Early Detection Of Health Problems Research Articles

Simulation Training Is an Effective Tool for Implementing the Practice of a Screening Nurse in Viamedis Clinics

Screening is a vital part of practical healthcare in the Republic of Kazakhstan. Screening is a process of mass examination or testing of a group of people to identify potential diseases or conditions at an early stage, before obvious symptoms appear. The purpose of screening is early detection of health problems, which allows for treatment or prevention to begin at an earlier stage and improve the effectiveness of medical intervention.

Peningkatan Edukasi, Kesehatan, dan Ekonomi Masyarakat Desa Ciantra Melalui Lomba, Penyuluhan, Cek Kesehatan dan Bazar UMKM

This research aims to improve public health and economic education in Ciantra Village through stunting counseling programs, health checks, and MSME development. The methods used include counseling at Posyandu to increase awareness about stunting and the importance of routine health checks, as well as MSME bazaars accompanied by marketing training to strengthen the local economy. The results of the study showed an increase in public understanding of stunting prevention and early detection of health problems, as well as an increase in sales of MSME products after rebranding. This program is not only successful in overcoming existing health and economic problems, but also strengthens the status of Ciantra Village as an independent village. The implementation of this program can be a model for other villages in an effort to achieve community independence and welfare.

Enhancing non-communicable disease detection and education among elderly

Non-Communicable Diseases (NCDs) often go undetected in their early stages due to a lack of evident symptoms or complaints. Among various NCDs, hypertension and diabetes mellitus are two of the most prevalent, closely linked to a range of health issues in older adults. To address this concern, this community service initiative aimed to conduct screening and provide education on NCDs as an initial step in early detection of health problems among the elderly. The implementation method involved health education and screenings, engaging midwives, village officials, and Aisyiyah health cadres. Screening results revealed that 71.43% of elderly individuals had hypertension, 34.39% were diagnosed with hyperglycemia, and 34.43% had blood sugar levels exceeding 200 mg/dl. Elderly individuals diagnosed with hypertension, diabetes mellitus, or at high risk for other NCDs were advised to seek further checkups and examinations at nearby healthcare facilities.

Cow Behavior Recognition Based on Wearable Nose Rings.

This study introduces a novel device designed to monitor dairy cow behavior, with a particular focus on feeding, rumination, and other behaviors. This study investigates the association between the cow behaviors and acceleration data collected using a three-axis, nose-mounted accelerometer, as well as the feasibility of improving the behavioral classification accuracy through machine learning. A total of 11 cows were used. We utilized three-axis acceleration sensors that were fixed to the cow's nose, and these devices provided detailed and unique data corresponding to their activity; in particular, a recorder was installed on each nasal device to obtain acceleration data, which were then used to calculate activity levels and changes. In addition, we visually observed the behavior of the cattle. The characteristic acceleration values during feeding, rumination, and other behavior were recorded; there were significant differences in the activity levels and changes between different behaviors. The results indicated that the nose ring device had the potential to accurately differentiate between eating and rumination behaviors, thus providing an effective method for the early detection of health problems and cattle management. The eating, rumination, and other behaviors of cows were classified with high accuracy using the machine learning technique, which can be used to calculate the activity levels and changes in cattle based on the data obtained from the nose-mounted, three-axis accelerometer.

Optimasi Deteksi Dini Masalah Kesehatan Bibit Kelapa Sawit dengan JST MLP Berbasis Citra Termal

Palm oil has an important role in the palm oil industry, but health problems in the seeds threaten production results. This research advocates an innovative approach by combining thermal imaging technology and artificial intelligence, especially Multilayer Perceptron Artificial Neural Networks (MLP ANN), for early detection of health problems in oil palm seedlings. The use of thermal cameras makes it easier to measure the temperature of plants and the surrounding environment. Thermal image analysis helps in evaluating thermal characteristics, especially plant temperature, which may be associated with health problems. Temperature data is classified into normal plants and plants affected by health problems, using statistical analysis to strengthen the relationship. A predictive model using MLP ANN was formulated to correlate thermal characteristics with the health condition of oil palm seedlings. The research results show that this model has high validity, with R2 of 0.933 for calibration and 0.930 for validation. In essence, this method accurately predicts the health condition of oil palm seedlings based on thermal images. This approach has the potential to provide early detection of plant health problems quickly, accurately, and efficiently. Through the application of this method, it is hoped that it can reduce losses due to health problems in oil palm seedlings, thereby making a major contribution to increasing productivity and welfare in the palm oil industry.

PKM PEMBERDAYAAN KADER DAN BIDAN DESA PEREAN KANGIN UNTUK MENINGKATKAN GERAKAN SADARI KANKER PAYUDARA

In 2021 there were 10 breast cases from the Baturiti sub-district which carried out examinations in the PA laboratory at Tabanan Hospital. 6 of them (60%) turned out to be cases of malignancy, and 4 of them (66.66%) stage III and IV which are the final stages of breast cancer. Perean Kangin Village which is one of the villages in the Baturiti sub-district with the most cases of breast tumors. The long-lasting pandemic has contributed to the decreased activity of cadres and increased delays in breast cancer screening. If the patient can be detected early and get treatment as soon as possible, it will give hope for recovery and a better life expectancy. One of the early detection of breast cancer is breast self-examination (BSE). It is hoped that re-activating midwives and village cadres will motivate the community to do BSE. The result of this PKM was an increase in the knowledge and attitude of cadres towards BSE. We also provide investment goods in the form of digital baby and adult weight measuring devices, stunting mats and height measuring devices for early detection of health problems.

Optimalisasi Kelas Ibu Hamil Sebagai Upaya Deteksi Dini Gangguan Neuromusculoskeletal Ibu Hamil

Indonesia's Maternal Mortality Rate (MMR) has yet to reach the SDGs goal by the end of 2030, which is less than 70% achievement targets. One of the reasons for the high MMR is the mother's inability to make decisions if there are problems during pregnancy. Providing knowledge to pregnant women and their families through pregnant women classes as an early detection effort to reduce MMR. Efforts for early detection are neuromusculoskeletal disorders of pregnant women. The purpose of this community service activity is to carry out early detection and increase the knowledge and skills of pregnant women about neuromusculoskeletal disorders in pregnant women. The method is carried out by conducting examinations for pregnant women, health education, and practical demonstrations on how to deal with neuromusculoskeletal disorders in pregnant women. The target of this activity is pregnant women who are in the working area of the Tirto Health Center, with as many as 51 pregnant women. The results of this community service activity are being able to carry out early detection of pregnant women by conducting health checks and increasing the knowledge of pregnant women about neuromusculoskeletal disorders with an average rating score of 23.06%. Pregnant women can practice how to deal with neuromusculoskeletal disorders in pregnant women. The Health Office can work with the Puskesmas and health workers, especially midwives, to carry out routine activities in conducting early detection of health problems in pregnant women through examinations, health counseling, or practice demonstrations on early detection of health in pregnant women.

Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET

Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions.

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient's heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected.

Framework for a Secure and Sustainable Internet of Medical Things, Requirements, Design Challenges, and Future Trends

The framework presented in this article provides a guide for designing secure and sustainable internet of medical things (IoMT) solutions. The main objective is to address the challenges related to safety and sustainability in the medical field. The critical conditions driving these challenges are identified, and future trends in the field of IoMT are discussed. To assess the effectiveness of the proposed framework, a case study was carried out in a private medical clinic. In this study, an IoMT system was implemented to monitor patients’ vital signs, even when they were not in the clinic. The positive results demonstrated that the implemented IoMT system met the established security and sustainability requirements. The main statistical findings of the case study include the real-time monitoring of the vital signs of the patients, which improved the quality of care and allowed for the early detection of possible complications. In addition, medical devices such as the blood pressure monitor, pulse oximeter, and electrocardiograph were selected, proving safe, durable, and energy and maintenance efficient. These results were consistent with previous research that had shown the benefits of IoMT in remote monitoring, the early detection of health problems, and improved medical decision-making.

Early Detection of Health Problems through Artificial Intelligence (Ai) Technology in Hospital Information Management: A Literature Review Study

Early detection of health problems needs to be supported by optimal hospital service quality subsystems through an integrated Hospital Management Information System. Artificial intelligence (AI) as a modern technology has entered the mainstream of clinical medicine. Diagnostics has traditionally been one of the main areas of AI in medicine. AI will broadly transform diagnostic and predictive analysis of medical images in the coming years. Studies in the fields of pathology and dermatology have demonstrated the ability of AI to outperform human diagnostics in accurately detecting and classifying various types of cancer. Artificial intelligence technology in the health aspect is useful in disease diagnosis and can analyze medical images to identify certain diseases so that they are able to distinguish between benign and malignant diseases. AI can also help identify the risk of mental illness and identify the risk of suicide among patients with psychiatric disorders or among certain populations, such as prisoners and soldiers. This helps doctors quickly get disease information and provide a more accurate disease diagnosis, thereby saving time for disease treatment.

Correlation Between History of Birth Control Usage and Incidence of Gingivitis among Pregnant Women in Ambulu Public Health Center, Jember District: A Cross-sectional Study

Using birth control (BC) can affect gingiva tissue, increasing the risk of developing gingivitis among women of reproductive age. Based on the Central Bureau of Statistics census, in 2021 61,77% of women in their reproductive age have used contraception and birth control in East Java. This study aims to find the correlation between the history and birth control usage and the incidence of gingivitis among pregnant women in Ambulu Public Health Center. This study uses a cross-sectional method with an analytical observational approach on pregnant women who visited the Dental Outpatient and received dental examinations in April and May of 2022. Thirty respondents were gathered with a history of birth control usage and evaluated using the dentist's Periodontal Index (PI). The results show that nine respondents (30%) had no history of using BC, two respondents (6.6%) used BC pills, ten respondents (33.3%) used 1-month injection BC, five respondents (16.6%) used 3-month injection BC, two respondents (6.6%) used implants BC and two respondents (6.6%) used IUD. From the cross table, one respondent (3.3%) was in the normal category, 1 respondent (3.3%) was in the simple gingivitis category, 15 respondents (50%) were in the early destructive periodontal disease category, and 13 respondents (43.4%) were in the destructive periodontitis. The data are analyzed using Chi-square result, showing that the significant value is p = 0,049 (p<0,05), so it can be concluded there is a significant relation between history of birth control usage and incidence of gingivitis in Ambulu Public Health Center. Dental and oral examinations are highly recommended in routine as early detection of health problems among pregnant women.

A correlated-variables model for monitoring individual growing-finishing pig's behavior by RFID registrations

Feeding and drinking behavior of individual growing-finishing pigs can be used for health and welfare monitoring by real time warning systems. The objective of this study was to develop and test a model detecting deviating feeding and drinking behavior of individual growing-finishing pigs based on Radio-Frequency Identification (RFID) registrations. Feeding and drinking behavior of growing-finishing pigs was recorded through low frequency RFID readings via ear tags. In three 16-week batches, twelve pens containing each twelve pigs were equipped with one drinker and two feeders. Four readers (each with eight antennas) recorded feeding and drinking activity of each individual pig in the pens. All tag readings were combined into visits, and subsequently visits into meals. The analyzed variables were number of meals per day, average interval between meals and maximum interval between meals per day for both feeding and drinking. A correlated-variables model with a Kalman filter was developed, generating alerts when the daily level was deviating from the expected level. For illustration of model results, the model was validated with culling recordings, which included all pigs that died or were euthanized during the experiment. Most cases of culled pigs corresponded with alerts given by the model, but sensitivity was hard to determine due to low number of cases and specific circumstances of the culled pigs. The specificity of the model was similar for feeding and drinking behavior, and was highest for number of meals (93–99%), followed by the average interval between meals (90–96%) and the maximum interval between meals (86–97%), depending on desired confidence interval. The developed model is promising for early detection of health problems in growing-finishing pigs, but further validation should occur on a bigger scale in order to increase accuracy of results and improve knowledge required for practical implication of the model.

Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens

Elevated platforms are a suitable possibility to enrich the environment of broiler chicken and, thus, improving their welfare. Early detection of health problems additionally contributes to an improved welfare of chickens and, at the same time, helps the farmer to optimize production efficiency. A prototype combined elevated platforms with weighing beams and an algorithm was developed to estimate number of chickens on the platform as well as their activity. To validate the prototype, during two consecutive trials, 1200 mixed-sex broiler chickens were kept for 35 days in 6 pens in total. In each pen, an elevated platform (height × length × width: 0.5 m × 4 m × 0.6 m), consisting of two rectangular steel frames and four load cells, was offered. Overall weight of broilers on the elevated platform was recorded continuously during the entire fattening period by the automatic weighing system. Algorithms were developed and implemented to estimate (i) daily mean weight of a single animal, (ii) number of animals on the platform, and two activity measures: (iii) movement on platform and (iv) number of platform changes from the weight recordings. To validate estimations, weight was recorded manually on 5 days throughout the trial and usage of elevated platforms was analysed via video records with scan sampling. From days 4 to 34, daily mean weight could be estimated using the proposed algorithm. Compared to manual weighing of a sample group at five days per pen, Spearman correlation coefficient was given by 0.9868 (p < 0.0001), and estimation error (RAE) was 11% on average. The number of chickens on the elevated platform could be approximated with deviations of about +19% and Spearman correlation of 0.97 (p < 0.0001) compared to video observations. An explanation for overestimation of animal number are occasional recalibration problems of the weighing system that resulted in measured weights being larger than actual weights. Furthermore, results of automatic approximation of usage behaviour and animal activity based on continuous recordings of the weighing system throughout days 4 to 34 of fattening are shown. In addition to providing an enrichment tool that could help farmers to control weight development of their livestock automatically and continually, first promising observations of anomalies due to heat and illness show that the proposed system could be used for early detection of animal welfare problems.

Development of an ADL-practice guideline: The next step towards guidance in essential nursing care activities

Supporting and respecting care receivers in Activities of Daily Living (ADL) lies at the essence of nursing care, irrespective of diagnosis or healthcare setting. ADL-care is an intimate form of caring, and therefore close and personal to the care receiver, aiming to enhance their independence and comfort. Even though ADL-care is indispensable and highly valued by care receivers, the scientific foundation of ADL-care is weak. This leaves nursing professionals with insufficient guidance as to what constitutes quality ADL-care and what activities are necessary and effective. Therefore, we developed an ADL practice guideline according to the framework of the Dutch Institute for Health (AQUA-guideline) and AGREE II. The guideline was developed over three stages: (1) Determination of the target population and scope, (2) Analysis of problems leading to guiding questions and answering these based on literature search and consensus, (3) Testing and validation. A multidisciplinary working group determined the purpose, target group, and five clinical questions. We used literature search and consensus procedures to answer these questions in close collaboration with care receivers and professional care providers. This guideline provides guidance for nursing professionals to choose appropriate ADL-care options in five modules covering recommendations: (1) Involving care receivers in ADL-related care choices, (2) Identifying ADL-care needs, (3) Choosing effective interventions to enhance ADL-independence and comfort, (4) Supporting informal caregivers in ADL-care, and (5) Using ADL-care for early detection of health problems. These modules are considered leading for future developments in essential nursing care and will be evaluated in a pilot implementation.

Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods.

Simple SummaryDiseases in dairy livestock farming can lead to important economic losses. Several studies have been conducted to identify illness such as lameness by using MIR spectrometry data and relying on partial least squares discriminant analysis. However, this method suffers some limitations. In this study, random forest, support vector machine, neural network, convolutional neural network and ensemble models were used to test the feasibility of identifying cow sickness among 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and Holstein-Friesian × Jersey crossbreed cows. The results obtained show that it is possible to identify a health problem with a reasonable level of accuracy using a neural network.The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques—random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models—were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%), the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.

Adherence to Self-Care Practices and Associated Factors Among Outpatient Adult Heart Failure Patients Attending a Cardiac Center in Addis Ababa, Ethiopia in 2020.

IntroductionProper self-care in patients with chronic illnesses, such as heart failure is allied with the prevention or early detection of health problems and improved clinical outcomes. Even though self-care among patients with heart failure is commonly poor, a low-sodium diet, regular exercise, and weight monitoring are essential to control heart failure symptoms and exacerbation. Poor adherence to these self-care practices contributes to an increase in hospitalization, morbidity, and mortality.ObjectiveTo assess adherence to self-care practices and associated factors among outpatient adult heart failure patients attending cardiac center in Ethiopia, Addis Ababa, 2020.MethodsInstitution-based cross-sectional study design was used to incorporate 396 heart failure patients who attended the cardiac center in Ethiopia, Addis Ababa. The study was conducted from March to April 2020. Study participants were selected by using a systematic sampling technique. Data were collected through face-to-face interviews and from the patients’ medical records. Epi-data version 3.1 and SPSS version 26 were used for data entry and analysis, respectively. Binary logistic regression analysis was performed to identify predictors of self-care practice. Those variables with p-value <0.25 in the bivariable regression analysis were entered into the multivariable regression analysis and the result were presented using tables, chart, and mean.ResultsOf 396 respondents, 111 (28%) of patients with heart failure had overall good self-care adherence. Comorbidity (AOR: 1.62; 95% CI: 1.07–2.624), level of knowledge (AOR: 3.58; 95% CI: 2.23–5.79) and depression (AOR: 2.45; 95% CI: 1.048–5.726) were factors significantly associated with adherence to self-care practice.ConclusionComorbidity, inadequate knowledge, and depression were predictors of self-care practice. As a result, nursing intervention programs regarding knowledge on heart failure are recommended for enhancing self-care practices. Self-care strategies shall target patients with depression and comorbidity.

Feasibility of Wireless Horse Monitoring Using a Kinetic Energy Harvester Model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 μW during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current.

Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults.

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods.Methods: Data were collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community-dwelling adults over a period of 1 to 2 years. Physical activity in the apartment, toilet visits, refrigerator use, and entrance door openings were quantified using a commercially available passive infrared motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd., Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data were correlated with health-related data collected from the weekly visits of the seniors by health professionals, including information about physical, psychological, cognitive, and behavior status, health problems, diseases, medication, and medical diagnoses.Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study; two participants had to stop their study participation because of severe health deterioration, whereas two participants died during the course of the study. A history of chronic disease was present in 12/24 seniors, including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes, and Parkinson's disease. In total, 242,232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of aging-relevant indicators and outcomes were performed. Several episodes of health deterioration, including heart failure worsening and heart rhythm disturbances, could be captured by sensor signals from different sources.Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well-accepted, with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.

Cognition and quality of life relationship among the elderly community: a cross-sectional study.

to assess cognitive functions and their association with quality of life among elderly people enrolled in a Family Health Unit (FHU) of Primary Health Care in Recife-PE. a quantitative, descriptive, cross-sectional study in which elderly aged 60 years and over were studied. 76.7% of the elderly were women and the age group was less than or equal to 70 years. 68.6% had cognitive impairment, and in the quality of life assessment it was found that the social participation facet had the highest mean score among the elderly (14.25), while the lowest was observed in the sensory functioning facet (9.10). There was an association between cognitive decline and quality of life. most of the elderly had good quality of life rates, but low cognitive level. Using screening tools allows early detection of health problems, guiding the nursing staff in the construction of preventive measures.