Development and psychometric evaluation of the healthy lifestyle questionnaire for elderly (heal)

Role of Lifestyle in Thoracic Outlet Syndrome: A Narrative Review

INTRODUCTION

The presence of a positional compression of the neurovascular bundle in the outlet between the thorax and the upper limb during arm movements (mainly abduction) is common but remains asymptomatic in most adults. Nevertheless, a certain number of subjects with thoracic outlet positional compression will develop incapacitating symptoms or clinical complications as a result of this condition. Symptomatic forms of positional neurovascular bundle compression are referred to as "thoracic outlet syndrome" (TOS).

MATERIALS AND METHODS

This paper aims to review the literature and discuss the interactions between aspects of patients' lifestyles in TOS. The manuscript will be organized to report (1) the historical importance of lifestyle evolution on TOS; (2) the evaluation of lifestyle in the clinical routine of TOS-suspected patients, with a description of both the methods for lifestyle evaluation in the clinical routine and the role of lifestyle in the occurrence and characteristics of TOS; and (3) the influence of lifestyle on the treatment options of TOS, with a description of both the treatment of TOS through lifestyle changes and the influence of lifestyle on the invasive treatment options of TOS.

RESULTS

We report that in patients with TOS, lifestyle (1) is closely related to anatomical changes with human evolution; (2) is poorly evaluated by questionnaires and is one of the factors that may induce symptoms; (3) influences the sex ratio in symptomatic athletes and likely explains why so many people with positional compression remain asymptomatic; and (4) can sometimes be modified to improve symptoms and potentially alter the range of interventional treatment options available.

CONCLUSIONS

Detailed descriptions of the lifestyles of patients with suspected TOS should be carefully analysed and reported.

Awareness of Healthy Lifestyle Among Elderly Population During Aging in Al-Ahsa, Saudi Arabia

INTRODUCTION

The research explores healthy aging among elderly individuals globally and in Saudi Arabia. Factors like health services, lifestyle, and chronic diseases affecting seniors are examined. However, there is a gap in culturally relevant research, particularly in Arabic-speaking countries. This study aims to understand elderly individuals' knowledge, attitudes, and practices regarding healthy lifestyles for effective functional preservation in aging.

METHODOLOGY

A cross-sectional study was conducted in the eastern part of Saudi Arabia, specifically Al-Ahsa, from February to May 2023. The Raosoft calculator was employed to determine a sample size of at least 384 participants. The data was analyzed using SPSS.

RESULTS

Regarding the associations between knowledge levels and demographics, education significantly impacts knowledge (p=0.003). Retired respondents exhibit higher knowledge (50.4%) compared to those with jobs (10.4%) (p=0.002). Smoking has a significant impact on knowledge (p=0.012). Regarding the opinions on elderly care, respondents agree on the importance of fresh fruits and vegetables (52.2%), increased protein intake (64.3%), less fat (83.5%), and regular exercise (44.3%). Supplements' necessity is disagreed upon (95.7%). Living with family is favored (67.8%), and elderly self-management is recognized (60.9%). Significant differences are seen in fruit and vegetable consumption (p=0.001), less fat usage (p=0.000), exercise habits (p=0.000), smoking (p=0.000), and using just salt in cooking (p=0.000).

CONCLUSION

Study findings underscore the importance of education in influencing healthy behaviors and informed choices, with education levels significantly impacting knowledge levels. Respondents' preferences for balanced diets, exercise, and self-management reflect a positive trend toward embracing healthy aging principles. Notably, the study identifies disparities between knowledge groups in various lifestyle factors, highlighting the potential of education to drive positive changes in behaviors.

Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms

INTRODUCTION

The global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and multidimensional concept with many ambiguities regarding its meaning or measuring. This study aimed to propose an intelligent predictive model to predict SA.

METHODS

In this retrospective study, the data of 784 elderly people were used to develop and validate machine learning (ML) methods. Data pre-processing was first performed. First, an adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict SA. Then, the predictive performance of the proposed model was compared with three ML algorithms, including multilayer perceptron (MLP) neural network, support vector machine (SVM), and random forest (RF) based on accuracy, sensitivity, precision, and F-score metrics.

RESULTS

The findings indicated that the ANFIS model with gauss2mf built-in membership function (MF) outperformed the other models with accuracy, sensitivity, precision, and F-score of 91.57%, 95.18%, 92.31%, and 92.94%, respectively.

CONCLUSIONS

The predictive performance of ANFIS is more efficient than the other ML models in SA prediction. The development of a decision support system (DSS) using our prediction model can provide healthcare administrators and policymakers with a reliable and responsive tool to improve elderly outcomes.

Developing a prediction model for successful aging among the elderly using machine learning algorithms

Objective

The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA.

Methods

The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC).

Results

Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA.

Conclusions

Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

Prediction of successful aging using ensemble machine learning algorithms

Background

Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA.

Methods

In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models.

Results

The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure.

Conclusions

Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.

Development and psychometric evaluation of the loneliness inventory for older adults (Lonely): A mixed-methods study

Aim

To develop and initially validate the Loneliness Inventory for Older Adults.

Design

Scale development and evaluation.

Methods

This was a two‐phase study. In phase 1, the initial items pool (126 items) was generated based on the concept analysis and literature review. Moreover, content validity was established by geriatric and psychometric experts. Phase 2 evaluated structural validity by performing item analysis, exploratory factor analysis and convergent validity. Reliability was evaluated by examining internal consistency, stability (ICC) and absolute reliability.

Results

Following the development process, 94 items were removed and a provisional version of the questionnaire with 32 items was subjected to psychometric evaluation. Three hundred and seventy older adults completed the questionnaire. After performing factor analysis, overall 3 items were removed due to low loading, and the questionnaire was reduced to 29 items tapping into five factors. The Cronbach's alpha for the instrument was 0.94, and the ICC value was 0.97.