Patients' perspectives on digital health tools

Nurse-led remote digital support for adults with chronic conditions: A systematic synthesis without meta-analysis

AIM

The systematic review aims to synthesize the literature examining the effectiveness of nurse-led remote digital support on health outcomes in adults with chronic conditions.

BACKGROUND

Adults with chronic diseases have increased rates of mortality and morbidity and use health care resources at a higher intensity than those without chronic conditions-placing strain on the patient, their caregivers and health systems. Nurse-led digital health disease self-management interventions have potential to improve outcomes for patients with chronic conditions by facilitating care in environments other that the hospital setting.

DESIGN AND METHODS

We searched PubMed/MEDLINE, Embase, PsycINFO and Cochrane Central databases from inception to 7 December 2022. We included randomized controlled trials assessing the impact of nurse-led remote digital support interventions compared to usual care on health-related outcomes in adults with chronic illness. The Cochrane risk-of-bias tool was used to assess bias in studies. Outcomes were organized into four categories: self-management, clinical outcomes, health care resource use and satisfaction with care. Results are presented narratively based on statistical significance.

RESULTS

Forty-four papers pertaining to 40 unique studies were included. Interventions most targeted diabetes (n = 11) and cardiovascular disease (n = 8). Websites (n = 10) and mobile applications (n = 10) were the most used digital modalities. Nurses supported patients either in response to incoming patient health data (n = 14), virtual appointment (n = 8), virtual health education (n = 5) or through a combination of these approaches (n = 13). Positive impacts of nurse-led digital chronic disease support were identified in each outcome category. Mobile applications were the most effective digital modality.

CONCLUSION AND RELEVANCE TO CLINICAL PRACTICE

Results show that nurse-led remote digital support interventions significantly improve self-management capacity, clinical health outcomes, health care resource use and satisfaction with care. Such interventions have potential to support overall health for adults with chronic conditions in their home environments.

Mapping the Evidence on the Impact of mHealth Interventions on Patient-Reported Outcomes in Patients With Breast Cancer: A Systematic Review

PURPOSE

To comprehensively synthesize the existing evidence concerning mHealth interventions for patients with breast cancer (BC).

DESIGN

On July 30, 2023, we searched PubMed, PsycINFO, and Google Scholar for articles using the following inclusion criteria: evaluation of mHealth interventions in patients with cancer, at least 30 participants with BC, randomized control trials or prospective pre-post studies, determinants of health (patient-reported outcomes [PROs] and quality of life [QoL]) as primary outcomes, interventions lasting at least 8 weeks, publication after January 2015. Publications were excluded if they evaluated telehealth or used web-based software for desktop devices only. The quality of the included studies was analyzed with the Cochrane Collaboration Risk of Bias Tool and the Methodological Index for Non-Randomized Studies.

RESULTS

We included 30 studies (20 focused on BC), encompassing 5,691 patients with cancer (median 113, IQR, 135.5). Among these, 3,606 had BC (median 99, IQR, 75). All studies contained multiple interventions, including physical activity, tailored information for self-management of the disease, and symptom tracker. Interventions showed better results on self-efficacy (3/3), QoL (10/14), and physical activity (5/7). Lifestyle programs (3/3), expert consulting (4/4), and tailored information (10/11) yielded the best results. Apps with interactive support had a higher rate of positive findings, while interventions targeted to survivors showed worse results. mHealth tools were not available to the public in most of the studies (17/30).

CONCLUSION

mHealth interventions yielded heterogeneous results on different outcomes. Identifying lack of evidence on clinical scenarios (eg, patients undergoing systemic therapy other than chemotherapy) could aid in refining strategic planning for forthcoming research endeavors within this field.

Tailoring remote patient management in cardiovascular risk management for healthcare professionals using panel management: a qualitative study

BACKGROUND

While remote patient management (RPM) has the potential to assist in achieving treatment targets for cardiovascular risk factors in primary care, its effectiveness may vary among different patient subgroups. Panel management, which involves proactive care for specific patient risk groups, could offer a promising approach to tailor RPM to these groups. This study aims to (i) assess the perception of healthcare professionals and other stakeholders regarding the adoption and (ii) identify the barriers and facilitators for successfully implementing such a panel management approach.

METHODS

In total, nineteen semi-structured interviews and two focus groups were conducted in the Netherlands. Three authors reviewed the audited transcripts. The Consolidated Framework for Implementation Strategies (CFIR) domains were used for the thematic analysis.

RESULTS

A total of 24 participants (GPs, nurses, health insurers, project managers, and IT consultants) participated. Overall, a panel management approach to RPM in primary care was considered valuable by various stakeholders. Implementation barriers encompassed concerns about missing necessary risk factors for patient stratification, additional clinical and technical tasks for nurses, and reimbursement agreements. Facilitators included tailoring consultation frequency and early detection of at-risk patients, an implementation manager accountable for supervising project procedures and establishing agreements on assessing implementation metrics, and ambassador roles.

CONCLUSION

Panel management could enhance proactive care and accurately identify which patients could benefit most from RPM to mitigate CVD risk. For successful implementation, we recommend having clear agreements on technical support, financial infrastructure and the criteria for measuring evaluation outcomes.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study

BACKGROUND

Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease.

OBJECTIVE

This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device.

METHODS

A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling.

RESULTS

Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively.

CONCLUSIONS

Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Digital health and health equity: How digital health can address healthcare disparities and improve access to quality care in Africa

The healthcare industry is constantly evolving to bridge the inequality gap and provide precision care to its diverse population. One of these approaches is the integration of digital health tools into healthcare delivery. Significant milestones such as reduced maternal mortality, rising and rapidly proliferating health tech start-ups, and the use of drones and smart devices for remote health service delivery, among others, have been reported. However, limited access to family planning, migration of health professionals, climate change, gender inequity, increased urbanization, and poor integration of private health firms into healthcare delivery rubrics continue to impair the attainment of universal health coverage and health equity. Health policy development for an integrated health system without stigma, addressing inequalities of all forms, should be implemented. Telehealth promotion, increased access to infrastructure, international collaborations, and investment in health interventions should be continuously advocated to upscale the current health landscape and achieve health equity.

Multilayer framework for digital multicomponent platform design for colorectal survivors and carers: a qualitative study

Background

The advent of eHealth services offers the potential to support colorectal cancer (CRC) survivors and their informal caregivers (ICs), yet research into user needs and design requirements remains scant. This exploratory qualitative study addresses this knowledge gap by focusing on the development of a Digital Multicomponent Platform (DMP) designed to provide comprehensive support to these populations.

Aims

The objective of this research is to use qualitative methodologies to identify key user needs and design requirements for eHealth services. It seeks to propose and apply a multi-tiered framework for creating a DMP that encapsulates the needs of CRC survivors and their ICs.

Methods

Skype-based focus groups (FGs) were utilized to gather qualitative data from CRC survivors and ICs. This approach served to elicit crucial themes integral to the design of the DMP. A multi-tiered framework was subsequently developed to integrate user-centered design (UCD) principles and requirements with predetermined outcomes, eHealth services, and IT infrastructure.

Results

The first stage of the analysis identified five crucial themes: (1) the importance of healthcare system interaction via eHealth, (2) interaction between healthcare providers and peers, (3) lifestyle and wellness considerations, (4) platform content and user interface requirements, (5) caregiver support. The second stage analysis applied the multi-tiered framework, to determine the DMP that was conceptualized from these themes, underscores the significance of personalized content, caregiver involvement, and integration with electronic health records (EHRs).

Conclusion

The study offers novel insights into the design and development of digital supportive care interventions for CRC survivors and their caregivers. The results highlight the utility of user-centered design principles, the significance of personalized content and caregiver involvement, and the need for a unified health data platform that promotes communication among patients, healthcare providers, and peers. This multi-tiered framework could serve as a prototype for future eHealth service designs.