BACKGROUND: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual\'s functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity.
OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.
METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition.
RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs.
CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

Aging Veterans face complex needs across multiple domains. However, the needs of older female Veterans and the degree to which unmet needs differ by sex are unknown. We analyzed responses to the HERO CARE survey from 7,955 Veterans aged 55 years and older (weighted N = 490,148), 93.9% males and 6.1% females. We evaluated needs and unmet needs across the following domains: activities of daily living (ADLs), instrumental ADLs (IADLs), health management, and social. We calculated weighted estimates and compared sex differences using age-adjusted prevalence ratios. On average, female Veterans were younger, more were Non-Hispanic Black and unmarried. Females and males reported a similar prevalence of problems across all domains. However, compared to males, female Veterans had a lesser prevalence of missed appointments due to transportation (aPR 0.49; 95% CI: 0.26-0.92), housework unmet needs (aPR: 0.44; 95% CI: 0.20-0.97), and medication management unmet needs (aPR: 0.33; 95% CI: 0.11-0.95) but a higher prevalence of healthcare communication unmet needs (aPR: 2.40; 95% CI: 1.13-5.05) and monitoring health conditions unmet needs (aPR: 2.13, 95% CI: 1.08-4.20). Female Veterans\' common experience of unmet needs in communicating with their healthcare teams could result in care that is less aligned with their preferences or needs. As the number of older female Veterans grows, these data and additional work to understand sex-specific unmet needs and ways to address them are essential to providing high-quality care for female Veterans.

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.

BACKGROUND: Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care needs among older adults. However, such information is often only documented in free-text clinical notes within the electronic health record and can be challenging to find.
OBJECTIVE: This study aims to develop and validate machine learning models to determine the status of ADL and iADL impairments based on clinical notes.
METHODS: This cross-sectional study leveraged electronic health record clinical notes from Mass General Brigham\'s Research Patient Data Repository linked with Medicare fee-for-service claims data from 2007 to 2017 to identify individuals aged 65 years or older with at least 1 diagnosis of dementia. Notes for encounters both 180 days before and after the first date of dementia diagnosis were randomly sampled. Models were trained and validated using note sentences filtered by expert-curated keywords (filtered cohort) and further evaluated using unfiltered sentences (unfiltered cohort). The model\'s performance was compared using area under the receiver operating characteristic curve and area under the precision-recall curve (AUPRC).
RESULTS: The study included 10,000 key-term-filtered sentences representing 441 people (n=283, 64.2% women; mean age 82.7, SD 7.9 years) and 1000 unfiltered sentences representing 80 people (n=56, 70% women; mean age 82.8, SD 7.5 years). Area under the receiver operating characteristic curve was high for the best-performing ADL and iADL models on both cohorts (>0.97). For ADL impairment identification, the random forest model achieved the best AUPRC (0.89, 95% CI 0.86-0.91) on the filtered cohort; the support vector machine model achieved the highest AUPRC (0.82, 95% CI 0.75-0.89) for the unfiltered cohort. For iADL impairment, the Bio+Clinical bidirectional encoder representations from transformers (BERT) model had the highest AUPRC (filtered: 0.76, 95% CI 0.68-0.82; unfiltered: 0.58, 95% CI 0.001-1.0). Compared with a keyword-search approach on the unfiltered cohort, machine learning reduced false-positive rates from 4.5% to 0.2% for ADL and 1.8% to 0.1% for iADL.
CONCLUSIONS: In this study, we demonstrated the ability of machine learning models to accurately identify ADL and iADL impairment based on free-text clinical notes, which could be useful in determining the severity of dementia.

BACKGROUND: The marital relationship is an important source of the well-being of older adults. Despite existing literature on marital dissatisfaction and adverse health outcomes, little is known about whether marital dissatisfaction is associated with functional performance in older adults.
OBJECTIVE: Drawing on stress process model and health behavior model, this study examined the longitudinal association between marital dissatisfaction and older adults\' functional performance. Furthermore, we sought to investigate whether this association varies based on educational level.
METHODS: Using seven waves (12 years) of the Korean Longitudinal Study of Ageing (KLoSA) from 2006 to 2019, this study estimated fixed effects models to account for unobserved individual-level confounders. Objectively measured hand grip strength and subjective assessments of vision, hearing, masticatory functions, as well as limitations in activities of daily living (ADLs) and instrumental activities of daily living (IADLs) were used to evaluate functional performance. An interaction model was used to determine whether educational level moderates the association.
RESULTS: Fixed effects estimates revealed that marital dissatisfaction is negatively associated with grip strength, as well as masticatory, vision, and hearing functions, while also showing a positive association with limitations in ADLs and IADLs. The results of this study provided evidence on heterogeneity in the association by educational level. The associations between marital dissatisfaction and functional performance, including grip strength, mastication, and hearing, were driven primarily by those with older adults with a higher level of education.
CONCLUSIONS: The findings of this study suggest that marital dissatisfaction is a robust predictor of functional performance in older adults. Efforts to address marital dissatisfaction has the potential to improve functional performance, particularly for older adults with higher levels of education.

Heart failure (HF) patients traditionally report dyspnoea as their main symptom. Although the cardiopulmonary exercise test (CPET) and 6 min walking test are the standardized tools in assessing functional capacity, neither cycle ergometers nor treadmill maximal efforts do fully represent the actual HF patients\' everyday activities [activities of daily living (ADLs)] (i.e. climbing the stairs). New-generation portable metabolimeters allow the clinician to measure task-related oxygen intake (VO2) in different scenarios and exercise protocols. In the last years, we have made considerable progress in understanding the ventilatory and metabolic behaviours of HF patients and healthy subjects during tasks aimed to reproduce ADLs. In this paper, we describe the most recent findings in the field, with special attention to the relationship between the metabolic variables obtained during ADLs and CPET parameters (i.e. peak VO2), demonstrating, for example, how exercises traditionally thought to be undemanding, such as a walk, instead represent supramaximal efforts, particularly for subjects with advanced HF and/or artificial heart (left ventricular assist devices) wearers.
This article summarizes the most recent evidence on the cardiometabolic behaviours of a full spectrum of heart failure (HF) patients of different severity during their daily life activities (i.e. walking, making a bed, and taking the stairs).Heart failure patients experience symptoms (mostly dyspnoea) during daily activities that sometimes represent maximal or supramaximal exercises for them, particularly for the most severe patients.Measuring metabolic parameters (O2 intake, ventilation, and CO2 production) through appropriate devices during these activities provides a better understanding of the pathophysiological mechanisms underlying HF patients’ symptoms and their adaptation. This can lead to the detection of new parameters that can become novel patient-centred prognostic markers or therapeutic targets for drugs and rehabilitation treatments.

BACKGROUND: Transitional care interventions have emerged as a promising method of ensuring treatment continuity and health care coordination when patients are discharged from hospital to home. However, few studies have investigated the frequency and duration of interventions and the effects of interventions on physical function. Therefore, this study aimed to determine the efficacy of transitional care for patients with stroke.
METHODS: Six databases and the grey literature were searched to obtain relevant articles from October 1, 2022 to March 10, 2023. The primary outcomes studied were motor performance, walking speed, activities of daily living (ADLs) and caregiver burden following hospital-to-home transitional care. The quality of the studies was assessed with Cochrane risk of bias version 2. The quality and sensitivity of the evidence were assessed to ensure rigour of the findings. Meta-analyses were performed using stata 17.0.
RESULTS: A total of 2966 patients were identified from 23 studies. Transitional care improved post-stroke motor performance, walking speed and ADLs, and reduced caregiver burden.
CONCLUSIONS: The findings suggest that provision of transitional care model implementation in patients with stroke is important because it reduces disability in stroke patients and helps to decrease caregivers\' burden.
CONCLUSIONS: The findings of the study emphasize the importance of transitional care programmes for stroke patients after they are discharged from the hospital and returned to their homes. To meet the needs of patients, all levels of health professionals including nurses should be aware of the discharge process and care plan.

BACKGROUND: The present study aimed to translate and validate the Knee Outcome Survey-Activities of Daily Living Scale (KOS-ADLS) in Iran.
METHODS: Following standard forward and backward translation procedure, content and face validity were tested by specialists and a sample of 32 patients. Then, in a cross sectional study, a sample of patients with knee disorders, recruited through simple sampling, completed the KOS-ADLS and the Short-Form Health Survey (SF-36) in their first visit to physiotherapy clinics in Tehran. Regarding construct validity, the Spearman\'s correlation (rs) and one-way ANOVA were employed to evaluate the correlations between the Persian KOS-ADLS and SF-36 subscales (convergent validity) and known groups comparison, respectively. Test-retest reliability and internal consistency were evaluated by intraclass correlation coefficient (ICC) and the Cronbach\'s α coefficient.
RESULTS: In total 101 patients were included in the study. The mean age of patients was 42.39 (SD = 9.2). The finding indicated that the KOS-ADLS had strong correlations with SF-36 physical functioning, bodily pain subscales, and also physical component summary while it had lower correlations with other subscales of the SF-36 as expected. The KOS-ADLS was able to differentiate between the subgroups of patients who differed in BMI. The acceptable level of intraclass correlation coefficient (ICC = 0.91) and Cronbach\'s α coefficient (α = 0.91) was obtained for the Persian KOS-ADLS. Also no floor and ceiling effects were observed for the questionnaire.
CONCLUSIONS: The Persian version of KOS-ADLS was found to be a reliable and valid outcome measure for assessing daily living activities in patients who suffer from knee pathological conditions.

Despite extensive research on cognitive impairment and limitations in basic activities of daily living, no study has investigated the burden of their co-occurrence (co-impairment). Using the Health and Retirement Study data and incidence-based multistate models, we study the population burden of co-impairment using three key indicators: mean age at onset, lifetime risk, and health expectancy. We examine patterns by gender, race, ethnicity, nativity, education, and their interactions for U.S. residents aged 50-100. Furthermore, we analyze what fractions of racial, ethnic, and nativity disparities in co-impairment are attributable to inequalities in educational attainment. Results reveal that an estimated 56% of women and 41% of men aged 50 will experience co-impairment in their remaining life expectancy. Men experience an earlier onset of co-impairment than women (74 vs. 77 years), and women live longer in co-impairment than men (3.4 vs. 1.9 years). Individuals who are Black, Latinx, and lower educated, especially those experiencing intersecting disadvantages, have substantially higher lifetime risk of co-impairment, earlier co-impairment onset, and longer life in co-impairment than their counterparts. Up to 75% of racial, ethnic, and nativity disparity is attributable to inequality in educational attainment. This study provides novel insights into the burden of co-impairment and offers evidence of dramatic disparities in the older U.S. population.

As populations age, multimorbidity (the presence of two or more chronic morbidities) is increasingly more common. These evolving demographics demand further research into the identification of morbidity patterns in different settings as well as the longitudinal effects of these patterns.
Prospectively collected data on 12,755 older persons aged 65+ years were derived from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS, www.topics-mds.eu). Latent class analyses were performed to identify unobserved relationship patterns between morbidities in older persons. Using linear mixed models, the average difference in health-related quality of life (EQ-5D) and general quality of life scores (Cantril\'s Self Anchoring Ladder) as well as limitations in Activities of Daily Living and Instrumental Activities of Daily Living (ADL/IADL) were examined over a 12-month period.
Five multimorbidity patterns were identified: sensory (n = 3882), cardio-metabolic (n = 2627), mental health (n = 920), osteo-articular (n = 4486), and system decline (n = 840). Relative to older persons in the sensory group, multimorbidity patterns did not have a strong effect on health-related quality of life, general quality of life or ADL/IADLs over a one-year period.
The observed multimorbidity patterns are similar to others based on different methodologies and study populations. When examining the effect of such patterns on quality of life, the EQ-5D and Cantril\'s Ladder may be insufficient outcome measures. Further investigations into the prognostic value of morbidity patterns would be of benefit.