Predictors for Telephone Outreach Post-hospital Discharge.

PURPOSE OF STUDY: The specific aims of this study were to examine whether sociodemographic variables and medical-surgical diagnoses were associated with telephone follow-up (TFU) reach rates, emergency department visits, and hospital readmissions. PRIMARY PRACTICE OF SETTING: Acute care inpatient units in an academic medical center. METHODOLOGY AND SAMPLE: A correlational design was utilized, and a prospective medical record review of patients was conducted while implementing face-to-face prehospital discharge meeting interventions. The study sample (N = 176) included adult patients in two neurosurgical wards who were admitted between June 2016 and September 2016. Parametric and nonparametric tests were used to explore the balance between the intervention group receiving a face-to-face prehospital discharge meeting and comparison group receiving standard prehospital discharge care. Bivariate statistics were employed to determine associations between variables. RESULTS: A total of 15 sociodemographic and medical-surgical variables were used to correlate TFU reach rates, emergency department (ED) visits, and readmission rates. Educational attainment (p = .002), employment status (p = .014), parental status (p = .010), and hospital service (p = .039) had significant differences between the intervention and comparison groups. Results demonstrated an improved reach rate for the intervention group but despite the differences in the groups, phi and Cramer's V coefficients did not correlate any associations with TFU reach rate, ED visits, and readmission rates with sociodemographic and surgical variables. This outcome affirmed that despite the similarities and differences in the sample, a face-to-face meeting prehospital discharge is an effective intervention to improve telephone outreach. IMPLICATIONS TO CASE MANAGEMENT PRACTICE: There is a need to determine the most cost-effective way to increase TFU reach rates to prevent subsequent ED visits and hospital readmissions. There is also a need to develop a tool that can predict the hardest-to-reach patients posthospital discharge, so that case managers can meet those patients before leaving the hospital. In addition, it is important to identify alternative methods of "face-to-face" interactions during the COVID-19 pandemic crises. Case managers must explore ways with caution to leverage secured digital technology to bridge the gap of communicating with patients and family members when hospital visitations are limited.

Factors Associated With Intention to Adopt mHealth Apps Among Dementia Caregivers With a Chronic Condition: Cross-sectional, Correlational Study.

BACKGROUND: In the United States, nearly 80% of family caregivers of people with dementia have at least one chronic condition. Dementia caregivers experience high stress and burden that adversely affect their health and self-management. mHealth apps can improve health and self-management among dementia caregivers with a chronic condition. However, mHealth app adoption by dementia caregivers is low, and reasons for this are not well understood. OBJECTIVE: The purpose of this study is to explore factors associated with dementia caregivers' intention to adopt mHealth apps for chronic disease self-management. METHODS: We conducted a cross-sectional, correlational study and recruited a convenience sample of dementia caregivers. We created a survey using validated instruments and collected data through computer-assisted telephone interviews and web-based surveys. Before the COVID-19 pandemic, we recruited dementia caregivers through community-based strategies, such as attending community events. After nationwide closures due to the pandemic, the team focused on web-based recruitment. Multiple logistic regression analyses were used to test the relationships between the independent and dependent variables. RESULTS: Our sample of 117 caregivers had an average age of 53 (SD 17.4) years, 16 (SD 3.3) years of education, and 4 (SD 2.5) chronic conditions. The caregivers were predominantly women (92/117, 78.6%) and minorities (63/117, 53.8%), experienced some to extreme income difficulties (64/117, 54.7%), and were the child or child-in-law (53/117, 45.3%) of the person with dementia. In logistic regression models adjusting for the control variables, caregiver burden (odds ratio [OR] 1.3, 95% CI 0.57-2.8; P=.57), time spent caregiving per week (OR 1.7, 95% CI 0.77-3.9; P=.18), and burden of chronic disease and treatment (OR 2.3, 95% CI 0.91-5.7; P=.08) were not significantly associated with the intention to adopt mHealth apps. In the final multiple logistic regression model, only perceived usefulness (OR 23, 95% CI 5.6-97; P<.001) and the interaction term for caregivers' education and burden of chronic disease and treatment (OR 31, 95% CI 2.2-430; P=.01) were significantly associated with their intention to adopt mHealth apps. Perceived ease of use (OR 2.4, 95% CI 0.67-8.7; P=.18) and social influence (OR 1.8, 95% CI 0.58-5.7; P=.31) were not significantly associated with the intention to adopt mHealth apps. CONCLUSIONS: When designing mHealth app interventions for dementia caregivers with a chronic condition, it is important to consider caregivers' perceptions about how well mHealth apps can help their self-management and which app features would be most useful for self-management. Caregiving factors may not be relevant to caregivers' intention to adopt mHealth apps. This is promising because mHealth strategies may overcome barriers to caregivers' self-management. Future research should investigate reasons why caregivers with a low education level and low burden of chronic disease and treatment have significantly lower intention to adopt mHealth apps for self-management.