Assessing the Impact of Mobile Health Apps on Family Caregiver Burden Levels and the Factors Predicting Mobile Health App Use
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Abstract
Assessing the Impact of Mobile Health Apps on Family Caregiver Burden Levels and the Factors Predicting Mobile Health App Use
Kathleen Frisbee, MPH, PhDc
Neil Evans, MD
Thomas Houston MD MPH
Kevin Todd
Thomas English PhD
Erin Borglund MS
Department of Veterans Affairs, Veterans Health Administration, Connected Health Office, Washington DC
Introduction: Family Caregivers play a significant role in healthcare by providing supplemental uncompensated care that delays or prevents the need for more expensive institutionalized care. Caregiver burden and stress is high as caregiving takes a physical and mental toll on the health and well-being of the caregiver. As family members and patients search for ways to help them better manage their healthcare, they are turning to mobile health (mHealth) applications. The VA created a suite of Mobile Health Apps designed to support Family Caregivers in their caregiving role. The objective of this research was twofold: First to assess the effectiveness of the Family Caregiver mHealth apps in reducing Caregiver burden levels and second to determine the characteristics of Family Caregivers and their care recipients that predict the use of these Apps
Methods: The effectiveness of the mobile health apps in reducing Caregiver burden was investigated using a quasi-experimental pre-posttest study design with repeated measures. The intervention consisted of Mobile Health Apps created by the VA that were loaded on an iPad and distributed to 881 Family Caregivers in the VA’s Family Caregiver Program who volunteered to participate in the mobile health pilot. Family Caregivers enrolled in the program but who did not volunteer to participate in the mobile health pilot formed the control group (3620). The outcome variable assessed was the change in the Pre and Post Zarit burden scores in the treatment group compared with the control. Pre and Posttest Zarit Burden scores were extracted from VA electronic notes and analyzed using ANCOVA. The follow-up rate for the treatment group and control groups were 70% and 65% respectively. Predictor variables were extracted from VA operational databases.
The characteristics of the Caregiver/Patient dyads that predict use of the mobile apps was studied in the treatment group receiving the iPads (881) and in a subset of the treatment participants (560) who completed a Caregiver characteristic survey. The outcome variable measured was the number of times the mobile health apps were used by the Caregiver/Patient dyads over a three-month time period ending September 18, 2013. Predictor variables were extracted from VA operational databases and the Caregiver characteristic survey. Negative Binomial regression models and zero inflated regression models were constructed to predict outcomes.
Results: A statistically significant difference was not found between the treatment and control groups in pre and post Zarit Burden scores. Statistically significant predictors of App Use varied by individual App but included: relationship of Caregiver to care recipient, urban/rural living location, polytrauma care, use of outpatient services, Veteran age, Caregiver age, IADLs – arranging services and giving medications.
Conclusion: This study has contributed to the mHealth evidence base by identifying user characteristics that predict the use of specific types of mobile health apps.
Kathleen Frisbee, MPH, PhDc
Neil Evans, MD
Thomas Houston MD MPH
Kevin Todd
Thomas English PhD
Erin Borglund MS
Department of Veterans Affairs, Veterans Health Administration, Connected Health Office, Washington DC
Introduction: Family Caregivers play a significant role in healthcare by providing supplemental uncompensated care that delays or prevents the need for more expensive institutionalized care. Caregiver burden and stress is high as caregiving takes a physical and mental toll on the health and well-being of the caregiver. As family members and patients search for ways to help them better manage their healthcare, they are turning to mobile health (mHealth) applications. The VA created a suite of Mobile Health Apps designed to support Family Caregivers in their caregiving role. The objective of this research was twofold: First to assess the effectiveness of the Family Caregiver mHealth apps in reducing Caregiver burden levels and second to determine the characteristics of Family Caregivers and their care recipients that predict the use of these Apps
Methods: The effectiveness of the mobile health apps in reducing Caregiver burden was investigated using a quasi-experimental pre-posttest study design with repeated measures. The intervention consisted of Mobile Health Apps created by the VA that were loaded on an iPad and distributed to 881 Family Caregivers in the VA’s Family Caregiver Program who volunteered to participate in the mobile health pilot. Family Caregivers enrolled in the program but who did not volunteer to participate in the mobile health pilot formed the control group (3620). The outcome variable assessed was the change in the Pre and Post Zarit burden scores in the treatment group compared with the control. Pre and Posttest Zarit Burden scores were extracted from VA electronic notes and analyzed using ANCOVA. The follow-up rate for the treatment group and control groups were 70% and 65% respectively. Predictor variables were extracted from VA operational databases.
The characteristics of the Caregiver/Patient dyads that predict use of the mobile apps was studied in the treatment group receiving the iPads (881) and in a subset of the treatment participants (560) who completed a Caregiver characteristic survey. The outcome variable measured was the number of times the mobile health apps were used by the Caregiver/Patient dyads over a three-month time period ending September 18, 2013. Predictor variables were extracted from VA operational databases and the Caregiver characteristic survey. Negative Binomial regression models and zero inflated regression models were constructed to predict outcomes.
Results: A statistically significant difference was not found between the treatment and control groups in pre and post Zarit Burden scores. Statistically significant predictors of App Use varied by individual App but included: relationship of Caregiver to care recipient, urban/rural living location, polytrauma care, use of outpatient services, Veteran age, Caregiver age, IADLs – arranging services and giving medications.
Conclusion: This study has contributed to the mHealth evidence base by identifying user characteristics that predict the use of specific types of mobile health apps.
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