Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis

Castelyn, Grant; Laranjo, Liliana; Schreier, Günter; Gallego, Blanca

doi:10.1016/j.ijmedinf.2021.104620

Cited by 25 publications

(21 citation statements)

References 96 publications

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“…These findings are similar to those involving apps for other chronic diseases, showing that enabling customization (eg, editing information and choosing which aspects to track) is among the most appealing features and may enhance the usability, motivation, and engagement with the apps [ 17 , 75 , 76 ]. Future studies may explore the delivery of core BCTs (self-monitoring, feedback, and instruction on how to perform the behavior) and provide other techniques in a personalized manner, according to patient preferences and self-reported information [ 77 ] or based on machine learning algorithms using patient data collected over time (eg, from smartphone sensors or wireless monitoring devices) [ 78 , 79 ].…”

Section: Discussionmentioning

confidence: 99%

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies

Bezerra¹,

Tong²,

Atherton³

et al. 2022

JMIR Cardio

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Background Heart failure self-management is essential to avoid decompensation and readmissions. Mobile apps seem promising in supporting heart failure self-management, and there has been a rapid growth in publications in this area. However, to date, systematic reviews have mostly focused on remote monitoring interventions using nonapp types of mobile technologies to transmit data to health care providers, rarely focusing on supporting patient self-management of heart failure. Objective This study aims to systematically review the evidence on the effect of heart failure self-management apps on health outcomes, patient-reported outcomes, and patient experience. Methods Four databases (PubMed, Embase, CINAHL, and PsycINFO) were searched for studies examining interventions that comprised a mobile app targeting heart failure self-management and reported any health-related outcomes or patient-reported outcomes or perspectives published from 2008 to December 2021. The studies were independently screened. The risk of bias was appraised using Cochrane tools. We performed a narrative synthesis of the results. The protocol was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42020158041). Results A total of 28 articles (randomized controlled trials [RCTs]: n=10, 36%), assessing 23 apps, and a total of 1397 participants were included. The most common app features were weight monitoring (19/23, 83%), symptom monitoring (18/23, 78%), and vital sign monitoring (15/23, 65%). Only 26% (6/23) of the apps provided all guideline-defined core components of heart failure self-management programs: education, symptom monitoring, medication support, and physical activity support. RCTs were small, involving altogether 717 participants, had ≤6 months of follow-up, and outcomes were predominantly self-reported. Approximately 20% (2/10) of RCTs reported a significant improvement in their primary outcomes: heart failure knowledge (P=.002) and self-care (P=.004). One of the RCTs found a significant reduction in readmissions (P=.02), and 20% (2/10) of RCTs reported higher unplanned clinic visits. Other experimental studies also found significant improvements in knowledge, self-care, and readmissions, among others. Less than half of the studies involved patients and clinicians in the design of apps. Engagement with the intervention was poorly reported, with only 11% (3/28) of studies quantifying app engagement metrics such as frequency of use over the study duration. The most desirable app features were automated self-monitoring and feedback, personalization, communication with clinicians, and data sharing and integration. Conclusions Mobile apps may improve heart failure self-management; however, more robust evaluation studies are needed to analyze key end points for heart failure. On the basis of the results of this review, we provide a road map for future studies in this area.

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Section: Discussionmentioning

confidence: 99%

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies

Bezerra¹,

Tong²,

Atherton³

et al. 2022

JMIR Cardio

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“…Remote home monitoring models have previously been used to provide care for chronic conditions. [24][25][26] During the pandemic, remote home monitoring models were used for acute conditions such as COVID-19, with the aim of shortening length of stay in hospital, reducing unnecessary hospital admissions or readmission and infection transmission and escalating care as needed. 27,28 Many different types of COVID-19 remote home monitoring models were implemented throughout England.…”

Section: Introductionmentioning

confidence: 99%

“…The COVID‐19 pandemic further enhanced and accelerated the need for healthcare services to use technology in care delivery 5 and escalated the need for patient self‐management. Remote home monitoring models have previously been used to provide care for chronic conditions 24–26 . During the pandemic, remote home monitoring models were used for acute conditions such as COVID‐19, with the aim of shortening length of stay in hospital, reducing unnecessary hospital admissions or readmission and infection transmission and escalating care as needed 27,28…”

Section: Introductionmentioning

confidence: 99%

Patients' experiences of, and engagement with, remote home monitoring services for COVID‐19 patients: A rapid mixed‐methods study

Walton

Vindrola‐Padros

Crellin

et al. 2022

Health Expectations

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Introduction: Remote home monitoring models were implemented during the COVID-19 pandemic to shorten hospital length of stay, reduce unnecessary hospital admission, readmission and infection and appropriately escalate care. Within these models, patients are asked to take and record readings and escalate care if advised.There is limited evidence on how patients and carers experience these services. This study aimed to evaluate patient experiences of, and engagement with, remote home monitoring models for COVID-19.Methods: A rapid mixed-methods study was carried out in England (conducted from March to June 2021). We remotely conducted a cross-sectional survey and semistructured interviews with patients and carers. Interview findings were summarized using rapid assessment procedures sheets and data were grouped into themes (using thematic analysis). Survey data were analysed using descriptive statistics.Results: We received 1069 surveys (18% response rate) and conducted interviews with patients (n = 59) or their carers (n = 3). 'Care' relied on support from staff members and family/friends. Patients and carers reported positive experiences and felt that the service and human contact reassured them and was easy to engage with. Yet, some patients and carers identified problems with engagement (e.g., hesitancy to self-escalate care). Engagement was influenced by patient factors such as health and knowledge, support from family/friends and staff, availability and ease of use of informational and material resources (e.g., equipment) and service factors.

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“…Con esto se busca evitar el desarrollo de dicha enfermedad y, en caso de darse, poder aplicar el tratamiento adecuado para prevenir su progresión a gran escala (Jaiswal et al, 2021). Por ello, el sector salud promueve la implementación de sistemas de apoyo al diagnóstico y prevención de la diabetes con el objetivo de reducir sus efectos, utilizando dispositivos inteligentes y sensores (Ray & Chaudhuri, 2021) e identificación de modelos predictivos mediante métodos computacionales asociados a la inteligencia artificial (Castelyn et al, 2021b), la cual tiene el potencial de revolucionar el cuidado de la salud debido a la disponibilidad de métodos y plataformas computacionales que son cada vez más poderosas, junto con las diversas fuentes informativas de datos que se tiene de cada paciente, tanto dentro como fuera de los entornos clínicos (Gautier et al, 2021).…”

Section: Introductionunclassified

Una revisión de las implementaciones de sistemas para la identificación de tendencias de la diabetes

Loja

Ygnacio

2022

Interfases

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La diabetes mellitus es una enfermedad crónica que aparece cuando el páncreas no secreta suficiente insulina o cuando el organismo no utiliza apropiadamente la insulina que produce. Dado que la insulina es una hormona que regula la concentración de glucosa en la sangre, uno de los efectos más comunes de la diabetes no controlada es la hiperglucemia, que con el tiempo daña gravemente muchos órganos y sistemas del cuerpo. Por ello, es importante el desarrollo de software predictivo para el diagnóstico y posterior tratamiento de esta enfermedad, en particular para la diabetes tipo 1 y 2, que concentran la mayoría de los casos. El presente trabajo realiza una revisión sistemática de literatura a fin de determinar los métodos y la problemática en la construcción de sistemas de identificación de tendencias orientados a la diabetes. Los resultados muestran 16 métodos diferentes de construcción utilizados en estos sistemas, de los cuales los más eficientes son la regresión logística y las redes neuronales artificiales.

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Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis

Cited by 25 publications

References 96 publications

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies

Patients' experiences of, and engagement with, remote home monitoring services for COVID‐19 patients: A rapid mixed‐methods study

Una revisión de las implementaciones de sistemas para la identificación de tendencias de la diabetes

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