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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙 | |
dc.contributor.author | Chih-Kuan Lai | en |
dc.contributor.author | 賴志冠 | zh_TW |
dc.date.accessioned | 2021-06-15T12:29:48Z | - |
dc.date.available | 2017-08-26 | |
dc.date.copyright | 2016-08-26 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-05 | |
dc.identifier.citation | 1. Abdullah AS, Driezen P, Quah AC, Nargis N, Fong GT. Predictors of smoking cessation behavior among Bangladeshi adults: findings from ITC Bangladesh survey. Tob Induc Dis. 2015;13(1):23.
2. Abraham C, Sheeran PJ. The health belief model. Cambridge University Press, 2014. 3. Ajzen I. From intentions to actions: A theory of planned behavior. Action control: Springer; 1985: 11-39. 4. Ajzen I. The theory of planned behavior. Organ Behav Hum Dec 1991; 50(2): 179-211. 5. Armitage CJ, Arden MA. How useful are the stages of change for targeting interventions? Randomized test of a brief intervention to reduce smoking. Health Psychol 2008; 27(6): 789-98. 6. Armitage CJ, Conner M. Efficacy of the Theory of Planned Behaviour: a meta-analytic review. Br J Soc Psychol 2001; 40: 471-99. 7. Aveyard P, Lawrence T, Cheng KK, Griffin C, Croghan E, Johnson C. A randomized controlled trial of smoking cessation for pregnant women to test the effect of a transtheoretical model-based intervention on movement in stage and interaction with baseline stage. Br J Health Psychol 2006; 11: 263-78. 8. Aveyard P, Massey L, Parsons A, Manaseki S, Griffin C. The effect of Transtheoretical Model based interventions on smoking cessation. Soc Sci Med 2009; 68(3): 397-403. 9. Balmford J, Borland R, Burney S. Is contemplation a separate stage of change to precontemplation? Int J Behav Med 2008; 15(2): 141-8. 10. Bandura A, Walters RH. Social learning theory. Englewood Cliffs, N.J.: Prentice-Hall; 1977. 11. Barnard J, McCulloch R, Meng, XL. Modeling Covariance Matrices in Terms of Standard Deviations and Correlations with Application to Shrinkage, Statistica Sinica, 2000; 10, 1281–1311. 12. Bartlett MS. On the Theory of Statistical Regression, Proceedings of the Royal Society of Edinburgh, 1933; 53, 260–283. 13. Becker GS, Murphy KM. A Theory of Rational Addiction. J Poli Econ 1988; 96(4): 675-700. 14. Bully P, Sanchez A, Zabaleta-del-Olmo E, Pombo H, Grandes G. Evidence from interventions based on theoretical models for lifestyle modification (physical activity, diet, alcohol and tobacco use) in primary care settings: A systematic review. Prev Med. 2015; 76: S76-93. 15. Cahill K, Lancaster T, Green N. Stage-based interventions for smoking cessation. Cochrane Database Syst Rev 2010; (11): CD004492 (11). 16. Caponnetto P, Polosa R. Common predictors of smoking cessation in clinical practice. Respir Med. 2008;102(8):1182-92. 17. Carlson LE, Taenzer P, Koopmans J, Casebeer A. Predictive value of aspects of the Transtheoretical Model on smoking cessation in a community-based, large-group cognitive behavioral program. Addict Behav 2003; 28(4): 725-40. 18. Carpenter CJ. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun 2010; 25(8): 661-9. 19. Chaloupka F. Rational Addictive Behavior and Cigarette Smoking. J Poli Econ 1991; 99(4): 722-42. 20. Chouinard MC, Robichaud-Ekstrand S. Predictive value of the transtheoretical model to smoking cessation in hospitalized patients with cardiovascular disease. Eur J Cardiovasc Prev Rehabil 2007; 14(1): 51-8. 21. Daniels MJ and Kass RE. Shrinkage Estimators for Covariance Matrices. Biometrics 2001; 57, 1173–1184. 22. Daniels MJ and Normand SL. Longitudinal Profiling of Health Care Units Based on Continuous and Discrete Patient Outcomes. Biostatistics, 2006; 7, 1–15. 23. DiClemente CC, Prochaska JO, Fairhurst SK, Velicer WF, Velasquez MM, Rossi JS. The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. J Consult Clin Psychol 1991; 59(2): 295-304. 24. Dijkstra A, Roijackers J, De Vries H. 'Smokers in four stages of readiness to change.' Addict Behav, 1998; 23(3): 339-350. 25. Dunson DB. Bayesian Latent Variable Models for Clustered Mixed Outcomes. Journal of the Royal Statistical Society, Ser. B, 2000; 62, 355–366. 26. Dunson DB. Dynamic Latent Trait Models for Multidimensional Longitudinal Data. Journal of the American Statistical Association, 2003; 98, 555–563. 27. Etter JF. Associations between smoking prevalence, stages of change, cigarette consumption, and quit attempts across the United States. Prev Med 2004; 38(3): 369-73. 28. Etter JF, Perneger TV. Associations between the stages of change and the pros and cons of smoking in a longitudinal study of Swiss smokers. Addict Behav 1999; 24(3): 419-24. 29. Feng G, Jiang Y, Li Q, Yong HH, Elton-Marshall T, Yang J, Li L, Sansone N, Fong GT. Individual-level factors associated with intentions to quit smoking among adult smokers in six cities of China: findings from the ITC China Survey. Tob Control. 2010;19 Suppl 2:i6-11. 30. Fitzmaurice GM and Laird NM. Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering. Journal of the American Statistical Association, 1995; 90, 845–852. 31. Font-Mayolas S, Planes M, Gras MA, Sullman MJ. Motivation for change and the pros and cons of smoking in a Spanish population. Addict Behav 2007; 32(1): 175-80. 32. George EI and McCulloch RE. Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 1993; 88, 881–889. 33. George EI and McCulloch RE. Approaches for Bayesian Variable Selection, Statistica Sinica, 1997; 7, 339–373. 34. Galvin KT. A critical review of the health belief model in relation to cigarette smoking behavior. J Clin Nurs 1992; 1(1): 13-8. 35. Geweke J. Efficient Simulation from the Multivariate Normal and Student t-Distributions Subject to Linear Constraints, Computer Sciences and Statistics, Proceedings of the 23rd Symposium Interface, 1991; 571–578. 36. Ghani WM, Razak IA, Yang YH, Talib NA, Ikeda N, Axell T, Gupta PC, Handa Y, Abdullah N, Zain RB. Factors affecting commencement and cessation of smoking behavior in Malaysian adults. BMC Public Health. 2012 Mar 19;12:207. 37. Godin G, Kok G. The theory of planned behavior: a review of its applications to health-related behaviors. Am J Health Promot 1996; 11(2): 87-98. 38. Godin G, Valois P, Lepage L, Desharnais R. Predictors of smoking behavior: an application of Ajzen's theory of planned behavior. Br J Addict 1992; 87(9): 1335-43. 39. Godtfredsen NS, Prescott E, Osler M, Vestbo J. Predictors of smoking reduction and cessation in a cohort of Danish moderate and heavy smokers. Prev Med. 2001 Jul;33(1):46-52. 40. Gueorguieva RV and Agresti A. A Correlated Probit Model for Joint Modelling of Clustered Binary and Continuous Responses. Journal of the American Statistical Association, 2001; 96, 1102–1112. 41. Gueorguieva RV and Sanacora G. Joint Analysis of Repeatedly Observed Continuous and Ordinal Measures of Disease Severity. Statistics in Medicine, 2006; 25, 1307–1322. 42. Hagimoto A, Nakamura M, Morita T, Masui S, Oshima A. Smoking cessation patterns and predictors of quitting smoking among the Japanese general population: a 1-year follow-up study. Addiction. 2010 Jan;105(1):164-73. 43. Horn D, Waingrow S. Some Dimensions of a Model for Smoking Behavior Change. Am J Public Health 1966; 56(12p2): 21-26 44. Hu SC, Lanese RR. The applicability of the theory of planned behavior to the intention to quit smoking across workplaces in southern Taiwan. Addic Behav 1998; 23(2): 225-37. 45. Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE. Measures of abstinence in clinical trials: issues and recommendations. Nicotine Tob Res. 2003 Feb;5(1):13-25. 46. Hyland A, Borland R, Li Q, Yong HH, McNeill A, Fong GT, O'Connor RJ, Cummings KM. Individual-level predictors of cessation behaviours among participants in the International Tobacco Control (ITC) Four Country Survey. Tob Control. 2006;15 Suppl 3:iii83-94. 47. Johnston DW, Johnston M, Pollard B, Kinmonth A-L, Mant D. Motivation is not enough: prediction of risk behavior following diagnosis of coronary heart disease from the theory of planned behavior. Health Psychol 2004; 23(5): 533. 48. Kalbfleisch JD, Lawless, Jerald F. The analysis of panel data under a Markov assumption. Journal of the American Statistical Association, 1985, 80.392: 863-871. J Am Stat Assoc. 2009;104(486):429-438. 49. Killeen PR. Markov model of smoking cessation. Proc Natl Acad Sci U S A. 2011;108 (Suppl 3):15549-56. 50. Lawrence T, Aveyard P, Evans O, Cheng KK. A cluster randomised controlled trial of smoking cessation in pregnant women comparing interventions based on the transtheoretical (stages of change) model to standard care. Tob Control 2003; 12(2): 168-77. 51. Lee CW, Kahende J. Factors associated with successful smoking cessation in the United States, 2000. Am J Public Health. 2007;97(8):1503-9. 52. Li L, Feng G, Jiang Y, Yong HH, Borland R, Fong GT. Prospective predictors of quitting behaviours among adult smokers in six cities in China: findings from the International Tobacco Control (ITC) China Survey. Addiction. 2011;106(7):1335-45. 53. Li YP, Chan W. Analysis of longitudinal multinomial outcome data. Biometrical J. 2006;48(2):319–326. 54. Liu X, Daniels MJ, Marcus B. Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking Cessation Trial. J Am Stat Assoc. 2009;104(486):429-438. 55. Lippke S, Ziegelmann JP. Theory‐based health behavior change: Developing, testing, and applying theories for evidence‐based interventions. Appl Psych 2008; 57(4): 698-716. 56. Luo S, Crainiceanu CM, Louis TA, Chatterjee N. Analysis of smoking cessation patterns using a stochastic mixed-effects model with a latent cured state. Journal of the American Statistical Association 2008;03(483):1002-1013. 57. Luo S, Crainiceanu CM, Louis TA, Chatterjee N.Bayesian inference for smoking cessation with a latent cure state. Biometrics. 2009;65(3):970-8. 58. Marcus BH, Lewis BA, King TK, Albrecht AE, Hogan J, Bock B, Parisi AF, Abrams DB. Rationale, Design, and Baseline Data for Commit to Quit II: An Evaluation of the Efficacy of Moderate-Intensity Physical Activity as an Aid to Smoking Cessation in Women. Preventive Medicine, 2003; 36, 479–492. 59. Marcus BH, Lewis BA, Hogan J, King TK, Albrecht AE, Bock B, Parisi AF, Niaura R, Abrams DB. The Efficacy of Moderate-Intensity Exercise as an Aid for Smoking Cessation in Women: A Randomized Controlled Trial. Nicotine & Tobacco Research, 2005; 7, 871–880. 60. Mason D, Gilbert H, Sutton S. Effectiveness of web-based tailored smoking cessation advice reports (iQuit): a randomized trial. Addiction 2012; 107(12): 2183-90. 61. Mhoon KB, Chan W, Del Junco DJ, Vernon SW. A continuous-time markov chain approach analyzing the stages of change construct from a health promotion intervention. JP J Biostat. 2010;4(3):213-226. 62. Norman P, Conner M, Bell R. The theory of planned behavior and smoking cessation. Health Psychol 1999; 18(1): 89-94. 63. Norman GJ, Velicer WF, Fava JL, Prochaska JO. Cluster subtypes within stage of change in a representative sample of smokers. Addict Behav, 2000; 25(2): 183-204. 64. Pollak KI, Carbonari JP, DiClemente CC, Niemann YF, Mullen PD. Causal relationships of processes of change and decisional balance: stage-specific models for smoking. Addict Behav 1998; 23(4): 437-48. 65. Pourahmadi M and Daniels MJ. Dynamic Conditionally Linear Mixed Models for Longitudinal Data. Biometrics, 2002; 58, 225–231. 66. Prapavessis H, De Jesus S, Fitzgeorge L, Faulkner G, Maddison R, Batten S. Exercise to Enhance Smoking Cessation: The Getting Physical on Cigarette Randomized Control Trial. Ann Behav Med. 2016 Jun;50(3):358-69. 67. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol 1992; 47(9): 1102-14. 68. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol 1992; 47(9): 1102-1114. 69. Prochaska JO and Velicer WF. 'The transtheoretical model of health behavior change. Am J Health Promot, 1997; 12(1): 38-48. 70. Robert CP. Simulation of Truncated Normal Variables. Statistics and Computing, 1995; 5, 121–125. 71. Rothman AJ. Toward a theory-based analysis of behavioral maintenance. Health Psychol 2000; 19(1 Suppl): 64-9. 72. Schauer GL, Wheaton AG, Malarcher AM, Croft JB. Smoking prevalence and cessation characteristics among U.S. adults with and without COPD: findings from the 2011 Behavioral Risk Factor Surveillance System. COPD. 2014;11(6):697-704. 73. Schumann A, Kohlmann T, Rumpf HJ, Hapke U, John U, Meyer C. Longitudinal relationships among transtheoretical model constructs for smokers in the precontemplation and contemplation stages of change. Ann Behav Med 2005; 30(1): 12-20. 74. Smith M and Kohn R. Parsimonious Covariance Matrix Estimation for Longitudinal Data. Journal of the American Statistical Association. 2002; 97, 1141–1153. 75. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson K P, Stefansson K. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 2008;452(7187):638-642. 76. van Dyk DA and Meng XL. The Art of Data Augmentation (with discussion). Journal of Computational and Graphical Statistics, 2001; 10, 1–111. 77. van Zundert RM, Nijhof LM, Engels RC. Testing Social Cognitive Theory as a theoretical framework to predict smoking relapse among daily smoking adolescents. Addict Behav 2009;34(3):281-286. 78. Vangeli E, Stapleton J, Smit ES, Borland R, West R. Predictors of attempts to stop smoking and their success in adult general population samples: a systematic review. Addiction 2011;106(12):2110-21. 79. Weinstein ND, Rothman AJ, Sutton SR. Stage theories of health behavior: conceptual and methodological issues. Health Psych 1998; 17(3): 290-9. 80. Zhou X, Nonnemaker J, Sherrill B, Gilsenan AW, Coste F, West R. Attempts to quit smoking and relapse: factors associated with success or failure from the ATTEMPT cohort study. Addict Behav. 2009;34(4):365-73. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50104 | - |
dc.description.abstract | 背景
眾多流行病學研究探討驅使成為習慣性吸菸者、戒菸者與復發性吸菸者個別動力之危險因子(如人口學特性、嚼食檳榔、飲酒、共病症與健康行為等),惟甚少研究提出以一動態過程模型同時解讀此三項過程(非吸菸者習慣性吸菸者、習慣性吸菸者戒菸者、戒菸者復發性吸菸者),如此可將三項過程視為相依結果,涵蓋於單一框架內,由單獨至全部過程的因素,進而估算成為習慣性吸菸者之「淨力」。除極少研究考量動態過程外,此類研究最欠缺處在於未特別將行為理論(如跨理論模式)同時運用於吸菸行為與流行病學分析。納入流行病學與行為改變變項,運用統計模型建構具行為階段相關特色的動態過程應是值得被研究的。 目的 本研究論文的方向期能達成統計的目標有: 一、 針對上述三種吸菸行為的單獨轉移過程,以Cox比例風險迴歸模型估算人口學特性、嚼食檳榔、飲酒、共病症(糖尿病與高血壓)以及健康行為之效應; 二、 針對吸菸行為轉移的動態過程(由三種過程所組成的連續轉移:非吸菸者習慣性吸菸者戒菸者,其中反向箭頭代表復發性吸菸),以三階段馬可夫轉移模型估算第一項中所列各個流行病學因子之效應; 三、 針對「跨理論模型」中不同行為改變階段轉移的動態過程(深思前期深思期準備期行動期),以四階段馬可夫轉移模型估算第一項中所列各個流行病學相關因子(例如早上起床後30分鐘內是否吸第一支菸、開始吸菸年齡)之效應; 四、 針對吸菸行為轉移的動態過程(由三種過程所組成的連續轉移:非吸菸者習慣性吸菸者戒菸者,其中反向箭頭代表復發性吸菸),以三階段馬可夫轉移模型內嵌四階段馬可夫轉移模型,估算第一項中所列各個流行病學因子之效應; 在吸菸習慣與行為領域的目標則為: 五、 當從非吸菸者轉移成為習慣性吸菸者的驅力與戒菸者轉移成為復發性吸菸者的驅力達成平衡後,同時考量第一項中所列流行病學因子,估算成為習慣性吸菸者之「淨力」; 六、 考量相關變項後(如起床30分鐘後是否吸第一支菸、開始吸菸年齡),估算跨理論模式改變階段(深思前期深思期準備期行動期)之「淨力」; 七、 當從非吸菸者轉移成為吸菸者的驅力與戒菸者轉移成為復發性吸菸者的驅力達成平衡後,同時考量嵌入的跨理論模式行為改變階段與第1項中所列流行病學因子,估算成為習慣性吸菸者之「淨力」。 材料與方法 本研究主要利用基隆市及彰化縣之整合式篩檢資料進行分析,基隆市自2000年開辦社區篩檢服務,以社區到點方式進行、提供五項癌症及三項慢性病篩檢,2005年該模式拓展至彰化縣實施。自2000-2005年共計有228,258人參與,其中包括基隆127,194人及彰化101,064人。參加篩檢民眾同時接受自填式問卷及抽血生化檢驗服務,問卷內容包括吸菸狀態、教育程度、婚姻狀態、生活型態(包括喝酒習慣、嚼檳榔)、居住區域、運動習慣、共病症及接受健檢經驗等。共病症資料則於篩檢同時由參加民眾自述過去病史或當場檢查判定。 本研究利用問卷依照個案吸菸狀態分為「從未抽菸」、「習慣吸菸」與「已戒菸」三類,習慣吸菸定義為每週至少有1次抽菸且每次至少1支,戒菸者則定義為已經持續戒菸半年以上。針對目前吸菸或已戒菸者,同時收集開始抽菸年齡、每日吸菸支數及戒菸年數等量性資料。此外,進一步詢問吸菸者每日起床後約多久抽第一支菸,並以跨理論模式(trans-theoretical models, TTM)定義之行為改變階段,詢問吸菸者準備戒菸之改變階段。 本研究首先以Cox 比例風險模式估計人口學變項、嚼檳榔習慣、喝酒習慣、共病症及健康相關行為分別對於三階段作用估計。我們進一步利用三階段馬可夫轉移模型估計各因子對吸菸習慣轉移的動態過程(由三種過程所組成的連續轉移:非吸菸者吸菸者戒菸者,其中反向箭頭代表再度吸菸)之階段別效應。對吸菸行為改變過程則採用跨理論模型提出四階段馬可夫轉移模型(深思前期深思期準備期行動期)。最後再將四階段跨理論馬可夫轉移模型嵌入三階段吸菸習慣轉移模型以估算行為改變理論的傾向對吸菸習慣改變之效應。 結果 本研究習慣性吸菸者與戒菸者的盛行率分別為17.6%與6.9%,吸菸率呈現顯著的性別差異,男性習慣性吸菸者與戒菸者的盛行率為39.3%與16.6%、女性則為5.3%與1.3%,我們的結果證實男性習慣性吸菸發生率與低教育程度、未婚/離婚、已戒除/目前嚼食檳榔和飲酒有關。 以Cox迴歸模式分析,習慣吸菸的加強因子有男性、中低度教育程度、未婚、離婚/喪偶、已戒除/目前嚼食檳榔以及飲酒;保護因子則為規律運動與具健檢經驗。與戒菸有關的因素,顯著正面影響者包括年紀、男性、規律運動與健檢經驗,阻礙吸菸者戒菸的顯著因素則是中低教育程度、已戒除/目前嚼食檳榔。造成復發性吸菸的顯著因素則包括低教育程度、未婚、目前嚼食檳榔,有規律運動與男性則較不易成為復發性吸菸。 運用動態馬可夫模型,成為習慣性吸菸、戒菸以及復發性吸菸之速率估計值分別為0.00027 (95% CI: 0.00025-0.00028)、0.0073 (95% CI: 0.0071-0.0042)及 0.0040 (95% CI: 0.0038-0.0042),由習慣性吸菸成為戒菸以及復發性吸菸之五年機率分別為32%與18%,統計模型估算最終之習慣吸菸與戒菸之比例分別為35%與65%。將三段轉移間之消長納入考量後估算成為習慣性吸菸之淨力中,嚼食檳榔對於此一淨力具有重要影響,嚼食檳榔習慣以及戒除檳榔嚼食者之偏量(drift)估計值分別為1.87與0.59,其所反應對於習慣吸菸之勝算比分別為6.5以及1.8。規律運動者成為習慣吸菸之淨力為負向偏量(-0.87),其可降低成為習慣吸菸之危險達60%。基礎跨理論模式階段中之行動期對於習慣吸菸具有顯著影響,其可增加戒菸成功率達73%,並可降低成為復發性吸菸達68%。跨理論模式期別中之深思期、準備期與行動期對於成為習慣性吸菸淨力之估計值分別為-0.41 、-0.20,以及-1.69,此一結果反應於降低成為習慣性吸菸風險分別達34%、18%以及81%。 由多變項羅吉斯迴歸分析結果可知開始吸菸年齡早於20歲者及早上起床後30分鐘內吸菸會較不容易由深思前期變成行動期,其勝算比分別為0.61 (95%CI: 0.30,1.23)及0.49 (95%CI: 0.26,0.94)。四階段跨理論模式動態馬可夫過程的分析結果亦相同,且在此動態模型中,更可看出早上起床後30分鐘內吸菸對個案從行動期返回至深思前期的影響極為明顯。 嵌入跨理論模式動態馬可夫過程於吸菸習慣三階段馬可夫過程分析結果顯示留在深思前期機率與習慣性吸菸呈負相關(淨力之迴歸係數為-5.55, 95% CI: -9.83, 0.00),而進入行動期的機率則與習慣性吸菸呈正相關(淨力之迴歸係數為8.10, 95% CI: -5.30, 9.87)。 結論 本論文以兩個社區整合式篩檢資料,成功證明如何運用馬可夫過程來建置吸菸行為(非吸菸者吸菸者戒菸者)與改變階段(深思前期深思期準備期行動期)的動態模型,以下所獲致之結論分成兩大面向。 由本次論文在方法學上所獲致之改進有: 一、 就階段特定的危險因子與階段動態轉移的效應力精準度而言,以三階段馬可夫模型所建立的吸菸行為動態過程優於以Cox比例風險迴歸模型所建立的三個單獨過程; 二、 調整其他干擾因素後,對每一階段特定之危險因子善用三階段馬可夫過程,估算三個轉移過程的「淨力」; 三、 善用四階段馬可夫過程,估算吸菸行為中兩個重要關聯狀態間四組前進與後退轉移之「淨力」; 四、 將跨理論模式動態過程之馬可夫過程整合融入吸菸、戒菸與再吸菸之吸菸習慣多階段過程; 在吸菸習慣與吸菸行為的實證方面,包括下列幾項發現: 五、 在狀態特定的流行病學因子中,嚼食檳榔成為習慣吸菸的最大淨力; 六、 影響成為習慣吸菸的淨力,顯著的狀態特定因子包括年紀輕、男性、低教育程度、未婚、目前嚼食檳榔、飲酒、糖尿病、未罹患高血壓、缺乏規律運動、以及無健檢經驗者; 七、 影響從深思前期演變為行動期的流行病學特質包括高齡、男性、規律運動、糖尿病、高血壓、不嚼食檳榔與戒酒,而從戒菸行動期演變為深思前期僅嚼食檳榔一項具最重要角色; 八、 清晨起床30分鐘內吸菸為從深思前期轉移至行動期之顯著障礙; 九、 在控制其他所有流行病學危險因子後,跨理論模式的改變階段、尤其是深思前期,為成為習慣吸菸淨力的獨立預測因子; 源自內嵌跨理論模式階段改變的馬可夫過程,維持在深思前期或從深思前期至行動期的轉移機率,經吸菸行為調整後,顯著地影響由吸菸習慣三階段馬可夫過程所導出成為習慣吸菸的淨力。 | zh_TW |
dc.description.abstract | Background
Numerous epidemiological studies have been conducted to study risk factors (including demographic features, betel quid chewing, alcohol drinking, co-morbidity, and health behavior) affecting the forces of being regular smoker (habitual use), quitting, and relapse separately, but very few studies have been proposed to elucidate three transitions (non-smoker regular smoker, regular smoker quitting, quitting relapse) with a dynamic process modelled by consolidating three processes as an unified framework that treats three processes as correlated outcomes to estimate the net force of being habitual use by each individual factor and a constellation of these factors. In addition to considering this dynamic process, what is the most lacking in such kind of epidemiological studies is the failure of considering those epidemiological correlates and smoking behavior particularly based on certain behavior theory (such as transtheoretical model, TTM) simultaneously. The application of statistical models to build up a dynamic process characterized by stage-dependent correlates that consist of both epidemiological and behavior change variables is worthy of being investigated. Aims The objectives of this thesis were in achieving statistical goal to (1) estimate the effects of demographic features, betel quid chewing, alcohol drinking, co-morbidity (diabetes mellitus and hypertension), and health behavior on three independent processes of smoking habits with conventional Cox proportional hazards regression models; (2) estimate the effects of all each of epidemiological factors indicated in the aim of (1) on a dynamic process (composed of three consecutive transitions, non-smoker regular smoker quitting; the reverse arrow represents the relapse) using a three-state Markov transition model ; (3) estimate the effects of all each of epidemiological factors indicated in the aim of (1) and relevant correlates (such as smoking within 30 minutes after wake-up and age at the commencement of smoking) on the stage of change of TTM (pre-contemplation contemplation preparation action) using a four-state Markov transition model; (4) estimate the effects of all each of epidemiological factors indicated in the aim of (1) on a dynamic process (composed of three consecutive transitions, non-smoker regular smoker quitting; the reverse arrow represents the relapse) using a three-state Markov transition model embedded with a four-state Markov model on cyclic four-state transition; and were in the province of smoking habit and behavior to (5) estimate the net force of being habitual use after the balance between the force of entering habitual use from non-smoker and the force of relapse from quitter by considering epidemiological factors indicated in the aim of (1) ; (6) estimate the net force of TTM-based stage of change (pre-contemplation contemplation preparation action) considering relevant correlates (such as smoking < 30 minutes after wake-up and age at the commencement of smoking); (7) estimate the net force of being habitual use after the balance between the force of entering habitual use from non-smoker and the force of relapse from quitter by considering the embedded probability of being TTM-based stage of change with respect to action or pre-contemplation epidemiological factors indicated in the aim of (1); Materials and Methods Study subjects of this thesis were derived from two Community-based Integrated Screening (CIS) programs from Keelung City and Changhua County. The two programs have been launched since 2000 and 2005 for Keelung and Changhua, respectively. A total of 228,258 subjects attended the CIS programs between 2000 and 2015, including 127,194 from Keelung and 101,064 from Changhua. Each participant completed a self-administered questionnaire to collect data on cigarette smoking status, betel-quid chewing, alcohol drinking habits, place of residence, physical activity, comorbidity, and health check-up experience. Data on comorbidity were ascertained by the on-site screening and self-reported status. The study population was categorized into three groups by smoking status: non- smokers, habitual use, and quitting. The habitual use was defined as those who regularly smoked at least 1 cigarette per week. We further recorded their age of smoking commencement, cigarettes per day and duration of being smoker. Those who have quit smoking longer than half year were defined as abstention. The period of having been a quitter was also collected. The questionnaire also collected data on smoker’s readiness to quit, which enables us to define the stage of change underpinning the transtheoretical model (TTM). We firstly estimated the effects of demographic features, betel quid chewing, alcohol drinking, comorbidity, and health behavior on three independent processes of smoking habits (nonsmoker habitual use, habitual use quitting, and quitting habitual use) with Cox proportional hazards regression models. A three-state Markov model was further proposed to estimate the state-specific effects of relevant risk factors on a dynamic process (composed of three consecutive transitions, non-smoker habitual use quitting). For the behavior change process, we used a four-state Markov transition model underpinning the TTM (pre-contemplation contemplation preparation action) to elucidate the state-specific effects of risk factors. The TTM-underpinned Markov model was then embedded in the proposed three-state Markov for smoking habit model to elucidate the net force of being habitual use after the balance between the force of quitting and the force of relapse by considering the embedded probability of being the stage of change of TTM with respect to action or pre-contemplation. Results The prevalence of habitual user and quitter were 17.7% and 6.9%, respectively. The smoking rates show significant gender differences in habitual use and quitting, 39.3% and 16.6% for males and 5.3% and 1.3% for females, respectively. Our results demonstrated the higher incidence rates of habitual use among the male, with lower education level, unmarried/divorced, quit/current betel-quid chewing or alcohol drinking. Using conventional Cox proportional hazards regression model, the aggravating factors responsible for habitual use were male, middle and low education, unmarried, divorced/widowed, quitting/current betel-quid chewer, alcohol drinking. The protective health-related behavior factors were consisting of regular exercise and experience of health check-up. For the factors responsible for quitting, the significant promoting factors included old age, male, regular exercise, and the uptake of health check-up experience. The significant factors preventing one from quitting were middle and lower education level and quitting/current betel-quid chewing. The significant factors responsible for relapse were lower education level, unmarried and current betel-quid chewing. Protective factors were regular exercise and male. The monthly rate of turning into habitual use, quitting, and relapse were estimated as 0.00027 (95% CI: 0.00025-0.00028), 0.0073 (95% CI: 0.0071-0.0042), and 0.0040 (95% CI: 0.0038-0.0042), respectively based on the dynamic Markov model. The five year probabilities of quitting for habitual users and relapse were estimated as 32% and 18%, respectively. In the end of statistical model, 35% of the population will be habitual uses and 65% be quitter. By looking at net force of the balance between three transitions, betel-quid chewing plays a major role toward habitual use with the drift estimated as 1.87 and 0.59 for current chewer and quitter, corresponding to the ORs of 6.5 and 1.8. Regular exercise revealed a negative drift of -0.87 toward habitual use with the corresponding risk reduction of 60%. The baseline TTM stage of action was significantly associated with a 73% increase of quitting and 68% reduction of relapse. The estimated net force toward habitual use of contemplation, preparation, and action were estimated as -0.41, -0.20, and -1.69, corresponding to the risk reductions of 34%, 18%, and 81%, respectively. As regards the factors associated with the stage of change of TTM, we found that smoking initiation before the age of 20 (OR=0.61, 95%CI:(0.30,1.23)) and first cigarette within 30 mins after waking up (OR=0.49, 95%CI:(0.26,0.94)) were less likely to go toward action stage from pre-contemplation stage in multivariate logistic regression analysis. The results based on the four-state Markov model were similar. First cigarette within 30 mins after waking up made significant contribution to the relapse from action to pre-contemplation of TTM stage. The results of adding the embedded four-state Markov model of the TTM to the dynamic three-state Markov model of smoking habit shows the probability of being in the action stage had a negative net force of being habitual use (net regression coefficient: -5.55, 95% CI: -9.83, 0.00) considering the balance between the force of departing from habitual use to quitting and of relapsing from quitting to smoking, whereas the probability of being in the pre-contemplation led to a positive net effect of being habitual use (net regression coefficient: 8.10, 95% CI: -5.30, 9.87). Conclusions This thesis has successfully demonstrated how to apply two Markov processes to modelling the corresponding two dynamic processes of smoking habits (non-smoker regular smoker abstention) and smoking behavior (pre-contemplation contemplation preparation action) by using two community-based integrated screening data with the conclusions drawn from this thesis on methodological improvements are three, including (1) dynamic process of smoking habit with the use of three-state Markov model is better than three independent processes with Cox proportional hazards regression model with respect to the precision of effect size on state-specific risk factors and dynamic transition between states; (2) the better use of three-state Markov process to estimate the net force of three transitions for each state-specific risk factor with adjustment for other confounding factors; (3) the better use of four-state Markov process to estimate the net force of four forward and backward transitions for two important correlates of smoking behavior; (4) the integration of the Markov process pertaining to the dynamic process of TMM behavior into the Markov process of smoking habits on regular smoker, quitting, and relapse; and those drawn from this thesis on empirical aspect of smoking habits and behavior include the respective findings as follows. (5) The largest net force of being habitual use (regular smoker) among these state-specific epidemiological factors was betel quid chewing; (6) significant state-specific factors for net force of being habitual use included young age, male, low education level, unmarried, current betel chewing, alcohol drinking, DM, no hypertension, lacking of regular exercise, and the failure of the uptake of health check-up; (7) epidemiological characteristics directing the change from pre-contemplation to action included old age, males, regular exercise, DM, hypertension, non-chewer, the quitter for drinking whereas only betel quid chewer play the most important role in the direction of the change from action to pre-contemplation; (8) Smoking behavior of first cigarette within 30 minutes when waking up made significant contribution to the resistance to the change from pre-contemplation to action and the drift toward pre-contemplation; (9) stage of change of TTM, particularly pre-contemplation, is still an independent predictor for the net force of being habitual use after controlling for all epidemiological risk factors using the three-state Markov model; smoking-behavior-adjusted transition probability of remaining in pre-contemplation or that from pre-contemplation to action derived from the embedded Markov process on the stage change of TMM behavior made independent significant contribution to the net force of being habitual use derived from three-state Markov process of smoking habits | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:29:48Z (GMT). No. of bitstreams: 1 ntu-105-D99849013-1.pdf: 3548123 bytes, checksum: c35785e7f303cb34d04555098d8e8e69 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 口試委員會審定書 ii
Abstract viii 中文摘要 xvi Chapter 1 Introduction 1 1.1 Dynamic process of habits on smoking, quitting, and relapse 1 1.2 Dynamic Process of Stage of smoking behavior change 3 1.3 Combining both Dynamic Processes 5 1.4 Aims 6 Chapter 2. Literature Review 9 2.1 Factors affecting the change of smoking habits 9 2.1.1 Factors affecting intention to quit 9 2.2 Behavioral Models in Change of Smoking Behavior 30 2.2.1 Theory of Planned Behavior (TPB) 32 2.2.2 Social Cognitive Theory (SCT) 35 2.2.3 Health Belief Model 39 2.2.4 Transtheoretical Model (TTM) of Behavior Change 42 2.2.5 Rational Addiction Model 48 2.3 Application of Markov model to smoking cessation 49 2.3.1 A three-state Markov Model 49 2.3.2 A Continuous-time Markov Chain Approach for TTM 53 2.3.3 Analysis of smoking cessation patterns using a stochastic mixed-effects model with a latent cured state 55 Chapter 3 Data Source and Variable Definition 61 3.1 Study cohort 61 3.2 Data collection and measurement of variables 62 3.2.1 Smoking status 62 3.2.2 Stage of behavior change for smoking cessation 63 3.2.3. Dose of smoking and nicotine dependence 64 3.2.4 Betel-quid chewing and alcohol drinking 64 3.2.5 Health-related behaviors 65 3.2.6 Transition of smoking status 65 Chapter 4. Statistical Methods 67 4.1 Study design 67 4.2 Traditional approaches for the independent processes of smoking habits 67 4.2.1 Cumulative Risk with Kaplan-Meier Estimator 68 4.2.2 Effects of risk factors on the three processes of smoking habit with conventional time-to-event regression models 68 4.3 Three-state Markov model for the dynamic process of smoking behavior 69 4.3.1 Three-state Markov model for smoking behavior in discrete time 69 4.3.2 Three-state Markov model for smoking behavior in continuous time 72 4.3.3 Derivation of net force toward habitual use 73 4.3.4 Bayesian Markov chain Monte Carlo method for the estimated of parameters 74 4.4 Four-state Markov transition model for the stage of change of TTM 75 4.5 Net force of being habitual use with the embedded Markov models 77 Chapter 5 Results 79 5.1. Characteristics and distribution of study population 79 5.1.1. Distribution of smoking status of study population 79 5.1.2. Person-years of follow-up and incidence rates by smoking behavioral change 80 5.2. Factors responsible for smoking habits 82 5.2.1. Factors responsible for habitual use 82 5.2.2. Factors responsible for quitting 83 5.2.3. Factors responsible for relapse 84 5.2.4. Factors responsible for smoking habits change by county 84 5.3 Dynamic process of habits on smoking, cessation, and relapse 87 5.4 Dynamic process of smoking behavior based on Transtheoretical model (TTM) 92 5.4.1 Factors associated with the smoking behavior change in logistic regression model 92 5.4.2 Estimation of transition probability for TTM 93 5.4.2.1 Age in association with smoking behavior in TTM 94 5.4.2.2 Gender in associated with smoking behavior in TTM 94 5.4.2.3 Education in associated with smoking behavior in TTM 94 5.4.2.4 Martial status in associated with smoking behavior in TTM 95 5.4.2.5 Physical activity in associated with smoking behavior in TTM 95 5.4.2.6 Health check-up in associated with smoking behavior in TTM 95 5.4.2.7 Diabetic mellitus in associated with smoking behavior in TTM 96 5.4.2.8 Hypertension in associated with smoking behavior in TTM 96 5.4.2.9 Betel quid chewing in associated with smoking behavior in TTM 97 5.4.2.10 Alcohol use in associated with smoking behavior in TTM 97 5.4.2.11 Age at smoking initiation and time for first cigarette in the morning in associated with smoking behavior in TTM 97 5.5 The dynamic process of smoking habit embedded with the four-state TTM Markov transition model 98 Chapter 6 Discussion 100 6.1 Advance in elucidating epidemiological and behavior science in smoking 100 6.2 Methodological improvements 101 6.2.1 Dynamic process of smoking habits 101 6.2.2 Markov dynamic processes of smoking habit embedded with Markov process on the stage of TMM change 103 6.3 Elucidating the multi-stage of smoking habits and smoking behavior 104 6.3.1 Net force of being habitual use (regular smoking) 104 6.3.2 Net force of being action and pre-contemplation 106 6.3.3 Implications for individually-tailored health promotion program for smoking cessation 107 6.4 Limitations 108 6.5 Conclusions 109 References 213 | |
dc.language.iso | en | |
dc.title | 應用馬可夫模式探討抽菸-戒菸-復發動態式內嵌型跨理論模式 | zh_TW |
dc.title | Dynamic Process of Health on Smoking-Cessation-Relapse Embedded with Transtheoretical Behavior Changes Using Markov Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 張淑惠,葉彥伯,林明薇,潘信良,嚴明芳 | |
dc.subject.keyword | 吸菸,戒菸,隨機過程,跨理論模式, | zh_TW |
dc.subject.keyword | smoking,smoking cessation,stochastic process,transtheoretical model, | en |
dc.relation.page | 218 | |
dc.identifier.doi | 10.6342/NTU201601964 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-05 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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