Dataset Information

Behavioral Model of Middle-Aged and Seniors for Bicycle Tourism.

ABSTRACT: This research centers on the behavioral tendency of the middle-aged and seniors in bicycle tourism at environmentally protected scenic areas and its relevant influence factors. The theory of planned behavior (TPB) is adopted as the basis of this study. The middle-aged and seniors are the subjects of this research. A questionnaire survey is conducted at environmentally protected national scenic areas in Taiwan. A total of 230 samples are drawn with a random sampling method, and 210 are valid. The findings indicate two things. First, when applying the TPB to different fields of the study, the level of predictability may vary. Another finding is that subjective norm shows a higher level of susceptibility to sport habit and predictability to behavioral intention than the other two constructs. With an empirical analysis, the study is able to provide middle-aged and senior participants and sport administration authorities with relevant suggestions for reference at the end of this paper.

SUBMITTER: Lin SW

PROVIDER: S-EPMC7160323 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Behavioral Model of Middle-Aged and Seniors for Bicycle Tourism.

Lin Shu-Wang SW Hsu Shih-Yun SY Ho Juei-Ling JL Lai Mei-Ying MY

Frontiers in psychology 20200409

This research centers on the behavioral tendency of the middle-aged and seniors in bicycle tourism at environmentally protected scenic areas and its relevant influence factors. The theory of planned behavior (TPB) is adopted as the basis of this study. The middle-aged and seniors are the subjects of this research. A questionnaire survey is conducted at environmentally protected national scenic areas in Taiwan. A total of 230 samples are drawn with a random sampling method, and 210 are valid. The ...[more]

PMID: 32328004

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Project description:Background/objectivesDue to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle-aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6-month risk of serious falls among middle-aged Veterans.DesignCohort study.SettingVeterans Health Administration (VA).ParticipantsVeterans, aged 45 to 65 years, who presented for care within the VA between 2012 and 2015 (N = 275,940).ExposuresThe exposures of primary interest were substance use (including alcohol and prescription opioid use), multimorbidity, and polypharmacy. Hazardous alcohol use was defined as an Alcohol Use Disorders Identification Test - Consumption (AUDIT-C) score of 3 or greater for women and 4 or greater for men. We used International Classification of Diseases, Ninth Revision (ICD-9), codes to identify alcohol and illicit substance use disorders and identified prescription opioid use from pharmacy fill-refill data. We included counts of chronic medications and of physical and mental health comorbidities.MeasurementsWe identified serious falls using external cause of injury codes and a machine-learning algorithm that identified serious falls in radiology reports. We used multivariable logistic regression with general estimating equations to calculate risk. We used an integrated predictiveness curve to identify intervention thresholds.ResultsMost of our sample (54%) was aged 60 years or younger. Duration of follow-up was up to 4 years. Veterans who fell were more likely to be female (11% vs 7%) and White (72% vs 68%). They experienced 43,641 serious falls during follow-up. We identified 16 key predictors of serious falls and five interaction terms. Model performance was enhanced by addition of opioid use, as evidenced by overall category-free net reclassification improvement of 0.32 (P < .001). Discrimination (C-statistic = 0.76) and calibration were excellent for both development and validation data sets.ConclusionWe developed and internally validated a model to predict 6-month risk of serious falls among middle-aged Veterans with excellent discrimination and calibration.

Dataset Information

Behavioral Model of Middle-Aged and Seniors for Bicycle Tourism.

Publications

Behavioral Model of Middle-Aged and Seniors for Bicycle Tourism.

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