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1.
Oncol Nurs Forum ; 47(5): 557-566, 2020 09 01.
Article in English | MEDLINE | ID: mdl-32830799

ABSTRACT

OBJECTIVES: To examine skeletal muscle mass change in a racially diverse sample of patients undergoing cancer treatment, determine significant predictors of muscle mass loss, and explore the interaction of race and cancer site. SAMPLE & SETTING: A retrospective analysis was conducted for 212 patients seeking treatment at a university hospital clinic. METHODS & VARIABLES: Skeletal muscle mass index (SMI) was determined by computed tomography at the time of cancer diagnosis and with cancer treatment. RESULTS: One hundred thirty-four patients (63%) had SMI loss with cancer treatment. Race and cancer site were found to be significant predictors of SMI loss. Compared to other racial groups, non-Hispanic Black (NHB) patients had the greatest SMI loss (p < 0.001) with cancer treatment. NHB patients with rectal cancer experienced the greatest SMI loss compared to patients of other races and cancer types. IMPLICATIONS FOR NURSING: To improve survivorship care for patients with cancer, it is essential to develop strategies for assessing and managing skeletal muscle mass loss throughout treatment, particularly for NHB patients with rectal cancer.


Subject(s)
Muscle, Skeletal , Neoplasms , Humans , Retrospective Studies , Tomography, X-Ray Computed
2.
Clin J Oncol Nurs ; 24(3): 42-48, 2020 06 01.
Article in English | MEDLINE | ID: mdl-32441697

ABSTRACT

BACKGROUND: eHealth can enhance the delivery of clinical cancer care by offering unique education opportunities for oncology nurses, patients, and family caregivers throughout the cancer trajectory. OBJECTIVES: This article reviews eHealth technology that can be applied to oncology education, such as mobile health applications, text messaging, web-based education, and audio- and videoconferencing. METHODS: Case studies provide exemplars of eHealth technologies used for delivering oncology education to nurses, patients, and caregivers. FINDINGS: By using eHealth technologies to obtain and provide education, oncology nurses are well positioned to improve the lives of patients and caregivers.


Subject(s)
Caregivers/education , Health Education/organization & administration , Neoplasms/nursing , Nurse Clinicians/education , Oncology Nursing/education , Patient Education as Topic/organization & administration , Telemedicine/organization & administration , Adult , Aged , Aged, 80 and over , Curriculum , Education, Nursing, Continuing , Family , Female , Humans , Male , Middle Aged , Mobile Applications
3.
Oncol Nurs Forum ; 46(6): 701-714, 2019 11 01.
Article in English | MEDLINE | ID: mdl-31626613

ABSTRACT

PROBLEM IDENTIFICATION: Many breast cancer survivors fail to engage in surveillance mammography to detect new and recurrent cancers. This review identifies factors promoting or inhibiting breast cancer survivors' participation in recommended surveillance mammography. LITERATURE SEARCH: This integrative review included all English-language studies published from 2000 to 2017, identified in CINAHL®, PsycINFO®, Embase®, and MEDLINE® via PubMed®. DATA EVALUATION: 23 studies met the inclusion criteria and were analyzed and synthesized. SYNTHESIS: 19 factors influencing mammography adherence were identified and organized into a conceptual model with two major categories. IMPLICATIONS FOR RESEARCH: The new model of predictors of mammography adherence can provide guidance for identifying individuals at greatest risk for nonadherence, as well as development of new interventions to address barriers to regular mammography screening, to promote early detection of new and recurrent cancers and improved survival rates.


Subject(s)
Breast Neoplasms/diagnosis , Breast Neoplasms/psychology , Cancer Survivors/psychology , Guideline Adherence/statistics & numerical data , Mammography/psychology , Neoplasm Recurrence, Local/diagnosis , Neoplasm Recurrence, Local/psychology , Adult , Aged , Aged, 80 and over , Cancer Survivors/statistics & numerical data , Female , Humans , Mammography/statistics & numerical data , Middle Aged
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