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Summary: Background: Patients with heart failure have high rehospitalisation rates and poor cardiovascular outcomes. Home-based monitoring (HBM) has emerged with promising results in different settings. However, its long-term effects on patients recently admitted for acute decompensated heart failure (ADHF) remain uncertain. Methods: We systematically searched PubMed, Embase, and Cochrane Library for randomised controlled trials (RCTs) comparing HBM with usual care (UC) that were published between database inception and June 24, 2023. We included studies with patients admitted for ADHF in the previous 6 months and with a minimum follow-up of 6 months. We excluded studies with patients hospitalised for reasons other than ADHF and studies with disproportional education interventions between arms. Statistical analyses were performed using R software version 4.3.2. We pooled risk ratios (RR) and mean differences (MD) with 95% confidence intervals (CI) for categorical and continuous outcomes, respectively. Cochrane Collaboration’s tool for assessing risk of bias in RCTs (RoB 2) was used to assess study quality. Publication bias was assessed via funnel plots and Egger’s test, and heterogeneity was assessed through I2 statistics and sensitivity analysis. The protocol for this systematic review and meta-analysis was registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42023465359). Findings: We included 16 RCTs comprising 4629 patients, of whom 2393 (51.7%) were randomised to HBM and 3150 (68%) were men. Follow-up ranged from six to fifteen months. As compared with UC, HBM significantly reduced all-cause mortality (RR 0.75; 95% CI 0.61, 0.91; p = 0.005), all-cause hospitalisations (RR 0.82; 95% CI 0.70, 0.97; p = 0.018), cardiovascular (CV) mortality (RR 0.53; 95% CI 0.36, 0.79; p = 0.002), hospitalisations for heart failure (RR 0.75; 95% CI 0.62, 0.91; p = 0.004), and CV hospitalisations (RR 0.72; 95% CI 0.55, 0.95; p = 0.018). There were no significant differences in length of hospital stay (MD 0.97 days; 95% CI -0.90, 2.84; p = 0.308). Interpretation: In patients recently admitted with ADHF, HBM significantly reduces long-term all-cause mortality and hospitalisations, CV mortality and hospitalisations, and hospitalisations for heart failure, as compared with UC. This supports the implementation of HBM as a standard practice to optimise patient outcomes following admissions for ADHF. However, future studies are warranted to evaluate the efficacy and safety of implementing HBM in the real-world setting. Funding: None.

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS. [ABSTRACT FROM AUTHOR]

Cycle monitoring via ultrasound and serum-based hormonal assays during medically assisted reproduction (MAR) can provide information on ovarian response and assist in optimizing treatment strategies in addition to reducing complications such as ovarian hyperstimulation syndrome (OHSS). Two surveys conducted in 2019 and 2020, including overall 24 fertility specialists from Europe, Asia and Latin America, confirmed that the majority of fertility practitioners routinely conduct hormone monitoring during MAR. However, blood tests may cause inconvenience to patients. The reported drawbacks of blood tests identified by the survey included the validity of results from different service providers, long waiting times and discomfort to patients due to travelling to clinics for tests and repeated venepunctures. Historically, urine-based assays were used by fertility specialists in clinics but were subsequently replaced by more practical and automated serum-based assays. A remote urine-based hormonal assay could be an alternative to current serum-based testing at clinics, reducing the inconvenience of blood tests and the frequency of appointments, waiting times and patient burden. Here we provide an overview of the current standard of care for cycle monitoring and review the literature to assess the correlation between urine-based hormonal assays and serum-based hormonal assays during MAR. In addition, in this review, we discuss the evidence supporting the introduction of remote urine-based hormonal monitoring as part of a novel digital health solution that includes remote ultrasound and tele-counselling to link clinics and patients at home. [ABSTRACT FROM AUTHOR]

Background: Muscle fatigue, characterized by reduced force generation during repetitive contractions, impacts older adults doing daily activities and athletes during sports activities. While various sensors detect muscle fatigue via muscle activity, biochemical markers, and kinematic parameters, a real-time wearable solution with high usability remains limited. Plantar pressure monitoring detects muscle fatigue through foot loading changes, seamlessly integrating into footwear to improve the usability and compliance for home-based monitoring., Objective: This study aimed to investigate the effects of muscle fatigue on plantar pressure measurements using a self-developed wearable plantar pressure system., Methods: Twelve healthy participants completed a 5-minute calf muscle fatigue protocol. The plantar pressures and surface electromyography (sEMG) activity of the gastrocnemius muscles were recorded before and after exercise. The plantar pressures at 6 regions and the median frequency (MDF) of sEMG were analyzed to quantify fatigue., Results: The self-developed foot pressure system showed a significant decrease in plantar pressure peak values at the heel of the left (P=.003) and right feet (P=.001) and at the lateral toe of the left (P=.001) and right feet (P=.026). A significant increase was observed at the metatarsal head of both the left foot (P=.001) and the right foot (P=.017). The MDF of sEMG signals significantly decreased in the left (P=.001) and right gastrocnemius (P<.001)., Conclusions: Plantar pressure changes and sEMG signals effectively detect gastrocnemius muscle fatigue using the proposed wearable system, supporting the development of a wearable solution for detecting muscle fatigue suitable for home-use., (© Fu-Yu Chen, Tzu-Yao Lin, Yi-Cheng Huang, Evina Widianawati. Originally published in JMIR Human Factors (https://humanfactors.jmir.org).)

Introduction: Congenital heart diseases are related to heart defects that develop during pregnancy and are present at birth. Children must regularly attend specialized clinical centers for treatment, which includes medical check-ups, hemodynamic procedures, and surgery. Nevertheless, the success of interventions largely depends on the education of parents and caregivers about the symptoms and warning signs that babies with complex congenital heart disease may present during the period between stages. Likewise, it is necessary for caregivers to have tools to face the barriers of the health system, such as, obstacles or delays in obtaining healthcare services. This facilitates timely health care for patients and avoids fatal outcomes or deterioration in quality of life. Materials and Methods: This document presents a system that provides support to patients with complex congenital heart disease in follow-up by a home monitoring program. The system consists of a mobile application that allows parents/caregivers to monitor physiological variables of the patients (weight, intake, and oximetry). The information registered in the application is sent for real-time evaluation by the medical team. Results: The application provides an effective means for sending data and communicating with the medical control center, in addition to offering educational and informative material. In this way, parents/caregivers get constant help and feedback about their baby's condition. Conclusions: The mobile application could help mitigate the costs of medical care and overcome the limitations of follow-up at home, giving doctors the possibility to see the patient's evolution and give timely recommendations. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the symptoms experienced by women at home and self-care agency after breast-conserving surgery. The study was carried out in a public hospital in Istanbul between January and June 2018. The sample of the study consisted of 65 women. The data were collected through face-to-face interviews using Patient Description Form, Symptom Check List and Self-Care Agency Scale during outpatient check-ups in the first and third weeks following discharge. The mean age of the women was 52.68 ± 13.24. Fatigue, weakness, pain and insomnia were mostly experienced symptoms in the first and third weeks after discharge while fever, discharge from the wound area and nausea-vomiting symptoms were the least experienced symptoms in these weeks. It was determined that women had moderate self-care agency in the first and third weeks of discharge. Women who were married and whose income was equal to their expenses had higher self-care agency, and those living alone had lower self-care agency than those living with their spouses and children (p < 0.05). The self-care agency scores of those who did not have a chronic disease were higher than those with chronic diseases (p < 0.05). It is found that women with breast-conserving surgery experience symptoms related to the operation in the first and third weeks of discharge. Women's self care agency was moderate during the follow up period, meaning that these patients should be supported by nurses with regard to self care agency and symptoms experienced at home. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely.

Introduction: Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life.Methods: We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling.Results: The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0).Conclusion: Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states.