Multiple Linear Models Research Articles

Prognostic Factors in Patients Undergoing Physiotherapy for Chronic Low Back Pain: A Level I Systematic Review

Background: Low back pain is common. For patients with mechanic or non-specific chronic LBP (cLBP), the current guidelines suggest conservative, nonpharmacologic treatment as a first-line treatment. Among the available strategies, physiotherapy represents a common option offered to patients presenting with cLBP. The present systematic review investigates the prognostic factors of patients with mechanic or non-specific cLBP undergoing physiotherapy. Methods: In September 2024, the following databases were accessed: PubMed, Web of Science, Google Scholar, and Embase. All the randomised controlled trials (RCTs) which evaluated the efficacy of a physiotherapy programme in patients with LBP were accessed. All studies evaluating non-specific or mechanical LBP were included. Data concerning the following PROMs were collected: the pain scale, Roland Morris Disability Questionnaire (RMQ), and Oswestry Disability Index (ODI). A multiple linear model regression analysis was conducted using the Pearson Product–Moment Correlation Coefficient. Results: Data from 2773 patients were retrieved. The mean length of symptoms before the treatment was 61.2 months. Conclusions: Age and BMI might exert a limited influence on the outcomes of the physiotherapeutic management of cLBP. Pain and disability at baseline might represent important predictors of health-related quality of life at the six-month follow-up. Further studies on a larger population with a longer follow-up are required to validate these results.

Energy Efficiency and Mathematical Modeling of Shrimp Pond Oxygenation: A Multiple Regression Experimental Study

Aquaculture is one of the key economic activities to reduce food shortages worldwide. Water recirculation systems using pumps are crucial to maintain oxygenation and water quality, consuming about 35% of the total energy in this economic activity. This research proposes a multiple linear regression mathematical model to optimize oxygenation systems in intensive shrimp aquaculture by reducing energy consumption and minimizing water changes in ponds. The proposed model is key to optimizing the operation of pumping systems, allowing us to significantly reduce water turnover without compromising dissolved oxygen levels as a function of key variables such as water turnover volume, biomass, solar radiation (0–1200 W/m2), water temperature (20 °C–32 °C), phytoplankton levels (0–1,000,000 cells/ml), zooplankton (0–500,000 cells/ml), and wind speed (0–15 m/s). These variables are integrated into the model, managing to explain 94.02% of the variation in dissolved oxygen, with an R2 of 92.9%, which adjusts the system conditions in real time, reducing the impact of environmental fluctuations on water quality. This leads to an estimated annual energy savings of 106,397.5 kWh, with a total consumption of 663.8 MWh. The research contributes to the development of a mathematical approach that not only improves oxygenation prediction, but also minimizes the use of water resources, improving the sustainability and profitability of shrimp farming systems, and is a robust tool that maximizes operational efficiency in intensive aquaculture, particularly where water and energy management are critical.

Pengaruh Strategi Pemasaran dan Inovasi Produk Terhadap Keputusan Pembelian pada Furniture Hj Vera di Kota Karawang

This is a The research aims to determine marketing strategies and product innovation on purchasing decisions for Hj Vera Furniture in Karawang City. The method used in this research is a quantitative method, this research was conducted on Hj Vera Furniture in Karawang City with a total research sample of 96 people with the criteria of consumers who had previously purchased Hj Vera Furniture in Karawang City, using a purpisive sampling method. The multiple linear regression analysis model used in this research uses the f test and t test with a significance threshold of 5%. The findings show that there is a partial contribution between marketing strategies and purchasing decisions at Hj Vera Furniture in Karawang City. The research results obtained are the influence of marketing strategy and product innovation on purchasing decisions at HJ Vera Furniture in Karawang City, there is an influence from all variables.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Research on the Development Trend of New Energy Electric Vehicles in China based on Mathematical Model

As the global economic development moves closer to the direction of green sustainability, the international automobile market pays more and more attention to the development of new energy vehicles. New energy electric vehicle (NEV), as a kind of new energy vehicles, has developed rapidly in recent years due to its characteristics of low pollution, low energy consumption, peak power consumption and adjustment capacity, and has been welcomed by global consumers and governments. The Chinese government conforms to the development of The Times and actively promotes the development of its industry. This paper aims to study the development trend of China's new energy electric vehicles in China, through the establishment of multiple linear regression equation model analysis of different factors of new energy electric vehicles, and using the stochastic comparison model compared the influence of new energy electric vehicles on the traditional energy vehicle industry, and establish the influence factor model, analyze the international policy influence on the development of new energy electric vehicles, in the whole research process to test and the advantages and disadvantages analysis.

A 10-year Retrospective Clinical Study to Identify Risk Indicators for Peri-Implant Bone Loss and Implant Failure.

To evaluate long-term survival and success of dental implants and evaluate indicators affecting the long-term outcome. Implant survival, success and crestal bone loss (BL) over time were evaluated. For covariates at patient level, Kaplan-Meier estimates of implant survival were compared between groups with the log-rank test. Observed mean bone loss (MBL) was plotted as a function of time. Cumulative frequencies of BL were plotted for different post-op times. Uni- and multivariate analysis was performed. Simple linear mixed and multiple linear mixed models for BL at 1, 5 and 10 years were fitted. 407 patients (221 women, 186 men; mean age 64.86 years (range 28-92, SD 10.11)), with 1482 implants, responded. Absolute implant survival was 94.74%; MBL was 0.81 mm (SD 1.58, range 0.00-17.00) after an average follow-up of 10.66 years (range 10-14, SD 0.87). Implant survival was influenced on implant level by smoking, implant width and early bone loss (EBL) > 0.5 mm; on patient level by a history of periodontitis. Indicators influencing MBL after the 1st year were abutment height, type of surgery and implant width, while after 5 and 10 years of function were abutment height, EBL > 0.5 mm and smoking. Implant survival was significantly affected by a history of periodontitis on patient level and by smoking, implant width and EBL > 0.5 mm on implant level. Late bone loss was significantly affected by abutment height, EBL > 0.5 mm and smoking. B670201524796.

Promoting motivation and reducing stress in medical students by utilizing self-determination theory - a randomized controlled trial in practical psychiatry courses.

Medical students experience high levels of stress and related mental health problems. Students' autonomous and controlled motivation and their mental well-being are interconnected. This study aimed to investigate whether an innovative teaching concept based on self-determination theory (SDT) could improve students' motivation and thereby reduce their stress levels, ultimately providing a healthier framework for learning. In a week-long practical psychiatry course for medical students, a new didactic concept was implemented in half the groups (n = 73) and compared with the preexisting concept (n = 75) as a randomized controlled trial (RCT). To promote the SDT-target factors of perceived autonomy, competence, and relatedness, the methods used included team building, exclusively positive feedback, group discussions, and choice in task distribution. Significant group differences in motivation, stress, performance, and their relationships were analyzed through t-tests, multiple linear regression analyses, mediation analyses, and hierarchical linear modeling (HLM) using questionnaires collected before (t0) and after (t1) the course, and students' exam results (t2). In the innovation group (n = 53), intrinsic motivation/interest (d = 0.41; p = .019) and perceived choice/autonomy (d = 0.33; p = .048) were greater than in the control group (n = 52). While autonomous regulation remained stable, the innovation group showed reduced controlled regulation (d = -0.36; p = .033) and reported significantly lower stress (d = -0.55; p = .003). The observed changes in motivation collectively mediated the stress reduction. However, students in the innovation group achieved lower exam scores, which seemed to result from the absence of critical feedback, but not from the observed differences in motivation or stress. This study demonstrated that enhancing intrinsic motivation through SDT-based teaching can effectively reduce stress in medical students. Exclusively strengths-based positive feedback may have hindered exam performance, but optimizing educational concepts to promote motivation and reduce stress will be a valuable step toward improving medical students' mental well-being.

Longitudinal associations of life space mobility and domain-specific cognitive measures in ACTIVE

ABSTRACT We tested the longitudinal associations between life space mobility (LSM) at baseline and cognitive performance in three domains (memory, reasoning, and speed of processing) over time. Our analytic sample includes 2,690 older adults (mean age = 73.0, 75.9% female) participating in the ACTIVE Study. We used multiple linear mixed-effects models to evaluate whether LSM, measured using the Life Space Questionnaire, at baseline was longitudinally associated with scores on eight cognitive tests and three composite scores across 10 years. In unadjusted models, there were significant main effects of baseline LSM on memory and reasoning domains, and one speed of processing test (beta: 0.019 to 0.055, p < 0.05). All effects were non-significant in adjusted models. Over time, baseline LSM was associated with one memory test score in adjusted models. Greater LSM at baseline is associated with marginally higher cognitive performance but does not appear to affect the rate of cognitive change at a clinically significant level.

Longitudinal evaluation of retinal neuroaxonal loss in epilepsy using optical coherence tomography.

People with epilepsy (PwE) suffer from progressive brain atrophy, which is reflected as neuroaxonal loss on the retinal level. This study aims to provide initial insight into the longitudinal dynamics of the retinal neuroaxonal loss and possible driving factors. PwE and healthy controls (HC; 18-55 years of age) underwent spectral domain optical coherence tomography at baseline and 7.0 ± 1.5 and 6.7 ± 1.0 months later, respectively. The change in retinal thickness/volume and annualized percentage change (APC) were calculated for the peripapillary retinal nerve fiber layer (pRNFL), the macular RNFL (mRNFL), the ganglion cell inner plexiform layer (GCIP), the inner nuclear layer, and the total macular volume (TMV). Group comparisons and multiple linear models with stepwise backward selection were performed to evaluate associations with demographic and clinical parameters. PwE (n = 44, 21 females, mean age = 35.6 ± 10.9 years) revealed a significant decrease in the pRNFL, mRNFL, GCIP, and TMV thickness or volume in the study interval. When compared to HC (n = 56, 37 females, mean age = 32.7 ± 8.3 years), the APC of the pRNFL (-.98 ± 3.13%/year) and the GCIP (-1.24 ± 2.56%/year) were significantly more pronounced in PwE (p = .01 and p = .046, respectively). Of note, atrophy of the mRNFL was significantly influenced by the number of antiseizure medications (ASMs; p = .047) and increasing age of PwE (p = .03). Contradictory results, however, were revealed for the impact of seizures. In epilepsy, progression of retinal neuroaxonal loss was already detectable at short-term follow-up. PwE who receive a high number of ASMs seem to be at risk for accelerated neuroaxonal loss, stressing the importance of well-considered and effective antiseizure therapy.

Resolving the Paradox: How mobile money drives economic growth through financial inclusion

The primary aim of this study is to identify the most relevant financial inclusion variables influencing gross domestic product (GDP) and to choose the optimal multiple linear regression model to quantify their contribution to economic growth. Finance is essential for every economic activity and financial inclusion is closely related to societal empowerment. It is observed in different nations that financial inclusion variables have a valuable impact on economic growth. For this, secondary data were gathered for this study from the IMF (International Monetary Fund) from 2011 to 2020. Graphical presentations were used to visualize the relationship between financial inclusion variables and economic growth. The most eligible variables and the best model among a collection of models based on empirical data were chosen using a stepwise backward elimination technique using multiple linear regression and model selection criteria. From the graph, it is seen that financial inclusion variables are proportionately related to GDP and the results demonstrate that the most eligible variables of financial inclusion that affect GDP are (1) the number of registered mobile money agent outlets, (2) the number of active mobile money accounts, (3) the number of mobile money transactions, and (4) outstanding balances on active mobile money accounts. These variables have a positive impact on economic growth, and the fitted model measures the more significant contribution to economic growth. As a result, it may be summarized that financial enclosure has to be enhanced for the development of rural and border areas of Bangladesh. This study will assist policymakers, development organizations, and scholars and make knowledgeable findings that will guide long-term improvements to minimize the gap between rich and poor, leading to the improvement of the community's wealth and welfare.

The Influence of Entrepreneurship Knowledge, Personality, Family Environment on Entrepreneurial Interest of Undergraduate Students Class of 2021, Faculty of Economics and Business, University of Riau

This research aims to determine 1) the influence of entrepreneurial knowledge on students' interest in entrepreneurship, 2) the influence of personality on students' interest in entrepreneurship, 3) the influence of the environment on students' interest in entrepreneurship and 4) the influence of personality, entrepreneurial knowledge and the environment on students' interest in entrepreneurship. This research is quantitative with a multiple linear regression analysis model. This research uses data obtained from distributing questionnaires to 90 respondents. The sampling method used was the simple random sampling method. This research uses statistical data analysis methods using SPSS version 25. The results of the research show that 1) Knowledge of entrepreneurship has a positive and significant effect on students' interest in entrepreneurship, 2) Personality has a positive and significant effect on students' interest in entrepreneurship, 3) Family environment has a positive and significant effect on students' interest in entrepreneurship, 4) Knowledge of entrepreneurship, personality, and The family environment together has a positive and significant effect on interest in entrepreneurship. This means that the influence of these factors interacts with each other, where individuals who have good knowledge about entrepreneurship, a strong personality, and a positive family environment will have a great interest in pursuing an entrepreneurial career.

Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000-2022.

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

Using Electric Vehicle Driver’s Driving Mode for Trip Planning and Routing

With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make comparisons between simulated and a real trips.

Analysis of high temperature and strain amplitude effects on low cycle fatigue behavior of pitting corroded killed E350 BR structural steel

High-tension structural steels are prone to accelerated fatigue damage from pitting corrosion and high-temperature. Despite adverse effects, research on their low cycle fatigue (LCF) behavior is limited. Specifically, studies analyzing temperature-dependent pit sensitivity effects, considering pit-related material’s susceptibility to surface topographic features variation and stress concentration are lacking. This study conducts LCF tests on pitting corroded killed E350 BR structural steel at multiple strain amplitudes and high temperatures. It develops temperature-dependent parameters, such as the cyclic softening pit sensitivity factor, suitable for integration into existing approaches like total cyclic plastic strain energy density (CPSED), power-law, average strain energy density (SED), Coffin-Manson, and pit stress intensity factor (pit-SIF). Further, it proposes multiple linear regression-based prediction models relating total CPSED and average SED with strain amplitude and temperature. Corroded specimens show higher plastic deformation and reduced peak stress, fatigue life, and total CPSED compared to uncorroded ones. The developed parameters, integrated with average SED approach, predicts LCF life within an error band of ±1.5, while power-law relationship reduces it to ±1.2. Moreover, pit-SIF approach estimates fatigue life within an error band of ±1.5. The findings provide critical knowledge for enhanced component design, leading to structural safety, performance, and fire resilience.

The ability of peripheral neurophysiological biomarkers to predict future psychological conditions among geriatric populations

ObjectiveThough the association between peripheral neurophysiological biomarkers and psychological conditions is widely discussed, there is still limited evidence about the ability of peripheral biomarkers to predict psychological outcomes, especially among geriatric populations. MethodsThe study is designed as a prospective cohort study. We collected information from participants aged over 55 years. The participants were evaluated at the start of the study (T0) and 6–9 months later (T1). Information about demographic profiles, peripheral neurophysiological biomarker recordings (including heart rate variability, finger temperature, skin conductance, and electromyogram), and psychological measurements (including Brief Symptom Rating Scale-5, Chinese Happiness Inventory, and Short Portable Mental Status Questionnaire) were collected at T0. At T1, participants reported self-rated questionnaires for psychological outcomes (Patient Health Questionnaire-15, health anxiety questionnaire, Beck Depression Inventory-II, and Beck Anxiety Inventory) and were evaluated with Mini-Mental State Examination by the staff. The association between the peripheral biomarkers and psychological outcomes was evaluated via multiple regression models. ResultsA total of 385 participants were included in the study and the average age was 74.49 ± 7.34 years. Both stepwise multiple linear and logistic models showed a significant association between decreased skin conductance and increased/presence of depression at T1. The receiver operating characteristic (ROC) curve analysis of skin conductance for depression was fair (area under curve = 0.812). ConclusionsThe ability of skin conductance to predict depression among geriatric populations may facilitate the detection of geriatric depression and future research on the pathophysiology.

Lifestyle deterioration linked to elevated inflammatory cytokines over a two-month follow-up

Healthy lifestyle reduces the risk of inflammation-related diseases. This study assessed how lifestyle changes affect inflammatory cytokines over 2 months. Involving 179 apparently healthy participants recruited from community, collecting data on lifestyles (smoking, alcohol, BMI, daily activity, sleep, diet) and measured inflammatory cytokines (TNF-α, IL-1β, IL-17A, CRP, IL-8, IL-18, IFN-γ) plus pepsinogens (PG I, PG II) at the baseline and 2-month follow-up. The combined adverse lifestyle score is the sum of scores across six lifestyles, with higher scores indicating more adverse lifestyle factors. Use multiple linear regression and mixed linear models to analyze the relationship between the changes in lifestyle and inflammatory cytokines (follow-up values minus baseline values). For every 1-point increase in combined adverse lifestyle score, IL-17A increased by 0.98 (95% CI 0.23, 1.73) pg/mL, IFN-γ increased by 1.79 (95% CI 0.39, 3.18) pg/mL. Decreased changes in daily activity were associated with higher IL-17A (β = 1.83, 95% CI 0.53, 3.13) and IFN-γ (β = 2.59, 95% CI 0.9, 4.98). Excluding daily activity, changes in combined adverse lifestyle scores were not associated with changes in inflammatory cytokines. Lifestyle improvements at 2-month intervals may reduce TNF-α, IL-17A and IFN-γ, with daily activity making the greatest contribution.

Pengaruh Kompetensi Sumber Daya Manusia, Gaya Kepemimpinan dan Pemanfaatan Teknologi Informasi terhadap Kualitas Laporan Keuangan Gereja Paroki Wilayah Kota Maumere

The aim of this research is to determine the influence of human resource competence, leadership style and use of information technology on the quality of financial reports of Parish Churches in the Maumere City area. This research uses an associative quantitative approach. The population in this study was the Financial Council taken from 5 Parish Churches in Maumere City, namely 32 respondents. The sampling method used in this research was saturated sampling so that the number of samples obtained was 32 respondents. The data collection process was carried out by distributing questionnaires to respondents, where the research instrument used a Likert scale. The data analysis technique used in this research uses a multiple linear regression analysis model with the help of the IBM SPSS Statistics 26 program. The results of this research show that the variables of human resource competency, leadership style and the use of information technology partially and simultaneously have a significant effect on the quality of the Church's financial reports. Maumere City Parish.

Pharmacological and non-pharmacological predictors of the LSD experience in healthy participants

The pharmacodynamic effects of lysergic acid diethylamide (LSD) are diverse and different in different individuals. Effects of other psychoactive substances have been shown to be critically influenced by non-pharmacological factors such as personality traits and mood states. The aim of this study was to determine pharmacological and psychological predictors of the LSD effects in healthy human subjects. This analysis is based on nine double-blind, placebo-controlled, cross-over studies with a total of 213 healthy subjects receiving between 25–200 µg LSD. The influence of sex, age, dose, body weight, pharmacogenetic, drug experience, personality, setting, and mood before drug intake on the peak autonomic and total subjective responses to LSD was investigated using multiple linear mixed effects models and Least Absolute Shrinkage and Selection Operator regression. Results were adjusted for LSD dose and corrected for multiple testing. LSD dose emerged as the most influential predictor, exhibiting a positive correlation with most response variables. Pre-drug mental states such as “Well-Being”, “Emotional Excitability”, and “Anxiety” were also important predictor for a range of subjective effects but also heart rate and body temperature. The trait “Openness to Experiences” was positively correlated with elevated ratings in “Oceanic Boundlessness” and mystical-type effects. Previous experiences with hallucinogens have been negatively associated with the overall altered state of consciousness and particularly with “Anxious Ego Dissolution”. Acute anxiety negatively correlated with the genetically determined functionality of the Cytochrome 2D6 enzyme. In summary, besides the amount of drug consumed, non-pharmacological factors such as personal traits and current mood also significantly predicted the subjective drug experience. Sex and body weight were not significant factors in influencing the drug experience.

The amyloid beta 42/38 ratio as a plasma biomarker of early memory deficits in cognitively unimpaired older adults

The amyloid beta (Aβ) 42/40 ratio has been widely studied as a biomarker in Alzheimer’s disease (AD); however, other Aβ peptides could also represent relevant biomarkers. We measured levels of Aβ38/40/42 in plasma samples from cognitively-unimpaired older adults and determined the relationships between Aβ levels and amyloid positron-emission-tomography (PET) and performance on a learning and memory task. We found that all Aβ peptides individually and the Aβ42/40 ratio, but not the Aβ42/38 ratio, were significantly correlated with brain amyloid (Aβ-PET). Multiple linear modeling, adjusting for age, sex, education, APOE4 and Aβ-PET showed significant associations between the Aβ42/38 ratio and memory. Further, associations between the Aβ42/38 ratio and learning scores were stronger in males and in Aβ-PET-negative individuals. In contrast, no significant associations were detected between the Aβ42/40 ratio and any learning measure. These studies implicate the Aβ42/38 ratio as a biomarker to assess early memory deficits and underscore the utility of the Aβ38 fragment as an important biomarker in the AD field.

Low public awareness opens up new opportunities for highlighting milk as an iodine dietary source

This international survey provides insights into public awareness of the importance of iodine as an essential trace mineral in human health along with knowledge of iodine dietary sources. The online questionnaire included sociodemographic aspects and dietary iodine consumer awareness on 7-point Likert-type questions. A total of 4,704 questionnaires from 16 countries were considered. Answers were analyzed through a multiple regression linear model including country, gender, age, education level, and employment status as fixed effects. Respondents were moderately aware of the importance of fish (4.86) and seafood (4.90) as dietary iodine sources, but less aware of milk as a primary iodine source (3.32). Respondent awareness varied considerably across countries. Age, education level, and employment status only modified their perception when asked about fish and seafood as a source of iodine, with elderly respondents, those highly educated and of working age being more aware of their relevance as dietary iodine sources. Respondent knowledge did not vary by age, education level, employment status, or gender when asked about cereals, vegetables and fruits, meat and milk as iodine-rich food sources. Consequently, labeling milk and dairy products as an iodine-rich food source should be considered. Public authorities can consider the results from this survey in promotional campaigns to improve the awareness of different iodine sources and their beneficial effect on health.