Psychological distress, chronic conditions and quality of life in elderly hematologic cancer patients: study protocol of a prospective study

Background This study compared the perceived social support of young and older adult cancer patients, examining possible influencing factors as well as associations with health-related quality of life. Methods A total of 179 young patients (18–39 years) and 200 older adult patients (> 70 years) with haematological malignancies completed questionnaires on their perceived social support (ISSS-8, scales: Positive Support and Detrimental Interactions, range 0–16) and health-related quality of life (EORTC QLQ-C30). Tests for mean differences, correlations and regression analyses to determine associated variables of social support were performed. Results No difference was reported between young (M = 13.40, SD = 2.81) and older adult patients (M = 13.04, SD = 3.82; p = .313) for Positive Support. However, young patients (M = 4.16, SD = 3.10) reported having had more Detrimental Interactions than older patients did (M = 1.63, SD = 2.42; p < .001, Cohen’s d = .910). Comparison of the EORTC QLQ-C30 Function scales showed poorer outcomes for young patients on Emotional, Cognitive and Social Functions and a higher outcome on Physical Function compared with older adult patients. Regression analyses indicated that age (young vs. older adult patients) significantly explained proportions of variance in all models, with young age having a negative impact on Emotional, Cognitive and Social Functions and a positive impact on Physical and Role Functions compared with old age. Significant associations between Detrimental Interactions and all the scales examined except Cognitive Function were found. Conclusions The difference in negative perceptions of social support in young vs. older adult patients and its impact on health-related quality of life emphasises the necessity of differentiating between positive and negative social support. Negative interactions should be addressed through psychosocial care, particularly with young cancer patients.

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“…Study participants were offered an allowance of 10€. More information about the 70+ study has been provided by Köhler et al [28].…”

Section: Methodsmentioning

confidence: 99%

Perceived social support and associations with health-related quality of life in young versus older adult patients with haematological malignancies

Geue

Götze

Friedrich

et al. 2019

Health Qual Life Outcomes

Self Cite

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“…Consequently, patient satisfaction with self-monitoring systems through mHealth is dependent on the availability of technical infrastructure, such as mobile networks and bandwidth strength, the ability of health workers to reach rural communities, and the availability of health personnel support. A number of researchers have also argued that the successful implementation of health systems depends on the support health institutions receive in terms of better policies, standards, and strategies, as well as their evaluation and reform [ 1 , 8 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

“…An mHealth self-monitoring application system will serve as a reminder to timely attend to patients’ health needs. Those living with a chronic disease often give up trying because of anxiety [ 1 , 8 ]. The World Health Organization [ 7 ] showed that inconsistency in drug intake is a major cause of high mortality rates in people with chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

mHealth Self-Monitoring Model for Medicine Adherence of Patients With Diabetes in Resource-Limited Countries: Structural Equation Modeling Approach

Kgasi¹,

Chimbo²,

Motsi³

2023

JMIR Form Res

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Background The COVID-19 pandemic has led to serious challenges and emphasized the importance of using technology for health care operational transformation. Consequently, the need for technological innovations has increased, thus empowering patients with chronic conditions to tighten their adherence to medical prescriptions. Objective This study aimed to develop a model for a mobile health (mHealth) self-monitoring system for patients with diabetes in rural communities within resource-limited countries. The developed model could be based on the implementation of a system for the self-monitoring of patients with diabetes to increase medical adherence. Methods This study followed a quantitative approach, in which data were collected from health care providers using a questionnaire with close-ended questions. Data were collected from district hospitals in 3 South African provinces that were selected based on the prevalence rates of diabetes and the number of patients with diabetes treated. The collected data were analyzed using smart partial least squares to validate the model and test the suggested hypotheses. Results Using variance-based structural equation modeling that leverages smart partial least squares, the analysis indicated that environmental factors significantly influence all the independent constructs that inform patients’ change of behavior toward the use of mHealth for self-monitoring of medication adherence. Technology characteristics such as effort expectancy, self-efficacy, and performance expectancy were equally significant; hence, their hypotheses were accepted. In contrast, the contributions of culture and social aspects were found to be insignificant, and their hypotheses were rejected. In addition, an analysis was conducted to determine the interaction effects of the moderating variables on the independent constructs. The results indicated that with the exception of cultural and social influences, there were significant interacting effects on other independent constructs influencing mHealth use for self-monitoring. Conclusions On the basis of the findings of this study, we conclude that behavioral changes are essential for the self-monitoring of chronic diseases. Therefore, it is important to enhance those effects that stimulate the behavior to change toward the use of mHealth for self-monitoring. Motivational aspects were also found to be highly significant as they triggered changes in behavior. The developed model can be used to extend the research on the self-monitoring of patients with chronic conditions. Moreover, the model will be used as a basic architecture for the implementation of fully fledged systems for self-monitoring of patients with diabetes.

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“…A more aggressive cancer diagnosis (based on biological and genetic markers) and lack of responsivity to treatment yields a higher risk for psychological distress (especially anxiety) and psychiatric comorbidities. 57 A higher burden of medical comorbidities bears a higher level of emotional distress. 31,58 Rehospitalization after the transplant increases the risk of psychological issues as well.…”

Section: Disease Factorsmentioning

confidence: 99%

Psychological Considerations in Hematopoietic Stem Cell Transplantation

et al. 2019

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Psychological distress, chronic conditions and quality of life in elderly hematologic cancer patients: study protocol of a prospective study

Cited by 12 publications

References 42 publications

Perceived social support and associations with health-related quality of life in young versus older adult patients with haematological malignancies

Perceived social support and associations with health-related quality of life in young versus older adult patients with haematological malignancies

mHealth Self-Monitoring Model for Medicine Adherence of Patients With Diabetes in Resource-Limited Countries: Structural Equation Modeling Approach

Psychological Considerations in Hematopoietic Stem Cell Transplantation

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