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The importance of simulating patient behavior for medical assessment training has grown in recent decades due to the increasing variety of simulation tools, including standardized/simulated patients, humanoid and android robot-patients. Yet, there is still a need for improvement of current android robot-patients to accurately simulate patient behavior, among which taking into account their hearing loss is of particular importance. This paper is the first to consider hearing loss simulation in an android robot-patient and its results provide valuable insights for future developments. For this purpose, an open-source dataset of audio data and audiograms from human listeners was used to simulate the effect of hearing loss on an automatic speech recognition (ASR) system. The performance of the system was evaluated in terms of both word error rate (WER) and word information preserved (WIP). Comparing different ASR models commonly used in robotics, it appears that the model size alone is insufficient to predict ASR performance in presence of simulated hearing loss. However, though absolute values of WER and WIP do not predict the intelligibility for human listeners, they do highly correlate with it and thus could be used, for example, to compare the performance of hearing aid algorithms.

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Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device's companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users' privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices.

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Objectives: To identify the effectiveness of an android-based paediatric assessment triangle application in emergency diagnostics. METHODS: The action research study was conducted in the emergency department of a hospital under the Ministry of Defence, Indonesia, located within the Ministry of Defence Rehabilitation Centre Complex, from April to December 2020 after approval from the ethics review committee of the Faculty of Nursing, Universitas Indonesia, Indonesia, employing quantitative and qualitative methods consisting of planning, acting, observing and reflecting stages. Emergency department nurses with at least D3 nursing graduation who were able to identify emergency status in children were included. The subjects were given training on paediatric assessment triangle application before using it in their professional life. The difference was noted through pre- and post-intervention tests. Qualitative data was collected using focus group discussion and system usability scale. RESULTS: Of the 9 nurses, 5(55.6%) were males, 4(44.4%) were females, 8(88.9%) were aged 26-35 years, and 2(22.2%) had professional experience 1-2 years. The mean baseline score was 36.1±11.4, while the mean post-intervention score was 70.9±14.4. The fastest application completion time was 13 seconds, while the slowest was 52 seconds. Qualitative data led to the emergence of 4 themes: time required to complete the application; preference for connectivity with the hospital's electronic record system; assessment of children's clinical status; and, unfamiliarity with the computerised system. The mean system usability scale score was 72.22±11.35 (range: 52.5-92.5). CONCLUSIONS: Paediatric assessment triangle application could be a valid tool for identifying emergency severity in patients during the triage process.

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Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

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Background: Fat distribution plays an important role in impaired glucose tolerance. Android adiposity (ANDROID) and gynoid adiposity (GYNOID) have been proven to be linked with insulin resistance. A higher risk of sarcopenia is associated with type 2 diabetes mellitus (T2DM). In this study, ANDROID, GYNOID, and ANDROID to GYNOID ratios (A/G ratios) were evaluated in T2DM patients to determine if they were associated with sarcopenia. Methods: We recruited 1086 T2DM patients, measured skeletal muscle index (SMI), ANDROID, GYNOID, and collected clinical data. Results: T2DM patients with 119 male subjects had sarcopenia (20.24%), and 72 female subjects had sarcopenia (16.51%). All patients with T2DM who had high ANDROID and A/G ratios were at a reduced risk of sarcopenia. The SMI showed a correlation with ANDROID and A/G ratios among subjects with T2DM. Conclusion: ANDROID and A/G ratios are inversely related to sarcopenia in T2DM patients.

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In behavioral sciences, there is growing concern about the inflation of false-positive rates due to the amount of under-powered studies that have been shared in the past years. While problematic, having the possibility to recruit (lots of) participants (for a lot of time) is realistically not achievable for many research facilities. Factors that hinder the reaching of optimal sample sizes are, to name but a few, research costs, participants' availability and commitment, and logistics. We challenge these issues by introducing PsySuite, an Android app designed to foster a remote approach to multimodal behavioral testing. To validate PsySuite, we first evaluated its ability to generate stimuli appropriate to rigorous psychophysical testing, measuring both the app's accuracy (i.e., stimuli's onset, offset, and multimodal simultaneity) and precision (i.e., the stability of a given pattern across trials), using two different smartphone models. We then evaluated PsySuite's ability to replicate perceptual performances obtained using a classic psychophysical paradigm, comparing sample data collected with the app against those measured via a PC-based setup. Our results showed that PsySuite could accurately reproduce stimuli with a minimum duration of 7 ms, 17 ms, and 30 ms for the auditory, visual, and tactile modalities, respectively, and that perceptual performances obtained with PsySuite were consistent with the perceptual behavior observed using the classical setup. Combined with the high accessibility inherently supported by PsySuite, here we ought to share the app to further boost psychophysical research, aiming at setting it to a cheap, user-friendly, and portable level.

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Background and Objectives: The literature suggests that physiological menopause (MP) seems linked with increased adiposity with a preference for intra-abdominal fat accumulation, greater than what can be attributed only by aging, which could magnify this period's increased cardiovascular risk. Materials and Methods: We retrospectively analyzed two age and body mass index (BMI) propensity-matched subgroups each formed of 90 clinically healthy, 40-60-year-old postmenopausal women, within the first 5 and 5-10 years of MP. The 10-year ASCVD risk was assessed using medical history, anthropometric data, and lipid profile blood tests. The android-to-gynoid (A/G) ratio was computed using Lunar osteodensitometry lumbar spine and hip scans. Results: The A/G ratio was significantly higher for the subgroup evaluated in years 5-10 of MP than in the first 5 years of MP, even after controlling for BMI (1.05 vs. 0.99, p = 0.005). While displaying a significant negative correlation with HDL cholesterol (r = 0.406), the A/G ratio also had positive correlations with systolic blood pressure (BP) values (r = 0.273), triglycerides (r = 0.367), and 10-year ASCVD risk (r = 0.277). After adjusting for smoking, hypertension treatment, and type 2 diabetes, the 10-year ASCVD risk became significantly different for women in the first 5 years (3.28%) compared to those in years 5-10 of MP (3.74%), p = 0.047. Conclusions: In women with similar age and BMI, the A/G ratio appears to vary based on the number of years since menopause onset and correlates with either independent cardiovascular risk parameters like BP, triglycerides, and HDL cholesterol or with composite scores, such as 10-year ASCVD risk.

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In the digitization era, the battery consumption factor plays a vital role for the devices that operate Android software, expecting them to deliver high performance and good maintainability.The study aims to analyze the Android-specific code smells, their impact on battery consumption, and the formulation of a mathematical model concerning static code metrics hampered by the code smells. We studied the impact on battery consumption by three Android-specific code smells, namely: No Low Memory Resolver (NLMR), Slow Loop (SL) and Unclosed Closable, considering 4,165 classes of 16 Android applications. We used a rule-based classification method that aids the refactoring ideology. Subsequently, multi-linear regression (MLR) modeling is used to evaluate battery usage against the software metrics of smelly code instances. Moreover, it was possible to devise a correlation for the software metric influenced by battery consumption and rule-based classifiers. The outcome confirms that the refactoring of the considered code smells minimizes the battery consumption levels. The refactoring method accounts for an accuracy of 87.47% cumulatively. The applied MLR model has an R-square value of 0.76 for NLMR and 0.668 for SL, respectively. This study can guide the developers towards a complete package for the focused development life cycle of Android code, helping them minimize smartphone battery consumption and use the saved battery lives for other operations, contributing to the green energy revolution in mobile devices.

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Pomegranate is an important fruit crop that is usually managed manually through experience. Intelligent management systems for pomegranate orchards can improve yields and address labor shortages. Fast and accurate detection of pomegranates is one of the key technologies of this management system, crucial for yield and scientific management. Currently, most solutions use deep learning to achieve pomegranate detection, but deep learning is not effective in detecting small targets and large parameters, and the computation speed is slow; therefore, there is room for improving the pomegranate detection task. Based on the improved You Only Look Once version 5 (YOLOv5) algorithm, a lightweight pomegranate growth period detection algorithm YOLO-Granada is proposed. A lightweight ShuffleNetv2 network is used as the backbone to extract pomegranate features. Using grouped convolution reduces the computational effort of ordinary convolution, and using channel shuffle increases the interaction between different channels. In addition, the attention mechanism can help the neural network suppress less significant features in the channels or space, and the Convolutional Block Attention Module attention mechanism can improve the effect of attention and optimize the object detection accuracy by using the contribution factor of weights. The average accuracy of the improved network reaches 0.922. It is only less than 1% lower than the original YOLOv5s model (0.929) but brings a speed increase and a compression of the model size. and the detection speed is 17.3% faster than the original network. The parameters, floating-point operations, and model size of this network are compressed to 54.7%, 51.3%, and 56.3% of the original network, respectively. In addition, the algorithm detects 8.66 images per second, achieving real-time results. In this study, the Nihui convolutional neural network framework was further utilized to develop an Android-based application for real-time pomegranate detection. The method provides a more accurate and lightweight solution for intelligent management devices in pomegranate orchards, which can provide a reference for the design of neural networks in agricultural applications.

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A growing dependence on real-time positioning apps for navigation, safety, and location-based services necessitates a deep understanding of latency challenges within cloud-based Global Navigation Satellite System (GNSS) solutions. This study analyses a GNSS real-time positioning app on smartphones that utilizes cloud computing for positioning data delivery. The study investigates and quantifies diverse latency contributors throughout the system architecture, including GNSS signal acquisition, data transmission, cloud processing, and result dissemination. Controlled experiments and real-world scenarios are employed to assess the influence of network conditions, device capabilities, and cloud server load on overall positioning latency. Findings highlight system bottlenecks and their relative contributions to latency. Additionally, practical recommendations are presented for developers and cloud service providers to mitigate these challenges and guarantee an optimal user experience for real-time positioning applications. This study not only elucidates the complex interplay of factors affecting GNSS app latency, but also paves the way for future advancements in cloud-based positioning solutions, ensuring the accuracy and timeliness critical for safety-critical and emerging applications.

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OBJECTIVES: Recognition of mobile applications within encrypted network traffic holds considerable effects across multiple domains, encompassing network administration, security, and digital marketing. The creation of network traffic classifiers capable of adjusting to dynamic and unforeseeable real-world settings presents a tremendous challenge. Presently available datasets exclusively encompass traffic data obtained from a singular network environment, thereby restricting their utility in evaluating the robustness and compatibility of a given model. DATA DESCRIPTION: This dataset was gathered from 60 popular Android applications in five different network scenarios, with the intention of overcoming the limitations of previous datasets. The scenarios were the same in the applications set but differed in terms of Internet service provider (ISP), geographic location, device, application version, and individual users. The traffic was generated through real human interactions on physical devices for 3-15 min. The method used to capture the traffic did not require root privileges on mobile phones and filtered out any background traffic. In total, the collected dataset comprises over 48 million packets, 450K bidirectional flows, and 36 GB of data.

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Introduction: In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. Methods: G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Results: Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. Discussion: This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.

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BACKGROUND: The android-to-gynoid fat ratio (A/G ratio), an emerging indicator of obesity independent of body mass index (BMI), has yet to be conclusively associated with arterial stiffness in type 2 diabetes mellitus (T2DM). This study aimed to construct a nomogram to estimate arterial stiffness risk in diabetics and explore the interaction effect between A/G ratio and traditional obesity indicators on arterial stiffness. METHODS: 1313 diabetics were divided into 2 groups based on arterial stiffness identified by brachial ankle pulse wave velocity (baPWV), and demographic and clinical features were measured. The LASSO and multivariate logistics regression were used to develop the nomogram. Calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) were applied to assess calibration and clinical usefulness. Interaction effect analysis was performed to quantify the interactive relationship of A/G ratio and obesity indicators on arterial stiffness. RESULTS: 6 independent predictors (age, gender, A/G ratio, SBP, LDL-C and HbA1C) were screened to construct a nomogram prediction model. The calibration curve demonstrated satisfactory agreement between predicted and actual probability, and the nomogram exhibited clinical beneficial at the threshold between 8 % and 95 % indicated by DCA. The area under curve (AUC) was 0.918 and 0.833 for training and external set, respectively. Further investigation revealed A/G ratio and BMI acted positively synergistically towards arterial stiffness, and in BMI-based subgroup analysis, elevated A/G ratio was a significant risk factor for arterial stiffness, especially in normal BMI. CONCLUSIONS: A/G ratio showed a substantial association with arterial stiffness, and the nomogram, incorporating age, gender, A/G ratio, SBP, LDL-C, and HbA1c, exhibited high predictive value. A/G ratio measurement in BMI-normal individuals assisted in identifying cardiovascular diseases early.

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The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

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Given the high relevance and impact of ransomware in companies, organizations, and individuals around the world, coupled with the widespread adoption of mobile and IoT-related devices for both personal and professional use, the development of effective and efficient ransomware mitigation schemes is a necessity nowadays. Although a number of proposals are available in the literature in this line, most of them rely on machine-learning schemes that usually involve high computational cost and resource consumption. Since current personal devices are small and limited in capacities and resources, the mentioned schemes are generally not feasible and usable in practical environments. Based on a honeyfile detection solution previously introduced by the authors for Linux and Window OSs, this paper presents a ransomware detection tool for Android platforms where the use of trap files is combined with a reactive monitoring scheme, with three main characteristics: (i) the trap files are properly deployed around the target file system, (ii) the FileObserver service is used to early alert events that access the traps following certain suspicious sequences, and (iii) the experimental results show high performance of the solution in terms of detection accuracy and efficiency.

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BACKGROUND: The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. OBJECTIVE: This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. METHODS: Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. RESULTS: Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = ß0 + ß1 × notification frequency + ß2 × GPS use + ß3 × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. CONCLUSIONS: The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

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BACKGROUND: Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis. METHODS: This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants' kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied. RESULTS: Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively). CONCLUSION: This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

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INTRODUCTION: WHO estimated the prevalence of disabling hearing loss is 5% of the world population (430 million). People with a risk of hearing loss from noise exposure, ototoxic drugs, and comorbidities need regular hearing assessments. It is done by pure tone audiometry (PTA), requiring a skilled audiologist, special equipment, and a soundproof room. Modern technologies can help in overcoming these barriers. This study aimed to fill the lacuna by developing a new android-based application "Shravana Mitra" (Hearing companion) with features of both air conduction (AC) and bone conduction (BC) testing. OBJECTIVES: To develop, corroborate and compare smartphone application-based audiometry with PTA. METHODOLOGY: This study was done in three phases -(i) development of a mobile application, (ii) app validation in healthy individuals (iii) testing and comparison of results with PTA in individuals visiting OPD. The third phase was done as a cross-sectional observational study including 780 individuals visiting OPD of 10-60 years of age. RESULTS: The mean age of the study population was 32.89 years with female preponderance (57%). In AC testing, 83% of the pure tone average of the mobile application was within 5 dB of PTA thresholds and 99% was within 10 dB and for BC testing, 81% was within 5 dB of PTA thresholds and 98% within 10 dB. CONCLUSION: Our user-friendly mobile application- Shravana Mitra is the first Indian application available in the google play store with both AC & BC testing, multiple language options and accuracy similar to PTA. Thus, it can be used as the best hearing screening tool in camps, high-risk individuals, or any healthcare setup requiring initial hearing assessment.

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In recent years, the development of robots that can engage in non-task-oriented dialogue with people, such as chat, has received increasing attention. This study aims to clarify the factors that improve the user's willingness to talk with robots in non-task oriented dialogues (e.g., chat). A previous study reported that exchanging subjective opinions makes such dialogue enjoyable and enthusiastic. In some cases, however, the robot's subjective opinions are not realistic, i.e., the user believes the robot does not have opinions, thus we cannot attribute the opinion to the robot. For example, if a robot says that alcohol tastes good, it may be difficult to imagine the robot having such an opinion. In this case, the user's motivation to exchange opinions may decrease. In this study, we hypothesize that regardless of the type of robot, opinion attribution affects the user's motivation to exchange opinions with humanoid robots. We examined the effect by preparing various opinions of two kinds of humanoid robots. The experimental result suggests that not only the users' interest in the topic but also the attribution of the subjective opinions to them influence their motivation to exchange opinions. Another analysis revealed that the android significantly increased the motivation when they are interested in the topic and do not attribute opinions, while the small robot significantly increased it when not interested and attributed opinions. In situations where there are opinions that cannot be attributed to humanoid robots, the result that androids are more motivating when users have the interests even if opinions are not attributed can indicate the usefulness of androids.

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OBJECTIVE: Inflammation has a crucial role in several metabolic diseases, such as obesity. The author aimed to investigate the relationship between the system inflammation response index (SIRI) and android fat composition and distribution. METHODS: Data for individuals aged 8-59 years, SIRI, android percent fat, and android-to-gynoid ratio from the 2017 to 2018 National Health and Nutrition Examination Survey were used. Weighted multiple linear regression and smooth curve fitting were used to test for linear and nonlinear associations. Additional subgroup analyses were performed. Threshold effect analysis was performed using a two-linear regression model. RESULTS: Multiple linear regression showed a positive correlation between SIRI and android percent fat (ß 0.92, 95% confidence interval [CI] 0.25-1.59) and between SIRI and the android-to-gynoid ratio (ß 0.01, 95% CI 0.00-0.03) in 3783 Americans aged 8-59 years. The results showed that the effect of factors, other than smoking status, on the relationship between SIRI and android percent fat and android-to-gynoid ratio was not significant. There was a nonlinear relationship between SIRI and both android percent fat and android-to-gynoid ratio. CONCLUSIONS: Elevated SIRI levels were associated with an increased android percent fat and android-to-gynoid ratio. Larger prospective studies are needed to validate the findings.

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