The Great Plains Internet Wellness Study for Aging

Obesity Clinical Trial

Official title:

The Great Plains Internet Wellness Study for Aging: The GP I-WAS Project

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04461184
• Other study ID #: NorthDakotaSU
• Status: Completed
• Phase: N/A
• Start date: January 1, 2021
• Est. completion date: May 21, 2021

Study information

• Verified date: May 2021
• Source: North Dakota State University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Obesity is a major public health concern in older adults, who are also one of the fastest growing populations in the United States. Engaging in healthy behaviors such as physical activity, a healthy diet, and adequate sleep have each shown to be influential in reducing obesity. The internet could be an effective tool for administering a wellness intervention for older adults. Our goal is to help older adults achieve healthy lifestyles that promote successful aging.

Description:

Obesity is a major public health concern in older adults, who are also one of the fastest growing populations in the United States. The health consequences associated with obesity in older adulthood are severe and include increased risk for chronic diseases, poor physical functioning and early mortality. Concurrently engaging in healthy behaviors such as physical activity, a healthy diet, and adequate sleep have each shown to be influential in reducing obesity. Despite evidence suggesting that these healthy behaviors reduce the risk for obesity, older adults have difficulty achieving such healthy lifestyles due to barriers such as geographical isolation from lack of transportation, motivation and education. The internet is an effective mode for relaying health information to a wide-ranging audience, including those that live in rural communities or are home bound. Further, internet usage among older adults continues to increase. Therefore, the internet could be an effective tool for administering a wellness intervention for older adults and incorporating community-based participatory research principles such as inviting stakeholders (i.e., older adults) in all phases of the research will magnify the impact of the research for the population in which it is intended to help. Our long-term goal is to help older adults achieve healthy lifestyles that promote successful aging. The overall objective of the proposed research, which is the next step in pursuit of that goal, is to improve healthy lifestyles in older adults by utilizing the internet for delivering a wellness intervention that is designed by both investigators and stakeholders. To propel toward accomplishing our overall objective, the following three specific aims will be pursued: 1) collaborate with stakeholders in all phases of the internet-based wellness intervention to gain knowledge on the perspectives of the older adult population, 2) assess the feasibility of an internet-based wellness intervention for obese older adults, and 3) determine if completing an internet based wellness intervention improves healthy behaviors among obese older adults. We will recruit 20 eligible older adults to participate in an internet-based wellness intervention. A prospective, within-participant design with multiple assessments across the 10-week study period and 1-month follow-up will allow us to optimize power with a smaller sample size and assess within-person change over time.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 14
• Est. completion date: May 21, 2021
• Est. primary completion date: May 21, 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 65 Years and older

Eligibility

Inclusion Criteria:

• Adults aged at least 65 years that can use the internet daily, have a body mass index of = 30 kg/m2, and are apparently healthy (i.e., medically able to participate in physical activity as determined by the PAR-Q+) Will be eligible for the study.

Exclusion Criteria:

• Those with a surgical implant, who are unable to read or speak the English language fluently, with a severe cognitive impairment, and unable to wear an accelerometer on their waist will be excluded.

Related Conditions & MeSH terms

• Adult Population
• Internet Based Wellness Program
• Obesity

Intervention

Behavioral:
• Internet Wellness Intervention for Aging
Internet technologies have emerged as a platform for performing wellness interventions that also have wide outreach. Previous studies that have used the internet for delivering health interventions have found that older adults valued this platform, used it for researching health information and social communications. Likewise, the effectiveness of delivering health-related information intended for behavior change through the internet is equal to that of print-based delivery, thereby lowering costs and expanding reach. Thus, the internet provides a unique platform for conducting interventions. The internet based wellness intervention will be a low cost method focused on older adults to help increase intrinsic motivation through autonomy, competence, and relatedness (Intrinsic Motivation) to help increase daily physical activity.

Locations

Country	Name	City	State
United States	North Dakota State University Health, Nutrition, and Exercise Sciences	Fargo	North Dakota

Sponsors (1)

Lead Sponsor	Collaborator
North Dakota State University

Country where clinical trial is conducted

United States, 

References & Publications (55)

Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O'Brien WL, Bassett DR Jr, Schmitz KH, Emplaincourt PO, Jacobs DR Jr, Leon AS. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S498-504. — View Citation

Bales CW, Porter Starr KN. Obesity Interventions for Older Adults: Diet as a Determinant of Physical Function. Adv Nutr. 2018 Mar 1;9(2):151-159. doi: 10.1093/advances/nmx016. Review. — View Citation

Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004 Apr;31(2):143-64. — View Citation

Batsis JA, Zagaria AB. Addressing Obesity in Aging Patients. Med Clin North Am. 2018 Jan;102(1):65-85. doi: 10.1016/j.mcna.2017.08.007. Epub 2017 Oct 21. Review. — View Citation

Beccuti G, Pannain S. Sleep and obesity. Curr Opin Clin Nutr Metab Care. 2011 Jul;14(4):402-12. doi: 10.1097/MCO.0b013e3283479109. Review. — View Citation

Befort CA, Nazir N, Perri MG. Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005-2008). J Rural Health. 2012 Fall;28(4):392-7. doi: 10.1111/j.1748-0361.2012.00411.x. Epub 2012 May 31. — View Citation

Bennett JA, Winters-Stone K. Motivating older adults to exercise: what works? Age Ageing. 2011 Mar;40(2):148-9. doi: 10.1093/ageing/afq182. Epub 2011 Jan 20. — View Citation

Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J Epidemiol. 1986 Sep;124(3):453-69. — View Citation

Bolin JN, Bellamy GR, Ferdinand AO, Vuong AM, Kash BA, Schulze A, Helduser JW. Rural Healthy People 2020: New Decade, Same Challenges. J Rural Health. 2015 Summer;31(3):326-33. doi: 10.1111/jrh.12116. Epub 2015 May 7. — View Citation

Bowen DJ, Kreuter M, Spring B, Cofta-Woerpel L, Linnan L, Weiner D, Bakken S, Kaplan CP, Squiers L, Fabrizio C, Fernandez M. How we design feasibility studies. Am J Prev Med. 2009 May;36(5):452-7. doi: 10.1016/j.amepre.2009.02.002. — View Citation

Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011 Feb;43(2):357-64. doi: 10.1249/MSS.0b013e3181ed61a3. — View Citation

Cohen SA, Greaney ML, Sabik NJ. Assessment of dietary patterns, physical activity and obesity from a national survey: Rural-urban health disparities in older adults. PLoS One. 2018 Dec 5;13(12):e0208268. doi: 10.1371/journal.pone.0208268. eCollection 2018. — View Citation

Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992 Oct;15(5):461-9. — View Citation

Douthit N, Kiv S, Dwolatzky T, Biswas S. Exposing some important barriers to health care access in the rural USA. Public Health. 2015 Jun;129(6):611-20. doi: 10.1016/j.puhe.2015.04.001. Epub 2015 May 27. Review. — View Citation

Fakhouri TH, Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity among older adults in the United States, 2007-2010. NCHS Data Brief. 2012 Sep;(106):1-8. — View Citation

Foley LS, Maddison R, Rush E, Olds TS, Ridley K, Jiang Y. Doubly labeled water validation of a computerized use-of-time recall in active young people. Metabolism. 2013 Jan;62(1):163-9. doi: 10.1016/j.metabol.2012.07.021. Epub 2012 Sep 11. — View Citation

Gardner B, Lally P, Wardle J. Making health habitual: the psychology of 'habit-formation' and general practice. Br J Gen Pract. 2012 Dec;62(605):664-6. doi: 10.3399/bjgp12X659466. — View Citation

Ghanvatkar S, Kankanhalli A, Rajan V. User Models for Personalized Physical Activity Interventions: Scoping Review. JMIR Mhealth Uhealth. 2019 Jan 16;7(1):e11098. doi: 10.2196/11098. Review. — View Citation

Gomersall SR, Olds TS, Ridley K. Development and evaluation of an adult use-of-time instrument with an energy expenditure focus. J Sci Med Sport. 2011 Mar;14(2):143-8. doi: 10.1016/j.jsams.2010.08.006. Epub 2010 Oct 6. — View Citation

Hargens TA, Kaleth AS, Edwards ES, Butner KL. Association between sleep disorders, obesity, and exercise: a review. Nat Sci Sleep. 2013 Mar 1;5:27-35. doi: 10.2147/NSS.S34838. Print 2013. — View Citation

Hernandez DC, Johnston CA. Individual and Environmental Barriers to Successful Aging: The Importance of Considering Environmental Supports. Am J Lifestyle Med. 2016 Oct 24;11(1):21-23. doi: 10.1177/1559827616672617. eCollection 2017 Jan-Feb. Review. — View Citation

Hill JO, Catenacci V, Wyatt HR. Obesity: overview of an epidemic. Psychiatr Clin North Am. 2005 Mar;28(1):1-23, vii. Review. — View Citation

Horowitz CR, Robinson M, Seifer S. Community-based participatory research from the margin to the mainstream: are researchers prepared? Circulation. 2009 May 19;119(19):2633-42. doi: 10.1161/CIRCULATIONAHA.107.729863. Review. — View Citation

Jakicic JM, Rogers RJ, Davis KK, Collins KA. Role of Physical Activity and Exercise in Treating Patients with Overweight and Obesity. Clin Chem. 2018 Jan;64(1):99-107. doi: 10.1373/clinchem.2017.272443. Epub 2017 Nov 20. Review. — View Citation

Jones RB, Ashurst EJ, Atkey J, Duffy B. Older people going online: its value and before-after evaluation of volunteer support. J Med Internet Res. 2015 May 18;17(5):e122. doi: 10.2196/jmir.3943. — View Citation

Jonkman NH, van Schooten KS, Maier AB, Pijnappels M. eHealth interventions to promote objectively measured physical activity in community-dwelling older people. Maturitas. 2018 Jul;113:32-39. doi: 10.1016/j.maturitas.2018.04.010. Epub 2018 Apr 25. Review. — View Citation

Jopp DS, Jung S, Damarin AK, Mirpuri S, Spini D. Who Is Your Successful Aging Role Model? J Gerontol B Psychol Sci Soc Sci. 2017 Mar 1;72(2):237-247. doi: 10.1093/geronb/gbw138. — View Citation

Keadle SK, Shiroma EJ, Freedson PS, Lee IM. Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study. BMC Public Health. 2014 Nov 24;14:1210. doi: 10.1186/1471-2458-14-1210. — View Citation

Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011 Aug;43(8):1561-7. doi: 10.1249/MSS.0b013e31820ce174. — View Citation

Ling CH, de Craen AJ, Slagboom PE, Gunn DA, Stokkel MP, Westendorp RG, Maier AB. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr. 2011 Oct;30(5):610-5. doi: 10.1016/j.clnu.2011.04.001. Epub 2011 May 8. — View Citation

Mace CJ, Maddison R, Olds T, Kerse N. Validation of a computerized use of time recall for activity measurement in advanced-aged adults. J Aging Phys Act. 2014 Apr;22(2):245-54. doi: 10.1123/japa.2012-0280. Epub 2013 May 22. — View Citation

Marcus BH, Lewis BA, Williams DM, Dunsiger S, Jakicic JM, Whiteley JA, Albrecht AE, Napolitano MA, Bock BC, Tate DF, Sciamanna CN, Parisi AF. A comparison of Internet and print-based physical activity interventions. Arch Intern Med. 2007 May 14;167(9):944-9. — View Citation

Montoye AHK, Nelson MB, Bock JM, Imboden MT, Kaminsky LA, Mackintosh KA, McNarry MA, Pfeiffer KA. Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers. Med Sci Sports Exerc. 2018 May;50(5):1103-1112. doi: 10.1249/MSS.0000000000001534. — View Citation

Ness-Abramof R, Apovian CM. Diet modification for treatment and prevention of obesity. Endocrine. 2006 Feb;29(1):5-9. Review. — View Citation

Nicklett EJ, Kadell AR. Fruit and vegetable intake among older adults: a scoping review. Maturitas. 2013 Aug;75(4):305-12. doi: 10.1016/j.maturitas.2013.05.005. Epub 2013 Jun 12. Review. — View Citation

Pontzer H, Durazo-Arvizu R, Dugas LR, Plange-Rhule J, Bovet P, Forrester TE, Lambert EV, Cooper RS, Schoeller DA, Luke A. Constrained Total Energy Expenditure and Metabolic Adaptation to Physical Activity in Adult Humans. Curr Biol. 2016 Feb 8;26(3):410-7. doi: 10.1016/j.cub.2015.12.046. Epub 2016 Jan 28. — View Citation

Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997 Sep-Oct;12(1):38-48. Review. — View Citation

Riffin C, Kenien C, Ghesquiere A, Dorime A, Villanueva C, Gardner D, Callahan J, Capezuti E, Reid MC. Community-based participatory research: understanding a promising approach to addressing knowledge gaps in palliative care. Ann Palliat Med. 2016 Jul;5(3):218-24. doi: 10.21037/apm.2016.05.03. — View Citation

Rogers MA, Lemmen K, Kramer R, Mann J, Chopra V. Internet-Delivered Health Interventions That Work: Systematic Review of Meta-Analyses and Evaluation of Website Availability. J Med Internet Res. 2017 Mar 24;19(3):e90. doi: 10.2196/jmir.7111. Review. — View Citation

Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices. Med Sci Sports Exerc. 2016 Mar;48(3):457-65. doi: 10.1249/MSS.0000000000000778. — View Citation

Ryan K, Dockray S, Linehan C. A systematic review of tailored eHealth interventions for weight loss. Digit Health. 2019 Feb 5;5:2055207619826685. doi: 10.1177/2055207619826685. eCollection 2019 Jan-Dec. — View Citation

Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000 Jan;55(1):68-78. — View Citation

Saint Onge JM, Krueger PM. Health Lifestyle Behaviors among U.S. Adults. SSM Popul Health. 2017 Dec;3:89-98. doi: 10.1016/j.ssmph.2016.12.009. — View Citation

Salimi Y, Shahandeh K, Malekafzali H, Loori N, Kheiltash A, Jamshidi E, Frouzan AS, Majdzadeh R. Is Community-based Participatory Research (CBPR) Useful? A Systematic Review on Papers in a Decade. Int J Prev Med. 2012 Jun;3(6):386-93. — View Citation

Sartorio A, Malavolti M, Agosti F, Marinone PG, Caiti O, Battistini N, Bedogni G. Body water distribution in severe obesity and its assessment from eight-polar bioelectrical impedance analysis. Eur J Clin Nutr. 2005 Feb;59(2):155-60. — View Citation

Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011 Sep;14(5):411-6. doi: 10.1016/j.jsams.2011.04.003. Epub 2011 May 25. — View Citation

Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, Vandelanotte C. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016 Dec 7;13(1):127. Review. — View Citation

Sheeran P, Maki A, Montanaro E, Avishai-Yitshak A, Bryan A, Klein WM, Miles E, Rothman AJ. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: A meta-analysis. Health Psychol. 2016 Nov;35(11):1178-1188. Epub 2016 Jun 9. Review. — View Citation

Teixeira PJ, Carraça EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. 2012 Jun 22;9:78. doi: 10.1186/1479-5868-9-78. Review. — View Citation

Teixeira PJ, Silva MN, Mata J, Palmeira AL, Markland D. Motivation, self-determination, and long-term weight control. Int J Behav Nutr Phys Act. 2012 Mar 2;9:22. doi: 10.1186/1479-5868-9-22. Review. — View Citation

Thomson CA, Giuliano A, Rock CL, Ritenbaugh CK, Flatt SW, Faerber S, Newman V, Caan B, Graver E, Hartz V, Whitacre R, Parker F, Pierce JP, Marshall JR. Measuring dietary change in a diet intervention trial: comparing food frequency questionnaire and dietary recalls. Am J Epidemiol. 2003 Apr 15;157(8):754-62. — View Citation

Tudor-Locke C, Camhi SM, Troiano RP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination Survey, 2003-2006. Prev Chronic Dis. 2012;9:E113. Epub 2012 Jun 14. — View Citation

Wallerstein NB, Duran B. Using community-based participatory research to address health disparities. Health Promot Pract. 2006 Jul;7(3):312-23. Epub 2006 Jun 7. — View Citation

Wiklund P. The role of physical activity and exercise in obesity and weight management: Time for critical appraisal. J Sport Health Sci. 2016 Jun;5(2):151-154. doi: 10.1016/j.jshs.2016.04.001. Epub 2016 Apr 8. Review. — View Citation

Zamboni M, Mazzali G, Fantin F, Rossi A, Di Francesco V. Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis. 2008 Jun;18(5):388-95. doi: 10.1016/j.numecd.2007.10.002. Epub 2008 Apr 18. Review. — View Citation

* Note: There are 55 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Increase Physical Activity and Participation	Actigraph accelerometer and physical activity recall will be used to measure and record physical activity throughout the 10-week internet wellness intervention.	10-weeks
Primary	Create a more balanced dietary intake based on nutrient dense foods	Participants will complete the Arizona Food Frequency Questionnaire (AFFQ) to assess dietary intake at the beginning and end of the intervention, and at 1-month follow-up. The AFFQ is a modified version of the Health Habits Questionnaire and has demonstrated strong reliability and validity for assessing dietary intake. In addition, each report will contain a personalized message from the dietitian to each participant. Intake of nutritionally dense foods (e.g., vegetables, lean proteins) and decreased intake of calorically dense foods (e.g., high sugar foods) will be compared to assess dietary change.	10-weeks

External Links

•   A review of Internet use among older adults.
•   l. Assessing sleep using hip and wrist actigraphy
•   Obesity in Elderly
•   Obesity in older adults
•   Projections of the Size and Composition of the US Population: 2014 to 2060
•   Self-determination theory: A macrotheory of human motivation, development, and health.
•   Social cognitive theory of mass communication.
•   Statistical power analysis for the behavioral sciences.
•   United States Department of Health and Human Services
•   United States Department of Health and Human Services. Centers for Diseasee Control and Prevention. Introduction to program evaluation for public health programs: a self-study guide
•   Web-based physical activity interventions for older adults: A review.