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Cheng YL, Wu YR, Lin KD, Lin CHR, Lin IM. Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus. Healthcare (Basel) 2023; 11:healthcare11081141. [PMID: 37107975 PMCID: PMC10138388 DOI: 10.3390/healthcare11081141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients. The study utilized a database from Lin et al. (2022) that included 647 T2DM patients. Regression tree analysis was conducted to identify the interactions among risk factors that contribute to glycated hemoglobin (HbA1c) values, and various machine learning methods were compared for their accuracy in classifying T2DM patients. The results of the regression tree analysis revealed that high depression scores may be a risk factor in one subgroup but not in others. When comparing different machine learning classification methods, the random forest algorithm emerged as the best-performing method with a small set of features. Specifically, the random forest algorithm achieved 84% accuracy, 95% area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine learning methods can provide significant value in accurately classifying patients with T2DM when considering depression as a risk factor.
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Affiliation(s)
- Yi-Ling Cheng
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
| | - Ying-Ru Wu
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
| | | | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - I-Mei Lin
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 807378, Taiwan
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Li J, Sun J, Wang R, Cui T, Tong Y. Warming of surface water in the large and shallow lakes across the Yangtze River Basin, China, and its driver analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:20121-20132. [PMID: 36251192 DOI: 10.1007/s11356-022-23608-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
A variety of physical, chemical, and biological processes within the lakes relies on the surface water temperature while the spatial pattern of large lakes of different warming trends and their connections with climate change remain unclear. Using correlation analysis, regression tree analysis (RTA), and general linear models (GLMs), we have estimated the warming trends of 192 lakes since 2000 in the populated Yangtze River Basin, China, to identify dominant climate drivers and quantify their contributions. The results show that surface water temperature has increased substantially in the majority of the investigated lakes (179 from a total of 192 lakes) at a rate of 0.29 (- 0.12 to 0.62) °C/decade (median and 95% confidence interval). The shallower lakes (< 13.1 m in depth) usually have the faster median warming rates than the deeper lakes (i.e., 0.37 °C/decade versus 0.16 °C/decade). We find that in the shallow lakes, rising air temperatures and declining wind speeds can explain the majority of variation in surface water temperature (i.e., 31.4‒80.3% and 13.0‒21.0%, respectively). In contrast, in deeper lakes, change of air temperatures plays a dominant role in water warming (75.4‒91.2%). This study has emphasized the importance of declining wind speed in water warming in large and shallow lakes and illustrated a difference of dominant climatic drivers in water warming between the shallow and deep lakes.
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Affiliation(s)
- Jing Li
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
- Tianjin Geospatial Information Technology Engineering Center, Tianjin Normal University, Tianjin, 300387, China
| | - Jingjing Sun
- School of Environmental Sciences and Engineering, Tianjin University, Tianjin, 300072, China
| | - Ruonan Wang
- Sichuan Ecological Environment Monitoring Station, Chengdu, 610074, China
| | - Tiejun Cui
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
- Tianjin Geospatial Information Technology Engineering Center, Tianjin Normal University, Tianjin, 300387, China
| | - Yindong Tong
- School of Environmental Sciences and Engineering, Tianjin University, Tianjin, 300072, China.
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Emerson C, Fuller-Tyszkiewicz M, Orr R, Lyall K, Beswick L, Olive L, Skvarc D, Cummins RA, Mikocka-Walus A. Low Subjective Wellbeing Is Associated with Psychological Distress in People Living with Inflammatory Bowel Disease. Dig Dis Sci 2022; 67:2059-2066. [PMID: 34052938 DOI: 10.1007/s10620-021-07065-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/15/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a common and debilitating disease of the gastrointestinal tract. Psychological distress is highly comorbid to IBD, especially during periods of active disease. However, a controversy exists on how to best manage its symptoms in the IBD population. AIMS This study aimed to explore protective and risk factors of psychological distress in IBD. METHODS A cross-sectional online survey was conducted via social media and online patient forums. Respondents (N = 235) filled out questionnaires on demographics, health characteristics and a range of psychological variables. Measures of pain, disease activity, comorbid functional symptom severity, social support, subjective wellbeing, sleep quality, fatigue, stress, age, BMI and gender were entered into the Classification and Regression Tree Analysis model. RESULTS Overall, 87 participants (37%) reported distress. Self-reported stress significantly discriminated between cases of probable psychological distress. In those with high stress, patients with and without probable psychological distress were separated by subjective wellbeing. Among patients with low stress, fatigue was the primary discriminator. CONCLUSIONS Monitoring patients for low subjective wellbeing and high stress in clinical settings could offer an opportunity to engage in early intervention to limit psychological distress development. Monitoring for fatigue in patients who seem otherwise psychologically well could offer preventative benefits.
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Affiliation(s)
- Catherine Emerson
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | | | - Rebecca Orr
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Kimina Lyall
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Lauren Beswick
- Department of Gastroenterology, Barwon Health, Geelong, Australia
| | - Lisa Olive
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia.,IMPACT Institute, Faculty of Health, Deakin University Geelong, Geelong, Australia
| | - David Skvarc
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Robert A Cummins
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Antonina Mikocka-Walus
- School of Psychology, Deakin University Geelong, 221 Burwood Highway, Burwood, VIC, 3125, Australia.
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4
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Short CE, DeSmet A, Woods C, Williams SL, Maher C, Middelweerd A, Müller AM, Wark PA, Vandelanotte C, Poppe L, Hingle MD, Crutzen R. Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies. J Med Internet Res 2018; 20:e292. [PMID: 30446482 PMCID: PMC6269627 DOI: 10.2196/jmir.9397] [Citation(s) in RCA: 192] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 08/01/2018] [Accepted: 09/10/2018] [Indexed: 12/30/2022] Open
Abstract
Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.
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Affiliation(s)
- Camille E Short
- Freemasons Foundation Centre for Men's Health, School of Medicine, University of Adelaide, Adelaide, Australia
| | - Ann DeSmet
- Department of Movement and Sports Sciences, Ghent University, Brussels, Belgium
| | - Catherine Woods
- Health Research Institute, Centre for Physical Activity and Health, Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
| | - Susan L Williams
- Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia
| | - Carol Maher
- Alliance for Research in Exercise, Nutrition and Activity, Sansom Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
| | - Anouk Middelweerd
- Department of Rheumatology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Andre Matthias Müller
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Centre for Sport and Exercise Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Petra A Wark
- Centre for Innovative Research Across the Life Course, Faculty of Health and Life Sciences, Coventry University, Coventry, United Kingdom
| | - Corneel Vandelanotte
- Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia
| | - Louise Poppe
- Department of Movement and Sports Sciences, Ghent University, Brussels, Belgium
| | - Melanie D Hingle
- Department of Nutritional Sciences, College of Agriculture & Life Sciences, University of Arizona, Tucson, AZ, United States
| | - Rik Crutzen
- Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
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Hamilton K, Marques MM, Johnson BT. Advanced analytic and statistical methods in health psychology. Health Psychol Rev 2018; 11:217-221. [PMID: 28659020 DOI: 10.1080/17437199.2017.1348905] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Kyra Hamilton
- a School of Applied Psychology, Menzies Health Institute Queensland , Griffith University , Brisbane , Australia.,b School of Psychology and Speech Pathology, Health Psychology and Behavioural Medicine Research Group , Curtin University , Perth , Australia
| | - Marta M Marques
- c Department of Clinical, Educational and Health Psychology , University College London , London , UK
| | - Blair T Johnson
- d Department of Psychological Sciences and Institute for Collaboration on Health, Intervention, and Policy (InCHIP) , University of Connecticut , Storrs , CT , USA
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