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

Bandari, Razieh; Shahboulaghi, Farahnaz Mohammadi; Montazeri, Ali

doi:10.1186/s12955-020-01529-3

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Lifestyle determination generally depends on the total score obtained and is calculated by obtaining a score of 42–98 indicating an unfavorable, 99–155 showing a medium, and 156–216 denoting a desirable lifestyle. It measures physical activity, exercise, recreation, healthy eating, stress management, and social and interpersonal relationships [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…The Stress-Management Questionnaire was used to describe the participant's ability to cope with difficult and stressful situations. The total scores were divided into three levels of low (0-30), moderate (31)(32)(33)(34)(35)(36)(37)(38)(39), and high (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). The cut-off point of this variable in our study is 31.…”

Section: Stress Managementmentioning

confidence: 99%

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Prediction of successful aging using ensemble machine learning algorithms

Varzaneh

Yazdani

Kazemi-Arpanahi³

2022

BMC Med Inform Decis Mak

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Stress Managementmentioning

confidence: 99%

Prediction of successful aging using ensemble machine learning algorithms

Varzaneh

Yazdani

Kazemi-Arpanahi³

2022

BMC Med Inform Decis Mak

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“…It can be obtained by specifying a grade ranging from 42 to 98 associated with the unpleasant lifestyle, 99 to 155 for medium lifestyle, and 156 to 211 pertained to a pleasant lifestyle. It evaluates exercise, healthy eating, stress management, social and interpersonal relationships, physical activity, and recreation (35).…”

Section: Satisfaction With Life Scale (Swls)mentioning

confidence: 99%

Designing a Predictive Model for Successful Aging among the Elderly Using Machine Learning Techniques

Ahmadi

Nopour

Nasiri

2022

Preprint

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Background: the aging phenomenon has an increasing trend worldwide which caused the emergence of the SA1 concept. It is believed that the SA prediction model can increase the QoL2 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 social factors by considering all factors affecting SA.Materials and methods: in this descriptive, applied, and retrospective study, the data of 975 related to SA and non-SA of the elderly were investigated. We used the Chi-square test at P<0.05 to determine the best factors affecting the SA. The AB3, J-48, RF4, ANN5, SVM6, BLR7, and NB8 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using the sensitivity, specificity, accuracy, F-measure, and AUC. Results: The Chi-square test showed that 28 variables had a meaningful relationship with SA. The results of comparing the ML9 model's performance showed that the RF with sensitivity=0.91, specificity= 0.98, accuracy= 0.95, F-test=0.9, and AUC-test= 0.884 is the best model for predicting the SA. Conclusion: 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.

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“…As recommended, such tools must be simple, comprehensible and easily applicable especially in clinical settings. In addition, as they should be more acceptable for and focused on the status of older people, they should be able to monitor and evaluate the changes in elderly loneliness more precisely (Bandari et al., 2020 ; Haywood et al., 2005 ). Thus, the present study was designed to develop a questionnaire to study loneliness among older adults and evaluate its validity and reliability.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 9 publications

References 18 publications

Prediction of successful aging using ensemble machine learning algorithms

Prediction of successful aging using ensemble machine learning algorithms

Designing a Predictive Model for Successful Aging among the Elderly Using Machine Learning Techniques

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

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