Heart failure decompensation alerts in a patient's home using an automated, AI-driven, point-of-care device.

Heart failure (HF) is a major challenge worldwide and needs continuous monitoring of patients even after hospital discharge. This case report summarises the data collected and experience gained from the first usage of an automated, point-of-care device (Heartfelt device) in a patient's home in the UK. The device monitors the onset of peripheral oedema and alerts clinicians if an increase in volume outside an expected normal range for the patient is detected. This may provide a reliable method of remotely and automatically monitoring HF patients in the home for those who do not reliably use weighing scales. The device successfully provided data for about 15 months and generated alerts in advance, which supported decisions for the patient's care. The rate of data acquisition was very high and consistent throughout this period. The patient was satisfied with the device and agreed that it helped in her decision to seek medical attention.

Compliance with Telemonitoring in Heart Failure. Are Study Findings Representative of Reality?: A Narrative Literature Review.

Introduction: Telemonitoring technologies enable medical teams to remotely manage outpatients with heart failure (HF) and reduce their risk of HF-related hospitalizations. However, noncompliance threatens the effectiveness of these approaches. This review aims to identify whether patients who are less likely or unable to comply with telemonitoring and their instructions for use are represented by interventional telemonitoring studies, and if their exclusion from studies is resulting in study findings not representative of clinical reality. Methods: A narrative literature review was conducted to identify interventional telemonitoring studies reporting compliance rates for HF patients. A search of PubMed and Medline databases identified eligible studies published between January 2000 and June 2021. Results: Twenty-five (n = 25) eligible studies with an interventional study design were identified. Reported compliance with telemonitoring ranged between 37% and 98.5%; however, 72% of studies reported good or medium compliance. A majority (76%) of studies had exclusion/inclusion criteria favoring the enrollment of patients who may be more likely to comply with telemonitoring and their instructions for use. Forty percent of studies had a sample with a mean or median age of <65 years. Participants were more likely to be male (majority in 92% of studies) and white (majority in 78% of studies that reported ethnicity). Conclusion: Compliance rates reported by current studies are unlikely to be generalizable to the wider HF population, particularly patients who are less likely or unable to comply with telemonitoring. Studies are therefore likely overestimating compliance rates. Future innovation should focus on designing "low compliance" solutions that require minimal engagement from users and future studies should aim to recruit a more generalizable cohort of patients. To achieve a more standardized metric of compliance, studies should report compliance (however defined) achieved by the 25th, 50th, and 75th percentile of all patients enrolled.

Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis.

Establish whether digital self-monitoring of diet and physical activity is effective at supporting weight loss, increasing physical activity and improving eating behavior in adults with obesity or overweight, and determine the intervention components that might explain variations in its effectiveness. A systematic search of MEDLINE, Embase, PsycINFO, Web of Science, Scopus, Cinahl, and CENTRAL identified 4068 studies, of which 12 randomized controlled trials were eligible and included in the review. A random-effect meta-analysis evaluated intervention effectiveness and subgroup analyses tested for effective intervention content. Twelve studies were included in the review and meta-analysis. Digital self-monitoring of both diet and physical activity had a statistically significant effect at supporting weight loss (mean difference [MD] = -2.87 [95% CI -3.78, -1.96], P < 0.001, I2  = 69%), improving moderate physical activity (standardized mean difference [SMD] = 0.44 [95% CI 0.26, 0.62], P < 0.001, I2  = 0%), and reducing calorie intake (MD = -181.71 [95% CI -304.72, -58.70], P < 0.01, I2  = 0%). Tailored interventions were significantly more effective than nontailored interventions (x2 = 12.92, P < 0.001). Digital self-monitoring of physical activity and diet is an effective intervention to support weight loss in adults with obesity or overweight. This effect is significantly associated with tailored advice. Future studies should use rigorous designs to explore intervention effectiveness to support weight loss as an adjunct to weight management services.