Background

As cancer care shifts from hospital to outpatient settings, the number of cancer caregivers continues to grow. However, little is known about the cancer caregiving experience. This gap in knowledge is especially evident for racially diverse caregivers.

Objective

This study, part of a multisite study of care recipients with either lung or colorectal cancer and their caregivers, examined the caregiving experiences of African American (AA) and white caregivers.

Methods

Caregivers were identified by cancer patients in the Cancer Care Outcomes Research and Surveillance consortium. Caregivers completed a self-administered, mailed questionnaire that assessed their characteristics and experiences. Analysis of covariance was used to compare racial groups by objective burden and caregiving resources while controlling for covariates.

Results

Despite greater preparedness for the caregiving role (P = .006), AA caregivers reported more weekly hours caregiving than whites did (26.5 ± 3.1 vs 18.0 ± 1.7; P = .01). In later phases of caregiving, AAs reported having more social support (P = .02), spending more hours caregiving (31.9 ± 3.5 vs 16.9 ± 1.9; P < .001), and performing more instrumental activities of daily living on behalf of their care recipient (P = .021).

Conclusion

Racial differences in the caregiving experience exist.

Implications for practice

Nurses play a key role in educating cancer patients and their caregivers on how to effectively cope with and manage cancer. Because AA caregivers seem to spend more time in the caregiving role and perform more caregiving tasks, AA caregivers may benefit from interventions tailored to their specific caregiving experience.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.