Reach and efficacy of the eHealth application Oncokompas, facilitating partners of incurably ill cancer patients to self-manage their caregiver needs: a randomized controlled trial

Schuit, Anouk S.; Rienks, Michelle M; Hooghiemstra, Nienke; Jansen, Femke; Lissenberg‐Witte, Birgit I.; Cuijpers, Pim; Leeuw, Irma M. Verdonck‐de; Holtmaat, Karen

doi:10.1007/s00520-022-07441-4

Cited by 6 publications

(16 citation statements)

References 42 publications

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“…Multimedia Appendix 2 [ 23 24 31-37 undefined undefined undefined undefined undefined undefined ] summarizes the main characteristics of the included studies. These studies were all RCTs published in 3 countries between 2015 and 2023: a total of 6 from the United States, 1 from the Netherlands, and 2 from China.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, 2 studies provided intervention through an application. Schuit et al [ 36 ] developed a program called Oncokompas to provide personalized information, suggestions, and supportive care solutions tailored to the caregiver’s situation. Chen et al [ 23 ] created a dyadic life review program for patients with advanced cancer and their caregivers using WeChat software to promote their QOL.…”

Section: Resultsmentioning

confidence: 99%

“…The control group in 1 study received enhanced usual care [ 33 ], whereas those in the remaining studies received usual care. Furthermore, in the study by Schuit et al [ 36 ], informal caregivers in the control group were allowed to use telemedicine equipment after the research ended.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 5 studies that evaluated caregiver burden were included in the meta-analysis [ 23 31 35-37 undefined undefined ]. Since no significant heterogeneity was observed among the included studies ( I 2 =0%; P =.64), a fixed-effect model was used for merging the data.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 4 studies that assessed QOL were included in the meta-analysis [ 23 24 32 36 ]. Due to moderate heterogeneity among the studies ( I 2 =73%; P =.01), a random-effects model was used.…”

Section: Resultsmentioning

confidence: 99%

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Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis

Yang,

Li,

Jiang

et al. 2024

JMIR Mhealth Uhealth

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Background Telemedicine technology is a rapidly developing field that shows immense potential for improving medical services. In palliative care, informal caregivers assume the primary responsibility in patient care and often face challenges such as increased physical and mental stress and declining health. In such cases, telemedicine interventions can provide support and improve their health outcomes. However, research findings regarding the use of telemedicine among informal caregivers are controversial, and the efficacy of telemedicine remains unclear. Objective This study aimed to evaluate the impacts of telemedicine on the burden, anxiety, depression, and quality of life of informal caregivers of patients in palliative care. Methods A systematic literature search was conducted using the PubMed, Embase, Web of Science, CENTRAL, PsycINFO, CINAHL Plus with Full Text, CBM, CNKI, WanFang, and VIP databases to identify relevant randomized controlled trials published from inception to March 2023. Two authors independently screened the studies and extracted the relevant information. The methodological quality of the included studies was assessed using the Cochrane risk-of-bias tool. Intervention effects were estimated and sensitivity analysis was conducted using Review Manager 5.4, whereas 95% prediction intervals (PIs) were calculated using R (version 4.3.2) and RStudio. Results A total of 9 randomized controlled trials were included in this study. The meta-analysis indicated that telemedicine has reduced the caregiving burden (standardized mean differences [SMD] −0.49, 95% CI −0.72 to −0.27; P<.001; 95% PI −0.86 to −0.13) and anxiety (SMD −0.23, 95% CI −0.40 to −0.06; P=.009; 95% PI −0.98 to 0.39) of informal caregivers; however, it did not affect depression (SMD −0.21, 95% CI −0.47 to 0.05; P=.11; 95% PI −0.94 to 0.51) or quality of life (SMD 0.35, 95% CI −0.20 to 0.89; P=.21; 95% PI −2.15 to 2.85). Conclusions Although telemedicine can alleviate the caregiving burden and anxiety of informal caregivers, it does not significantly reduce depression or improve their quality of life. Further high-quality, large-sample studies are needed to validate the effects of telemedicine. Furthermore, personalized intervention programs based on theoretical foundations are required to support caregivers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis

Yang,

Li,

Jiang

et al. 2024

JMIR Mhealth Uhealth

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Palliative Care for Patients With Cancer: ASCO Guideline Update

Sanders,

Temin,

Ghoshal

et al. 2024

JCO

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ASCO Guidelines provide recommendations with comprehensive review and analyses of the relevant literature for each recommendation, following the guideline development process as outlined in the ASCO Guidelines Methodology Manual . ASCO Guidelines follow the ASCO Conflict of Interest Policy for Clinical Practice Guidelines . Clinical Practice Guidelines and other guidance (“Guidance”) provided by ASCO is not a comprehensive or definitive guide to treatment options. It is intended for voluntary use by providers and should be used in conjunction with independent professional judgment. Guidance may not be applicable to all patients, interventions, diseases, or stages of diseases. Guidance is based on review and analysis of relevant literature, and is not intended as a statement of the standard of care. ASCO does not endorse third-party drugs, devices, services, or therapies and assumes no responsibility for any harm arising from or related to the use of this information. See complete disclaimer in Appendix 1 and 2 (online only) for more. PURPOSE To provide evidence-based guidance to oncology clinicians, patients, nonprofessional caregivers, and palliative care clinicians to update the 2016 ASCO guideline on the integration of palliative care into standard oncology for all patients diagnosed with cancer. METHODS ASCO convened an Expert Panel of medical, radiation, hematology-oncology, oncology nursing, palliative care, social work, ethics, advocacy, and psycho-oncology experts. The Panel conducted a literature search, including systematic reviews, meta-analyses, and randomized controlled trials published from 2015-2023. Outcomes of interest included quality of life (QOL), patient satisfaction, physical and psychological symptoms, survival, and caregiver burden. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS The literature search identified 52 relevant studies to inform the evidence base for this guideline. RECOMMENDATIONS Evidence-based recommendations address the integration of palliative care in oncology. Oncology clinicians should refer patients with advanced solid tumors and hematologic malignancies to specialized interdisciplinary palliative care teams that provide outpatient and inpatient care beginning early in the course of the disease, alongside active treatment of their cancer. For patients with cancer with unaddressed physical, psychosocial, or spiritual distress, cancer care programs should provide dedicated specialist palliative care services complementing existing or emerging supportive care interventions. Oncology clinicians from across the interdisciplinary cancer care team may refer the caregivers (eg, family, chosen family, and friends) of patients with cancer to palliative care teams for additional support. The Expert Panel suggests early palliative care involvement, especially for patients with uncontrolled symptoms and QOL concerns. Clinicians caring for patients with solid tumors on phase I cancer trials may also refer them to specialist palliative care. Additional information is available at www.asco.org/supportive-care-guidelines .

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Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study (Preprint)

Liu,

Shih,

Huang

et al. 2023

Preprint

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BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning–based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. CLINICALTRIAL ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907

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Reach and efficacy of the eHealth application Oncokompas, facilitating partners of incurably ill cancer patients to self-manage their caregiver needs: a randomized controlled trial

Cited by 6 publications

References 42 publications

Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis

Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis

Palliative Care for Patients With Cancer: ASCO Guideline Update

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study (Preprint)

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