Mobile technology intervention for weight loss in rural men: protocol for a pilot pragmatic randomised controlled trial

Christine M Eisenhauer, Fabiana Almeida Brito, Aaron M Yoder, Kevin A Kupzyk, Carol H Pullen, Katherine E Salinas, Jessica Miller, Patricia A Hageman, Christine M Eisenhauer, Fabiana Almeida Brito, Aaron M Yoder, Kevin A Kupzyk, Carol H Pullen, Katherine E Salinas, Jessica Miller, Patricia A Hageman

Abstract

Introduction: Men who are overweight or obese in the rural Midwestern USA are an unrepresented, at-risk group exhibiting rising rates of cardiovascular disease, poor access to preventive care and poor lifestyle behaviours that contribute to sedentary lifestyle and unhealthy diet. Self-monitoring of eating and activity has demonstrated efficacy for weight loss. Use of mobile technologies for self-monitoring eating and activity may address rural men's access disparities to preventive health resources and support weight loss. Our pilot trial will assess the feasibility and acceptability of two mobile applications for weight loss in rural men to inform a future, full-scale trial.

Methods and analysis: A 6-month randomised controlled trial with contextual evaluation will randomise 80 men using a 1:1 ratio to either a Mobile Technology Plus (MT+) intervention or a basic Mobile Technology (MT) intervention in rural, midlife men (aged 40-69 years). The MT+ intervention consists of a smartphone self-monitoring application enhanced with discussion group (Lose-It premium), short message service text-based support and Wi-Fi scale. The MT group will receive only a self-monitoring application (Lose-It basic). Feasibility and acceptability will be evaluated using number of men recruited and retained, and evaluative focus group feedback. We seek to determine point estimates and variability of outcome measures of weight loss (kg and % body weight) and improved dietary and physical activity behaviours (Behavioral Risk Factor Surveillance System (BRFSS) physical activity and fruit and vegetable consumption surveys, data from Lose-It! application (kcal/day, steps/day)). Community capacity will be assessed using standard best practice methods. Descriptive content analysis will evaluate intervention acceptability and contextual sensitivity.

Ethics and dissemination: This protocol was approved by the University of Nebraska Medical Center Institutional Review Board (IRB# 594-17-EP). Dissemination of findings will occur through ClinicalTrials.gov and publish pilot data to inform the design of a larger clinical trial.

Trial registration number: NCT03329079; preresults. Protocol V.10, study completion date 31 August 2020. Roles and responsibilities funder: NIH/NINR Health Disparities Section 1R15NR017522-01.

Keywords: preventive medicine; primary care; public health.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Participant timeline.
Figure 2
Figure 2
Figure 2Outcome Measures.

References

    1. Hales C, Carroll M, Fryar C, et al. . Prevalence of obesity among adults and youth: United States, 2015-2016. Hyattsville, MD: National Center for Health Statistics, 2017.
    1. Ogden CL, Carroll MD, Kit BK, et al. . Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14. 10.1001/jama.2014.732
    1. Meit M, Knudson A, Gilbert T, et al. . The 2014 update of the rural-urban chartbook. Bethesda. Rural Health Reform Policy Research Center, 2014.
    1. Shelton JB, Rajfer J. Androgen deficiency in aging and metabolically challenged men. Urol Clin North Am 2012;39:63–75. 10.1016/j.ucl.2011.09.007
    1. Eisenhauer CM, Hageman PA, Rowland S, et al. . Acceptability of mHealth technology for self-monitoring eating and activity among rural men. Public Health Nurs 2017;34:138–46. 10.1111/phn.12297
    1. Lundeen E, Park S, Liping P, et al. . Obesity prevalence among adults living in metropolitan and nonmetropolitan countries-Unitied states. Morbidity and Mortality Weekly Report 2016:653–8.
    1. Guo Z, Jiang Y, Huffman SK. Agricultural mechanization and BMI for rural workers: a field experiment in China. Economics Working Papers 2018.
    1. Pickett W, King N, Lawson J, et al. . Farmers, mechanized work, and links to obesity. Prev Med 2015;70:59–63. 10.1016/j.ypmed.2014.11.012
    1. Han M, Lee E. Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials. Healthc Inform Res 2018;24:207. 10.4258/hir.2018.24.3.207
    1. Beleigoli A, Andrade A, Cançado A, et al. . The impact of web-based digital health interventions on weight loss and lifestyle habits changes in overweight and obese adults: a systematic review and meta-analysis (Preprint). Journal of Medical Internet Research 2017;21.
    1. Fortuin J, Salie F, Abdullahi LH, et al. . The impact of mHealth interventions on health systems: a systematic review protocol. Syst Rev 2016;5:200. 10.1186/s13643-016-0387-1
    1. Anderson-Lewis C, Darville G, Mercado RE, et al. . mHealth technology use and implications in historically underserved and minority populations in the United States: systematic literature review. JMIR Mhealth Uhealth 2018;6:e128. 10.2196/mhealth.8383
    1. Robertson C, Avenell A, Stewart F, et al. . Clinical effectiveness of weight loss and weight maintenance interventions for men: a systematic review of men-only randomized controlled trials (the ROMEO project). Am J Mens Health 2017;11:1096–123. 10.1177/1557988315587550
    1. Neumark-Sztainer D, Sherwood NE, French SA, et al. . Weight control behaviors among adult men and women: cause for concern? Obes Res 1999;7:179–88. 10.1002/j.1550-8528.1999.tb00700.x
    1. Lemon SC, Rosal MC, Zapka J, et al. . Contributions of weight perceptions to weight loss attempts: differences by body mass index and gender. Body Image 2009;6:90–6. 10.1016/j.bodyim.2008.11.004
    1. French SA, Jeffery RW, Wing RR. Sex differences among participants in a weight-control program. Addict Behav 1994;19:147–58. 10.1016/0306-4603(94)90039-6
    1. Lovejoy JC, Sainsbury A, Stock Conference 2008 Working Group . Sex differences in obesity and the regulation of energy homeostasis. Obes Rev 2009;10:154–67. 10.1111/j.1467-789X.2008.00529.x
    1. Klitzman P, Armstrong B, Janicke DM. Distance as a predictor of treatment attendance in a family based pediatric weight management program in rural areas. J Rural Health 2015;31:19–26. 10.1111/jrh.12078
    1. Hiebert B, Leipert B, Regan S, et al. . Rural men's health, health information seeking, and gender identities: a conceptual theoretical review of the literature. Am J Mens Health 2018;12:863–76. 10.1177/1557988316649177
    1. Graham LJ, Connelly DM. "Any movement at all is exercise": a focused ethnography of rural community-dwelling older adults' perceptions and experiences of exercise as self-care. Physiother Can 2013;65:333–41. 10.3138/ptc.2012-31
    1. Whitehead AL, Sully BGO, Campbell MJ. Pilot and feasibility studies: is there a difference from each other and from a randomised controlled trial? Contemp Clin Trials 2014;38:130–3. 10.1016/j.cct.2014.04.001
    1. Zuidgeest MGP, Goetz I, Groenwold RHH, et al. . Series: pragmatic trials and real world evidence: paper 1. Introduction. J Clin Epidemiol 2017;88:7–13. 10.1016/j.jclinepi.2016.12.023
    1. Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: recommendations for good practice. J Eval Clin Pract 2004;10:307–12. 10.1111/j.2002.384.doc.x
    1. Browne RH. On the use of a pilot sample for sample size determination. Stat Med 1995;14:1933–40. 10.1002/sim.4780141709
    1. National Institutes of Health Academic research enhancement award (parent R15) PA-16-200 : Department of health and human services Part 1 overview information. National Institutes of Health, 2017.
    1. Robsahm TE, Heir T, Sandvik L, et al. . Changes in midlife fitness, body mass index, and smoking influence cancer incidence and mortality: a prospective cohort study in men. Cancer Med 2019;8:4875–82. 10.1002/cam4.2383
    1. Strandberg TE, Sirola J, Pitkälä KH, et al. . Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men. Int J Obes 2012;36:1153–7. 10.1038/ijo.2012.83
    1. Holme I, Tonstad S. Survival in elderly men in relation to midlife and current BMI. Age Ageing 2015;44:434–9. 10.1093/ageing/afu202
    1. Shapiro JR, Koro T, Doran N, et al. . Text4Diet: a randomized controlled study using text messaging for weight loss behaviors. Prev Med 2012;55:412–7. 10.1016/j.ypmed.2012.08.011
    1. Shaw RJ, Bosworth HB, Hess JC, et al. . Development of a theoretically driven mHealth text messaging application for sustaining recent weight loss. JMIR Mhealth Uhealth 2013;1:e5. 10.2196/mhealth.2343
    1. Patrick K, Raab F, Adams MA, et al. . A text message-based intervention for weight loss: randomized controlled trial. J Med Internet Res 2009;11:e1. 10.2196/jmir.1100
    1. Gerber BS, Stolley MR, Thompson AL, et al. . Mobile phone text messaging to promote healthy behaviors and weight loss maintenance: a feasibility study. Health Informatics J 2009;15:17–25. 10.1177/1460458208099865
    1. Joo N-S, Kim B-T. Mobile phone short message service messaging for behaviour modification in a community-based weight control programme in Korea. J Telemed Telecare 2007;13:416–20. 10.1258/135763307783064331
    1. Lee D, Moon J, Kim YJ, et al. . Antecedents and consequences of mobile phone usability: linking simplicity and interactivity to satisfaction, trust, and brand Loyalty. Inf Manage 2015;52:295–304. 10.1016/j.im.2014.12.001
    1. United States Department of Agriculture Dietary guidlines for Americans online: USDA, 2018. Available:
    1. Centers for Disease Control and Prevention Surveillance of fruit and vegetable intake using the behavioral risk factor surveillance system, 2015. Available:
    1. Hydén L-C, Bülow PH. Who's talking: drawing conclusions from focus groups—some methodological considerations. Int J Soc Res Methodol 2003;6:305–21. 10.1080/13645570210124865
    1. Rodgers BL, Cowles KV. The qualitative research audit TRAIL: a complex collection of documentation. Res Nurs Health 1993;16:219–26. 10.1002/nur.4770160309
    1. Mannell J, Davis K. Evaluating complex health interventions with randomized controlled trials: how do we improve the use of qualitative methods? Qual Health Res 2019;29:623–31. 10.1177/1049732319831032
    1. Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res 2005;15:1277–88. 10.1177/1049732305276687
    1. Haussen DC, Doppelheuer S, Schindler K, et al. . Utilization of a smartphone platform for electronic informed consent in acute stroke trials. Stroke 2017;48:3156–60. 10.1161/STROKEAHA.117.018380
    1. Civelek ME, Uca N, Çemberci M. eUCP and electronic commerce investments: E-signature and paperless foreign trade. Eurasian Academy of Sciences, Eurasian Business & Economics Journal 2015;3.
    1. Tsui EY, Gao XJ, Zinman B. Bioelectrical impedance analysis (BIA) using bipolar foot electrodes in the assessment of body composition in type 2 diabetes mellitus. Diabet Med 1998;15:125–8. 10.1002/(SICI)1096-9136(199802)15:2<125::AID-DIA532>;2-N
    1. Brownson RC, Jones DA, Pratt M, et al. . Measuring physical activity with the behavioral risk factor surveillance system. Med Sci Sports Exerc 2000;32:1913–8. 10.1097/00005768-200011000-00015
    1. Hedrick VE, Savla J, Comber DL, et al. . Development of a brief questionnaire to assess habitual beverage intake (BEVQ-15): sugar-sweetened beverages and total beverage energy intake. J Acad Nutr Diet 2012;112:840–9. 10.1016/j.jand.2012.01.023
    1. Zoellner J, Hill JL, Brock D, et al. . One-Year mixed-methods case study of a Community-Academic Advisory board addressing childhood obesity. Health Promot Pract 2017;18:833–53. 10.1177/1524839916689550
    1. Lundeen EA, Park S, Pan L, et al. . Daily intake of sugar-sweetened beverages among US adults in 9 states, by state and sociodemographic and behavioral characteristics, 2016. Prev Chronic Dis 2018;15:E154–E54. 10.5888/pcd15.180335
    1. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr 2006;84:274–88. 10.1093/ajcn/84.2.274
    1. Singh GM, Micha R, Khatibzadeh S, et al. . Global, regional, and national consumption of sugar-sweetened beverages, fruit juices, and milk: a systematic assessment of beverage intake in 187 countries. PLoS One 2015;10:e0124845. 10.1371/journal.pone.0124845
    1. Sharkey JR, Johnson CM, Dean WR. Less-healthy eating behaviors have a greater association with a high level of sugar-sweetened beverage consumption among rural adults than among urban adults. Food Nutr Res 2011;55:5819. 10.3402/fnr.v55i0.5819
    1. Pickering TG, Hall JE, Appel LJ, et al. . Recommendations for blood pressure measurement in humans and experimental animals. Hypertension 2005;45:142–61. 10.1161/01.HYP.0000150859.47929.8e
    1. Hageman PA, Pullen CH, Hertzog M, et al. . Web-Based interventions alone or supplemented with peer-led support or professional email counseling for weight loss and weight maintenance in women from rural communities: results of a clinical trial. J Obes 2017;2017:1–21. 10.1155/2017/1602627
    1. Perloff D, Grim C, Flack J, et al. . Human blood pressure determination by sphygmomanometry. Circulation 1993;88:2460–70. 10.1161/01.CIR.88.5.2460
    1. Eisenhauer C, et al. Partnering with rural farm women for participatory action and ethnography. Online Journal of Rural Nursing and Health Care 2016;16:196–216. 10.14574/ojrnhc.v16i1.397
    1. Yen P-Y, Wantland D, Bakken S. Development of a customizable health it usability evaluation scale. AMIA Annu Symp Proc 2010;2010:917–21.
    1. Morgan PJ, Callister R, Collins CE, et al. . The SHED-IT community trial: a randomized controlled trial of internet- and paper-based weight loss programs tailored for overweight and obese men. Ann Behav Med 2013;45:139–52. 10.1007/s12160-012-9424-z
    1. Yen P-Y, Sousa KH, Bakken S. Examining construct and predictive validity of the Health-IT usability evaluation scale: confirmatory factor analysis and structural equation modeling results. J Am Med Inform Assoc 2014;21:e241–8. 10.1136/amiajnl-2013-001811
    1. Saldaña J. The coding manual for qualitative researchers. 3rd edn Thoursand Oaks: Sage, 2015.
    1. Liberato SC, Brimblecombe J, Ritchie J, et al. . Measuring capacity building in communities: a review of the literature. BMC Public Health 2011;11:850. 10.1186/1471-2458-11-850

Source: PubMed

スポンサーと協力者

医学的状態

薬物療法

3
購読する