Purpose: This study estimated the long-term individual-level productivity costs of physical inactivity.
Methods: The data were drawn from the Northern Finland Birth Cohort 1966, to which the productivity cost variables (sick leaves and disability pensions) from Finnish registries were linked. Individuals ( N = 6261) were categorized into physical activity groups based on their level of physical activity, which was measured in three ways: 1) self-reported leisure-time moderate- to vigorous-intensity physical activity (MVPA) at 46 yr old, 2) longitudinal self-reported leisure-time MVPA at 31-46 yr old, and 3) accelerometer-measured overall MVPA at 46 yr old. The human capital approach was applied to calculate the observed costs (years 2012-2020) and the expected costs (years 2012-2031).
Results: The results showed that the average individual-level productivity costs were higher among physically inactive compared with the costs among physically active. The results were consistent regardless of the measurement type of physical activity or the period used. On average, the observed long-term productivity costs among physically inactive individuals were €1900 higher based on self-reported MVPA, €1800 higher based on longitudinal MVPA, and €4300 higher based on accelerometer-measured MVPA compared with the corresponding productivity costs among physically active individuals. The corresponding difference in the expected costs was €2800, €1200, and €8700, respectively.
Conclusions: The results provide evidence that productivity costs differ according to an individual's level of physical activity. Therefore, investments in physical activity may decrease not only the direct healthcare costs but also the indirect productivity costs paid by the employee, the employer, and the government.
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http://dx.doi.org/10.1249/MSS.0000000000003037 | DOI Listing |
Eur Radiol
January 2025
Department of Radiology, Montpellier Research Center Institute, PINKCC Laboratory, Montpellier, France.
Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).
Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.
BMJ Open Gastroenterol
December 2024
Department of English Language, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Objectives: Our aim was to systematically review the cost-effectiveness of proton pump inhibitor (PPI) therapies and surgical interventions for gastro-oesophageal reflux disease (GORD).
Design: The study design was a systematic review of economic evaluations.
Data Sources: We searched PubMed, Embase, Scopus, and Web of Science for publications from January 1990 to March 2023.
J Clin Med
January 2025
Ocular Surface Unit, ISPRE Ophthalmics, 16129 Genoa, Italy.
Dry eye disease (DED) is a multifactorial, chronic, and often relapsing condition with a significant impact on patient quality of life (QoL). Symptoms such as ocular discomfort and visual disturbances are diverse and frequently misaligned with objective clinical signs, complicating diagnosis and management. DED not only interferes with daily activities like reading, driving, and computer use but also imposes a substantial economic burden due to direct healthcare costs and reduced work productivity.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time.
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