Protocol for a cluster randomised controlled trial on information technology-enabled nutrition intervention among urban adults in Chandigarh (India): SMART eating trial

References

• 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.
   Google Scholar
• Rangan AM, Schindeler S, Hector DJ, et al. Consumption of ‘extra’ foods by Australian adults: types, quantities and contribution to energy and nutrient intakes. Eur J Clin Nutr. 2009;63:865–12.
   PubMed Web of Science ®Google Scholar
• Mohan V, Shanthirani S, Deepa R, et al. Intra-urban differences in the prevalence of the metabolic syndrome in southern India – the Chennai Urban Population Study (CUPS No. 4). Diabet Med. 2001;18:280–287.
   PubMed Web of Science ®Google Scholar
• O’Donnell M, Mente A, Rangarajan S. Urinary sodium and potassium excretion, mortality, and cardiovascular events. N Engl J Med. 2014;371:612–623.
   PubMed Web of Science ®Google Scholar
• Johnson C, Praveen D, Pope A, et al. Mean population salt consumption in India: a systematic review. J Hypertens. 2017;35:3–9.
   Web of Science ®Google Scholar
• Hall JN, Moore S, Harper SB, et al. Global variability in fruit and vegetable consumption. Am J Prev Med. 2009;36:402–409, e405.
   PubMed Web of Science ®Google Scholar
• Australian Bureau of Statistics. Australian Health Survey: First Results, 2011-12; [cited 2017 May 31]. Available from: http://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/1680ECA402368CCFCA257AC90015AA4E/$File/4364.0.55.001.pdf
   Google Scholar
• Sachdeva S, Sachdev TR, Sachdeva R. Increasing fruit and vegetable consumption: challenges and opportunities. Indian J Community Med. 2013;38:192–197.
   Google Scholar
• Government of India. Ministry of Health and Family welfare. National Family Health Survey-4 (2015-16): India fact sheet; [cited 2017 Sep 20]. Available from: http://rchiips.org/NFHS/pdf/NFHS4/India.pdf
   Google Scholar
• World Health Organization. The Growing Crisis of Non-communicable Diseases in the South-East Asia Region: Fact sheet; 2011 [cited 2017 Sep 21]. Available from: http://www.searo.who.int/entity/noncommunicable_diseases/media/non_communicable_diseases_fact_sheet.pdf
   Google Scholar
• Contento I, Balch GI, Bronner YL, et al. The effectiveness of nutrition education and implications for nutrition education policy, programs, and research: a review of research. J Nutr Educ. 1995;27:227–418.
   Google Scholar
• Ammerman AS, Lindquist CH, Lohr KN, et al. The efficacy of behavioral interventions to modify dietary fat and fruit and vegetable intake: a review of the evidence. Prev Med. 2002;35:25–41.
   PubMed Web of Science ®Google Scholar
• Pomerleau J, Lock K, Knai C, et al. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr. 2005;135:2486–2495.
   PubMed Web of Science ®Google Scholar
• Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med. 2010;51:214–221.
   PubMed Web of Science ®Google Scholar
• 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;17:1620–1627.
   Web of Science ®Google Scholar
• Gupta MK, Chandraprabha. RV. Momentum in penetration of technology in Indian healthcare: a way forward. Int J Recent Sci Res. 2015;6:3881–3886.
   Google Scholar
• Afshin A, Babalola D, McLean M, et al. Information technology and lifestyle: a systematic evaluation of internet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J Am Heart Assoc. 2016;5:e003058.
   Web of Science ®Google Scholar
• Harris J, Felix L, Miners A, et al. Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2011;15:1–160.
   Web of Science ®Google Scholar
• International Telecommunication Union. ICT Data and Statistics Division, Telecommunication Development Bureau. ICT Facts and Figures: The world in 2015; [cited 2017 Sep 21]. Available from: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf
   Google Scholar
• Clark CC, Paraska KK. Health promotion for nurses: a practical guide. USA: Jones and Barlett Learning; 2014.
   Google Scholar
• Biswas R State wise Mobile Phone users in India Census 2011; 2012 [cited 2017 Sep 20]. Available from: https://updateox.com/india/state-wise-mobile-phone-users-in-india-census-2011/
   Google Scholar
• Internet and Mobile Association of India & Kantar Indian Market Research Bureau. Internet in India - 2016: An IAMAI & Kantar IMRB report; [cited 2017 Sep 16]. Available from: http://bestmediainfo.com/wp-content/uploads/2017/03/Internet-in-India-2016.pdf
   Google Scholar
• Internet and Mobile Association of India. IAMAI Press Release, November 17, 2015; [cited 2017 Sep 16]. Available from: http://www.iamai.in/media/details/4486
   Google Scholar
• Press Trust of India. The Times of India. Google: Internet users in India to touch 500 million by 2017 February 21; 2016 [cited 2017 Sep 16]. Available from: http://www.gadgetsnow.com/tech-news/Google-Internet-users-in-India-to-touch-500-million-by-2017/articleshow/51077664.cms
   Google Scholar
• Land MA, Webster J, Christoforou A, et al. Salt intake assessed by 24 h urinary sodium excretion in a random and opportunistic sample in Australia. BMJ Open. 2014;4:e003720.
   Web of Science ®Google Scholar
• Hayes RJ, Bennett S. Simple sample size calculation for cluster-randomized trials. Int J Epidemiol. 1999;28:319–326.
   PubMed Web of Science ®Google Scholar
• Fewtrell MS, Kennedy K, Singhal A, et al. How much loss to follow-up is acceptable in long-term randomised trials and prospective studies? Arch Dis Child. 2008;93:458–461.
   PubMed Web of Science ®Google Scholar
• Painter JE, Borba CP, Hynes M, et al. The use of theory in health behavior research from 2000 to 2005: a systematic review. Ann Behav Med. 2008;35:358–362.
   PubMed Web of Science ®Google Scholar
• National Institute of Nutrition. Indian Council of Medical Research. Dietary guidelines for Indians: A manual; 2011 [cited 2017 Sep 20]. Available from: http://ninindia.org/DietaryGuidelinesforNINwebsite.pdf
   Google Scholar
• Warwick-Booth L, Cross R, Woodall J, et al. Health champions and their circles of influence as a communication mechanism for health promotion. Int Rev Soc Res. 2013;3:113–129.
   Google Scholar
• Woodall J, Raine G, South J, et al. Empowerment and Health & Well-being: Evidence Review; 2010 [cited 2017 Sep 20]. Available from: http://eprints.leedsbeckett.ac.uk/2172/1/FINAL%20EMPOWERMENT%20EVIDENCE%20REVIEW.pdf
   Google Scholar
• Lechner L, Brug J, De Vries H, et al. Stages of change for fruit, vegetable and fat intake: consequences of misconception. Health Educ Res. 1998;13:1–11.
   Web of Science ®Google Scholar
• Engbers LH, Van Poppel MN, Chin APM, et al. 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.
   PubMed Web of Science ®Google Scholar
• Mahajan R, Malik M, Bharathi AV, 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;26:266–272.
   PubMed Web of Science ®Google Scholar
• Pickering TG, Hall JE, Appel LJ, et al. AHA scientific statement: recommendations for blood pressure measurement in humans and experimental animals - Part 1: blood pressure measurement in humans. Circulation. 2005;111:697–716.
   PubMed Web of Science ®Google Scholar
• 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
   Google Scholar
• Guido I, Wooldridge J Difference‐in‐Differences Estimation, Lecture Notes 10, summer 2007. [cited 2017 Sep 20]. Available from: http://www.nber.org/WNE/lect_10_diffindiffs.pdf
   Google Scholar
• Kawasaki T, Itoh K, Uezono K, et al. A simple method for estimating 24 h urinary sodium and potassium excretion from second morning voiding urine specimen in adults. Clin Exp Pharmacol Physiol. 1993;20:7–14.
   PubMed Web of Science ®Google Scholar
• Campbell MK, Elbourne DR, Altman DG, et al. CONSORT statement: extension to cluster randomised trials. Bmj. 2004;328:702–708.
   PubMed Web of Science ®Google Scholar
• Paineau DL, Beaufils F, Boulier A, et al. Family dietary coaching to improve nutritional intakes and body weight control: a randomized controlled trial. Arch Pediatr Adolesc Med. 2008;162:34–43.
   PubMedGoogle Scholar
• Takashashi Y, Sasaki S, Takahashi M, et al. 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;37:432–441.
   PubMed Web of Science ®Google Scholar
• Ireland DM, Clifton PM, Keogh JB. Achieving the salt intake target of 6 g/day in the current food supply in free-living adults using two dietary education strategies. J Am Diet Assoc. 2010;110:763–767.
   Google Scholar
• Wright JL, Sherriff JL, Dhaliwal SS, et al. 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. Int J Behav Nutr Phys Act. 2011;8:43.
   Web of Science ®Google Scholar
• Kennedy BM, Katzmarzyk PT, Johnson WD, et al. People United to Sustain Health (PUSH): a community-based participatory research study. Clin Transl Sci. 2014;7:108–114.
   Web of Science ®Google Scholar
• Sacks FM, Svetkey LP, Vollmer WM, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. N Engl J Med. 2001;344:3–10.
   PubMed Web of Science ®Google Scholar
• Gatenby SJ, Aaron JI, Jack VA, et al. Extended use of foods modified in fat and sugar content: nutritional implications in a free-living female population. Am J Clin Nutr. 1997;65:1867–1873.
   PubMed Web of Science ®Google Scholar
• Toral N, Slater B. Intervention based exclusively on stage-matched printed educational materials regarding healthy eating does not result in changes to adolescents’ dietary behavior. Sci World J. 2012;2012:174640.
   Google Scholar
• Lanza E, Schatzkin A, Daston C, 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;74:387–401.
   PubMed Web of Science ®Google Scholar
• Stevens VJ, Glasgow RE, Toobert DJ, et al. One-year results from a brief, computer-assisted intervention to decrease consumption of fat and increase consumption of fruits and vegetables. Prev Med. 2003;36:594–600.
   PubMed Web of Science ®Google Scholar
• Havas S, Anliker J, Greenberg D, et al. Final results of the Maryland WIC food for life program. Prev Med. 2003;37:406–416.
   PubMed Web of Science ®Google Scholar
• Pfammatter A, Spring B, Saligram N, et al. mHealth intervention to improve diabetes risk behaviors in India: a prospective, parallel group cohort study. J Med Internet Res. 2016;18:e207.
   Web of Science ®Google Scholar
• Radhika G, Sudha V, Mohan Sathya R, et al. Association of fruit and vegetable intake with cardiovascular risk factors in urban south Indians. Br J Nutr. 2008;99:398–405.
   PubMed Web of Science ®Google Scholar
• Joshipura KJ, Hu FB, Manson JE, et al. The effect of fruit and vegetable intake on risk for coronary heart disease. Ann Intern Med. 2001;134:1106–1114.
   PubMed Web of Science ®Google Scholar
• Rezazadeh A, Rashidkhani B. The association of general and central obesity with major dietary patterns of adult women living in Tehran, Iran. J Nutr Sci Vitaminol (Tokyo). 2010;56:132–138.
   Web of Science ®Google Scholar
• Benetou V, Orfanos P, Lagiou P, et al. Vegetables and fruits in relation to cancer risk: evidence from the Greek EPIC cohort study. Cancer Epidemiol Biomarkers Prev. 2008;17:387–392.
   PubMed Web of Science ®Google Scholar
• Riboli E, Norat T. Epidemiologic evidence of the protective effect of fruit and vegetables on cancer risk. Am J Clin Nutr. 2003;78:559S–569S.
   PubMed Web of Science ®Google Scholar
• Pavia M, Pileggi C, Nobile CG, et al. Association between fruit and vegetable consumption and oral cancer: a meta-analysis of observational studies. Am J Clin Nutr. 2006;83:1126–1134.
   PubMed Web of Science ®Google Scholar
• World Health Organisation. Fruit and Vegatable Promotion Initiative: A Meeting Report; 2003 [cited 2017 Sep 23]. Available from: http://www.who.int/dietphysicalactivity/publications/f&v_promotion_initiative_report.pdf
   Google Scholar
• Michels KB, Giovannucci E, Chan AT, et al. Fruit and vegetable consumption and colorectal adenomas in the Nurses’ Health Study. Cancer Res. 2006;66:3942–3953.
   PubMed Web of Science ®Google Scholar
• Daivadanam M, Wahlstrom R, Sundari Ravindran TK, et al. Design and methodology of a community-based cluster-randomized controlled trial for dietary behaviour change in rural Kerala. Glob Health Action. 2013;6:20993.
   PubMed Web of Science ®Google Scholar
• Mohan S, Prabhakaran D, Kishnan A. Promoting populationwide salt reduction in the South-East Asia Region: current status and future directions. Regional Health Forum. 2013;17:72–79.
   Google Scholar
• World Health Organization. Strategies to monitor and evaluate population sodium consumption and sources of sodium in the diet: report of a joint technical meeting convened by WHO and the Government of Canada; 2011 [cited 2017 Sep 21]. Available from: http://apps.who.int/iris/bitstream/10665/44614/1/9789241501699_eng.pdf
   Google Scholar
• Petersen KS, Johnson C, Mohan S, et al. Estimating population salt intake in India using spot urine samples. J Hypertens. 2017;35:2207–2213.
   Web of Science ®Google Scholar
• John JH, Ziebland S. Reported barriers to eating more fruit and vegetables before and after participation in a randomized controlled trial: a qualitative study. Health Educ Res. 2004;19:165–174.
   PubMed Web of Science ®Google Scholar
• Godinho CA, Alvarez MJ, Lima ML. Formative research on HAPA model determinants for fruit and vegetable intake: target beliefs for audiences at different stages of change. Health Educ Res. 2013;28:1014–1028.
   PubMed Web of Science ®Google Scholar
• Daivadanam M, Wahlstrom R, Ravindran TK, et al. Conceptual model for dietary behaviour change at household level: a ‘best-fit’ qualitative study using primary data. BMC Public Health. 2014;14:574.
   Web of Science ®Google Scholar
• Clinical Trial Registry of India. Details of trial registration; 2016 [cited 2017 Sep 16]. Available from: http://www.ctri.nic.in/Clinicaltrials/pdf_generate.php?trialid=15058&EncHid=&modid=&compid=%27,%2715058det%27
   Google Scholar