Effectiveness of information technology–enabled ‘SMART Eating’ health promotion intervention: A cluster randomized controlled trial


Autoři: Jasvir Kaur aff001;  Manmeet Kaur aff001;  Venkatesan Chakrapani aff001;  Jacqui Webster aff003;  Joseph Alvin Santos aff003;  Rajesh Kumar aff001
Působiště autorů: Department of Community Medicine and School of Public Health, Post-graduate Institute of Medical Education and Research, Chandigarh, India aff001;  Centre for Sexuality and Health Research and Policy (C-SHaRP), Chennai, India aff002;  The George Institute for Global Health, University of New South Wales, Sydney, Australia aff003;  School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225892

Souhrn

Background

Unhealthy dietary behaviour–high intake of fat, sugar, and salt, and low intake of fruits and vegetables–is a major risk factor for chronic diseases. There is a lack of evidence-based interventions to promote healthy dietary intake among Indian populations. Therefore, we tested the effectiveness of an information technology-enabled ‘SMART Eating’ intervention to reduce the intake of fat, sugar and salt, and to increase the intake of fruits and vegetables.

Methods

In Chandigarh, a North Indian city, a cluster randomized controlled trial was implemented in twelve geographical clusters, based on the type of housing (i.e., LIG: Low-income group; MIG; Middle-income group, and HIG: High-income group–a proxy for socio-economic status). Computer-generated randomization was used to allocate clusters to intervention and comparison arms after pairing on the basis of socioeconomic status and geographical distance between clusters. The sample size was 366 families per arm (N = 732). One adult per family was randomly selected as an index case to measure the change in the outcomes. For behaviour change, a multi-channel communication approach was used, which included information technology–short message service (SMS), email, social networking app and ‘SMART Eating’ website, and interpersonal communication along with distribution of a ‘SMART Eating’ kit—kitchen calendar, dining table mat, and measuring spoons. The intervention was implemented at the family level over a period of six months. The comparison group received pamphlets on nutrition education. Outcome measurements were made at 0 and 6 months post-intervention at the individual level. Primary outcomes were changes in mean dietary intakes of fat, sugar, salt, and fruit and vegetables. Secondary outcomes included changes in body mass index (BMI), blood pressure, haemoglobin, fasting plasma glucose (FPG), and serum lipids. Mixed-effects linear regression models were used to determine the net change in the outcomes in the intervention group relative to the comparison group.

Results

Participants’ mean age was 53 years, a majority were women (76%), most were married (90%) and 51% had completed a college degree. All families had mobile phones, and more than 90% of these families had access to Internet through mobile phones. The intervention group had significant net mean changes of -12.5 g/day (p<0.001), -11.4 g/day (p<0.001), -0.5 g/day (p<0.001), and +71.6 g/day (p<0.001) in the intake of fat, sugar, salt, and fruit and vegetables, respectively. Similarly, significant net changes occurred for secondary outcomes: BMI -0.25 kg/m2, diastolic blood pressure -2.77 mm Hg, FPG -5.7 mg/dl, and triglycerides -24.2mg/dl. The intervention had no effect on haemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, or high-density lipoprotein cholesterol.

Conclusion

The IT-enabled ‘SMART Eating’ intervention was found to be effective in reducing fat, sugar, and salt intake, and increasing fruit and vegetable consumption among urban adults from diverse socio-economic backgrounds.

Trial registration

Clinical Trial Registry of India CTRI/2016/11/007457.

Klíčová slova:

Blood plasma – Blood pressure – Eating – Fats – Cholesterol – India – Nutrition – Sodium chloride


Zdroje

1. World Health Organization. Diet, nutrition and the prevention of chronic diseases: report of a Joint WHO/FAO Expert Consultation. WHO Technical Report Series 916. Geneva: World Health Organization; 2003.

2. World Health Organization. Global Strategy on Diet, Physical Activity and Health. Geneva: World Health Organization; 2004.

3. NACO. Annual Report—2016–17. National AIDS Control Organization (NACO). New Delhi: 2017.

4. World Health Organization. Non-communicable diseases—Fact sheet. 2018 [cited 2018 May 30]. Available from: http://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases.

5. Meenakshi JV. Trends and patterns in the triple burden of malnutrition in India. Agricultural Economics. 2016;47:115–34. doi: 10.1111/agec.12304

6. International Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-4), 2015–16: India. Mumbai: International Institute for Population Sciences; 2017.

7. National Institute of Nutrition (NIN), India. Diet and Nutrition Status of Urban Population of India: Report on Diet and Nutritional Status of Urban Population and prevalence of obesity, hypertension, diabetes and its associated non-communicable diseases released by NIN. 2017 [cited 2018 Feb 22]. Available from: http://icmr.nic.in/icmrnews/nin/ANNEXURE%20TO%20MEDIA%20RELEASE.pdf.

8. National White Paper (3rd Edition). Synergizing Efforts in Diabetes Care at the Tertiary Level—Strengthening Policies and Practices around Diabetes Management. A joint initiative of Confederation of India Industry, Ministry of Health and Family Welfare Government of India, the Lilly NCD partnership and Knowledge partner psi India. http://www.psi.org/wp-content/uploads/2015/09/National-NCD-White-Paper-2015.pdf. Accessed 20 Aug 2017.

9. World Health Organization. Diet and Physical Activity: a public health priority. http://www.who.int/dietphysicalactivity/background/en/. Accessed 25 Aug 2017.

10. Contento I, Balch GI, Bronner YL, Lytle LA, Maloney SK, Olson CM, et al. The effectiveness of nutrition education and implications for nutrition education policy, programs, and research: a review of research. J Nutr Educ. 1995;27(6):227–418.

11. Pomerleau J, Lock K, Knai C, McKee M. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr. 2005;135(10):2486–95. doi: 10.1093/jn/135.10.2486 16177217

12. Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med. 2010;51(3–4):214–21. doi: 10.1016/j.ypmed.2010.06.004 20558196

13. Jaime PC, Bandoni DH, Sarno F. Impact of an education intervention using email for the prevention of weight gain among adult workers. Public Health Nutr. 2014;17(7):1620–7. doi: 10.1017/S1368980013001936 23962422

14. Afshin A, Babalola D, McLean M, Yu Z, Ma W, Chen CY, et al. Information Technology and Lifestyle: A Systematic Evaluation of Internet and Mobile Interventions for Improving Diet, Physical Activity, Obesity, Tobacco, and Alcohol Use. Journal of the American Heart Association. 2016;5(9):e003058. doi: 10.1161/JAHA.115.003058 27581172

15. Harris J, Felix L, Miners A, Murray E, Michie S, Ferguson E, et al. Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2011;15(37):1–160. doi: 10.3310/hta15370 22030014

16. Feinberg L, Menon J, Smith R, Rajeev JG, Kumar RK, Banerjee A. Potential for mobile health (mHealth) prevention of cardiovascular diseases in Kerala: A population-based survey. Indian Heart J. 2017;69(2):182–99. doi: 10.1016/j.ihj.2016.11.004 28460766

17. Pfammatter A, Spring B, Saligram N, Dave R, Gowda A, Blais L, et al. mHealth Intervention to Improve Diabetes Risk Behaviors in India: A Prospective, Parallel Group Cohort Study. J Med Internet Res. 2016;18(8):e207. doi: 10.2196/jmir.5712 27496271

18. Svetkey LP, Batch BC, Lin PH, Intille SS, Corsino L, Tyson CC, et al. Cell phone Intervention for You (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology. Obesity. 2015;23(11):2133–41. doi: 10.1002/oby.21226 26530929

19. Campbell MK, Piaggio G, Elbourne DR, Altman DG. Consort 2010 statement: extension to cluster randomised trials. BMJ: British Medical Journal. 2012;345.

20. Kaur J, Kaur M, Webster J, Kumar R. Protocol for a cluster randomised controlled trial on information technology-enabled nutrition intervention among urban adults in Chandigarh (India): SMART eating trial. Global Health Action. 2018;11(1):1419738. doi: 10.1080/16549716.2017.1419738 29370744

21. Land MA, Webster J, Christoforou A, Praveen D, Jeffery P, Chalmers J, et al. Salt intake assessed by 24 h urinary sodium excretion in a random and opportunistic sample in Australia. BMJ open. 2014;4:e003720. doi: 10.1136/bmjopen-2013-003720 24440795

22. Hayes RJ, Bennett S. Simple sample size calculation for cluster-randomized trials. Int J Epidemiol. 1999;28(2):319–26. doi: 10.1093/ije/28.2.319 10342698

23. Fewtrell MS, Kennedy K, Singhal A, Martin RM, Ness A, Hadders-Algra M, et al. How much loss to follow-up is acceptable in long-term randomised trials and prospective studies? Arch Dis Child. 2008;93(6):458–61. doi: 10.1136/adc.2007.127316 18495909

24. Clark CC, Paraska KK. Chapter 2, Concepts, Models, and Theories. In: Health Promotion for Nurses: A Practical Guide. USA: Johns and Bartlett Learning; 2014.

25. National Institute of Nutrition. Indian Council of Medical Research. Dietary guidelines for Indians: A manual. Hyderabad, India: ICMR; 2011.

26. Kaur J, Kaur M, Chakrapani V, Kumar R. Multi-level influences on dietary behaviours among urban Indians: Application of the Social Ecological Model. SAGE Open.

27. Mahajan R, Malik M, Bharathi AV, Lakshmi PV, Patro BK, Rana SK, et al. Reproducibility and validity of a quantitative food frequency questionnaire in an urban and rural area of northern India. Natl Med J India. 2013;26(5):266–72. 25017832

28. Tsai AC, Burns BF. Syndemics of psychosocial problems and HIV risk: A systematic review of empirical tests of the disease interaction concept. Soc Sci Med. 2015;139:26–35. doi: 10.1016/j.socscimed.2015.06.024 26150065

29. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, et al. AHA Scientific Statement: Recommendations for blood pressure measurement in humans and experimental animals—Part 1: Blood pressure measurement in humans. Circulation. 2005;111(5):697–716. doi: 10.1161/01.CIR.0000154900.76284.F6 15699287

30. World Health Organisation. WHO STEPS surveillance manual: The WHO STEPwise approach to chronic disease risk factor surveillance. 2005 [cited 2017 Aug 28]. Available from: http://apps.who.int/iris/bitstream/10665/43376/1/9241593830_eng.pdf.

31. White IR, Carpenter J, Horton NJ. Including all individuals is not enough: lessons for intention-to-treat analysis. Clin Trials. 2012;9(4):396–407. doi: 10.1177/1740774512450098 22752633

32. Mustanski B, Garofalo R, Herrick A, Donenberg G. Psychosocial health problems increase risk for HIV among urban young men who have sex with men: preliminary evidence of a syndemic in need of attention. Ann Behav Med. 2007;34(1):37–45. doi: 10.1080/08836610701495268 17688395

33. Bhardwaj S, Misra A, Gulati S, Anoop S, Kamal VK, Pandey RM. A randomized controlled trial to evaluate the effects of high Protein Complete (lActo) VEgetaRian (PACER) diet in non-diabetic obese Asian Indians in North India. Heliyon. 2017;3(12):e00472. doi: 10.1016/j.heliyon.2017.e00472 29387815

34. Chen S-Y, Feng Z, Yi X. A general introduction to adjustment for multiple comparisons. J Thorac Dis. 2017;9(6):1725–9. doi: 10.21037/jtd.2017.05.34 28740688

35. UNIT 8 Assessment of nutritional status in community settings -11 [cited 2017 Nov 15]. Available from: http://egyankosh.ac.in/bitstream/123456789/33465/1/Unit-8.pdf.

36. Shrivastava S, Shrivastava P, Ramasamy J. Assessment of nutritional status in the community and clinical settings. J Med Sci. 2014;34(5):211–3.

37. Mohan S, D. P. World Health Organization. Technical paper—Review of salt and health: Situation in South-East Asia Region. Technical working group meeting on regional action plan and targets for prevention and control of NCDs Bangkok, Thailand, 11–13 June 2013. [cited 2018 Feb 20]. Available from: http://www.searo.who.int/entity/noncommunicable_diseases/events/ncd_twg_bangkok_technical_paper_review_of_salt_and_health.pdf.

38. Daivadanam M, Wahlström R, Ravindran TKS, Sarma PS, Sivasankaran S, Thankappan KR. Changing household dietary behaviours through community-based networks: A pragmatic cluster randomized controlled trial in rural Kerala, India. PLoS One. 2018;13(8):e0201877. doi: 10.1371/journal.pone.0201877 30133467

39. Wright JL, Sherriff JL, Dhaliwal SS, Mamo JC. Tailored, iterative, printed dietary feedback is as effective as group education in improving dietary behaviours: results from a randomised control trial in middle-aged adults with cardiovascular risk factors. The international journal of behavioral nutrition and physical activity. 2011;8:43. doi: 10.1186/1479-5868-8-43 21595978

40. Djuric Z, Poore KM, Depper JB, Uhley VE, Lababidi S, Covington C, et al. Methods to increase fruit and vegetable intake with and without a decrease in fat intake: compliance and effects on body weight in the nutrition and breast health study. Nutr Cancer. 2002;43(2):141–51. doi: 10.1207/S15327914NC432_4 12588694

41. John JH, Ziebland S, Yudkin P, Roe LS, Neil HA, Oxford F, et al. Effects of fruit and vegetable consumption on plasma antioxidant concentrations and blood pressure: a randomised controlled trial. Lancet. 2002;359(9322):1969–74. doi: 10.1016/s0140-6736(02)98858-6 12076551

42. Lanza E, Schatzkin A, Daston C, Corle D, Freedman L, Ballard-Barbash R, et al. Implementation of a 4-y, high-fiber, high-fruit-and-vegetable, low-fat dietary intervention: results of dietary changes in the Polyp Prevention Trial. Am J Clin Nutr. 2001;74(3):387–401. doi: 10.1093/ajcn/74.3.387 11522565

43. Engbers LH, van Poppel MN, Chin APM, van Mechelen W. The effects of a controlled worksite environmental intervention on determinants of dietary behavior and self-reported fruit, vegetable and fat intake. BMC Public Health. 2006;6:253. doi: 10.1186/1471-2458-6-253 17044935

44. Kennedy BM, Katzmarzyk PT, Johnson WD, Johnson GS, McGee BB, Champagne CM, et al. People United to Sustain Health (PUSH): a community-based participatory research study. Clin Transl Sci. 2014;7(2):108–14. doi: 10.1111/cts.12133 24405579

45. Paineau DL, Beaufils F, Boulier A, Cassuto DA, Chwalow J, Combris P, et al. Family dietary coaching to improve nutritional intakes and body weight control: a randomized controlled trial. Arch Pediatr Adolesc Med. 2008;162(1):34–43. doi: 10.1001/archpediatrics.2007.2 18180410

46. Takashashi Y, Sasaki S, Takahashi M, Okubo S, Hayashi M, Tsugane S. A population-based dietary intervention trial in a high-risk area for stomach cancer and stroke: changes in intakes and related biomarkers. Prev Med. 2003;37(5):432–41. doi: 10.1016/s0091-7435(03)00164-6 14572428

47. Greene GW, Fey-Yensan N, Padula C, Rossi SR, Rossi JS, Clark PG. Change in fruit and vegetable intake over 24 months in older adults: results of the SENIOR project intervention. Gerontologist. 2008;48(3):378–87. doi: 10.1093/geront/48.3.378 18591363

48. Stevens VJ, Glasgow RE, Toobert DJ, Karanja N, Smith KS. One-year results from a brief, computer-assisted intervention to decrease consumption of fat and increase consumption of fruits and vegetables. Prev Med. 2003;36(5):594–600. doi: 10.1016/s0091-7435(03)00019-7 12689805

49. Siddiqui F, Winther V, Kurbasic A, Sonestedt E, Lundgren KB, Lindeberg S, et al. Changes in dietary intake following a culturally adapted lifestyle intervention among Iraqi immigrants to Sweden at high risk of type 2 diabetes: a randomised trial. Public Health Nutr. 2017;20(15):2827–38. doi: 10.1017/S136898001700146X 28738912

50. Springvloet L, Lechner L, de Vries H, Oenema A. Long-term efficacy of a Web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: a randomized controlled trial. BMC Public Health. 2015;15:372. doi: 10.1186/s12889-015-1707-4 25887891

51. Bazzano LA, Hu T, Reynolds K, Yao L, Bunol C, Liu Y, et al. Effects of low-carbohydrate and low-fat diets: a randomized trial. Ann Intern Med. 2014;161(5):309–18. doi: 10.7326/M14-0180 25178568

52. He FJ, Wu Y, Feng XX, Ma J, Ma Y, Wang H, et al. School based education programme to reduce salt intake in children and their families (School-EduSalt): cluster randomised controlled trial. BMJ. 2015;350:h770. doi: 10.1136/bmj.h770 25788018

53. Costa Ede A, Rose G, Klein CH, Achutti AC. Diastolic pressure as an index of salt sensitivity. J Hum Hypertens. 1994;8(9):703–9. 7807501

54. Cheung BMY, Ho SPC, Cheung AHK, Lau CP. Diastolic blood pressure is related to urinary sodium excretion in hypertensive Chinese patients. Q J Med. 2000;93(3):163–8.

55. Dehghan M, Mente A, Zhang X, Swaminathan S, Li W, Mohan V, et al. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet. 2017;390(10107):2050–62. doi: 10.1016/S0140-6736(17)32252-3 28864332

56. Jung CH, Choi KM. Impact of High-Carbohydrate Diet on Metabolic Parameters in Patients with Type 2 Diabetes. Nutrients. 2017;9(4):E322. doi: 10.3390/nu9040322 28338608

57. Steinemann N, Grize L, Ziesemer K, Kauf P, Probst-Hensch N, Brombach C. Relative validation of a food frequency questionnaire to estimate food intake in an adult population. Food & nutrition research. 2017;61(1):1305193.

58. Naska A, Lagiou A, Lagiou P. Dietary assessment methods in epidemiological research: current state of the art and future prospects. F1000Research. 2017;6:926. doi: 10.12688/f1000research.10703.1 28690835

59. Heritier SR, Gebski VJ, Keech AC. Inclusion of patients in clinical trial analysis: the intention-to-treat principle. Med J Aust. 2003;179(8):438–40. 14558871

60. Fergusson D, Aaron SD, Guyatt G, Hebert P. Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis. BMJ. 2002;325(7365):652–4. doi: 10.1136/bmj.325.7365.652 12242181

61. Gupta SK. Intention-to-treat concept: A review. Perspect Clin Res. 2011;2(3):109–12. doi: 10.4103/2229-3485.83221 21897887

62. Pagoto SL, Schneider KL, Oleski JL, Luciani JM, Bodenlos JS, Whited MC. Male inclusion in randomized controlled trials of lifestyle weight loss interventions. Obesity. 2012;20(6):1234–9. doi: 10.1038/oby.2011.140 21633403

63. Daivadanam M, Wahlstrom R, Ravindran TKS, Sarma PS, Sivasankaran S, Thankappan KR. Design and methodology of a community-based cluster-randomized controlled trial for dietary behaviour change in rural Kerala. Global Health Action. 2013;6(1):20993.

64. Thankappan K, Shah B, Mathur P, Sarma P, Srinivas G, Mini G, et al. Risk factor profile for chronic non-communicable diseases: results of a community-based study in Kerala, India. Indian J Med Res. 2010;131(1):53–63.


Článek vyšel v časopise

PLOS One


2020 Číslo 1