scholarly journals Kesehatan Mental dan Perubahan Aktivitas-Perjalanan Saat Pandemi COVID-19 di Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 125-135
Author(s):  
Muhamad Rizki ◽  
Dwi Prasetyanto ◽  
Andrean Maulana
Keyword(s):  
Mental Health ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Well Being ◽  
The Internet ◽  
Online Data ◽  
Daily Lives ◽  
Online Data Collection

ABSTRAKPandemi COVID-19 telah secara signifikan mempengaruhi bagaimana kita menjalani kehidupan sehari-hari kita. Studi ini bertujuan untuk menginvestigasi dampak perubahan kesehatan mental kepada perubahan aktivitas dan perjalanan saat pandemi di Indonesia. Convenient sampling digunakan untuk menentukan jumlah sampel dan pengumpulan data dilakukan secara online pada masa pandemi dengan kuesioner. Adapun metode regresi linear berganda digunakan untuk menganalisis data. Hasil analisis menujukkan bahwa telah terjadinya perubahan aktivitas dan perjalanan akibat dari pandemi COVID-19. Tipe kesehatan mental seperti depresi dan bosan sangat berkaitan dengan penurunan pola perjalanan, sedangkan kelelahan berkaitan dengan berkurangnya kegiatan berbasis online. Studi ini juga menemukan bahwa masyarakat berpendapatan tinggi cenderung memiliki akses lebih baik terhadap platform online dan melakukan kegiatan online lebih banyak. Kelompok tersebut juga cenderung mengurangi perjalanan keluar tempat tinggal. Studi ini merekomendasikan pembenahan kualitas internet dan pembangunan fasilitas aktif (taman) dekat tempat tinggal untuk mengendalikan pandemi bersamaan dengan menjaga penurunan kesehatan mental.Kata kunci: COVID-19, Aktivitas, Perjalanan, Regresi linear berganda ABSTRACTThe COVID-19 pandemic has significantly affected how we do our daily lives. This study aims to investigate the effect of emotional well-being to the changes in activity and travel during COVID-19 pandemic in Indonesia. Convenient sampling is used for dermine sampling size and online data collection was carried out during a pandemic using a questionnaire. Moreover, the multiple linear regression model is used for data analysis. It is found that there has been a change in activity and travel as a result of the COVID-19 pandemic. The results of the analysis show that several issues related to mental health, such as depression and boredom, are strongly associated with the decrease of out-of-home activities, while fatigue is associated with a lower ICT activities. This study also found that high-income people, which have higher accessibility to ICT, tend to do more online activities and also reduce their out-of-home activities during pandemic. This study proposeimproving the quality of the internet and building active facilities (parks) near residential location to manage the pandemic while maintaining a decline in mental health.Keywords: COVID-19, Activity, Travel, Multiple linear regression

scholarly journals Selection of Appropriate Model for Quality of Life Data of Strabismus Patients Despite Censorship

2021 ◽  
Author(s):  
Zahra Ghassemi ◽  
Mehdi Yaseri ◽  
Mostafa Hosseini
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Tobit Model ◽  
P Value ◽  
Response Variable

Introduction: Previous studies on the quality of life of strabismus patients have not examined the existence of censoring to express the relation between the response variable and its predictors. Methods & Materials: The information used in this study is a conducted cross-sectional study in 2012. The sample size is 90 children in the age range (4-18) years and with congenital strabismus. We used the RAND Health Insurance Study questionnaire with ten subscales to evaluate the quality of life, which was increased to 11 dimensions by adding some items related to eye alignment concerns introduced by Archer et al. The demographic profile is also recorded by 13 other questions. We have expressed the relationship between the independent and response variables in each of the 11 dimensions of the questionnaire and the overall quality of life score by fitting the multiple linear regression model. Then we fitted the two models of classic Tobit and CLAD, which are for censoring, to all dimensions of the questionnaire. Results: We showed that in fitting the models to the overall quality of life scale variable, the best model is the multiple linear regression. Because the response variable was normal, and there was no censoring (ceiling and floor effect). However, in the depression subscale, due to the high censoring (28.89% of the ceiling effect) and the almost normal distribution of the response variable (p-value of skewness< 0.05), the appropriate model according to the criteria is the classic Tobit (AIC = 546.33). That is, the classic Tobit model is the best alternative to the multiple linear regression model in the presence of censoring. But these conditions did not exist in all variables. In the subscale, there was a severe censoring performance constraint (67.78% of the ceiling effect). When censoring is high, the distribution of the response variable becomes very skewed, and the distribution of response variables deviates drastically from normal. The distribution of the performance constraint variable was very skewed (p-value <0.001). Here the RMSE standard scale for the classic Tobit model was 28.74, which is much higher than the standard scale for the multiple linear regression model (14.23). The best model for the high censoring was CLAD. Conclusion: To use the appropriate statistical method in the analysis, one must look at how the response variable is distributed. The multiple linear regression model is very widely used, but in the presence of censoring, the use of this model gives skewed results. In this case, the classic Tobit model and its derived model, CLAD, are replaced. The nonparametric CLAD model calculates accurate estimates with minimum defaults and censoring.

scholarly journals Bio-psychosocial factor of vaginismus in Iranian women

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Mojdeh Banaei ◽  
Nourossadat Kariman ◽  
Giti Ozgoli ◽  
Maliheh Nasiri
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Psychosocial Factor ◽  
Multiple Linear Regression Model ◽  
Sexual Life ◽  
Sexual Intimacy ◽  
Iranian Women ◽  
Self Image ◽  
The Mean

Abstract Background and aim Various physical, psychological, social and cultural factors contribute to vaginismus. Therefore, given the multidimensionality of this disorder and the need to pay more attention to all biological, psychological and social dimensions in its treatment, the present study was conducted to investigate the bio-psychological factors contributing to vaginismus. Methods This descriptive cross-sectional study was conducted on 180 Iranian women with vaginismus who had been referred to sexual health clinics of Tehran province in 2020. Multistage random sampling method was used in this study, and vaginismus was diagnosed in women by a specialist through using a questionnaire. Data collection tools included demographic and obstetric information form, valid and reliable Sexual Function Questionnaire, Depression Anxiety Stress Scales (DASS), Sex Fear Questionnaire, Vaginal Penetration Cognition Questionnaire, Sexual Self-Efficacy Scale, Sexual Knowledge and Attitude Scale, Sexual Quality of Life-Female, Inventory of Sexual Satisfaction, ENRICH Marital Satisfaction Scale, Sexual Intimacy Scale and Questionnaire for Diagnosis of Vaginismus. In order to determine the factors related to vaginismus, multiple linear regression model was used through SPSS software version 25 (SPSS Inc., Chicago, IL). Results Based on the results of the present study, the mean age of women and the mean duration of their marriage were 27.77 ± 5.36 and 4.07 ± 3.87 years respectively. As the results of multiple linear regression revealed, the variables of fear of sex (B = 0.141, P = 0.036), positive cognition (B = 0.197, P = 0.046), self-image (B = 0.651, P = 0.001), sexual intimacy (B = -0.116, P = 0.021), quality of sexual life (B = 0.115, P = 0.002) and education (B = 2.129, P = 0.024) from the bio-psychosocial model were the final predictors of vaginismus diagnosis score in women with this disorder. According to the results of multiple linear regression, 45.5% of the variance of vaginismus diagnosis total score was explained by these variables (R = 0.706, R2 = 0.498 and ADJ.R2 = 0.455). Conclusion The results of the present study showed that the variables of fear of sex, positive cognition and negative self-image, sexual intimacy, quality of sexual life and education were the final predictors of vaginismus diagnosis score. This disorder is, thus, considered to be multidimensional.

scholarly journals Finite-Difference Models Application for Short-Term Forecasting of the Natural Resource Potential of the Perm Region

2021 ◽  
Vol 21 (2) ◽  
pp. 154-166
Author(s):  
N.A. Sirotina ◽  
◽  
A.V. Kopoteva ◽  
A.V. Zatonskiy
Keyword(s):  
Linear Regression ◽  
Finite Difference ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Natural Resource ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Resource Potential ◽  
Predicted Values

The article is about a problem of mathematical modeling of the natural resource potential of the Perm Territory by 1st and 2nd order finite-difference models. Such models can obtain better forecasts of complex socio-economic processes in comparison with the traditionally used linear multiple regression models. A high quality model of the natural resource potential with forecast possibi¬lities is one of the necessary conditions for the effective management of the natural resources of the region in order to ensure its sustainable economic development. Purpose of work. Aim of this work is work construction of finite-difference models of a natural resource potential complex indicators and an assessment of their prognostic properties. Materials and methods. Our research is based on Perm region statistical data for the period from 2001 to 2018. A multiple linear regression model is used as a comparison base. The natural resource potential complex indicator is calculated as a weighted sum of particular criteria characterizing the natural resources of the region. First and second order finite difference models are obtained by adding autoregressive terms of the first and second orders, respectively, to the multiple linear regression model. An estimation of the unknown parameters of the equations is carried out by a modified least squares method, which preserves the signs of the coefficients with the factors the same as in the original linear model. At the same time, the selection of explanatory factors and the assessment of the quality of the models are carried out based on the accuracy of the predicted values of the studied indicator. The results of the study. Components and factors of the natural resource potential is obtained, and a procedure for constructing finite-difference models is performed for three different time intervals: 2001–2018, 2001–2008, and 2008–2018. These intervals are chooseen because changes in the methodology for generating statistical data nearly 2008. Discussion and conclusions. The number of calculated predicted values was 18, and only in 4 out of 18 cases (22,2%) their quality is worse than forecasts obtained by the linear multiple model. So proposed modification of the multiple linear regression model with the addition of autoregressive terms makes it possible to improve the forecasting quality of the complex indicator of the natural resource potential of the region and, therefore, to make more effective decisions when managing its level.

scholarly journals Physical Condition Factors that Predict a Better Quality of Life in Women with Fibromyalgia

2019 ◽  
Vol 16 (17) ◽  
pp. 3173 ◽  
Author(s):  
Sempere-Rubio ◽  
Aguilar-Rodríguez ◽  
Inglés ◽  
Izquierdo-Alventosa ◽  
Serra-Añó
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Functional Capacity ◽  
Muscular Strength ◽  
Pain Threshold ◽  
Handgrip Strength ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model

What physical qualities can predict the quality of life (QoL) in women with fibromyalgia (FM)? QoL is a very complex outcome affected by multiple comorbidities in people with fibromyalgia. This study aims to determine which physical qualities can predict the quality of life in women with FM. Also, a comparison between the physical qualities of women with FM and healthy counterparts was conducted. In total, 223 women participated in this cross-sectional study, 123 with FM, with ages ranging between 45 and 70 years. The study was conducted at several fibromyalgia associations and specialized medical units. QoL was measured as the main outcome. In addition, functional capacity, muscular strength, maintenance of thoracic posture, postural control, flexibility, pain threshold, and anxiety were measured. Prediction of the QoL was conducted with multiple linear regression analysis and comparison between groups, using the Mann–Whitney U test. There were significant differences between groups in all the variables measured (p < 0.01). The multiple linear regression model showed that factors influencing QoL in women with FM for all the variables measured were functional capacity, handgrip strength and bicep strength, maintenance of thoracic posture, pain threshold, and anxiety (R2 = 0.53, p < 0.05). To conclude, women with FM show a significantly lower QoL than their healthy counterparts, and the factors that predict their perceived QoL are functional capacity, muscular strength, postural maintenance, pain threshold, and anxiety.

PENGARUH LOKASI, KUALITAS PRODUK TABUNGAN, KUALITAS PELAYANAN TERHADAP KEPUASAN NASABAH DALAM MENABUNG DI BMT ADZKIYAH KHIDMATUL UMMAH DI KOTA METRO

2019 ◽  
Vol 3 (2) ◽  
pp. 439
Author(s):  
Suwarto Suwarto ◽  
Risa Anggraini
Keyword(s):  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Requirements Analysis ◽  
Primary Data ◽  
Homogeneity Test ◽  
Linearity Test

This research is motivated by customer satisfaction which is a customer action to save. The purpose of this study was to determine the ef ect of location, quality of savings products, service quality on customer satisfaction. In this study using primary data collected by explanatory research methods and sample collection techniques in the form of accidental sampling of BMT customers Adzkiyah Khidmatul Ummah using a questionnaire with a likert skla in BMT Adzkiyah Khidmatul Ummah in Metro City. Testing the instrument requirements used include validity, reliability testing. Requirements analysis using normality test, linearity test, homogeneity test. And analysis tools using multiple linear regression with partial test (t test), simultaneous test (f test), coef icient of determination test (R2). As testing requirements analysis and hypothesis testing. Based on the results of research using multiple linear regression analysis obtained location influences customer satisfaction, the quality of savings products does not af ect customer satisfaction, and service quality influences customer satisfaction.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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