Health Outcomes ◽  
Care Utilization ◽  
Conversation Map

Health Outcomes and Health Care Utilization Among Obstetric Deliveries With Concurrent CKD in the United States

2020 ◽  
Vol 75 (1) ◽  
pp. 148-150 ◽  
Author(s):  
Andrea L. Oliverio ◽  
Lindsay K. Admon ◽  
Laura H. Mariani ◽  
Tyler N.A. Winkelman ◽  
Vanessa K. Dalton
Keyword(s):  
United States ◽  
Health Care ◽  
Health Care Utilization ◽  
Health Outcomes ◽  
The United States ◽  
Care Utilization

Factors associated with health care utilization in pediatric multiple sclerosis

2020 ◽  
Vol 38 ◽  
pp. 101511
Author(s):  
Ruth Ann Marrie ◽  
Julia O'Mahony ◽  
Colleen Maxwell ◽  
Vicki Ling ◽  
Christine Till ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Health Care ◽  
Health Care Utilization ◽  
Care Utilization ◽  
Factors Associated ◽  
Pediatric Multiple Sclerosis

Health and Situations

2017 ◽  
pp. 425-436
Author(s):  
Nicolas A. Brown ◽  
David M. Condon ◽  
Daniel K. Mroczek
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Health Outcomes ◽  
Situational Factors ◽  
Care Utilization ◽  
Personality Research ◽  
Important Health ◽  
Joint Study ◽  
Methodological Considerations ◽  
Illness And Disease

Research has demonstrated that personality is consistently linked to important health outcomes such as longevity, health care utilization, and adherence to prescribed treatment of illness and disease. However, situational factors may amplify, block, or modify person-level characteristics that affect these outcomes. Therefore, the integration of situation and personality research may add predictive validity for important outcomes such as health behavior and health care utilization. This chapter discusses the importance of context in the joint study of personality and health. We introduce a roadmap for studying health and situations emphasizing the need for theoretical and measurement frameworks. Lastly, the chapter reviews relevant methodological considerations for studying health situations, including tools for assessing situations (e.g., experience sampling, wearable cameras), and recruiting from appropriate populations.

scholarly journals Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

10.2196/17075 ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. e17075
Author(s):  
Shefali Kumar ◽  
Jennifer L A Tran ◽  
Ernesto Ramirez ◽  
Wei-Nchih Lee ◽  
Luca Foschini ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Health Care Utilization ◽  
Health Outcomes ◽  
Behavioral Data ◽  
Care Utilization ◽  
Depression And Anxiety ◽  
Activity Tracker ◽  
Study Population ◽  
Study Participants

Background Depression and anxiety greatly impact daily behaviors, such as sleep and activity levels. With the increasing use of activity tracking wearables among the general population, there has been a growing interest in how data collected from these devices can be used to further understand the severity and progression of mental health conditions. Objective This virtual 1-year observational study was designed with the objective of creating a longitudinal data set combining self-reported health outcomes, health care utilization, and quality of life data with activity tracker and app-based behavioral data for individuals with depression and anxiety. We provide an overview of the study design, report on baseline health and behavioral characteristics of the study population, and provide initial insights into how behavioral characteristics differ between groups of individuals with varying levels of disease severity. Methods Individuals who were existing members of an online health community (Achievement, Evidation Health Inc) and were 18 years or older who had self-reported a diagnosis of depression or anxiety were eligible to enroll in this virtual 1-year study. Participants agreed to connect wearable activity trackers that captured data related to physical activity and sleep behavior. Mental health outcomes such as the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Questionnaire (GAD-7), mental health hospitalizations, and medication use were captured with surveys completed at baseline and months 3, 6, 9, and 12. In this analysis, we report on baseline characteristics of the sample, including mental health disease severity and health care utilization. Additionally, we explore the relationship between passively collected behavioral data and baseline mental health status and health care utilization. Results Of the 1304 participants enrolled in the study, 1277 individuals completed the baseline survey and 1068 individuals had sufficient activity tracker data. Mean age was 33 (SD 9) years, and the majority of the study population was female (77.2%, 994/1288) and identified as Caucasian (88.3%, 1137/1288). At baseline, 94.8% (1211/1277) of study participants reported experiencing depression or anxiety symptoms in the last year. This baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression. Individuals with depressive symptoms were less active than those with minimal depressive symptoms. Severe forms of depression were also significantly associated with inconsistent sleep patterns and more disordered sleep. Conclusions These initial findings suggest that longitudinal behavioral and health outcomes data may be useful for developing digital measures of health for mental health symptom severity and progression.

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