Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic
Abstract
:1. The Pandemic Information Age
2. The Advent of SmartPhone Apps in Dietary Surveillance
3. Requirements of Nutrition Apps
4. Scientific Applications of Nutrition Apps and Their Pitfalls
5. Practicing Personalized Diet with Nutrition Apps
6. Future Prospects
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Quer, G.; Radin, J.M.; Gadaleta, M.; Baca-Motes, K.; Ariniello, L.; Ramos, E.; Kheterpal, V.; Topol, E.J.; Steinhubl, S.R. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat. Med. 2020, 27, 73–77. [Google Scholar] [CrossRef] [PubMed]
- Segal, E.; Zhang, F.; Lin, X.; King, G.; Shalem, O.; Shilo, S.; Allen, W.E.; Alquaddoomi, F.; Altae-Tran, H.; Anders, S.; et al. Building an international consortium for tracking coronavirus health status. Nat. Med. 2020, 26, 1161–1165. [Google Scholar] [CrossRef]
- Menni, C.; Valdes, A.M.; Freidin, M.B.; Sudre, C.H.; Nguyen, L.H.; Drew, D.A.; Ganesh, S.; Varsavsky, T.; Cardoso, M.J.; El-Sayed Moustafa, J.S.; et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 2020, 26, 1037–1040. [Google Scholar] [CrossRef]
- Drew, D.A.; Nguyen, L.H.; Steves, C.J.; Menni, C.; Freydin, M.; Varsavsky, T.; Sudre, C.H.; Jorge Cardoso, M.; Ourselin, S.; Wolf, J.; et al. Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science 2020, 368, 1362–1367. [Google Scholar] [CrossRef]
- Jia, J.S.; Lu, X.; Yuan, Y.; Xu, G.; Jia, J.; Christakis, N.A. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 2020, 582, 389–394. [Google Scholar] [CrossRef]
- Servick, K. Can phone apps slow the spread of the coronavirus? Science 2020, 368, 1296–1297. [Google Scholar] [CrossRef]
- Ferretti, L.; Wymant, C.; Kendall, M.; Zhao, L.; Nurtay, A.; Abeler-Dörner, L.; Parker, M.; Bonsall, D.; Fraser, C. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 2020, 368, eabb6936. [Google Scholar] [CrossRef] [Green Version]
- Ganguli, A.; Mostafa, A.; Berger, J.; Aydin, M.Y.; Sun, F.; Stewart de Ramirez, S.A.; Valera, E.; Cunningham, B.T.; King, W.P.; Bashir, R. Rapid isothermal amplification and portable detection system for SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 22727–22735. [Google Scholar] [CrossRef] [PubMed]
- Anthes, E. Alexa, do I have COVID-19? Nature 2020, 586, 22–25. [Google Scholar] [CrossRef] [PubMed]
- Budd, J.; Miller, B.S.; Manning, E.M.; Lampos, V.; Zhuang, M.; Edelstein, M.; Rees, G.; Emery, V.C.; Stevens, M.M.; Keegan, N.; et al. Digital technologies in the public-health response to COVID-19. Nat. Med. 2020, 26, 1183–1192. [Google Scholar] [CrossRef] [PubMed]
- Carter, M.C.; Burley, V.J.; Nykjaer, C.; Cade, J.E. My Meal Mate (MMM): Validation of the diet measures captured on a smartphone application to facilitate weight loss. Br. J. Nutr. 2013, 109, 539–546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chmurzynska, A.; Mlodzik-Czyzewska, M.A.; Malinowska, A.M.; Czarnocinska, J.; Wiebe, D. Use of a smartphone application can improve assessment of high-fat food consumption in overweight individuals. Nutrients 2018, 10, 1692. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wharton, C.M.; Johnston, C.S.; Cunningham, B.K.; Sterner, D. Dietary self-monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial. J. Nutr. Educ. Behav. 2014, 46, 440–444. [Google Scholar] [CrossRef] [PubMed]
- Spring, B.; Pellegrini, C.A.; Pfammatter, A.; Duncan, J.M.; Pictor, A.; McFadden, H.G.; Siddique, J.; Hedeker, D. Effects of an abbreviated obesity intervention supported by mobile technology: The ENGAGED randomized clinical trial. Obesity 2017, 25, 1191–1198. [Google Scholar] [CrossRef] [Green Version]
- Vasiloglou, M.F.; Christodoulidis, S.; Reber, E.; Stathopoulou, T.; Lu, Y.; Stanga, Z.; Mougiakakou, S. What healthcare professionals think of ″nutrition & diet″ apps: An international survey. Nutrients 2020, 12, 2214. [Google Scholar] [CrossRef]
- Bakırcı-Taylor, A.L.; Reed, D.B.; McCool, B.; Dawson, J.A. mHealth improved fruit and vegetable accessibility and intake in young children. J. Nutr. Educ. Behav. 2019, 51, 556–566. [Google Scholar] [CrossRef]
- Spring, B.; Pellegrini, C.; McFadden, H.G.; Pfammatter, A.F.; Stump, T.K.; Siddique, J.; King, A.C.; Hedeker, D. Multicomponent mHealth intervention for large, sustained change in multiple diet and activity risk behaviors: The make better choices 2 randomized controlled trial. J. Med. Internet Res. 2018, 20, e10528. [Google Scholar] [CrossRef]
- Recio-Rodriguez, J.I.; Conde, C.A.; Calvo-Aponte, M.J.; Gonzalez-Viejo, N.; Fernandez-Alonso, C.; Mendizabal-Gallastegui, N.; Rodriguez-Martin, B.; Maderuelo-Fernandez, J.A.; Rodriguez-Sanchez, E.; Gomez-Marcos, M.A.; et al. The effectiveness of a smartphone application on modifying the intakes of macro and micronutrients in primary care: A randomized controlled trial. The EVIDENT II study. Nutrients 2018, 10, 1473. [Google Scholar] [CrossRef] [Green Version]
- Nezami, B.T.; Ward, D.S.; Lytle, L.A.; Ennett, S.T.; Tate, D.F. A mHealth randomized controlled trial to reduce sugar-sweetened beverage intake in preschool-aged children. Pediatr. Obes. 2018, 13, 668–676. [Google Scholar] [CrossRef]
- Liu, Z.; Wu, Y.; Niu, W.Y.; Feng, X.; Lin, Y.; Gao, A.; Zhang, F.; Fang, H.; Gao, P.; Li, H.J.; et al. A school-based, multi-faceted health promotion programme to prevent obesity among children: Protocol of a cluster-randomised controlled trial (the DECIDE-Children study). BMJ Open 2019, 9, e027902. [Google Scholar] [CrossRef] [Green Version]
- Smith, J.J.; Morgan, P.J.; Plotnikoff, R.C.; Dally, K.A.; Salmon, J.; Okely, A.D.; Finn, T.L.; Lubans, D.R. Smart-phone obesity prevention trial for adolescent boys in low-income communities: The ATLAS RCT. Pediatrics 2014, 134, e723–e731. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ainscough, K.M.; O’Brien, E.C.; Lindsay, K.L.; Kennelly, M.A.; O’Sullivan, E.J.; O’Brien, O.A.; McCarthy, M.; De Vito, G.; McAuliffe, F.M. Nutrition, behavior change and physical activity outcomes from the PEARS RCT—An mHealth-supported, lifestyle intervention among pregnant women with overweight and obesity. Front. Endocrinol. 2020, 10, 938. [Google Scholar] [CrossRef] [PubMed]
- Kim, E.K.; Kwak, S.H.; Jung, H.S.; Koo, B.K.; Moon, M.K.; Lim, S.; Jang, H.C.; Park, K.S.; Cho, Y.M. The effect of a smartphone-based, patient-centered diabetes care system in patients with type 2 diabetes: A randomized, controlled trial for 24 weeks. Diabetes Care 2019, 42, 3–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dobson, R.; Whittaker, R.; Jiang, Y.; Maddison, R.; Shepherd, M.; McNamara, C.; Cutfield, R.; Khanolkar, M.; Murphy, R. Effectiveness of text message based, diabetes self management support programme (SMS4BG): Two arm, parallel randomised controlled trial. BMJ 2018, 361, 1959. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Eyles, H.; McLean, R.; Neal, B.; Jiang, Y.; Doughty, R.N.; McLean, R.; Ni Mhurchu, C. A salt-reduction smartphone app supports lower-salt food purchases for people with cardiovascular disease: Findings from the SaltSwitch randomised controlled trial. Eur. J. Prev. Cardiol. 2017, 24, 1435–1444. [Google Scholar] [CrossRef]
- Stevens, D.J.; McKenzie, K.; Cui, H.W.; Noble, J.G.; Turney, B.W. Smartphone apps for urolithiasis. Urolithiasis 2014, 43, 13–19. [Google Scholar] [CrossRef]
- Costello, N.; Deighton, K.; Dyson, J.; Mckenna, J.; Jones, B. Snap-N-Send: A valid and reliable method for assessing the energy intake of elite adolescent athletes. Eur. J. Sport Sci. 2017, 17, 1044–1055. [Google Scholar] [CrossRef]
- Simpson, A.; Gemming, L.; Baker, D.; Braakhuis, A. Do image-assisted mobile applications improve dietary habits, knowledge, and behaviours in elite athletes? A pilot study. Sports 2017, 5, 60. [Google Scholar] [CrossRef] [Green Version]
- Belanger, M.J.; Hill, M.A.; Angelidi, A.M.; Dalamaga, M.; Sowers, J.R.; Mantzoros, C.S. Covid-19 and disparities in nutrition and obesity. N. Engl. J. Med. 2020, 383, e69. [Google Scholar] [CrossRef]
- Im, J.H.; Je, Y.S.; Baek, J.; Chung, M.H.; Kwon, H.Y.; Lee, J.S. Nutritional status of patients with COVID-19. Int. J. Infect. Dis. 2020, 100, 390–393. [Google Scholar] [CrossRef]
- Barazzoni, R.; Bischoff, S.C.; Breda, J.; Wickramasinghe, K.; Krznaric, Z.; Nitzan, D.; Pirlich, M.; Singer, P. ESPEN expert statements and practical guidance for nutritional management of individuals with SARS-CoV-2 infection. Clin. Nutr. 2020, 39, 1631–1638. [Google Scholar] [CrossRef]
- Elezi, B.; Abazaj, E.; Kasa, M.; Topi, S. Prevention of frailty in the elderly through physical activity and nutrition. J. Geriatr. Med. Gerontol. 2020, 24, 6. [Google Scholar] [CrossRef] [Green Version]
- Dunn, C.G.; Kenney, E.; Fleischhacker, S.E.; Bleich, S.N. Feeding low-income children during the Covid-19 pandemic. N. Engl. J. Med. 2020, 382, e40. [Google Scholar] [CrossRef]
- Meyer, J.; McDowell, C.; Lansing, J.; Brower, C.; Smith, L.; Tully, M.; Herring, M. Changes in physical activity and sedentary behavior in response to COVID-19 and their associations with mental health in 3052 US Adults. Int. J. Environ. Res. Public Health. 2020, 17, 6469. [Google Scholar] [CrossRef] [PubMed]
- Maffoni, S.I.; Kalmpourtzidou, A.; Cena, H. The potential role of nutrition in mitigating the psychological impact of COVID-19 in healthcare workers. NFS J. 2021, 22, 6–8. [Google Scholar] [CrossRef]
- Swan, W.I.; Vivanti, A.; Hakel-Smith, N.A.; Hotson, B.; Orrevall, Y.; Trostler, N.; Howarter, K.B.; Papoutsakis, C. Nutrition care process and model update: Toward realizing people-centered care and outcomes management. J. Acad. Nutr. Diet. 2017, 117, 2003–2014. [Google Scholar] [CrossRef] [PubMed]
- Pellegrini, C.A.; Conroy, D.E.; Phillips, S.M.; Pfammatter, A.F.; McFadden, H.G.; Spring, B. Daily and seasonal influences on dietary self-monitoring using a smartphone application. J. Nutr. Educ. Behav. 2018, 50, 56–61.e1. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chin, S.O.; Keum, C.; Woo, J.; Park, J.; Choi, H.J.; Woo, J.T.; Rhee, S.Y. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci. Rep. 2016, 6, 34563. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Domhardt, M.; Tiefengrabner, M.; Dinic, R.; Fotschl, U.; Oostingh, G.J.; Stutz, T.; Stechemesser, L.; Weitgasser, R.; Ginzinger, S.W. Training of carbohydrate estimation for people with diabetes using mobile augmented reality. J. Diabetes Sci. Technol. 2015, 9, 516–524. [Google Scholar] [CrossRef] [Green Version]
- Lu, Y.; Stathopoulou, T.; Vasiloglou, M.F.; Pinault, L.F.; Kiley, C.; Spanakis, E.K.; Mougiakakou, S. goFOODTM: An artificial intelligence system for dietary assessment. Sensors 2020, 20, 4283. [Google Scholar] [CrossRef] [PubMed]
- Vasiloglou, M.F.; Mougiakakou, S.; Aubry, E.; Bokelmann, A.; Fricker, R.; Gomes, F.; Guntermann, C.; Meyer, A.; Studerus, D.; Stanga, Z. A comparative study on carbohydrate estimation: GoCARB vs. Dietitians. Nutrients 2018, 10, 741. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rollo, M.E.; Ash, S.; Lyons-Wall, P.; Russell, A. Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: Evaluation and implications for future applications. J. Telemed. Telecare 2011, 17, 318–323. [Google Scholar] [CrossRef] [PubMed]
- Mezgec, S.; Seljak, B.K. Nutrinet: A deep learning food and drink image recognition system for dietary assessment. Nutrients 2017, 9, 657. [Google Scholar] [CrossRef] [PubMed]
- Krznarić, Ž.; Bender, D.V.; Laviano, A.; Cuerda, C.; Landi, F.; Monteiro, R.; Pirlich, M.; Barazzoni, R. A simple remote nutritional screening tool and practical guidance for nutritional care in primary practice during the COVID-19 pandemic. Clin. Nutr. 2020, 39, 1983–1987. [Google Scholar] [CrossRef]
- Fernández-Quintela, A.; Milton-Laskibar, I.; Trepiana, J.; Gómez-Zorita, S.; Kajarabille, N.; Léniz, A.; González, M.; Portillo, M.P. Key aspects in nutritional management of COVID-19 patients. J. Clin. Med. 2020, 9, 2589. [Google Scholar] [CrossRef]
- Alexander, J.; Tinkov, A.; Strand, T.A.; Alehagen, U.; Skalny, A.; Aaseth, J. Early nutritional interventions with zinc, selenium and vitamin D for raising anti-viral resistance against progressive COVID-19. Nutrients 2020, 12, 2358. [Google Scholar] [CrossRef]
- Ipjian, M.L.; Johnston, C.S. Smartphone technology facilitates dietary change in healthy adults. Nutrition 2017, 33, 343–347. [Google Scholar] [CrossRef]
- Sharp, D.B.; Allman-Farinelli, M. Feasibility and validity of mobile phones to assess dietary intake. Nutrition 2014, 30, 1257–1266. [Google Scholar] [CrossRef]
- Gonzalez-Sanchez, J.; Recio-Rodriguez, J.I.; Fernandez-delRio, A.; Sanchez-Perez, A.; Magdalena-Belio, J.F.; Gomez-Marcos, M.A.; Garcia-Ortiz, L. Using a smartphone app in changing cardiovascular risk factors: A randomized controlled trial (EVIDENT II study). Int. J. Med. Inform. 2019, 125, 13–21. [Google Scholar] [CrossRef]
- Whitelock, V.; Kersbergen, I.; Higgs, S.; Aveyard, P.; Halford, J.C.G.; Robinson, E. A smartphone based attentive eating intervention for energy intake and weight loss: Results from a randomised controlled trial. BMC Public Health 2019, 19, 6923. [Google Scholar] [CrossRef]
- Kennelly, M.A.; Ainscough, K.; Lindsay, K.L.; O’Sullivan, E.; Gibney, E.R.; McCarthy, M.; Segurado, R.; DeVito, G.; Maguire, O.; Smith, T.; et al. Pregnancy exercise and nutrition with smartphone application support a randomized controlled trial. Proc. Obstet. Gynecol. 2018, 131, 818–826. [Google Scholar] [CrossRef]
- Vega-López, S.; Ausman, L.M.; Griffith, J.L.; Lichtenstein, A.H. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Diabetes Care 2007, 30, 1412–1417. [Google Scholar] [CrossRef] [Green Version]
- McMorrow, A.M.; Connaughton, R.M.; Magalhães, T.R.; McGillicuddy, F.C.; Hughes, M.F.; Cheishvili, D.D.; Morine, M.J.; Ennis, S.; Healy, M.L.; Roche, E.F.; et al. Personalized cardio-metabolic responses to an anti-inflammatory nutrition intervention in obese adolescents: A randomized controlled crossover trial. Mol. Nutr. Food Res. 2018, 62, 1008. [Google Scholar] [CrossRef] [Green Version]
- Seto, E.; Hua, J.; Wu, L.; Bestick, A.; Shia, V.; Eom, S.; Han, J.; Wang, M.; Li, Y. The Kunming CalFit study: Modeling dietary behavioral patterns using smartphone data. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2014. [Google Scholar] [CrossRef]
- Seto, E.; Hua, J.; Wu, L.; Shia, V.; Eom, S.; Wang, M.; Li, Y. Models of individual dietary behavior based on smartphone data: The influence of routine, physical activity, emotion, and food environment. PLoS ONE 2016, 11, e0153085. [Google Scholar] [CrossRef] [Green Version]
- Goldstein, S.P.; Zhang, F.; Thomas, J.G.; Butryn, M.L.; Herbert, J.D.; Forman, E.M. Application of machine learning to predict dietary lapses during weight loss. J. Diabetes Sci. Technol. 2018, 12, 1045–1052. [Google Scholar] [CrossRef]
- Hjorth, M.F.; Roager, H.M.; Larsen, T.M.; Poulsen, S.K.; Licht, T.R.; Bahl, M.I.; Zohar, Y.; Astrup, A. Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention. Int. J. Obes. 2018, 42, 580–583. [Google Scholar] [CrossRef] [Green Version]
- Kovatcheva-Datchary, P.; Nilsson, A.; Akrami, R.; Lee, Y.S.; De Vadder, F.; Arora, T.; Hallen, A.; Martens, E.; Björck, I.; Bäckhed, F. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015, 22, 971–982. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.; Avnit-Sagi, T.; Lotan-Pompan, M.; et al. Personalized nutrition by prediction of glycemic responses. Cell 2015, 163, 1079–1095. [Google Scholar] [CrossRef] [Green Version]
- Albert, L.; Capel, I.; García-Sáez, G.; Martín-Redondo, P.; Hernando, M.E.; Rigla, M. Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine. Diabetes Res. Clin. Pract. 2020, 169, 108396. [Google Scholar] [CrossRef] [PubMed]
- Dávila, L.A.; Bermúdez, V.; Aparicio, D.; Céspedes, V.; Escobar, M.C.; Durán-Agüero, S.; Cisternas, S.; Costa, J.D.A.; Rojas-Gómez, D.; Reyna, N.; et al. Effect of oral nutritional supplements with sucromalt and isomaltulose versus standard formula on glycaemic index, entero-insular axis peptides and subjective appetite in patients with type 2 diabetes: A randomised cross-over study. Nutrients 2019, 11, 1477. [Google Scholar] [CrossRef] [Green Version]
- Meng, H.; Matthan, N.R.; Ausman, L.M.; Lichtenstein, A.H. Effect of prior meal macronutrient composition on postprandial glycemic responses and glycemic index and glycemic load value determinations. Am. J. Clin. Nutr. 2017, 106, 1246–1256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- McHill, A.W.; Czeisler, C.A.; Phillips, A.J.K.; Keating, L.; Barger, L.K.; Garaulet, M.; Scheer, F.A.J.L.; Klerman, E.B. Caloric and macronutrient intake differ with circadian phase and between lean and overweight young adults. Nutrients 2019, 11, 587. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gill, S.; Panda, S. A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell Metab. 2015, 22, 789–798. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lu, Z.; O’Dell, D.; Srinivasan, B.; Rey, E.; Wang, R.; Vemulapati, S.; Mehta, S.; Erickson, D.; Sommer, A. Rapid diagnostic testing platform for iron and Vitamin A deficiency. Proc. Natl. Acad. Sci. USA 2017, 114, 13513–13518. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, S.; O’Dell, D.; Hohenstein, J.; Colt, S.; Mehta, S.; Erickson, D. NutriPhone: A mobile platform for low-cost point-of-care quantification of Vitamin B12 concentrations. Sci. Rep. 2016, 6, 28237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Howe, K.B.; Suharlim, C.; Ueda, P.; Howe, D.; Kawachi, I.; Rimm, E.B. Gotta catch’em all! Pokémon GO and physical activity among young adults: Difference in differences study. BMJ 2016, 355, i6270. [Google Scholar] [CrossRef] [Green Version]
- Puigdomenech, E.; Martin, A.; Lang, A.; Adorni, F.; Gomez, S.F.; McKinstry, B.; Prinelli, F.; Condon, L.; Rashid, R.; Caon, M.; et al. Promoting healthy teenage behaviour across three European countries through the use of a novel smartphone technology platform, PEGASO fit for future: Study protocol of a quasi-experimental, controlled, multi-Centre trial. BMC Med. Inform. Decis. Mak. 2019, 19, 278. [Google Scholar] [CrossRef] [Green Version]
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Zmora, N.; Elinav, E. Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic. Nutrients 2021, 13, 422. https://doi.org/10.3390/nu13020422
Zmora N, Elinav E. Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic. Nutrients. 2021; 13(2):422. https://doi.org/10.3390/nu13020422
Chicago/Turabian StyleZmora, Niv, and Eran Elinav. 2021. "Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic" Nutrients 13, no. 2: 422. https://doi.org/10.3390/nu13020422
APA StyleZmora, N., & Elinav, E. (2021). Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic. Nutrients, 13(2), 422. https://doi.org/10.3390/nu13020422