Telemedicine in Your Pocket: An Alternative Teleconsultation Tool in a Pandemic and in Resource-Poor Settings.

Introduction: Owing to the coronavirus disease 2019 (COVID-19) pandemic, digitalization is booming in all sectors, including health care. This study provides evidence of using a social networking mobile app as a telemedicine tool during the pandemic in India. Methods: The data include telemedicine conversations of 318 COVID-19 patients who were consulted over WhatsApp during the period March 31 to April 16, 2021, and monitored during a quarantine period of 17 days. Results: In total, 2,089 consultations were made by these patients during the study period. Diagnostic reports and recommendations were suggested through WhatsApp 74 times, and 30 patients were advised to admit themselves to the hospital due to increasing symptom severity. Conclusions: WhatsApp efficiently helped remotely manage hundreds of patients, thereby avoiding unnecessary travel to hospitals. Future study implies the need to acquire information on patient satisfaction during the teleconsultations and further encourage applications of commonly used social networking apps as alternative tools for consultations during a pandemic situation.

Virtual reality among the elderly: a usefulness and acceptance study from Taiwan.

BACKGROUND: Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. METHODS: This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach's alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson's product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. RESULTS: In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. CONCLUSIONS: Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology.

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data.

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

Cone Beam CT Derived Laser-Guided Percutaneous Lung Ablation: Minimizing Needle-Related Complications Under General Anesthesia with Lung Separation.

RATIONALE AND OBJECTIVES: Percutaneous lung tumor ablations are mostly performed in computed tomography (CT) rooms under local anesthesia with conscious sedation. However, maintaining the breath-hold phase during this can be challenging, affecting image quality and increasing complications. With the advent of hybrid operating rooms (HORs), this procedure can be performed with endotracheal tube (ETGA) intubation under general anesthesia with lung separation, ensuring precise imaging in a single-stage setting. Lung separation provides surgical exposure of one lung while ensuring ample gas exchange with the other. This study evaluated tumor ablations performed in an HOR equipped with cone beam CT and laser guidance. MATERIALS AND METHODS: This retrospective study included patients who underwent lung tumor ablation under general anesthesia with an ETGA in an HOR between July 2020 and May 2023. Anesthesia considerations, perioperative management, and postoperative follow-ups were evaluated. RESULTS: 65 patients (78 tumors) underwent ablation using two types of lung ventilation methods including a single-lumen tube with a blocker (SLT/BL) (n = 15) and double-lumen tube (DLT) (n = 50). Most patients experienced desaturation during the apnea phase of dynamic CT and needling. The average SpO2 value was significantly lower in the DLT group than in the SLT/BL group during the procedure (81.1% versus 88.7%, P = 0.033). Five, three, and two patients developed pneumothorax, subcutaneous emphysema, and pleural effusion, respectively. CONCLUSION: Percutaneous ablation under general anesthesia with endotracheal intubation and lung separation performed in HORs was feasible and safe. The setup minimized complication risks and maintained a balance between patient safety and successful procedures.

Nontraumatic intraoperative pulmonary nodule localization with laser guide stamping in a hybrid operating room.

Lung nodule localization using conventional image-guided video-assisted thoracoscopic surgery involves lung puncture, which increases the risk of needle-related complications. We aimed to evaluate the feasibility and safety of a single-stage non-invasive laser-guided stamping localization technique followed by resection under general anesthesia in a hybrid operating room. We retrospectively reviewed consecutive patients who underwent thoracoscopic surgery for small pulmonary nodules using laser-guided dye-stamping localization methods in a hybrid operating room between June 2023 and October 2023. During the study period, 18 patients with 20 lesions underwent single-stage intraoperative image-guided stamping video-assisted thoracoscopic surgery in the hybrid operating room. The median size of the nodules was 7.4 mm (interquartile range [IQR] 5.7-9.8 mm), and median distance from the pleural surface was 9.8 mm (IQR 7.7-14.6 mm). The median localization time was 26 min (IQR 23-34 min), whereas median operation time was 69 min (IQR 62-87 min). The total median operating room time was 146 min (IQR 136-157 min). Twelve patients underwent less than two cone-beam computed tomography scans, while 6 underwent more than two scans. The total median dose area product, including cone-beam computed tomography scans, was 5731.4 uGym2. No localization-related complications were observed, and the postoperative length of stay was 1 day (IQR 1-2 days). The single-stage image-guided pleural stamping technique for localizing small pulmonary nodules in a hybrid operating room is feasible and safe. Future research with larger cohorts is required to further explore the benefits of this workflow.

Augmented fluoroscopy-guided dye localization for small pulmonary nodules in hybrid operating room: intrathoracic stamping versus transbronchial marking.

PURPOSE: We developed a novel augmented fluoroscopy-guided intrathoracic stamping technique for localizing small pulmonary nodules in the hybrid operating room. We conducted an observational study to investigate the feasibility of this technique and retrospectively compared two augmented fluoroscopy-guided approaches: intrathoracic and transbronchial. METHODS: From August 2020 to March 2023, consecutive patients underwent single-stage augmented fluoroscopy-guided localization under general anaesthesia. This included intrathoracic stamping and bronchoscopic lung marking, followed by thoracoscopic resection in a hybrid operating room. Comparative analyses were performed between the two groups. RESULTS: The data of 50 patients in the intrathoracic stamping and 67 patients in the bronchoscopic lung marking groups were analysed. No significant difference was noted in demographic data between the groups, except a larger lesion depth in the bronchoscopic lung marking group (14.7 ± 11.7 vs 11.0 ± 5.8 mm, p = 0.029). Dye localization was successfully performed in 49 intrathoracic stamping group patients (98.0%) and 67 bronchoscopic lung marking group patients (100%). No major procedure-related complications occurred in either group; however, the time flow (total anaesthesia time/global operating room time) was longer, and the radiation exposure (fluoroscopy duration/total dose area product) was larger in the bronchoscopic lung marking group. CONCLUSIONS: Augmented fluoroscopic stamping localization under intubated general anaesthesia is feasible and safe, providing an alternative with less global operating room time and lower radiation exposure for image-guided thoracoscopic surgery in the hybrid operating room.

Accelerating digital health literacy for the treatment of growth disorders: The impact of a massive open online course.

Background: Growth hormone deficiency (GHD) is a rare disorder characterized by inadequate secretion of growth hormone (GH) from the anterior pituitary gland. One of the challenges in optimizing GH therapy is improving adherence. Using digital interventions may overcome barriers to optimum treatment delivery. Massive open online courses (MOOCs), first introduced in 2008, are courses made available over the internet without charge to a large number of people. Here, we describe a MOOC aiming to improve digital health literacy among healthcare professionals managing patients with GHD. Based on pre- and post-course assessments, we evaluate the improvement in participants' knowledge upon completion of the MOOC. Methods: The MOOC entitled 'Telemedicine: Tools to Support Growth Disorders in a Post-COVID Era' was launched in 2021. It was designed to cover 4 weeks of online learning with an expected commitment of 2 h per week, and with two courses running per year. Learners' knowledge was assessed using pre- and post-course surveys via the FutureLearn platform. Results: Out of 219 learners enrolled in the MOOC, 31 completed both the pre- and post-course assessments. Of the evaluated learners, 74% showed improved scores in the post-course assessment, resulting in a mean score increase of 21.3%. No learner achieved 100% in the pre-course assessment, compared with 12 learners (40%) who achieved 100% in the post-course assessment. The highest score increase comparing the pre- and the post-course assessments was 40%, observed in 16% of learners. There was a statistically significant improvement in post-course assessment scores from 58.1 ± 18.9% to 72.6 ± 22.4% reflecting an improvement of 14.5% (p < 0.0005) compared to the pre-course assessment. Conclusion: This "first-of-its-kind" MOOC can improve digital health literacy in the management of growth disorders. This is a crucial step toward improving the digital capability and confidence of healthcare providers and users, and to prepare them for the technological innovations in the field of growth disorders and growth hormone therapy, with the aim of improving patient care and experience. MOOCs provide an innovative, scalable and ubiquitous solution to train large numbers of healthcare professionals in limited resource settings.

Cone-Beam Computed-Tomography-Derived Augmented Fluoroscopy-Guided Biopsy for Peripheral Pulmonary Nodules in a Hybrid Operating Room: A Case Series.

Lung cancer is the most lethal cancer type in Taiwan and worldwide. Early detection and treatment advancements have improved survival. However, small peripheral pulmonary nodules (PPN) biopsy is often challenging, relying solely on bronchoscopy with radial endobronchial ultrasound (EBUS). Augmented fluoroscopy overlays the intra-procedural cone-beam computed tomography (CBCT) images with fluoroscopy enabling real-time three-dimensional localization during bronchoscopic transbronchial biopsy. The hybrid operating room (HOR), equipped with various types of C-arm CBCT, is a perfect suite for PPN diagnosis and other interventional pulmonology. This study shares the single institute experience of EBUS transbronchial biopsy of PPN with the aid of augmented fluoroscopic bronchoscopy (AFB) and CBCT in an HOR. We retrospectively enrolled patients who underwent robotic CBCT, augmented fluoroscopy-guided, radial endobronchial ultrasound-confirmed transbronchial biopsy and cryobiopsy in a hybrid operating room. Patient demographic characteristics, computed tomography images, rapid on-site evaluation cytology, and final pathology reports were collected. Forty-one patients underwent transbronchial biopsy and 6 received additional percutaneous transthoracic core-needle biopsy during the same procedure. The overall diagnostic yield was 88%. The complications included three patients with pneumothorax after receiving subsequent CT-guided percutaneous transthoracic needle biopsy, and two patients with hemothorax who underwent transbronchial cryobiopsy. Overall, the bronchoscopic biopsy of PPN using AFB and CBCT as precise guidance in the hybrid operating room is feasible and can be performed safely with a high diagnostic yield.

A Scoping Review and a Taxonomy to Assess the Impact of Mobile Apps on Cancer Care Management.

Mobile Health (mHealth) has a great potential to enhance the self-management of cancer patients and survivors. Our study aimed to perform a scoping review to evaluate the impact and trends of mobile application-based interventions on adherence and their effects on health outcomes among the cancer population. In addition, we aimed to develop a taxonomy of mobile-app-based interventions to assist app developers and healthcare researchers in creating future mHealth cancer care solutions. Relevant articles were screened from the online databases PubMed, EMBASE, and Scopus, spanning the time period from 1 January 2016 to 31 December 2022. Of the 4135 articles initially identified, 55 were finally selected for the review. In the selected studies, breast cancer was the focus of 20 studies (36%), while mixed cancers were the subject of 23 studies (42%). The studies revealed that the usage rate of mHealth was over 80% in 41 of the 55 studies, with factors such as guided supervision, personalized suggestions, theoretical intervention foundations, and wearable technology enhancing adherence and efficacy. However, cancer progression, technical challenges, and unfamiliarity with devices were common factors that led to dropouts. We also proposed a taxonomy based on diverse theoretical foundations of mHealth interventions, delivery methods, psycho-educational programs, and social platforms. We suggest that future research should investigate, improve, and verify this taxonomy classification to enhance the design and efficacy of mHealth interventions.

Using a Wearable Device and Patient Reported Outcome to Evaluate the Influence of Sleep on Quality of Life Among Breast and Prostate Cancer Patients.

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy.

Information Flow and Data Gaps in COVID-19 Recording and Reporting at National and Provincial Levels in Indonesia.

Epidemiological surveillance is an essential component of public health practice especially during infectious disease outbreaks. It is critical to offer transparent epidemiological information in a rigorous manner at different regional levels in countries for managing the outbreak situations. The objectives of this research are to better understand the information flow of COVID-19 health monitoring systems and to determine the data gaps of COVID-19 incidence at the national and provincial levels in Indonesia. COVID-19 information flow was researched using government websites at the national and various provincial levels. To find the disparities, we assessed the number of cases reported at both levels at the same time and displayed the absolute and relative differences. The findings revealed that out of a total of 34 provinces in Indonesia, data differences were seen in 25 (73.52%) provinces in terms of positive cases, 31 (91.18%) provinces in terms of cured cases, and 28 (82.35%) provinces of the number of deaths. Our results showed a pressing need for high-quality, transparent, and timely information. The integration of COVID-19 data in Indonesia has not been optimal, implying that the reported COVID-19 incidence rate may be biased or delayed. COVID-19 incidents must be better monitored to disrupt the disease's transmission chain.

Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India.

INTRODUCTION: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. METHODS: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. RESULTS: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. CONCLUSION: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.

The smart pill sticker: Introducing a smart pill management system based on touch-point technology.

Older adults tend to suffer from multi-morbidity, requiring complex treatment methodologies demanding poly-pharmacy. The increasing medication usage can tend towards the mismanagement of prescriptions and irregular or faulty administration. Thus, there arises an urgent need for a proper pill management system for these prescribed medicines. To tackle this grave concern, we propose a mobile, cost-effective, robust, and easy to use solution involving the extension to the human body-smartphones and conductive stickers. The technology utilizes a unique combination of touch-points on the smartphone screen to recognize the medication and give information regarding the proper usage and dosage and gives a reminder of the intake of the medicine. Our tool is comprised of two components-(1) the conductive ink stickers containing a unique combination of conductive inks to be applied to the pill container and (2) the mobile application utilizing touch-points generated by the conductive ink sticker to give information of the corresponding medicine. The following functionalities could be performed by the application-detection of pill container: providing essential information about pill container and dosage; keeping a count of pills already taken, to be taken and remaining pills; reordering the medication and reminding about the medicine intake at the correct designated time.

Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF. OBJECTIVE: The objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy. METHODS: This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. RESULTS: Our search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements. CONCLUSIONS: Both non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937.

Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and hypertension (HTN), both non-communicable diseases, are leading causes of death globally, with more imbalances in lower middle-income countries. Furthermore, poor treatment and management are known to lead to intensified healthcare utilization and increased medical care costs and impose a significant societal burden, in these countries, including Indonesia. Predicting future clinical outcomes can determine the line of treatment and value of healthcare costs, while ensuring effective patient care. In this paper, we present the prediction of length of stay (LoS) and mortality among hospitalized patients at a tertiary referral hospital in Tasikmalaya, Indonesia, between 2016 and 2019. We also aimed to determine how socio-demographic characteristics, and T2DM- or HTN-related comorbidities affect inpatient LoS and mortality. METHODS: We analyzed insurance claims data of 4376 patients with T2DM or HTN hospitalized in the referral hospital. We used four prediction models based on machine-learning algorithms for LoS prediction, in relation to disease severity, physician-in-charge, room type, co-morbidities, and types of procedures performed. We used five classifiers based on multilayer perceptron (MLP) to predict inpatient mortality and compared them according to training time, testing time, and Area under Receiver Operative Curve (AUROC). Classifier accuracy measures, which included positive predictive value (PPV), negative predictive value (NPV), F-Measure, and recall, were used as performance evaluation methods. RESULTS: A Random forest best predicted inpatient LoS (R2, 0.70; root mean square error [RMSE], 1.96; mean absolute error [MAE], 0.935), and the gradient boosting regression model also performed similarly (R2, 0.69; RMSE, 1.96; MAE, 0.935). For inpatient mortality, best results were observed using MLP with back propagation (AUROC 0.899; 69.33 and 98.61 for PPV and NPV, respectively). The other classifiers, stochastic gradient descent with regression loss function, Huber, and random forest models all showed an average performance. CONCLUSIONS: Linear regression model best predicted LoS and mortality was best predicted using MLP. Patients with primary diseases such as T2DM or HTN may have comorbidities that can prolong inpatient LoS. Physicians play an important role in disseminating health related information. These predictions could assist in the development of health policies and strategies that reduce disease burden in resource-limited settings.

Effects of Virtual Reality Sessions on the Quality of Life, Happiness, and Functional Fitness among the Older People: A Randomized Controlled Trial from Taiwan.

BACKGROUND AND OBJECTIVE: Ageing is a complex process with physical, psychological, and social changes, which can lead to diseases and disability, and further reduce happiness levels. Virtual reality (VR) is an emerging technology with the potential to improve overall well-being, quality of life (QoL), muscle activity and balance. Our study aimed to determine the influence of VR sessions on the QoL, happiness, and functional fitness components of an elderly cohort. METHODS: A non-blinded randomized controlled trial was conducted. Sixty participants, who visited the active ageing center at the university were randomized into two groups- intervention and control. The intervention group received VR experience sessions for 15 min twice a week for a duration of 6 weeks, while the control group received no sessions. Participants filled out a questionnaire for QoL assessment and happiness assessment. They were also tested for several functional fitness components. Both questionnaires and fitness tests were conducted at the beginning and at the end of study. RESULTS: QoL improved by some metrics assessed (Pain/Discomfort and Anxiety/Depression). Happiness significantly improved in the intervention group relative to the control group. Among the functional fitness tests, the back scratch test 1st and back scratch test 2nd were measured to be significantly improved in the intervention group in comparison to control group. CONCLUSIONS: VR sessions have potential to influence the well-being and functional fitness of older adults and further support the process of healthy and active ageing. Future considerations could focus on supporting more physical and psychological aspects of the older people through VR content. TRIAL REGISTRATION: NCT04166747.

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

Erratum: Barsasella et al. Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements. Healthcare 2021, 9, 785.

The authors wish to make the following erratum to this paper [...].

Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements.

Breast and prostate cancer patients may experience physical and psychological distress, and a possible decrease in sleep quality. Subjective and objective methods measure different aspects of sleep quality. Our study attempted to determine differences between objective and subjective measurements of sleep quality using bivariate and Pearson's correlation data analysis. Forty breast (n = 20) and prostate (n = 20) cancer patients were recruited in this observational study. Participants were given an actigraphy device (ACT) and asked to continuously wear it for seven consecutive days, for objective data collection. Following this period, they filled out the Pittsburgh Sleep Quality Index Questionnaire (PSQI) to collect subjective data on sleep quality. The correlation results showed that, for breast cancer patients, PSQI sleep duration was moderately correlated with ACT total sleeping time (TST) (r = -0.534, p < 0.05), and PSQI daytime dysfunction was related to ACT efficiency (r = 0.521, p < 0.05). For prostate cancer patients, PSQI sleep disturbances were related to ACT TST (r = 0.626, p < 0.05). Both objective and subjective measurements are important in validating and determining details of sleep quality, with combined results being more insightful, and can also help in personalized care to further improve quality of life among cancer patients.

COVID-19 preventive measures showing an unintended decline in infectious diseases in Taiwan.

Most of the communicable diseases have contact, airborne and/or droplet mode of transsmission. Following the outbreak of COVID-19, the Taiwan government implemented the use of masks and sanitizer, as well as other preventive measures like social distancing for prevention. This public response likely contributed significantly to the decline in the outbreak of other infectious diseases.