Dietary Intake and Health Status of Elderly Patients With Type 2 Diabetes Mellitus: Cross-sectional Study Using a Mobile App in Primary Care.

Joane Diomara Coleone, Ericles Andrei Bellei, Mateus Klein Roman, Vanessa Ramos Kirsten, Ana Carolina Bertoletti De Marchi
Author Information
  1. Joane Diomara Coleone: School of Physical Education and Physiotherapy, University of Passo Fundo, Passo Fundo, RS, Brazil. ORCID
  2. Ericles Andrei Bellei: Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, RS, Brazil. ORCID
  3. Mateus Klein Roman: Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, RS, Brazil. ORCID
  4. Vanessa Ramos Kirsten: Department of Foods and Nutrition, Federal University of Santa Maria, Palmeira das Missões, RS, Brazil. ORCID
  5. Ana Carolina Bertoletti De Marchi: School of Physical Education and Physiotherapy, University of Passo Fundo, Passo Fundo, RS, Brazil. ORCID

Abstract

BACKGROUND: Healthy dietary intake reduces the risk of complications of diabetes mellitus. Using assessment methods helps to understand these circumstances, and an electronic application may optimize this practice.
OBJECTIVE: In this study, we aimed to (1) assess the dietary intake and health status of elderly patients with type 2 diabetes mellitus (T2DM) in primary care, (2) use a mobile app as a tool for data collection and analysis in the context of primary care, and (3) verify the perceptions of multidisciplinary health professionals regarding app use.
METHODS: First, we developed a mobile app comprised of the questions of the Food and Nutrition Surveillance System (SISVAN) of Brazil, which includes a food frequency questionnaire of food categories with a recall of the previous 7 days. Thereafter, we used the app to collect data on the health status and dietary intake of 154 participants, aged 60-96 years, diagnosed with T2DM, and under treatment in primary care centers in the northern region of Rio Grande do Sul, Brazil. We also collected participants' demographic, anthropometric, biochemical, and lifestyle variables. The associations between dietary intake and other variables were tested using chi-square tests with a 5% significance level. Regarding the app, we assessed usability and acceptance with 20 health professionals.
RESULTS: Between August 2018 and December 2018, participants had an intake in line with recommended guidelines for raw salads (57.1%), fruits (76.6%), milk products (68.2%), fried foods (72.7%), savory biscuits (60.4%), cookies or sweets (72.1%), and sugary drinks (92.9%) Meanwhile, the consumption of beans (59.7%), pulses and cooked vegetables (73.4%), and processed meat products (59.7%) was not in line with the guidelines. There were statistically significant differences in meeting the recommended guidelines among participants of different genders (P=.006 and P=.035 for the intake of fried foods and sugary drinks, respectively), place of residence (P=.034 for the intake of cookies and sweets), family history of diabetes (P<.001 for the intake of beans), physical activity engagement (P=.003 for the intake fresh fruits), history of smoking (P=.001 for the intake of raw salads), and presence of coronary disease (P=.050 for the intake of pulses and cooked vegetables). The assessment of usability resulted in a mean score of 71.75 points. Similarly, the assessment of the 15 acceptance questions revealed high scores, and the qualitative questions revealed positive perceptions.
CONCLUSIONS: We identified that most participants complied with recommended intake guidelines for 7 of 10 categories in the SISVAN guidelines. However, most participants were overweight and had nutritional and clinical disorders, which justifies further investigations in this population. The app was well-rated by health professionals and considered a useful and promising tool for collecting and analyzing data in primary care settings.

Keywords

References

  1. Endocrinol Metab (Seoul). 2015 Sep;30(3):334-42 [PMID: 26435135]
  2. Br J Nutr. 2016 Jun;115(12):2219-26 [PMID: 27121045]
  3. Diabetes Metab Syndr. 2019 Sep - Oct;13(5):3005-3010 [PMID: 30057070]
  4. Lancet. 2014 Jun 7;383(9933):1999-2007 [PMID: 24910231]
  5. Public Health Nutr. 2019 Apr;22(6):976-987 [PMID: 30767843]
  6. World Health Organ Tech Rep Ser. 1995;854:1-452 [PMID: 8594834]
  7. AIMS Public Health. 2015 Jul 30;2(3):402-410 [PMID: 29546116]
  8. PeerJ. 2021 May 28;9:e11491 [PMID: 34123593]
  9. J Biomed Inform. 2020 Jul;107:103461 [PMID: 32504669]
  10. Cien Saude Colet. 2018 Dec;23(12):4199-4208 [PMID: 30540003]
  11. Front Psychol. 2018 Jun 18;9:977 [PMID: 29967588]
  12. Rev Panam Salud Publica. 2012 Mar;31(3):240-5 [PMID: 22569699]
  13. J Acad Nutr Diet. 2016 Aug;116(8):1336-8 [PMID: 27236643]
  14. JMIR Res Protoc. 2020 Jan 20;9(1):e15299 [PMID: 31958068]
  15. Lancet. 2011 May 21;377(9779):1778-97 [PMID: 21561655]
  16. JAMA. 1996 Feb 14;275(6):447-51 [PMID: 8627965]
  17. J Hum Nutr Diet. 2018 Aug;31(4):573-583 [PMID: 29473238]
  18. Telemed J E Health. 2020 Feb;26(2):205-217 [PMID: 30724717]
  19. Nutrients. 2018 Aug 23;10(9): [PMID: 30142929]
  20. Appetite. 2019 Mar 1;134:155-161 [PMID: 30593836]
  21. Proc Nutr Soc. 2017 Aug;76(3):276-282 [PMID: 27976605]
  22. Sci Rep. 2020 Apr 7;10(1):6009 [PMID: 32265476]
  23. Value Health. 2011 Jul-Aug;14(5 Suppl 1):S137-40 [PMID: 21839888]
  24. Nutrients. 2017 Jan 18;9(1): [PMID: 28106767]
  25. Clin Nutr. 2014 Jun;33(3):545-9 [PMID: 23954218]
  26. Clin Diabetes. 2019 Jan;37(1):11-34 [PMID: 30705493]
  27. Am J Clin Nutr. 2014 Aug;100(2):667-75 [PMID: 24944061]
  28. Arch Gerontol Geriatr. 2017 Sep;72:174-180 [PMID: 28688369]
  29. BMC Med. 2017 Nov 15;15(1):202 [PMID: 29137630]
  30. Circulation. 2015 Nov 24;132(21):1990-8 [PMID: 26503880]
  31. Rev Esc Enferm USP. 2015 Aug;49(4):619-25 [PMID: 26353099]
  32. Arq Neuropsiquiatr. 2003 Sep;61(3B):777-81 [PMID: 14595482]
  33. Int J Behav Nutr Phys Act. 2006 Nov 26;3:42 [PMID: 17125525]
  34. Nutr J. 2016 Feb 04;15:15 [PMID: 26847556]
  35. Rev Bras Epidemiol. 2015 Oct-Dec;18(4):953-65 [PMID: 26982308]

Word Cloud

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