skip to main content
research-article
Public Access

SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors

Published:18 March 2020Publication History
Skip Abstract Section

Abstract

Context plays a key role in impulsive adverse behaviors such as fights, suicide attempts, binge-drinking, and smoking lapse. Several contexts dissuade such behaviors, but some may trigger adverse impulsive behaviors. We define these latter contexts as 'opportunity' contexts, as their passive detection from sensors can be used to deliver context-sensitive interventions.

In this paper, we define the general concept of 'opportunity' contexts and apply it to the case of smoking cessation. We operationalize the smoking 'opportunity' context, using self-reported smoking allowance and cigarette availability. We show its clinical utility by establishing its association with smoking occurrences using Granger causality. Next, we mine several informative features from GPS traces, including the novel location context of smoking spots, to develop the SmokingOpp model for automatically detecting the smoking 'opportunity' context. Finally, we train and evaluate the SmokingOpp model using 15 million GPS points and 3,432 self-reports from 90 newly abstinent smokers in a smoking cessation study.

Skip Supplemental Material Section

Supplemental Material

References

  1. Accessed April, 2019. CDC: Smoking is the leading cause of preventable death. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/index.htmGoogle ScholarGoogle Scholar
  2. Accessed February, 2018. Texas Alcoholic Beverage Commission. "Licensing.". https://www.tabc.state.tx.us/Google ScholarGoogle Scholar
  3. Accessed February, 2018. Texas Comptroller. Active Cigarette/Tobacco Retailers, Open Data Portal. https://data.texas.gov/Government-and-Taxes/Active-Cigarette-Tobacco-Retailers/u5nd-4vpg/dataGoogle ScholarGoogle Scholar
  4. Accessed May, 2019. CDC: Smoking Banned indoors. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/secondhand_smoke/protection/improve_health/index.htmGoogle ScholarGoogle Scholar
  5. Accessed September, 2018. Southeast Texas Addressing and Referencing Map. www.h-gac.com/rds/gis_data/starmapGoogle ScholarGoogle Scholar
  6. Accessed September, 2018. TIGER/Line Shapefiles and TIGER/Line Files Technical Documentation. https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlGoogle ScholarGoogle Scholar
  7. Gregory D Abowd, Anind K Dey, Peter J Brown, Nigel Davies, Mark Smith, and Pete Steggles. 1999. Towards a better understanding of context and context-awareness. In International symposium on handheld and ubiquitous computing. Springer, 304--307.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Rebecca L Ashare and Larry W Hawk. 2012. Effects of smoking abstinence on impulsive behavior among smokers high and low in ADHD-like symptoms. Psychopharmacology 219, 2 (2012), 537--547.Google ScholarGoogle ScholarCross RefCross Ref
  9. Timothy B Baker, Megan E Piper, Danielle E McCarthy, Daniel M Bolt, Stevens S Smith, Su-Young Kim, Suzanne Colby, David Conti, Gary A Giovino, Dorothy Hatsukami, et al. 2007. Time to first cigarette in the morning as an index of ability to quit smoking: implications for nicotine dependence. Nicotine & Tobacco Research 9, Suppl_4 (2007), S555--S570.Google ScholarGoogle Scholar
  10. Doug Beeferman and Adam Berger. 2000. Agglomerative clustering of a search engine query log. In KDD, Vol. 2000. 407--416.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Richard W Bohannon. 1997. Comfortable and maximum walking speed of adults aged 20--79 years: reference values and determinants. Age and ageing 26, 1 (1997), 15--19.Google ScholarGoogle Scholar
  12. Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Michelle Nicole Burns, Mark Begale, Jennifer Duffecy, Darren Gergle, Chris J Karr, Emily Giangrande, and David C Mohr. 2011. Harnessing context sensing to develop a mobile intervention for depression. Journal of medical Internet research 13, 3 (2011), e55.Google ScholarGoogle ScholarCross RefCross Ref
  14. Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 1293--1304.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Samuel R Chamberlain and Barbara J Sahakian. 2007. The neuropsychiatry of impulsivity. Current opinion in psychiatry 20, 3 (2007), 255--261.Google ScholarGoogle Scholar
  16. Soujanya Chatterjee, Karen Hovsepian, Hillol Sarker, Nazir Saleheen, Mustafa al'Absi, Gowtham Atluri, Emre Ertin, Cho Lam, Andrine Lemieux, Motohiro Nakajima, et al. 2016. mCrave: Continuous estimation of craving during smoking cessation. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 863--874.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. W Jay Christian. 2012. Using geospatial technologies to explore activity-based retail food environments. Spatial and spatio-temporal epidemiology 3, 4 (2012), 287--295.Google ScholarGoogle Scholar
  18. Stefany Coxe, Stephen G West, and Leona S Aiken. 2009. The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of personality assessment 91, 2 (2009), 121--136.Google ScholarGoogle ScholarCross RefCross Ref
  19. Jonathan F Deiches, Timothy B Baker, Stephanie Lanza, and Megan E Piper. 2013. Early lapses in a cessation attempt: lapse contexts, cessation success, and predictors of early lapse. nicotine & tobacco research 15, 11 (2013), 1883--1891.Google ScholarGoogle Scholar
  20. Anind K Dey. 2001. Understanding and using context. Personal and ubiquitous computing 5, 1 (2001), 4--7.Google ScholarGoogle Scholar
  21. Katherine Ellis, Jacqueline Kerr, Suneeta Godbole, Gert Lanckriet, David Wing, and Simon Marshall. 2014. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiological measurement 35, 11 (2014), 2191.Google ScholarGoogle Scholar
  22. Emre Ertin, Nathan Stohs, Santosh Kumar, Andrew Raij, Mustafa al'Absi, and Siddharth Shah. 2011. AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. ACM, 274--287.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yoav Freund, Robert E Schapire, et al. 1996. Experiments with a new boosting algorithm. In icml, Vol. 96. Citeseer, 148--156.Google ScholarGoogle Scholar
  24. Zhongliang Fu, Zongshun Tian, Yanqing Xu, and Changjian Qiao. 2016. A two-step clustering approach to extract locations from individual GPS trajectory data. ISPRS International Journal of Geo-Information 5, 10 (2016), 166.Google ScholarGoogle ScholarCross RefCross Ref
  25. Clive WJ Granger. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society (1969), 424--438.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Paul D Groves, HFS Martin, Kimon Voutsis, DJ Walter, and Lei Wang. 2013. Context detection, categorization and connectivity for advanced adaptive integrated navigation. The Institute of Navigation.Google ScholarGoogle Scholar
  27. Chad J Gwaltney, Saul Shiffman, Mark H Balabanis, and Jean A Paty. 2005. Dynamic self-efficacy and outcome expectancies: prediction of smoking lapse and relapse. Journal of abnormal psychology 114, 4 (2005), 661.Google ScholarGoogle ScholarCross RefCross Ref
  28. Timothy Hnat, Syed Monowar Hossain, Nasir Ali, Simona Carini, Tyson Condie, Ida Sim, Mani B Srivastava, and Santosh Kumar. 2017. mCerebrum and Cerebral Cortex: A Real-time Collection, Analytic, and Intervention Platform for High-frequency Mobile Sensor Data.. In AMIA.Google ScholarGoogle Scholar
  29. Tim Horberry, Janet Anderson, Michael A Regan, Thomas J Triggs, and John Brown. 2006. Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accident Analysis & Prevention 38, 1 (2006), 185--191.Google ScholarGoogle ScholarCross RefCross Ref
  30. David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant. 2013. Applied logistic regression. Vol. 398. John Wiley & Sons.Google ScholarGoogle ScholarCross RefCross Ref
  31. Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM, 7.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Karen Hovsepian, Mustafa al'Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 493--504.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Jidong Huang, Zongshuan Duan, Julian Kwok, Steven Binns, Lisa E Vera, Yoonsang Kim, Glen Szczypka, and Sherry L Emery. 2019. Vaping versus JUULing: how the extraordinary growth and marketing of JUUL transformed the US retail e-cigarette market. Tobacco control 28, 2 (2019), 146--151.Google ScholarGoogle Scholar
  34. Michael A Ichiyama and Marc I Kruse. 1998. The social contexts of binge drinking among private university freshmen. Journal of Alcohol and Drug Education 44, 1 (1998), 18.Google ScholarGoogle Scholar
  35. Anita Jansen. 1998. A learning model of binge eating: cue reactivity and cue exposure. Behaviour research and therapy 36, 3 (1998), 257--272.Google ScholarGoogle Scholar
  36. Alireza Karbasivar and Hasti Yarahmadi. 2011. Evaluating effective factors on consumer impulse buying behavior. Asian Journal of Business Management Studies 2, 4 (2011), 174--181.Google ScholarGoogle Scholar
  37. Thomas R Kirchner, Jennifer Cantrell, Andrew Anesetti-Rothermel, Ollie Ganz, Donna M Vallone, and David B Abrams. 2013. Geospatial exposure to point-of-sale tobacco: real-time craving and smoking-cessation outcomes. American journal of preventive medicine 45, 4 (2013), 379--385.Google ScholarGoogle ScholarCross RefCross Ref
  38. Mingqi Lv, Ling Chen, and Gencai Chen. 2012. Discovering personally semantic places from GPS trajectories. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 1552--1556.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Abhinav Mehrotra and Mirco Musolesi. 2018. Using autoencoders to automatically extract mobility features for predicting depressive states. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 127.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Ganesan Muruganantham and Ravi Shankar Bhakat. 2013. A review of impulse buying behavior. International Journal of Marketing Studies 5, 3 (2013), 149.Google ScholarGoogle Scholar
  41. Felix Naughton, Sarah Hopewell, Neal Lathia, Rik Schalbroeck, Chloë Brown, Cecilia Mascolo, Andy McEwen, and Stephen Sutton. 2016. A context-sensing mobile phone app (Q sense) for smoking cessation: a mixed-methods study. JMIR mHealth and uHealth 4, 3 (2016), e106.Google ScholarGoogle Scholar
  42. John Ashworth Nelder and Robert WM Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society: Series A (General) 135, 3 (1972), 370--384.Google ScholarGoogle ScholarCross RefCross Ref
  43. Maria A Oquendo, Hanga Galfalvy, Stefani Russo, Steven P Ellis, Michael F Grunebaum, Ainsley Burke, and J John Mann. 2004. Prospective study of clinical predictors of suicidal acts after a major depressive episode in patients with major depressive disorder or bipolar disorder. American Journal of Psychiatry 161, 8 (2004), 1433--1441.Google ScholarGoogle ScholarCross RefCross Ref
  44. Abhinav Parate, Meng-Chieh Chiu, Chaniel Chadowitz, Deepak Ganesan, and Evangelos Kalogerakis. 2014. Risq: Recognizing smoking gestures with inertial sensors on a wristband. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services. ACM, 149--161.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Toby G Pavey, Nicholas D Gilson, Sjaan R Gomersall, Bronwyn Clark, and Stewart G Trost. 2017. Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. Journal of science and medicine in sport 20, 1 (2017), 75--80.Google ScholarGoogle ScholarCross RefCross Ref
  46. Jennifer L Pearson, Amanda Richardson, Raymond S Niaura, Donna M Vallone, and David B Abrams. 2012. e-Cigarette awareness, use, and harm perceptions in US adults. American journal of public health 102, 9 (2012), 1758--1766.Google ScholarGoogle Scholar
  47. Jane Powell, Lynne Dawkins, Robert West, John Powell, and Alan Pickering. 2010. Relapse to smoking during unaided cessation: clinical, cognitive and motivational predictors. Psychopharmacology 212, 4 (2010), 537--549.Google ScholarGoogle ScholarCross RefCross Ref
  48. Anna Pulakka, Jaana I Halonen, Ichiro Kawachi, Jaana Pentti, Sari Stenholm, Markus Jokela, Ilkka Kaate, Markku Koskenvuo, Jussi Vahtera, and Mika Kivimäki. 2016. Association between distance from home to tobacco outlet and smoking cessation and relapse. JAMA internal medicine 176, 10 (2016), 1512--1519.Google ScholarGoogle Scholar
  49. Valentin Radu, Panagiota Katsikouli, Rik Sarkar, and Mahesh K Marina. 2014. A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems. ACM, 280--294.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Jerry H Ratcliffe. 2004. Geocoding crime and a first estimate of a minimum acceptable hit rate. International Journal of Geographical Information Science 18, 1 (2004), 61--72.Google ScholarGoogle ScholarCross RefCross Ref
  51. Lorraine R Reitzel, Ellen K Cromley, Yisheng Li, Yumei Cao, Richard Dela Mater, Carlos A Mazas, Ludmila Cofta-Woerpel, Paul M Cinciripini, and David W Wetter. 2011. The effect of tobacco outlet density and proximity on smoking cessation. American Journal of Public Health 101, 2 (2011), 315--320.Google ScholarGoogle ScholarCross RefCross Ref
  52. Alex Rodriguez and Alessandro Laio. 2014. Clustering by fast search and find of density peaks. Science 344, 6191 (2014), 1492--1496.Google ScholarGoogle Scholar
  53. Dennis W Rook and Robert J Fisher. 1995. Normative influences on impulsive buying behavior. Journal of consumer research 22, 3 (1995), 305--313.Google ScholarGoogle ScholarCross RefCross Ref
  54. Adam Sadilek and Henry Kautz. 2013. Modeling the impact of lifestyle on health at scale. In Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 637--646.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa Al'Absi, and Santosh Kumar. 2015. puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 999--1010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Hillol Sarker, Matthew Tyburski, Md Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David H Epstein, Kenzie L Preston, C Debra Furr-Holden, Adam Milam, Inbal Nahum-Shani, et al. 2016. Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, 4489--4501.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Edward Sazonov, Kristopher Metcalfe, Paulo Lopez-Meyer, and Stephen Tiffany. [n. d.]. RF hand gesture sensor for monitoring of cigarette smoking. In 2011 Fifth International Conference on Sensing Technology. IEEE, 426--430.Google ScholarGoogle Scholar
  58. K Schag, J Schönleber, M Teufel, S Zipfel, and KE Giel. 2013. Food-related impulsivity in obesity and Binge Eating Disorder-a systematic review. Obesity Reviews 14, 6 (2013), 477--495.Google ScholarGoogle ScholarCross RefCross Ref
  59. Philipp M Scholl, Nagihan Kücükyildiz, and Kristof Van Laerhoven. 2013. When do you light a fire? Capturing tobacco use with situated, wearable sensors. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. 1295--1304.Google ScholarGoogle Scholar
  60. Roberto Secades-Villa, Victor Martínez-Loredo, Aris Grande-Gosende, and José Ramón Fernández-Hermida. 2016. The relationship between impulsivity and problem gambling in adolescence. Frontiers in Psychology 7 (2016), 1931.Google ScholarGoogle ScholarCross RefCross Ref
  61. Volkan Senyurek, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. 2019. Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter. Sensors 19, 3 (2019), 570.Google ScholarGoogle ScholarCross RefCross Ref
  62. Cindy Shearer, Daniel Rainham, Chris Blanchard, Trevor Dummer, Renee Lyons, and Sara Kirk. 2015. Measuring food availability and accessibility among adolescents: Moving beyond the neighbourhood boundary. Social Science & Medicine 133 (2015), 322--330.Google ScholarGoogle ScholarCross RefCross Ref
  63. Saul Shiffman. 2005. Dynamic influences on smoking relapse process. Journal of personality 73, 6 (2005), 1715--1748.Google ScholarGoogle ScholarCross RefCross Ref
  64. Saul Shiffman, Jean A Paty, Maryann Gnys, Jon A Kassel, and Mary Hickcox. 1996. First lapses to smoking: within-subjects analysis of real-time reports. Journal of consulting and clinical psychology 64, 2 (1996), 366.Google ScholarGoogle ScholarCross RefCross Ref
  65. Muhammad Shoaib, Ozlem Durmaz Incel, Hans Scholten, and Paul Havinga. 2018. Smokesense: Online activity recognition framework on smartwatches. In International conference on mobile computing, applications, and services. Springer, 106--124.Google ScholarGoogle ScholarCross RefCross Ref
  66. Roger W Sinnott. 1984. Virtues of the Haversine. Sky Telesc. 68 (1984), 159.Google ScholarGoogle Scholar
  67. Scott L Stephens. 2005. Forest fire causes and extent on United States Forest Service lands. International Journal of Wildland Fire 14, 3 (2005), 213--222.Google ScholarGoogle ScholarCross RefCross Ref
  68. Julia M Townshend, Nicolas Kambouropoulos, Alison Griffin, Frances J Hunt, and Raffaella M Milani. 2014. Binge drinking, reflection impulsivity, and unplanned sexual behavior: impaired decision-making in young social drinkers. Alcoholism: Clinical and Experimental Research 38, 4 (2014), 1143--1150.Google ScholarGoogle ScholarCross RefCross Ref
  69. Jan Van den Broek. 1995. A score test for zero inflation in a Poisson distribution. Biometrics (1995), 738--743.Google ScholarGoogle Scholar
  70. Frank van Diggelen. 2002. Indoor GPS theory & implementation. In 2002 IEEE Position Location and Navigation Symposium (IEEE Cat. No. 02CH37284). IEEE, 240--247.Google ScholarGoogle ScholarCross RefCross Ref
  71. Michael W Wiederman and Tamara Pryor. 1996. Substance use and impulsive behaviors among adolescents with eating disorders. Addictive behaviors 21, 2 (1996), 269--272.Google ScholarGoogle Scholar
  72. Pin Wu, Jun-Wei Hsieh, Jiun-Cheng Cheng, Shyi-Chyi Cheng, and Shau-Yin Tseng. 2010. Human smoking event detection using visual interaction clues. In 2010 20th International Conference on Pattern Recognition. IEEE, 4344--4347.Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Zhi-Qiang Zeng, Hong-Bin Yu, Hua-Rong Xu, Yan-Qi Xie, and Ji Gao. 2008. Fast training support vector machines using parallel sequential minimal optimization. In 2008 3rd international conference on intelligent system and knowledge engineering, Vol. 1. IEEE, 997--1001.Google ScholarGoogle ScholarCross RefCross Ref
  74. Vincent W Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative location and activity recommendations with GPS history data. In Proceedings of the 19th international conference on World wide web. ACM, 1029--1038.Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Yu Zheng. 2015. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 3 (2015), 29.Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Yu Zheng, Like Liu, Longhao Wang, and Xing Xie. 2008. Learning transportation mode from raw gps data for geographic applications on the web. In Proceedings of the 17th international conference on World Wide Web. ACM, 247--256.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 4, Issue 1
      March 2020
      1006 pages
      EISSN:2474-9567
      DOI:10.1145/3388993
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 March 2020
      Published in imwut Volume 4, Issue 1

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader