Sensor Generated Health Data for Behavior Change in Nurse Coaching: A Case Study

2.50
Hdl Handle:
http://hdl.handle.net/10755/616306
Category:
Full-text
Type:
Presentation
Title:
Sensor Generated Health Data for Behavior Change in Nurse Coaching: A Case Study
Other Titles:
Symposium: Engaging Persons With Diabetes in Nurse Coaching With Enabling Technology to Improve Health
Author(s):
Fazio, Sarina; Dharmar, Madan; Miyamoto, Sheridan; Young, Heather M.
Lead Author STTI Affiliation:
Non-member
Author Details:
Sarina Fazio, RN, safazio@ucdavis.edu; Madan Dharmar, MBBS; Sheridan Miyamoto, FNP, RN; Heather M. Young, RN, FAAN
Abstract:
Session presented on Sunday, July 24, 2016: Purpose: This work presents a case study describing outcomes of two participants who engaged in a nurse coaching intervention using mobile health (mHealth) technology, wireless wearable sensors and patient generated health data (PGHD) in an effort to improve their health and physical fitness.' In response to the growing burden of chronic disease, health coaching interventions targeting lifestyle management have become widely adopted among health systems and organizations. Motivational interviewing, a patient centered health coaching approach, has been shown to be effective in improving a number of health behaviors such as physical activity, nutritional habits, weight loss, and smoking cessation. Traditionally, health coaching has relied on patient self-report of behavior and activity patterns to guide coaching practices. The availability of commercial activity trackers and mHealth applications to capture health behaviors offers an objective view of daily activity not previously available.' Methods: The health coaching intervention was part of a randomized clinical trial in which intervention participants were assigned a nurse health coach and given a Fitbit One, a commercially available physical activity and sleep tracking sensor to wear over a three month period. Through bi-weekly telephone calls, the nurses utilized motivational interviewing techniques to support patients in setting health goals and to make sense of their PGHD passively collected by the Fitbit sensor. Two participants from the study, a 53 year old Latino woman (participant ML) and a 53 year old mixed race male (participant OB), were selected to illustrate two examples of how PGHD and mHealth technologies can be utilized to inform and improve health coaching and health behavior change. Results: Throughout the intervention ML and OB set bi-weekly goals related to their physical activity (steps, stairs, active minutes), nutritional habits (calories consumed), and sleep (quality, duration) in an effort to improve their overall health and fitness. ML and OB reached varying degrees of success in accomplishing their self-identified goals. By the end of the three month intervention, both participants achieved meaningful improvements to their anthropometric measurements, cardiovascular fitness and exercise habits. Visualization of participants? PGHD demonstrated the increased level of weekly physical activity had improved over the course of the intervention. Both participants also self-reported higher quality of life and health status ratings through questionnaires.' Conclusion: Emerging mHealth technologies and other health applications can track relevant information to assist individuals in making and sustaining lifestyle change. Integrating PGHD and mHealth technologies into health coaching practice allows nurses to perform meaningful analysis and correlate patient data with health behaviors to evaluate patient goal progression and provide timely and personal feedback based on their health goals. These case studies highlight the positive outcomes of two individuals who participated in a clinical trial, suggesting that the addition of sensor data adds value to nurse health coaching practice.' However, further research is necessary to determine the generalizability and effectiveness of pairing mHealth technologies with evidence-based nurse coaching interventions among larger numbers of diverse subjects.
Keywords:
chronic disease management; sensor and mobile technologies; case study
Repository Posting Date:
13-Jul-2016
Date of Publication:
13-Jul-2016 ; 13-Jul-2016
Other Identifiers:
INRC16M01; INRC16M01
Conference Date:
2016
Conference Name:
27th International Nursing Research Congress
Conference Host:
Sigma Theta Tau International, the Honor Society of Nursing
Conference Location:
Cape Town, South Africa
Description:
Theme: Leading Global Research: Advancing Practice, Advocacy, and Policy

Full metadata record

DC FieldValue Language
dc.language.isoenen
dc.type.categoryFull-texten
dc.typePresentationen
dc.titleSensor Generated Health Data for Behavior Change in Nurse Coaching: A Case Studyen
dc.title.alternativeSymposium: Engaging Persons With Diabetes in Nurse Coaching With Enabling Technology to Improve Healthen
dc.contributor.authorFazio, Sarinaen
dc.contributor.authorDharmar, Madanen
dc.contributor.authorMiyamoto, Sheridanen
dc.contributor.authorYoung, Heather M.en
dc.contributor.departmentNon-memberen
dc.author.detailsSarina Fazio, RN, safazio@ucdavis.edu; Madan Dharmar, MBBS; Sheridan Miyamoto, FNP, RN; Heather M. Young, RN, FAANen
dc.identifier.urihttp://hdl.handle.net/10755/616306-
dc.description.abstractSession presented on Sunday, July 24, 2016: Purpose: This work presents a case study describing outcomes of two participants who engaged in a nurse coaching intervention using mobile health (mHealth) technology, wireless wearable sensors and patient generated health data (PGHD) in an effort to improve their health and physical fitness.' In response to the growing burden of chronic disease, health coaching interventions targeting lifestyle management have become widely adopted among health systems and organizations. Motivational interviewing, a patient centered health coaching approach, has been shown to be effective in improving a number of health behaviors such as physical activity, nutritional habits, weight loss, and smoking cessation. Traditionally, health coaching has relied on patient self-report of behavior and activity patterns to guide coaching practices. The availability of commercial activity trackers and mHealth applications to capture health behaviors offers an objective view of daily activity not previously available.' Methods: The health coaching intervention was part of a randomized clinical trial in which intervention participants were assigned a nurse health coach and given a Fitbit One, a commercially available physical activity and sleep tracking sensor to wear over a three month period. Through bi-weekly telephone calls, the nurses utilized motivational interviewing techniques to support patients in setting health goals and to make sense of their PGHD passively collected by the Fitbit sensor. Two participants from the study, a 53 year old Latino woman (participant ML) and a 53 year old mixed race male (participant OB), were selected to illustrate two examples of how PGHD and mHealth technologies can be utilized to inform and improve health coaching and health behavior change. Results: Throughout the intervention ML and OB set bi-weekly goals related to their physical activity (steps, stairs, active minutes), nutritional habits (calories consumed), and sleep (quality, duration) in an effort to improve their overall health and fitness. ML and OB reached varying degrees of success in accomplishing their self-identified goals. By the end of the three month intervention, both participants achieved meaningful improvements to their anthropometric measurements, cardiovascular fitness and exercise habits. Visualization of participants? PGHD demonstrated the increased level of weekly physical activity had improved over the course of the intervention. Both participants also self-reported higher quality of life and health status ratings through questionnaires.' Conclusion: Emerging mHealth technologies and other health applications can track relevant information to assist individuals in making and sustaining lifestyle change. Integrating PGHD and mHealth technologies into health coaching practice allows nurses to perform meaningful analysis and correlate patient data with health behaviors to evaluate patient goal progression and provide timely and personal feedback based on their health goals. These case studies highlight the positive outcomes of two individuals who participated in a clinical trial, suggesting that the addition of sensor data adds value to nurse health coaching practice.' However, further research is necessary to determine the generalizability and effectiveness of pairing mHealth technologies with evidence-based nurse coaching interventions among larger numbers of diverse subjects.en
dc.subjectchronic disease managementen
dc.subjectsensor and mobile technologiesen
dc.subjectcase studyen
dc.date.available2016-07-13T11:09:29Z-
dc.date.issued2016-07-13-
dc.date.issued2016-07-13en
dc.date.accessioned2016-07-13T11:09:29Z-
dc.conference.date2016en
dc.conference.name27th International Nursing Research Congressen
dc.conference.hostSigma Theta Tau International, the Honor Society of Nursingen
dc.conference.locationCape Town, South Africaen
dc.descriptionTheme: Leading Global Research: Advancing Practice, Advocacy, and Policyen
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