Statistical Regression Models Research Articles

Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South

This study quantitatively evaluates the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in Greece, a country in southern Europe. The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021–2040 and 2041–2060 and compared with those under the historical local climate of 1986–2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined resulted in crop yields that are higher than those under the historical climate. However, the effectiveness of the measures diminished significantly in 2041–2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. Assessing the effectiveness of combined adaptation options and evaluating additional criteria (e.g., feasibility) represent essential areas for future research.

Prevalence of Depressive Symptoms in a Predominantly Hispanic/Latinx South Texas Community in the Aftermath of the COVID-19 Pandemic.

COVID-19 has a lasting impact on mental health, particularly within the Hispanic/Latinx communities. This paper empirically investigates the post-COVID-19 presence and severities of depression, one of the most common mental health disorders, among adults in a predominantly U.S.-born Hispanic/Latinx community in South Texas composed primarily of Mexican Americans. Multiple statistic regression models were applied to data from 515 adults in Nueces County who completed all questions in a survey from convenience sampling between June 2022 and May 2023. Depression was assessed using both standard PHQ-2 and PHQ-9 measurements. Of the 515 participants, 377 (64.5%) were Hispanic, and 441 (85.6%) had a high school education or higher, reflecting the county's demographics. About half of the participants (47%) reported mild/moderate to severe depression. The regression model estimation results reveal that female participants, those not in full-time employment, and individuals with disabilities were more likely to feel depressed after COVID-19. Middle-aged adults demonstrated greater resilience to depression compared to other age groups. Notably, non-Hispanic participants in the study reported higher levels of depression compared to their Hispanic counterparts. Additionally, COVID-19-related experiences, such as testing positive for the virus, being hospitalized, or having a history of depression before COVID-19, were associated with higher levels of reported depression. COVID-19 has significantly impacted the mental health of this predominantly U.S.-born Hispanic/Latinx community. These findings can assist healthcare providers and policymakers in developing targeted strategies to tailor interventions aimed at enhancing mental health well-being, reducing disparities, and fostering overall improvement within the Hispanic/Latinx community.

Analytical Auditing Methods and their Role in Improving the Performance of the Accounting Audit Process using Statistical Regression Models

Analytical Auditing Methods and their Role in Improving the Performance of the Accounting Audit Process using Statistical Regression Models

Impact of Urbanization on Air Quality in Largest Сities of Kazakhstan

In the context of the rapid growth of urbanization and industrialization of Kazakhstan, the air pollution problem in large cities is becoming increasingly urgent. It is essential to assess the impact of economic growth on the environmental situation, as this allows us to develop strategies to improve air quality and ensure sustainable development. Thus, this study examines the impact of urbanization and economic growth on air pollution across four major cities in Kazakhstan from 2016 to 2022. Statistical methods were used to analyze the relationship between pollution levels and economic indicators, including gross regional product (GRP) and industrialization. The study covers the period from 2016 to 2022 and is based on data on concentrations of critical pollutants collected from the National Bureau of Statistics of the Republic of Kazakhstan. National monitoring sources collected air quality data, including PM2.5, PM10, SO2, NO2, and CO concentrations. Statistical analyses, as correlation and regression models, were applied to establish relationships between pollution levels and economic growth indicators. The study found significant correlations between economic activities and air pollution levels. In Almaty, a 10% increase in GRP corresponds to a 5% increase in pollutant concentrations. Almaty experienced the highest average PM2.5 concentration at 42 µg/m³, exceeding permissible norms by 20%. Other cities showed varied levels of pollution influenced by specific economic and industrial profiles. The results underscore the pressing need for effective urban management and strategic policy-making to mitigate the adverse effects of economic development on air quality.

Modified reverse degree descriptors for combined topological and entropy characterizations of 2D metal organic frameworks: applications in graph energy prediction.

Topological descriptors are widely utilized as graph theoretical measures for evaluating the physicochemical properties of organic frameworks by examining their molecular structures. Our current research validates the usage of topological descriptors in studying frameworks such as metal-butylated hydroxytoluene, NH-substituted coronene transition metal, transition metal-phthalocyanine, and conductive metal-octa amino phthalocyanine. These metal organic frameworks are crucial in nanoscale research for their porosity, adaptability, and conductivity, making them essential for advanced materials and modern technology. In this study, we provide the topological and entropy characterizations of these frameworks by employing robust reverse degree based descriptors, which offer insightful information on structural complexities. This structural information is applied to predict the graph energy of the considered metal organic frameworks using statistical regression models.

Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth monitoring in agricultural management. Recent advancements in unmanned aerial vehicle (UAV) multispectral remote sensing have enabled fast and cost-effective measurements of these traits. However, traditional statistical regression models trained on specific datasets lack scalability and transferability across practical field conditions without retraining. This study proposed an efficient physics-informed transfer learning model (PITL) for winter wheat biochemical traits estimation from UAV multispectral data. The PITL integrates the strengths of physical radiative transfer simulations and deep neural network architectures through transfer learning to improve the estimation of biochemical traits from UAV multispectral data. The PITL was tested with convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM) architectures. Results indicated that PITLDNN had better accuracy than PITLCNN and PITLLSTM models in predicting LAI (R2=0.94, RMSE = 0.32 m2/m2), LCC (R2=0.81, RMSE = 5.20 μg/cm2) and CCC (R2=0.928, RMSE = 0.2 g/m2). Moreover, PITLDNN demonstrated higher capability in computational efficiency, making it suitable for processing large volumes of UAV multispectral data in crop growth monitoring applications. Furthermore, PITL's integration of radiative transfer knowledge with labeled field data yielded higher predictive accuracy compared to physically-based inversion model, pure data-driven deep neural network approaches, and hybrid models. This study highlighted the performance of PITLDNN in accurately and efficiently quantifing biochemical traits from UAV multispectral data, thereby providing timely and accurate information for guiding crop growth monitoring applications.

Fitting deep neural networks into the statistical regression modelling setting

Fitting deep neural networks into the statistical regression modelling setting

Investigating the relationship between students’ executive functions, emotion regulation, and academic achievement

Our research aimed to identify interrelationships between performance in the Stroop test and emotion regulation and academic achievement of students. The present research method was descriptive of correlation type. The statistical population in this study included all students of Mashhad in Iran. Using the cluster sampling method and within each cluster using random sampling method, 120 students were recruited from three different educational levels. In this study, the Stroop test, Cognitive Emotion Regulation Questionnaire (CERQ-P), and GPA of the students were used to measure the research variables. To analyze the data, a multivariate regression statistical model was used to determine the relationship between the desired variables. The analysis shows that there is a significant relationship between academic achievement and performance on the Stroop test and emotion regulation (p < 0.05). It is included that training based on improving executive functions and emotion regulation can be used to improve students’ academic status.

Criteria for Forcast of Proton Events by Real-Time Solar Observations

The sequence of overcoming threshold values of a number of physical characteristics for proton event forecast in real time is discussed. Each characteristic adds a new physical meaning that refines the forecast. To take into account all the characteristics, the following continuous patrol observations are necessary: 1) the magnetic field of the active region (ascent of the flux) and the general magnetic field of the Sun, which can predict the onset of flare activity several days before the main events; 2) soft X-ray radiation in two channels to calculate the temperature (T) and the measure emission of plasma, which can show the preheating to T 10 MK required to begin proton acceleration (the first minutes before the start of hard X-ray (HXR) radiation with energies 100 keV); 3) HXR radiation 100 keV or microwave radiation (3 GHz), which indicate the intensity and duration of operation of the electron accelerator (units and tens of minutes before the arrival of protons with energies 100 MeV); 4) radio emission at plasma frequencies ( 1000 MHz), showing the development of the flare process upward into the corona and leading to a coronal mass ejection (CME) several minutes before the onset of radio bursts of types II and IV (the first tens of minutes before the appearance of a CME in the field of view of the coronagraph); 5) the direction and speed of CME propagation, which determine the conditions for the release of accelerated protons into the heliosphere. These stages of solar proton flares are illustrated by observations of proton events on August 2—9, 2011. To quantitatively predict the onset time, maximum and magnitude of the proton flux, as well as its fluence, it is necessary to create statistical regression models based on all of the listed characteristics of past solar proton events.

Testing, Validation, and Simulation of a Novel Economizer Damper Control Strategy to Enhance HVAC System Efficiency

Buildings account for over 40% of global carbon dioxide (CO2) emissions, with supply and return fans in air handling units consuming a significant portion of energy. To address this, researchers have explored innovative economizer damper control methods and identified the “split-signal” strategy, which optimizes supply airflow using a single damper as a promising approach. In this study, split-signal was further refined for practical application and energy simulation, aiming to demonstrate its effectiveness and encourage adoption in real-world building mechanical systems. Laboratory testing on chilled water variable air volume (VAV) system showed fan energy savings of 0.2–5% compared to traditional “three-coupled” control, depending on ventilation air proportions, and prevented reverse airflow. A statistical regression model, based on experimental data, was developed to predict energy savings and streamline comparisons. Energy simulations were conducted across various U.S. climate zones and revealed potential savings of 15–20% in energy use, operational costs, and CO2 emissions. With minimal financial investment, split-signal control offers a cost-effective solution to improve energy efficiency and reduce environmental impact, promoting its adoption in real-world building applications.

Rate expression model from thermodynamics application and optimal kinetic parameters determination for urea synthesis and production process

Urea, an essential organic fertilizer, enhances soil fertility by providing 0.466 nitrogen for maximum crop yield. In this study, urea is synthesized from NH3 and CO2 in an equilibrium reaction process adhering to Le Chatelier's principle, maintained under process conditions: flow rate of 63.5 kg/s, temperature of 184 °C, and pressure of 160 kg/cm2. A new rate expression model, formulated in terms of extent of reaction and mole fraction, was developed based on mass action relations and thermodynamic models. Two industrial reactors were considered: a plug flow reactor (PFR) at Notore and a continuous stirred tank reactor (CSTR) at Indorama plants. Transient reactor models, based on material and energy balance conservation principles, were numerically resolved using MATLAB version 2020 with specified input conditions. A non-linear regression statistical optimization model was employed to refine kinetic parameter values, ensuring optimal and high-quality urea yield. Model validations were conducted using literature data, revealing higher urea yields of 0.726 and 0.7032 for the CSTR and PFR, respectively. Deviations (0.134, 0.10 to 1.135 and 0.635, 0.326 to 0.850) and root mean square errors (RMSE) (0.043, 0.033 to 0.193 and 0.137, 0.087 to 0.162) were observed when validated against plant and literature values for the CSTR and PFR respectively. The refined kinetic parameters (activation energies, Arrhenius constants, and rate constants) exhibited negligible deviations (0.0004–0.0466 and 0.0004 to 0.0491) and RMSE (0.0228, 0.0055, and 0.0256 and 0.0241, 0.0096, and 0.0269) when validated against plant data, significantly enhancing urea yield in CSTR and PFR reactors respectively.

Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression.

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Statistical and machine learning regression models of biological age have been trained on various sources of high dimensional data to predict chronological age. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose a machine learning approach that simplifies gene selection by using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We construct the LASSO-GAGE (L-GAGE) model in an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC and then test for accelerated biological aging in MDD. When testing L-GAGE association with MDD, we account for factors such as sex and chronological age to mitigate regression to the mean effects. The L-GAGE shows higher biological aging in MDD subjects than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age is significant in L-GAGE model. This effect remains statistically significant even after adjusting for chronological age and sex. We find cytomegalovirus (CMV) serostatus is associated with elevated L-GAGE. We also investigate feature selection methods Random Forest and nearest neighbor projected distance regression (NPDR) to characterize age related genes, and we find functional enrichment of infectious disease and SARS-COV pathways.

Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations

IntroductionGenetic testing is used across medical disciplines leading to unprecedented demand for genetic services. This has resulted in excessive waitlists and unsustainable pressure on the standard model of genetic healthcare....

Exploring the Cross-Sectional Relationship Between Implementation Supports for Adopting Nutrition Standards and the Nutritional Quality of School Lunches Served

BackgroundThe evidence-based nutrition standards of the Healthy, Hunger-Free Kids Act of 2010 significantly improved school meal nutrition, yet little is known about how school food authorities (SFAs) were supported to translate the standards into practice in schools. ObjectiveThis study tested whether or not 2 implementation supports, SFA receipt of training/technical assistance (TA) and purchase of new equipment, for implementing the nutrition standards were associated with the nutritional quality of school lunches. DesignThis study used a cross-sectional design. Participants/settingThe study sample included 365 SFAs derived from the US Department of Agriculture’s School Nutrition and Meal Cost Study (2014 to 2015 school year), the only national data of school nutrition environments since the Healthy, Hunger-Free Kids Act of 2010. Main outcome measuresImplementation supports included reported participation in training/TA and the purchase of new equipment between the 2012 to 2013 (year of policy adoption) and 2014 to 2015 school years. The primary outcome, nutritional quality of school lunches served, was defined as low/high Healthy Eating Index 2010 scores during the 2014 to 2015 school year. Secondary implementation supports included the number of areas covered by training/TA, the adequacy of training/TA, and the degree of implementation challenges. Statistical analysesMultivariable-adjusted logistic regression models assessed if receipt of implementation supports was associated with lunch Healthy Eating Index 2010 scores. ResultsThe median lunch Healthy Eating Index 2010 score was 81.7 (95% CI 80.4 to 82.9). Most SFAs (78.4%, 95% CI 72.0% to 85.0%) reported having participated in training/TA and one-third (33.8%, 95% CI 24.4% to 43.2%) reported having purchased new equipment—neither were associated with the odds of having higher Healthy Eating Index 2010 scores for lunches served at the time of data collection. ConclusionsMany SFAs accessed implementation supports to adopt the nutrition standards. Information on dose, quality, and nutrition-related impact of implementation supports using measures of change are needed to determine how best to support SFAs with implementation of new nutrition standards.

Evaluating the Impacts of Fertilization and Rainfall on Multi-Form Phosphorus Losses from Agricultural Fields: A Case Study on the North China Plain

Excessive fertilizer application increases the risk of eutrophication and agricultural non-point source pollution (ANPS) in rivers near farmland. However, the processes and mechanisms of runoff and phosphorus losses, particularly in the interflow, under various fertilizer treatments and rainfall scenarios are not well understood. This study used orthogonal experimental methods to investigate the combined effects of fertilization schemes and rainfall intensity on multi-form phosphorus runoff losses and to establish statistical relationships and regression models between phosphorus losses and environmental factors in surface runoff and interflow. The results indicated that (1) the optimized fertilization scheme, compared with conventional fertilization, enhanced pak choi (Brassica rapa) growth while reducing phosphorus runoff losses. By reducing phosphorus fertilization by 35.7%, total phosphorus losses decreased by 29.3%, 34.2%, and 29.8% under light, moderate, and heavy rainfall, respectively. (2) Different fertilizer applications and rainfall intensities had varying effects on phosphorus losses through different pathways. Fertilizer application was the primary factor affecting phosphorus losses in surface runoff, while rainfall intensity mainly influenced phosphorus losses through interflow. (3) Surface runoff was the dominant pathway for phosphorus losses from farmland (&gt;92.0%), with particulate phosphorus (&gt;89.4%) being the predominant form. However, under high-intensity and long-duration rainfall, interflow became a significant pathway for phosphorus losses. This study highlights the importance of optimized fertilization in reducing phosphorus losses and improving fertilizer efficiency in agricultural fields. The findings will help develop strategies to mitigate ANPS and soil nutrient losses in the North China Plain.

Morphologic predictors of mandibular changes induced by Sander's Bite Jumping Appliance.

This study aimed to identify pretreatment cephalometric variables as possible predictors of the mandibular length increase in Class II patients with mandibular retrusion, treated by means of the Bite Jumping Appliance (BJA). Forty-three subjects (22 males and 21 females) with Class II malocclusion, treated with a BJA, were selected on the basis of the following inclusion criteria: full Class II molar relationship, Overjet (OVJ) ≥ 6 mm and a skeletal Class II malocclusion with mandibular retrusion at the start of the treatment (T0); cervical vertebral maturation stage 2 or 3 at time 0 (T0). The following mandibular structural features were measured on lateral cephalograms at time 0 and time 1 (15 months of treatment): the width and height of the mandibular symphysis and its width/height ratio, the width and height of the mandibular ramus and its width/height ratio, the antegonial notch depth and the Condilion-Gonion-Menton (Co-Go-Me) angle. Post-treatment changes were assessed by Pancherz's cephalometric analysis, evaluating the increases in mandibular length. A regression statistical model was used to test the association between morphologic variables and mandibular length changes. At T1, a significant increase in mandibular length (7.1 + 3.4 mm, p < .001) was measured. A significant negative association between the pretreatment Co-Go-Me angle and mandibular length change was found (p < .05). IMPA angle was negatively associated with mandibular length change. All the others morphological feature were not statistically related to mandibular length change. Co-Go-Me angle and IMPA angle at T0 can be used as predictors for mandibular response to the treatment with BJA.

Regression without regrets –initial data analysis is a prerequisite for multivariable regression

Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.

Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.

Long COVID and the Higher Risk of Food Insecurity Among Participants and Nonparticipants of Food Assistance Programs in the United States

BackgroundIn the aftermath of the COVID-19 public health emergency, it is important to understand the extent of socioeconomic burdens of long COVID, defined as continuation of symptoms after initial infection, including food insecurity. ObjectiveThis study aimed to assess the association between long COVID and family food insecurity among low-income individuals (or any of their family members living with them) who were participants and nonparticipants in public food assistance programs (Supplemental Nutrition Assistance Program [SNAP], Special Supplemental Nutrition Program for Women, Infants, and Children [WIC], and National School Lunch Program [NSLP]) in the United States. DesignThe study used an observational cross-sectional design. Participants/settingData on 7151 adults (aged 18+ years), with family income of < 200% of the Federal Poverty line, from the 2022 National Health Interview Survey, were analyzed. Main outcome measuresLevel of family food security, based on responses to a set of 10 questions measuring family’s food security during the past 30 days. Statistical analyses performedMultinomial logistic regression models were estimated to obtain relative risk ratios of marginal and low/very low food security, relative to the base outcome of high food security, for long COVID status. Multivariable models were estimated separately for the full sample and for subgroups of food assistance participants and nonparticipants. A seemingly unrelated regression (SUR) specification was estimated to assess whether the estimates were different across the participant and nonparticipant subgroups. ResultsCompared with individuals who never had COVID-19, the adjusted relative risks of experiencing marginal and low/very low food security were 1.42 (95% CI, 1.00–2.02) and 1.43 (95% CI, 1.08–1.91) times higher, respectively, for individuals who had long COVID. Although the adjusted risks were not observed to be statistically significant in the participant subgroup, among nonparticipants, adjusted relative risk ratios were 2.34 (95% CI, 1.43–3.82) and 1.56 (95% CI, 1.02–2.39), respectively. SUR results showed that relationships between long COVID and food insecurity were only different for marginal and not low/very low levels of food security between food assistance participant and nonparticipant groups. ConclusionsStudy findings highlight a significantly higher risk of marginal and low/very low- food security among low-income adults who had long COVID, especially those who were nonparticipants in public food assistance programs. Further research is warranted to explore the causal pathways of this relationship for informing policies to mitigate the burden of long COVID.

Effect of a pragmatic lifestyle modification intervention on physical activity levels and body mass index among obese and overweight adolescents in Udupi, India: a cluster randomized trial.

Determine the effects of a multifactorial lifestyle intervention on physical activity (PA), BMI and health-related quality of life (QoL) in obese and overweight adolescents. Nine schools in India were clustered randomly in a 12-month study with students allocated to a multifactorial intervention (MFI), or exercise only (EX) or control (CON) group. Participants were adolescents aged 11-16 years (n=671). In the MFI group, adolescents and their parents received lifestyle education using a validated booklet combined with a PA intervention for school students. The EX group received school-based PA only; the CON group continued regular activities. Primary outcomes were PA levels measured with the PAQ-A, and BMI; the secondary outcome was health-related QoL. A linear regression statistical model was used to analyse time, group effects and interactions, with Bonferroni correction for within-group differences at baseline (T0) and at 12-weeks (T1) (post-intervention), 6-month (T2) and 12-month (T3) follow-ups. Significant time and group effects observed for all groups with PA scores (p<0.001), with MFI group having largest increase in PA; with BMI (p<0.001) and MFI showing the least gain in BMI; and HRQOL (p<0.001), with MFI group showing greatest improvement in scores. There were significant increases in PA at T1 and T3 time-points with the EX group, and at T3 time-point only for MFI and CON, with MFI group showing largest increase in HRQOL scores. BMI increased significantly for all groups at T2 (MFI p=0.001, EX p<0.001) and T3 (p<0.001), while HRQOL increased significantly for both MFI and EX at both follow-ups (p<0.001). School-based lifestyle MFI was more effective for improving PA, lifestyle behaviours and HRQOL than exercise alone for adolescents, although BMI was not reduced. MFI with PA could be an effective school-based approach for behaviour modification but BMI has limitations for measuring body composition changes. CTRI/2019/04/018834 (30/04/2019).