scholarly journals Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

2020 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Mikko Kärmeniemi ◽  
Soile Puhakka ◽  
Maarit Kangas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Mining ◽  
Decision Tree ◽  
Sitting Time ◽  
Accelerometer Data ◽  
Relative Importance ◽  
Interaction Detection ◽  
Data Mining Approach ◽  
Input Variables

Abstract Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior.Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors.Results: Of the 4,582 participants with valid accelerometer data at the latest follow-up, 2,701 and 1,881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds after exercise (SED: B=-16.1, LPA: B=9.9, and MVPA: B=9.6), average weekday total sitting time (SED: B=34.1, LPA: B=-25.3, and MVPA: B=-5.8), and extravagance score (SED: B=6.3 and LPA: B=-3.7).Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

scholarly journals Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

2020 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Mikko Kärmeniemi ◽  
Soile Puhakka ◽  
Maarit Kangas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Mining ◽  
Decision Tree ◽  
Sitting Time ◽  
Accelerometer Data ◽  
Relative Importance ◽  
Interaction Detection ◽  
Data Mining Approach ◽  
Input Variables

Abstract Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive depending on participants’ activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results: Of the 4,582 participants with valid accelerometer data at the latest follow-up, 2,701 and 1,881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds after exercise (SED: B=-16.1, LPA: B=9.9, and MVPA: B=9.6), average weekday total sitting time (SED: B=34.1, LPA: B=-25.3, and MVPA: B=-5.8), and extravagance score (SED: B=6.3 and LPA: B=-3.7). Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

scholarly journals Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

2020 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Mikko Kärmeniemi ◽  
Soile Puhakka ◽  
Maarit Kangas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Mining ◽  
Decision Tree ◽  
Sitting Time ◽  
Physically Active ◽  
Additional Hypothesis ◽  
Interaction Detection ◽  
Data Mining Approach ◽  
Modifiable Factors ◽  
Physically Inactive

Abstract Purpose A data mining approach was applied to establish a multilevel hierarchy explaining physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods The 46-year follow-up data from the population-based Northern Finland Birth Cohort 1966 were used to create a hierarchy using Chi-square Automatic Interaction Detection (CHAID) decision tree technique for predicting PA behavior. The study’s subjects were classified as physically active or physically inactive based on their activity profiles derived from objective measurement of PA. The variables were a wide list of potentially modifiable factors including self-reported, clinical, and environmental measures. We then analyzed the association of the factors emerging from the model with three PA metrics including sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA) minutes per day. Results Model fitting was performed using a total of 168 factors as input variables to classify the PA behavior of 2,701 physically active and 1,881 physically inactive subjects. The decision tree selected a total of 36 factors of different domains by which 54 subgroups of subjects were formed. Factors emerging from the model were associated with the PA metrics, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds after exercise (SED: B=-16.1, LPA: B=9.9, and MVPA: B=9.6), average weekday total sitting time (SED: B=34.1, LPA: B=-25.3, and MVPA: B=-5.8), and extravagance score (SED: B=6.3 and LPA: B=-3.7). Conclusions Using data mining, a data-driven model was established from empirical data that can be potentially utilized to identify subgroups for multilevel intervention allocation. An extensive set of factors was methodologically discovered that can be a basis for additional hypothesis testing in PA correlates research.

scholarly journals Longitudinal Changes in Sitting Patterns, Physical Activity, and Health Outcomes in Adolescents

Children ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Ana Contardo Ayala ◽  
Jo Salmon ◽  
David Dunstan ◽  
Lauren Arundell ◽  
Kate Parker ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Activity Patterns ◽  
Sitting Time ◽  
Linear Regression Models ◽  
Anthropometric Measures ◽  
Prolonged Sitting ◽  
Intensity Physical Activity ◽  
Changes Over Time

This study examined two-year changes in patterns of activity and associations with body mass index (BMI) and waist circumference (WC) among adolescents. Inclinometers (activPAL) assessed sitting, sitting bouts, standing, stepping, and breaks from sitting. ActiGraph-accelerometers assessed sedentary time (SED), light-intensity physical activity (LIPA, stratified as low- and high-LIPA), and moderate-to-vigorous physical activity (MVPA). Anthropometric measures were objectively assessed at baseline and self-reported at follow-up. Data from 324 and 67 participants were obtained at baseline and follow-up, respectively. Multilevel mixed-effects linear regression models examined changes over time, and associations between baseline values and BMI and WC at follow-up. There were significant increases in BMI (0.6 kg/m2) and durations of prolonged sitting (26.4 min/day) and SED (52 min/day), and significant decreases in stepping (−19 min/day), LIPA (−33 min/day), low-LIPA (−26 min/day), high-LIPA (−6.3 min/day), MVPA (−19 min/day), and the number of breaks/day (−8). High baseline sitting time was associated (p = 0.086) with higher BMI at follow-up. There were no significant associations between baseline sitting, prolonged sitting, LIPA, or MVPA with WC. Although changes in daily activity patterns were not in a favourable direction, there were no clear associations with BMI or WC. Research with larger sample sizes and more time points is needed.

scholarly journals Association between physical activity, occupational sitting time and mortality in a general population: An 18-year prospective survey in Tanushimaru, Japan

2018 ◽  
Vol 27 (7) ◽  
pp. 758-766 ◽  
Author(s):  
Akiko Sakaue ◽  
Hisashi Adachi ◽  
Mika Enomoto ◽  
Ako Fukami ◽  
Eita Kumagai ◽  
...  
Keyword(s):  
Physical Activity ◽  
General Population ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Sitting Time ◽  
Inverse Association ◽  
Physical Activity Questionnaire ◽  
Occupational Sitting ◽  
Activity Questionnaire

Aims It is well known that a decline in physical activity is associated with an increase of all-cause death including cardiovascular events and cancer. Few studies have examined the association between occupational sitting time and mortality. Therefore, we investigated this issue in a general population. Methods Physical activity and occupational sitting time were measured using the Baecke physical activity questionnaire in 1999. The questionnaire generated indices in three physical activity categories: work, sport and leisure-time. A total physical activity index was calculated by adding these three indices. The Baecke physical activity questionnaire was able to evaluate occupational sitting time. Hazard ratios and 95% confidence intervals (CIs) were calculated using Cox's proportional hazard regression models. Results We enrolled a total of 1680 participants, who were followed up for 15.9 ± 3.8 years. The final follow-up rate was 93%. During the follow-up period, 397 subjects died. A significant inverse association ( p < 0.0001) was found between physical activity and mortality after adjustment for age and sex. Compared with lower levels of physical activity, the adjusted hazard ratio for mortality at higher levels of physical activity was 0.85 (95% CI: 0.78–0.92). Longer occupational sitting time was also significantly associated with higher mortality ( p < 0.01). The adjusted hazard ratio for mortality at longer occupational sitting time was 1.16 (95% CI: 1.05–1.27). These findings were observed in males, but not in females. Conclusions Our data demonstrated that higher levels of physical activity are associated with a reduced risk of cancer and cardiovascular death. Further, longer occupational sitting time is associated with increased mortality.

scholarly journals Data mining untuk memprediksi prestasi siswa berdasarkan sosial ekonomi, motivasi, kedisiplinan dan prestasi masa lalu

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Heri Susanto ◽  
Sudiyatno Sudiyatno
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Multiple Regression ◽  
Economic Status ◽  
Predictive Analysis ◽  
Research Approach ◽  
Significance Level ◽  
Achievement Prediction ◽  
Interaction Detection ◽  
Chi Squared

Penelitian ini bertujuan untuk membuat prediksi prestasi belajar siswa berdasarkan status sosial ekonomi orang tua, motivasi, kedisiplinan siswa dan prestasi masa lalu menggunakan metode data mining dengan algoritma J48. Sebagai perbandingan, data penelitian dianalisis juga dengan CHAID (Chi Squared Automatic Interaction Detection) dan regresi ganda. Pendekatan penelitian yang digunakan adalah kuantitatif. Subyek penelitian ini adalah siswa tingkat X SMK Negeri 4 Surakarta berjumlah 416 siswa. Teknik pengumpulan data yang digunakan adalah dokumentasi dan angket. Hasil penelitian menunjukkan bahwa analisis prediksi menggunakan decision tree algoritma J48 memiliki akurasi sebesar 95,7%, sedangkan analisis prediksi menggunakan CHAID memiliki tingat akurasi 82,1% dan analisis regresi ganda menghasilkan tingkat signifikansi sebesar 90,6%. Berdasarkan hasil tersebut bisa disimpulkan bahwa metode J48 lebih baik dibandingkan dengan metode CHAID dan regresi ganda. DATA MINING TO PREDICT STUDENT’S ACHIEVEMENT BASED ON SOCIO-ECONOMIC, MOTIVATION, DISCIPLINE AND ACHIEVEMENT OF THE PASTAbstractThis study aims to make student achievement prediction based on socio-economic status of parents, motivation, discipline students and past achievements using data mining methods with the J48 algorithm. For comparison, the data were analyzed also with CHAID (Chi Squared Automatic Interaction Detection) and multiple regression. The research approach is quantitative. The subjects of this study were student-first level at SMK Negeri 4 Surakarta totaled 416 students. Data collection techniques used are documentation and questionnaires. The results showed that the predictive analysis using J48 decision tree algorithm has an accuracy of 95.7%, while the predictive analysis using CHAID has the rank of an accuracy of 82.1% and a multiple regression analysis resulted in a significance level of 90.6%. Based on these results it can be concluded that the J48 method is better than the CHAID and multiple regression methods.

scholarly journals Behavioural interventions to promote physical activity in a multiethnic population at high risk of diabetes: PROPELS three-arm RCT

2021 ◽  
Vol 25 (77) ◽  
pp. 1-190
Author(s):  
Kamlesh Khunti ◽  
Simon Griffin ◽  
Alan Brennan ◽  
Helen Dallosso ◽  
Melanie Davies ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Confidence Interval ◽  
Primary Outcome ◽  
Health Technology ◽  
Accelerometer Data ◽  
Ambulatory Activity ◽  
Steps Per Day

Background Type 2 diabetes is a leading cause of mortality globally and accounts for significant health resource expenditure. Increased physical activity can reduce the risk of diabetes. However, the longer-term clinical effectiveness and cost-effectiveness of physical activity interventions in those at high risk of type 2 diabetes is unknown. Objectives To investigate whether or not Walking Away from Diabetes (Walking Away) – a low-resource, 3-hour group-based behavioural intervention designed to promote physical activity through pedometer use in those with prediabetes – leads to sustained increases in physical activity when delivered with and without an integrated mobile health intervention compared with control. Design Three-arm, parallel-group, pragmatic, superiority randomised controlled trial with follow-up conducted at 12 and 48 months. Setting Primary care and the community. Participants Adults whose primary care record included a prediabetic blood glucose measurement recorded within the past 5 years [HbA1c ≥ 42 mmol/mol (6.0%), < 48 mmol/mol (6.5%) mmol/mol; fasting glucose ≥ 5.5 mmol/l, < 7.0 mmol/l; or 2-hour post-challenge glucose ≥ 7.8 mmol/l, < 11.1 mmol/l] were recruited between December 2013 and February 2015. Data collection was completed in July 2019. Interventions Participants were randomised (1 : 1 : 1) using a web-based tool to (1) control (information leaflet), (2) Walking Away with annual group-based support or (3) Walking Away Plus (comprising Walking Away, annual group-based support and a mobile health intervention that provided automated, individually tailored text messages to prompt pedometer use and goal-setting and provide feedback, in addition to biannual telephone calls). Participants and data collectors were not blinded; however, the staff who processed the accelerometer data were blinded to allocation. Main outcome measures The primary outcome was accelerometer-measured ambulatory activity (steps per day) at 48 months. Other objective and self-reported measures of physical activity were also assessed. Results A total of 1366 individuals were randomised (median age 61 years, median body mass index 28.4 kg/m2, median ambulatory activity 6638 steps per day, women 49%, black and minority ethnicity 28%). Accelerometer data were available for 1017 (74%) and 993 (73%) individuals at 12 and 48 months, respectively. The primary outcome assessment at 48 months found no differences in ambulatory activity compared with control in either group (Walking Away Plus: 121 steps per day, 97.5% confidence interval –290 to 532 steps per day; Walking Away: 91 steps per day, 97.5% confidence interval –282 to 463). This was consistent across ethnic groups. At the intermediate 12-month assessment, the Walking Away Plus group had increased their ambulatory activity by 547 (97.5% confidence interval 211 to 882) steps per day compared with control and were 1.61 (97.5% confidence interval 1.05 to 2.45) times more likely to achieve 150 minutes per week of objectively assessed unbouted moderate to vigorous physical activity. In the Walking Away group, there were no differences compared with control at 12 months. Secondary anthropometric, biomechanical and mental health outcomes were unaltered in either intervention study arm compared with control at 12 or 48 months, with the exception of small, but sustained, reductions in body weight in the Walking Away study arm (≈ 1 kg) at the 12- and 48-month follow-ups. Lifetime cost-effectiveness modelling suggested that usual care had the highest probability of being cost-effective at a threshold of £20,000 per quality-adjusted life-year. Of 50 serious adverse events, only one (myocardial infarction) was deemed possibly related to the intervention and led to the withdrawal of the participant from the study. Limitations Loss to follow-up, although the results were unaltered when missing data were replaced using multiple imputation. Conclusions Combining a physical activity intervention with text messaging and telephone support resulted in modest, but clinically meaningful, changes in physical activity at 12 months, but the changes were not sustained at 48 months. Future work Future research is needed to investigate which intervention types, components and features can help to maintain physical activity behaviour change over the longer term. Trial registration Current Controlled Trials ISRCTN83465245. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 77. See the NIHR Journals Library website for further project information.

Physical activity, sedentary behavior, and incidence of obstructive sleep apnea in three prospective US cohorts

2021 ◽  
pp. 2100606
Author(s):  
Yue Liu ◽  
Lin Yang ◽  
Meir J. Stampfer ◽  
Susan Redline ◽  
Shelley S. Tworoger ◽  
...  
Keyword(s):  
Physical Activity ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sedentary Behavior ◽  
Sitting Time ◽  
Health Study ◽  
Metabolic Factors ◽  
Obstructive Sleep ◽  
Watching Tv

Reduced physical activity and increased sedentary behavior may independently contribute to development of obstructive sleep apnea (OSA) through increased adiposity, inflammation, insulin resistance and body fluid retention. However, epidemiologic evidence remains sparse, and is primarily limited to cross-sectional studies.We prospectively followed 50 332 women from the Nurses’ Health Study (2002–2012), 68 265 women from the Nurses’ Health Study II (1995–2013), and 19 320 men from the Health Professionals Follow-up Study (1996–2012). Recreational physical activity (quantified by metabolic equivalent of task [MET]-hours/week) and sitting time spent watching TV and at work/away from home were assessed by questionnaires every 2–4 years. Physician-diagnosed OSA was identified by validated self-report. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for OSA incidence associated with physical activity and sedentary behavior.During 2 004 663 person-years of follow-up, we documented 8733 incident OSA cases. After adjusting for potential confounders, the pooled HR for OSA comparing participants with ≥36.0 versus <6.0 MET-hours/week of physical activity was 0.46 (95% CI: 0.43, 0.50; ptrend<0.001). Compared with participants spending <4.0 h/week sitting watching TV, the multivariable-adjusted HR (95% CI) was 1.78 (1.60, 1.98) for participants spending ≥28.0 h/week (ptrend<0.001). The comparable HR (95% CI) was 1.49 (1.38, 1.62) for sitting hours at work/away from home (ptrend<0.001). With additional adjustment for several metabolic factors including BMI and waist circumference, the associations with physical activity and sitting hours at work/away from home were attenuated but remained significant (ptrend<0.001), whereas the association with sitting hours watching TV was no longer statistically significant (ptrend=0.18).Higher levels of physical activity and fewer sedentary hours were associated with lower OSA incidence. The potential mediating role of metabolic factors in the association between sedentary behavior and OSA incidence may depend on type of sedentary behavior. Our results suggest that promoting an active lifestyle may reduce OSA incidence.

scholarly journals The association of exercise and sedentary behaviours with incident end-stage renal disease: the Southern Community Cohort Study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e030661 ◽  
Author(s):  
Mindy Pike ◽  
Jacob Taylor ◽  
Edmond Kabagambe ◽  
Thomas G Stewart ◽  
Cassianne Robinson-Cohen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cohort Study ◽  
Kidney Disease ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Sedentary Time ◽  
Sitting Time ◽  
Stage Renal Disease ◽  
End Stage

ObjectiveTo examine whether lifestyle factors, including sedentary time and physical activity, could independently contribute to risk of end-stage renal disease (ESRD).Study designCase-cohort study.SettingSouth-eastern USA.ParticipantsThe Southern Community Cohort Study recruited ~86 000 black and white participants from 2002 to 2009. We assembled a case cohort of 692 incident ESRD cases and a probability sample of 4113 participants.PredictorsSedentary time was calculated as hours/day from daily sitting activities. Physical activity was calculated as metabolic equivalent (MET)-hours/day from engagement in light, moderate and vigorous activities.OutcomesIncident ESRD.ResultsAt baseline, among the subcohort, mean (SD) age was 52 (8.6) years, and median (25th, 75th centile) estimated glomerular filtration rate (eGFR) was 102.8 (85.9–117.9) mL/min/1.73 m2. Medians (25th–75th centile) for sedentary time and physical activity were 8.0 (5.5–12.0) hours/day and 17.2 (8.7–31.9) MET-hours/day, respectively. Median follow-up was 9.4 years. We observed significant interactions between eGFR and both physical activity and sedentary behaviour (p<0.001). The partial effect plot of the association between physical activity and log relative hazard of ESRD suggests that ESRD risk decreases as physical activity increases when eGFR is 90 mL/min/1.73 m2. The inverse association is most pronounced at physical activity levels >27 MET-hours/day. High levels of sitting time were associated with increased ESRD risk only among those with reduced kidney function (eGFR ≤30 mL/min/1.73 m2); this association was attenuated after excluding the first 2 years of follow-up.ConclusionsIn a population with a high prevalence of chronic kidney disease risk factors such as hypertension and diabetes, physical activity appears to be associated with reduced risk of ESRD among those with preserved kidney function. A positive association between sitting time and ESRD observed among those with advanced kidney disease is likely due to reverse causation.

Modelling Tyre-Road Noise with Data Mining Techniques

2015 ◽  
Vol 40 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Elisabete Freitas ◽  
Joaquim Tinoco ◽  
Francisco Soares ◽  
Jocilene Costa ◽  
Paulo Cortez ◽  
...  
Keyword(s):  
Data Mining ◽  
Predictive Model ◽  
Early Stage ◽  
Aggregate Size ◽  
Noise Prediction ◽  
Road Noise ◽  
Predictive Capacity ◽  
Data Mining Approach ◽  
Relevant Variables ◽  
Input Variables

Abstract The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

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