scholarly journals Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data (Preprint)

2018 ◽  
Author(s):  
Afsaneh Doryab ◽  
Daniella K Villalba ◽  
Prerna Chikersal ◽  
Janine M Dutcher ◽  
Michael Tumminia ◽  
...  
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Data Mining ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Behavioral Patterns ◽  
Machine Learning Classification ◽  
Passive Sensing ◽  
The Mean ◽  
The Impact

BACKGROUND Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

scholarly journals Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

10.2196/13209 ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. e13209 ◽  
Author(s):  
Afsaneh Doryab ◽  
Daniella K Villalba ◽  
Prerna Chikersal ◽  
Janine M Dutcher ◽  
Michael Tumminia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Analysis ◽  
Social Isolation ◽  
Analysis Data ◽  
Behavioral Phenotypes ◽  
Passive Sensing

scholarly journals Stiffness and Strength of Stabilized Organic Soils—Part I/II: Experimental Database and Statistical Description for Machine Learning Modelling

Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 243
Author(s):  
Hernandez-Martinez Francisco G. ◽  
Al-Tabbaa Abir ◽  
Medina-Cetina Zenon ◽  
Yousefpour Negin
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Portland Cement ◽  
Soil Stabilization ◽  
Data Availability ◽  
Data Repository ◽  
Organic Soils ◽  
Experimental Database ◽  
Soil Mixing ◽  
The Impact

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.

MMPI-2 Scores of Puerto Rican, Mexican, and U.S. Latino College Students: A Research Note

2000 ◽  
Vol 87 (1) ◽  
pp. 266-268 ◽  
Author(s):  
Jose J. Cabiya ◽  
Denise A. Chavira ◽  
Francisco C. Gomez ◽  
Emilia Lucio ◽  
Jeanett Castellanos ◽  
...  
Keyword(s):  
United States ◽  
College Students ◽  
Puerto Rico ◽  
Standard Deviation ◽  
Puerto Rican ◽  
The United States ◽  
Clinical Scale ◽  
The Mean ◽  
The One ◽  
Scale Data

In this brief report, we present MMPI-2 basic validity and clinical scale data of Latino-descent persons from Puerto Rico ( n = 290), Mexico ( n = 1,920), and the United States ( n = 28). All were administered one of three Spanish translations of the MMPI-2. A review of the mean scores of these respective groups indicates similarities across all scales. Differences among these three groups, with the exception of the Mf scale (which is keyed to sex), were well within the one standard deviation band. More importantly, these findings are promising given the fact that three different translations of the MMPI-2 were applied.

Factors associated with mobility apprehension in persons with lower limb amputation

2020 ◽  
Vol 44 (4) ◽  
pp. 208-214
Author(s):  
Shannon L Mathis
Keyword(s):  
Standard Deviation ◽  
Lower Limb ◽  
Avoidance Behavior ◽  
Pain Catastrophizing ◽  
Lower Limb Amputation ◽  
Limb Amputation ◽  
Walking Ability ◽  
Factors Associated ◽  
The Mean ◽  
The Impact

Background: Factors that are related to mobility apprehension were measured in a sample of persons with lower-limb amputation. Objectives: The purpose was to determine whether intensity, interference, or catastrophizing are associated with mobility apprehension. Study design: Cross-sectional study. Methods: Persons with amputation of a lower limb who were attending a national limb loss conference were recruited to complete a survey. Subjects were administered the Tampa Scale for Kinesiophobia to measure mobility apprehension. The Brief Pain Inventory was administered to quantify the affect of pain on general activity, walking ability, and enjoyment of life. The Pain Catastrophizing Scale was administered to assess the tendency to ruminate and magnify pain sensations. A multivariable linear regression was performed to determine factors associated with mobility apprehension. Results: Fifty-three people with lower-limb amputation participated in the study. The mean (standard deviation) score for mobility apprehension was 34.2 (6.0). Mean (standard deviation) pain intensity and interference scores were 1.6 (1.7) and 2.5 (2.6), respectively. The mean (standard deviation) pain catastrophizing score was 9.1 (10). Pain catastrophizing was the only variable associated with higher mobility apprehension ( β = 0.31, p < 0.001, R2 = 0.32). Results suggest that for every one-point increase in the pain catastrophizing score, mobility apprehension will increase by 0.3 of a point. Conclusion: These preliminary results suggest that pain catastrophizing was related to mobility apprehension in this cohort of persons with lower-limb amputation. This relationship indicates that the exploration of avoidance behaviors, such as pain catastrophizing, may be useful when developing a program for physical rehabilitation. Clinical relevance Pain catastrophizing, an avoidance behavior, may be associated with higher levels of mobility apprehension in persons with major lower-limb amputation. Understanding the impact of fear-avoidance behavior will allow clinicians to identify individuals at risk for poor outcomes following amputation surgery and to develop psychological strategies to complement treatment.

scholarly journals Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Cristiana Valente ◽  
Elisa D’Alessandro ◽  
Michele Iester
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Visual Field ◽  
Trend Analysis ◽  
Open Angle Glaucoma ◽  
Mean Deviation ◽  
Threshold Strategy ◽  
Visual Field Progression ◽  
The Mean

Aim. To evaluate the agreement between different methods in detection of glaucomatous visual field progression using two classification-based methods and four statistical approaches based on trend analysis. Methods. This is a retrospective and longitudinal study. Twenty Caucasian patients (mean age 73.8 ± 13.43 years) with open-angle glaucoma were recruited in the study. Each visual field was assessed by Humphrey Field Analyzer, program SITA standard 30-2 or 24-2 (Carl Zeiss Meditec, Inc., Dublin, CA). Full threshold strategy was also accepted for baseline tests. Progression was analyzed by using Hodapp–Parrish–Anderson classification and the Advanced Glaucoma Intervention Study visual field defect score. For the statistical analysis, linear regression (r2) was calculated for mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), and when it was significant, each series of visual field was considered progressive. We also used Progressor to look for a significant progression of each visual field series. The agreement between methods, based on statistical analysis and classification, was evaluated using a weighted kappa statistic. Results. Thirty-eight visual field series were analyzed. The mean follow-up time was 6.2 ± 1.53 years (mean ± standard deviation). At baseline, the mean MD was −7.34 ± 7.18 dB; at the end of the follow-up, the mean MD was −9.25 ± 8.65 dB; this difference was statistically significant (p<0.001). The agreement to detect progression was fair between all methods based on statistical analysis and classification except for PSD r2. A substantial agreement (κ = 0.698 ± 0.126) was found between MD r2 and VFI r2. With the use of all the statistical analysis, there was a better time-saving. Conclusions. The best agreement to detect progression was found between MD r2 and VFI r2. VFI r2 showed the best agreement with all the other methods. GPA2 can help ophthalmologists to detect glaucoma progression and to help in treatment decisions. PSD r2 was the worse method to detect progression.

scholarly journals The Effect of Re-engineering of Administrative Processes in Achieving the Competitive Advantage of Sustainable in Five-Star Hotels in Jordan - Field Study

2019 ◽  
Vol 11 (1) ◽  
pp. 96
Author(s):  
Suleiman Mustafa EL-Dalahmeh
Keyword(s):  
Information Technology ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Competitive Advantage ◽  
Organizational Communication ◽  
Sustainable Competitive Advantage ◽  
Statistical Effect ◽  
The Mean ◽  
Level Of Significance ◽  
Very High

The main aim of the research was to identify the effect of Re-engineering of Administration Processes in Achieving the competitive Advantage of Sustainable in Five Star Hotels in Jordan. To achieve the objective of this study, a questionnaire distributed on 120 persons in Five Star Hotels in Jordan. 90 returned with a rate of 75%. The results of the study showed that there is a significant statistical effect at the level of significance of α ≤ 0.05 for the re - engineering of administrative processes in achieving sustainable competitive advantage in five - star hotels in Jordan in the following dimensions:* - Leadership * - Ability to analyze * - Advanced design * - Organizational communication * - Continuous improvementStrategic Planning. The total score of the mean of the study instrument was 4.43 and with a standard deviation of 0.35 and 88.6% at a very high degree.The results of the statistical analysis revealed the realization of the sample of the study in the investigated hotels, the extent of the effect of re-engineering the administrative processes in all its dimensions in achieving the competitive advantage.Based on the results of hypotheses tested, the six null hypotheses of the study were rejected. In the light of the findings, the researcher recommended that:1- The need to convince the management of hotels and hotel staff the importance of the application of re-engineering of administrative processes to achieve competitive advantage sustainable2- Utilizing the potential of graduates of new universities from the faculties of economics, administrative sciences and information technology. 

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

2020 ◽  
Author(s):  
Alex Sun ◽  
Bridget Scanlon ◽  
Himanshu Save ◽  
Ashraf Rateb
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Large Scale ◽  
Water Storage ◽  
Total Water ◽  
New Paradigm ◽  
Satellite Mission ◽  
Automated Machine Learning ◽  
The Mean ◽  
The Impact

&lt;p&gt;The GRACE satellite mission and its follow-on, GRACE-FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The approximately one-year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. There is strong interest in using machine learning (ML) algorithms to reconstruct GRACE-like data to fill this gap. So far, most studies attempted to train and select a single ML algorithm to work for global basins. However, hydrometeorological predictors may exhibit strong spatial variability which, in turn, may affect the performance of ML models. Existing studies have already shown that no single algorithm consistently outperformed others over all global basins. In this study, we applied an automated machine learning (AutoML) workflow to perform GRACE data reconstruction. AutoML represents a new paradigm for optimal model structure selection, hyperparameter tuning, and model ensemble stacking, addressing some of the most challenging issues related to ML applications. We demonstrated the AutoML workflow over the conterminous U.S. (CONUS) using six types of ML algorithms and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML-assisted gap filling achieved satisfactory performance over the CONUS. For the testing period (2014/06&amp;#8211;2017/06), the mean gridwise Nash-Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root-mean square error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE-FO periods (after 2017/06). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end-to-end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large-scale hydrological prediction systems and when predictor importance exhibits strong spatial variability.&lt;/p&gt;

scholarly journals Validation of SCIAMACHY HDO/H<sub>2</sub>O measurements using the TCCON and NDACC-MUSICA networks

2015 ◽  
Vol 8 (4) ◽  
pp. 1799-1818 ◽  
Author(s):  
R. A. Scheepmaker ◽  
C. Frankenberg ◽  
N. M. Deutscher ◽  
M. Schneider ◽  
S. Barthlott ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Hydrological Cycle ◽  
Source Region ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
The Tibetan Plateau ◽  
Wet Season ◽  
Data Set ◽  
The Mean ◽  
The Impact

Abstract. Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of −35 ± 30‰ compared to TCCON and −69 ± 15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (∼ −60 to −80‰) at the highest latitudes and smallest (∼ −20 to −30‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ∼ 115‰, which is reduced by a factor ∼ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity.

scholarly journals Terapi psikoreligi terhadap penurunan perilaku kekerasan pada pasien Skizofrenia di ruang rawat inap Rumah Sakit Jiwa Daerah Provinsi Lampung

2020 ◽  
Vol 13 (4) ◽  
pp. 373-380
Author(s):  
Teguh Pribadi ◽  
Djunizar Djamaludin
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Mental Hospital ◽  
Violent Behavior ◽  
Hospital Management ◽  
P Value ◽  
Spiritual Power ◽  
Research Instrument ◽  
Inpatient Ward ◽  
The Mean

Psycho-religious therapy in reducing violent behavior among   patients  with  SchizophreniaBackground: The main problem that often occurs to patients with schizophrenia is violent behavior. Violent behavior is a state in which a person performs a physically harmfulserve to himself or to another person. Psychoreligious therapy is a therapy that is usually through a religiousreligion approach such as client believed according their riligion   and tends to touch the spiritual side of man and also rather to awaken the spiritual power against of with illness. Data reported to Lampung Mental Hospital, the trend  number of violent behavior  was increasing and data in 2018 as many as 194 patients with violent behavior.Purpose: Knowing effectiveness of psycho-religious therapy in reducing violent behavior among patients with schizophrenia Lampung Mental Hospital 2019Methods: A quantitative and quasi experiment with two group pretest postest design. the population of this study were all patients with violent behavior in the inpatient ward of Lampung mental hospital as many as 30 patients. this research instrument used observation sheet. statistical analysis used t dependent tests.Result: Finding the mean score of violent behavior behavior before psychoreligious therapy were of 16.87 with a standard deviation of 1.46, and after psycho religious  therapy was 13.0 with a standard deviation of 1.0.Conclusion: There was an influence of psycho religious therapy in reducing violent behavior inpatients with schizophrenia (p value 0,000).Suggestion: The hospital management to apply psychoreligious therapy programs to patients with  schizophrenia specially patients has risk of violent behaviorKeywords: Psychoreligious Therapy; Violence Behavior; SchizophreniaPendahuluan: Permasalahan utama yang sering terjadi pada pasien Skizofrenia adalah perilaku kekerasan. Perilaku kekerasan adalah suatu keadaan dimana seseorang melakukan tindakan yang membahayakan secara fisik, baik kepada diri sendiri, maupun orang lain. Terapi psikoreligius adalah terapi yang biasanya melalui pendekatan  keagamaan yang dianut oleh klien dan cenderung untuk menyentuh sisi  spiritual manusia, untuk membangkitkan kekuatan spiritual dalam menghadapi penyakit yang dideritanya  Dari data yang diperoleh dari Rumah Sakit Jiwa Daerah Provinsi Lampung jumlah pasien dengan prilaku kekerasan  cenderung meningkat dan data di tahun 2018 sebanyak 194 pasien dengan perilaku kekerasan.Tujuan: Mengetahui pengaruh terapi psikoreligi terhadap penurunan perilaku kekerasan pada pasien skizopfrenia di ruang rawat inap  Rumah Sakit Jiwa Daerah  Provinsi Lampung Tahun 2019.Metode: Penelitian kuantitatif menggunakan Quasi Eksperiment dengan two group pretest postest. Populasi dan sampelnya seluruh pasien dengan perilaku kekerasan di Ruang Rawat Inap Rumah Sakit Jiwa Daerah Provinsi Lampung  sebanyak 30 pasien. Instrumen penelitian ini menggunakan lembar observasi. Analisastatistik yang digunakan yaitu uji t Dependen.Hasil: Didapatkan nilai rata-rata skor perilaku kekerasan Sebelum Terapi psikoreligi adalah 16,87 dengan standar deviasi 1,46, rata-rata Skor perilaku kekerasan sesudah Terapi psikoreligi adalah 13.0 dengan standar deviasi 1,0.Simpulan: Ada pengaruh terapi psikoreligi terhadap penurunan perilaku kekerasan pada pasien skizopfrenia di Ruang Rawat Inap  Rumah Sakit Jiwa Daerah  Provinsi Lampung Tahun 2019 (p value 0,000).Saran: Ditujukan kepada pihak manajemen rumah sakit agar menerapakan program terapi psikoreligius pada pasien schizophrenia dengan perilaku kekerasan.

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