Effects of COVID-19 lockdown on the dietary habits and lifestyle in a population in southern Spain: a cross-sectional questionnaire

1 Effects of COVID-19 lockdown on the dietary habits and lifestyle in a population 1 in southern Spain: a cross-sectional questionnaire 2 3 Carmen Flores Navarro-Pérez1, Ángel Fernández-Aparicio2, Emilio González- 4 Jiménez2*, Miguel Ángel Montero-Alonso3, Jacqueline Schmidt-RioValle2 5 6 1. Department of Nursing, Faculty of Nursing, Physiotherapy and Podiatry, University 7 of Seville, 41009, Seville, Spain. 8 2. Department of Nursing, Faculty of Health Sciences, University of Granada, Av. 9 Ilustración, 60, 18016, Granada, Spain 10 3. Department of Statistics and O.I., Faculty of Medicine, University of Granada, 18016 11 Granada, Spain 12 *Corresponding author: Emilio González-Jiménez; email: [email protected]; Telephone: 13 +34-958-243-496 14 15 16 17 18 19 20 21 22 23 24 2 25 Abstract: 26 Background/ Objective: Few studies have assessed the effect of lockdown on physical 27 activity and eating behaviours in a population from the Autonomous Community of 28 Andalusia in southern Spain. The aim of our study was to describe the effect of 29 COVID-19 pandemic home lockdown on eating habits and lifestyle in the Andalusian 30 population. 31 Subjects/Methods: A cross-sectional observational study was carried out on a 32 population from southern Spain, Andalusian population. An online questionnaire was 33 shared through social networks and snowball sampling. A total of 1,140 people filled in 34 the questionnaire. The questionnaire consisted of 34 items classified into three sections: 35 sociodemographic data, work and leisure activities, and questions on food consumption. 36 Each item offered pre- and post-lockdown information. 37 Results: The participants were classified into three age groups: 18 to 35, 36 to 65, and 38 over 65. Statistically significant differences were found between the three groups, with 39 the younger age group undergoing greater changes, increasing their physical activity 40 and consumption of fresh food, and decreasing both their consumption of fast food at 41 home and alcohol intake. 42 Conclusions: These findings suggest that, in the current social and health crisis, the 43 citizens of southern Spain have become aware of the importance of maintaining an 44 appropriate lifestyle to remain healthy, particularly the younger population with less 45 well-consolidated habits. 46 Keywords: 47 Lockdown, physical activity, diet, COVID-19, age, lifestyle, snacking, alcohol 48 49 3 50 1. Introduction 51 On 31 December, 2019, a cluster of cases of SARS-CoV-2 viral pneumonia, referred to 52 as COVID-19, was reported in the Chinese region of Wuhan 1. With 161,237 confirmed 53 cases and 6,000 deaths 2, in March 2020, the World Health Organisation (WHO) 54 declared a global pandemic 1. 55 In Spain, on March 14, 2020, to curb the increase in cases and the collapse of the 56 healthcare system, a state of alarm was declared 3. This declaration obliged the entire 57 Spanish population to remain at home, allowing them to go out only to buy basic 58 products, with the exception of people working jobs classified as essential. Initially, 15 59 days of house lockdown were imposed, which was eventually extended to 50 days. The 60 entry into force of this lockdown motivated a social urgency to hoard food and 61 household supplies to provide for homes in the days after March 14. This was 62 characterised by the compulsive purchase of pulses, beer, and hygiene articles, resulting 63 in shortages of basic products 4. 64 Numerous studies reflect the appearance of stress in people as a result of the lockdown 65 5,6. In turn, a relationship has been found between situations of stress and uncertainty, 66 and the consumption of hyperpalatable foods 7,8. The first published data on 67 consumption in the initial weeks of the lockdown and the weeks prior to it, report a 68 disproportionate increase in the purchase of food, with some non-perishable foodstuffs 69 even increasing by 100% compared to the same month of the previous year 4,9–11. In this 70 context, the WHO published guidelines on healthy eating 12 with the intention of 71 preventing further negative effects of the pandemic deriving from an unhealthy diet, 72 such as overweight and obesity, which are involved in the development of non- 73 communicable diseases (NCDs) 13. 74 4 In addition, the WHO also published recommendations highlighting the importance of 75 regular physical activity, its benefits for the body and mind, as well as its involvement 76 in lower morbidity and mortality rates 14. 77 The first published studies warned, as expected, of changes in eating habits, including 78 increased snacking, a greater number of meals consumed, and changes in the weight of 79 the participants and their physical activity levels, with two distinct trends, on the one 80 hand, there were many who decreased their daily exercise 15,16, while there was also a 81 significant number of people who increased their physical activity over this period 17. 82 These habit changes depended largely on age. In this sense, in the area of 83 Developmental Psychology there are 3 major stages in adulthood: The youth stage, 84 between approximately 20 and 40 years of age; the mature stage, between 40 and 65, 85 and finally the over 65s. The first stage is a period characterised by the appearance of a 86 certain independence from the family, including a period of higher education and 87 incorporation into the world of work. This decreases available time, and food is pushed 88 into the background. The second stage is based on assuming and overcoming 89 responsibilities, as well as social commitments, developing a certain emotional and 90 work stability. The older stage is characterised by more rigid thinking, with less 91 flexibility and greater rejection of changes involve in altered routines, as well as the 92 cessation of working activity 18,19. In Spain, people generally retire between 65 and 67 93 years of age 20. 94 Despite the volume of research published so far, few studies have evaluated the effect of 95 lockdown on the physical activity and eating behaviours of the population from the 96 Autonomous Community of Andalusia, in southern Spain. The aim of our work is, 97 therefore, to describe the effect of COVID-19 pandemic home lockdown on the dietary 98 5 habits and lifestyle of the Andalusian population, to prevent any negative impact of 99 future outbreaks and subsequent lockdown measures. 100 2. Materials and Methods 101 2.1. Study design 102 A cross-sectional observational study was carried out on a population from southern 103 Spain, Andalusian population. Access was provided to an anonymous online 104 questionnaire, which was active from 21 April (the sixth week of lockdown) until 2 105 May, 2020, coinciding with the start of the lockdown de-escalation in Spain. The 106 questionnaire was shared via social networks such as Instagram, Facebook, WhatsApp, 107 virtual classrooms in which the researchers themselves were teaching, as well as by 108 email, making snowball sampling possible. The questionnaire could be completed using 109 any mobile device, tablet, or computer. In addition, the number of days the participants 110 had been confined to their homes, from the start of lockdown to the time they filled in 111 the survey, was taken into account. 112 113 2.2. Population 114 The study population consisted of adults of 18 years of age or above, who used digital 115 technology. The start of the questionnaire included a brief description of the purpose of 116 the survey, information about the researchers responsible, and the fact that the data 117 collected would be both anonymous and confidential. Filling in the questionnaire was 118 completely voluntary. In addition, the participants were able to leave the study at any 119 time before submitting the survey. Responses were saved only when the submit button 120 was clicked. The Ethical Principles established by the World Medical Association 121 (WMA) in the Declaration of Helsinki were followed in the development of this study. 122 This study was also in line with current Spanish legislation, the General Data Protection 123 6 Regulation, and Organic Law 3/2018, of 5 December, on Personal Data Protection and 124 the guarantee of digital rights. 125 126 2.3. Data collection 127 The data was collected by means of a structured self-administered questionnaire, 128 created on the Google Form platform. This questionnaire collected the questions to be 129 answered sequentially and according to the scenarios "before" and "during" lockdown. 130 It included 34 items and was divided into three main sections, organised as follows: one 131 section on sociodemographic data; a second section containing questions about general 132 habits, work and leisure activities; and finally, questions to determine variation in food 133 consumption. The sociodemographic information collected included the variables age, 134 sex, educational level, place where the lockdown was taking place, people with whom 135 the participants were living at that time, information on the home they were confined to, 136 their employment situation and the need to go out to work, as well as changes in 137 financial income and body weight. The second section included questions on sleep 138 habits and physical activity levels. The items referring to eating habits were: eating 139 schedules and the number of meals/day, snacking between meals, consumption of ultra- 140 processed food groups, fast food at home, and the consumption of fresh food. Finally, 141 we included questions aimed at investigating changes in the frequency of food 142 consumption, with the intention of finding out whether the participants had increased, 143 decreased, or maintained their habits, for which we presented the foods organised into 144 rows with 4 possible response options: no consumption, more, less, or the same level. 145 These foods included beverages such as soft drinks, stimulant drinks, and alcohol. 146 147 2.4. Data analysis 148 7 The participants were divided into 3 groups according to their age, a first group of 149 participants aged between 18 and 35, a second group between 36 and 65, and finally, 150 those over 65 years of age, according to the classification of the Spain´s National 151 Institute of Statistics 21. Quantitative variables were analysed using a one-way ANOVA 152 and qualitative variables were analysed using a Chi-square test, both with a significance 153 of 0.05. Odds ratios were calculated using binary logistic regression analysis with 154 dietary assessment (less or the same vs. more) as the dependent variable (confidence 155 intervals at 95%). The first step included models evaluating the relationship between 156 each determinant and the assessment of food, less or the same versus more and the 157 corresponding odds ratio, adjusted for the following variables: modified weight, degree 158 of physical activity, amount of food, and modified sleep. In the second step, the odds 159 ratios were adjusted for sex and educational level. The normality of the distributions 160 was checked using the Kolmogorov-Smirnov test. The significance level was p<0.05. 161 All of the analyses were performed with version 24 of the SPSS software package 162 (IBM, Armonk, NY, USA). 163 164 3. Results 165 Table 1 shows the sociodemographic characteristics of the participants classified into 166 three age groups: 18-35, 36-65, and over 65 years of age. A total of 1,140 people 167 participated, all of whom were of legal age. Highly significant statistical differences 168 (p<0.001) were found between the three age groups in relation to the number of 169 cohabitants, leaving the home to go to work, and the reduction of income during 170 lockdown. 171 In relation to the habits of the participants (Table 2), statistically significant differences 172 were found, (p<0.001) and (p<0.05), in all the items presented between the different age 173 8 ranges. In relation to weight change during lockdown, more than half of the participants 174 reported that their weight changed over this period. 175 In terms of frequency of food consumption (Tables 3-5), statistically significant 176 differences were found (p<0.001 and p<0.05) with respect to age ranges for cereals and 177 derivatives, both refined and wholemeal, sweets and pastries, potatoes, pulses, nuts, 178 milk and dairy products, lean meats, cold cuts and sausages, lean and fatty fish, seafood, 179 seed oils other than olive oil, precooked foods, stimulant drinks, and alcoholic 180 beverages. In the case of alcoholic beverages, a third of all participants reported a 181 decrease in their consumption, with this reduction being greatest among the youngest 182 group, where there was a 50% drop. 183 Table 6 shows the different relationships between the assessment of eating and the 184 variables: modified weight, level of physical activity, amount of food consumed, and 185 modified sleep. When diet was assessed in relation to physical activity, there was a 186 positive association (OR = 2.56, 95% CI = 1.96; 3.34), i.e., people who were more 187 physically active were two and a half times more likely to evaluate their diet positively. 188 On the other hand, there was a negative association with regard to quantity eaten 189 (OR = 0.60, 95% CI = 0.46; 0.78), meaning that eating was negatively valued among 190 those who ate more food. The same degree of association holds for both the raw and 191 adjusted data, in such a way that, when corrected for sex and educational level, the OR 192 increased slightly in both cases, and the CIs remained virtually unchanged. 193 194 4. Discussion 195 The global COVID-19 pandemic led to home isolation measures in many parts of the 196 world. Changes in dietary patterns have been identified as a result of this lockdown, as 197 well as lifestyle changes that affect sleep and physical activity. As in other studies, our 198 9 data shows that a small number of people spent the lockdown alone, which is consistent 199 with other studies 16,22,23. Age is shown to be a determining factor in the changes 200 observed; around 50% of those surveyed who are of working age have seen their 201 income reduced as a result of the shutdown of non-essential activity, as demonstrated by 202 the National Statistics Institute in Spain 24,25. With the closure of non-essential activity 203 and the shift to online education, few people had to leave home to go to their workplace. 204 This affected the middle-aged population to the greatest extent, as they have more stable 205 jobs, similarly to that reported in other work 17. 206 In terms of body weight, most of the participants claim to have undergone changes, 207 coinciding with that described by Cancello et al. 16 in their study on an Italian 208 population. In this sense, the population over 35 years of age considers that they have 209 undergone fewer changes in their eating habits. The Kantar report 26 shows that as the 210 weeks of lockdown passed, consumers became more aware of the importance of 211 healthier, waste-free eating, and gained an interest in getting into the kitchen to make 212 healthy dishes, results that are consistent with those from another study conducted in 213 France over the same weeks 27. In view of our data, it is possible to think that the same 214 phenomenon occurred; a progressive awareness of the importance of food. However, we 215 could also consider that the preparation of healthy dishes served as an escape and a way 216 to keep busy 16,23,28,29. 217 On a general level, the participants consumed more food as they had more time, quicker 218 access to food, and greater stress levels 7,30–32. To cope with this high demand for 219 products, all links in the food production chain had to adapt rapidly 4,33. Related to this 220 higher level of consumption, is the group of 18-35 year olds who claimed to have 221 increased the variety in their diets; they are the most irregular in terms of schedule, and 222 they are the group who showed the greatest increase in the number of meals eaten every 223 16 12. World Health Organization (WHO). Food and nutrition tips during self- 372 quarantine. 2020. https://www.euro.who.int/en/health-topics/health- 373 emergencies/coronavirus-covid-19/publications-and-technical-guidance/food-and- 374 nutrition-tips-during-self-quarantine. Accessed 22 Jan 2021. 375 13. Buzzachera CF, Correale L, Liberali G. Physical activity and cardiovascular 376 health. In: Brain and Heart Dynamics. Springer Nature Switzerland AG, 377 Gewerbestrasse; 2020. pp. 871-880. 378 14. World Health Organization (WHO). Coronavirus disease (COVID-19): Staying 379 active. 2020. https://www.who.int/emergencies/diseases/novel-coronavirus- 380 2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19-staying-active. 381 Accessed 20 Nov 2020. 382 15. Ammar A, Brach M, Trabelsi K, Chtourou H, Boukhris O, Masmoudi L, et al. 383 Effects of COVID-19 home confinement on eating behaviour and physical activity: 384 results of the ECLB-COVID19 International Online Survey. Nutrients. 2020;12:1583. 385 https://doi.org/10.3390/nu12061583 386 16. Cancello R, Soranna D, Zambra G, Zambon A, Invitti C. Determinants of the 387 lifestyle changes during COVID-19 pandemic in the residents of Northern Italy. Int J 388 Environ Res Public Health. 2020;17:6287. https://doi.org/10.3390/ijerph17176287 389 17. di Renzo L, Gualtieri P, Pivari F, Soldati L, Attinà A, Cinelli G et al. Eating 390 habits and lifestyle changes during COVID-19 lockdown: an Italian survey. J Transl 391 Med. 2020;18:229. https://doi.org/10.1186/s12967-020-02399-5 392 18. Ruíz J. Los factores definitorios de los grandes grupos de edad de la población: 393 tipos, subgrupos y umbrales. Scripta Nova: Revista electrónica de geografía y ciencias 394 sociales. 2005;9:181-204. 395 17 19. Delgado-Losada ML. Psicología del desarrollo. In: Fundamentos de Psicología 396 para ciencias sociales y de la salud. Médica Panamericana; 2015. pp. 267-293. 397 20. Boletín Oficial del Estado (BOE). Real Decreto Legislativo 8/2015, de 30 de 398 octubre, por el que se aprueba el texto refundido de la Ley General de la Seguridad 399 Social. 2015. https://www.boe.es/buscar/pdf/2015/BOE-A-2015-11724- 400 consolidado.pdf. Accessed 20 Nov 2020. 401 21. Population (Spaniards/foreigners) since 1998 by age (3 age groups), sex and 402 year. 403 https://www.ine.es/jaxiPx/Tabla.htm?path=/t20/e245/p08/l0/&file=01001.px&L=1. 404 Accessed 29 Jun 2021. 405 22. Górnicka M, Drywień ME, Zielinska MA, Hamułka J. Dietary and lifestyle 406 changes during covid-19 and the subsequent lockdowns among polish adults: A cross- 407 sectional online survey PLifeCOVID-19 study. Nutrients. 2020;12:2324. 408 https://doi.org/10.3390/nu12082324 409 23. Pérez-Rodrigo C, Gianzo Citores M, Hervás Bárbara G, Ruiz-Litago F, Casis 410 Sáenz L, Aranceta-Bartrina J, et al. Changes in eating habits during lockdown period 411 due to the COVID-19 pandemic in Spain. Rev Esp Nutr Comunitaria. 2020;26:28010. 412 https://doi.org/10.14642/RENC.2020.26.2.5213 413 24. Instituto Nacional de Estadística. Porcentaje de establecimientos que durante el 414 estado de alarma han tenido que adoptar medidas referentes al empleo: Sectores de 415 actividad. 2020. 416 https://www.ine.es/jaxi/Datos.htm?path=/COVID/ice/p01/l1/&file=01005.px. Accessed 417 5 Apr 2021. 418 18 25. Instituto Nacional de Estadística. Indicador de Confianza Empresarial (ICE). 419 Módulo de opinión sobre el impacto de la COVID-19. Estado de alarma y segundo 420 semestre de 2020. https://www.ine.es/daco/daco42/ice/ice_mod_covid_0320.pdf 421 26. Kantar. El confinamiento lleva a récords en ticket medio. 2020. 422 https://www.kantar.com/es/inspiracion/coronavirus/el-confinamiento-lleva-a-records- 423 en-ticket-medio. Accessed 24 Jan 2021. 424 27. Rossinot H, Fantin R, Venne J. Behavioral changes during COVID-19 425 confinement in France: a web-based study. Int J Environ Res Public Health. 426 2020;17:8444. https://doi.org/10.3390/ijerph17228444 427 28. Balluerka M, Gómez J, Hidalgo M, Gorostiaga M, Espada J, Padilla J, 428 et al. Las consecuencias psicológicas de la COVID-19 y el confinamiento. Informe de 429 Investigación.; 2020. 430 29. Comunidad de Madrid. Hábitos saludables en época de pandemia. 2020. 431 https://www.comunidad.madrid/servicios/salud/habitos-saludables-epoca-pandemia. 432 Accessed 22 Apr 2021. 433 30. Scarmozzino F, Visioli F. Covid-19 and the subsequent lockdown modified 434 dietary habits of almost half the population in an Italian sample. Foods. 2020;9:675. 435 https://doi.org/10.3390/foods9050675 436 31. Zachary Z, Brianna F, Brianna L, Garrett P, Jade W, Alyssa D, et al. Self- 437 quarantine and weight gain related risk factors during the COVID-19 pandemic. Obes 438 Res Clin Pract. 2020;14:210-6. https://doi.org/10.1016/j.orcp.2020.05.004 439 32. Sadler J, Thapaliya G, Jansen E, Aghababian A, Smith K, Carnell S. COVID-19 440 stress and food intake: protective and risk factors for stress-related palatable food intake 441 in U.S. adults. Nutrients. 2021;13:901. https://doi.org/10.3390/nu13030901 442 19 33. Fundación Europea para la Innovación: Smart AgriFood. Los supermercados y 443 el sector retail se alinean para mejorar la respuesta ante el covid-19 en 444 #agrifoodbeatscoronavirus. 2020. http://smartagrifood.org/los-supermercados-y-el- 445 sector-retail-se-alinean-para-mejorar-la-respuesta-ante-el-covid-19-en- 446 agrifoodbeatscoronavirus/ 447 34. Sidor A, Rzymski P. Dietary Choices and Habits during COVID-19 Lockdown: 448 Experience from Poland. Nutrients. 2020;12:1657. https://doi.org/10.3390/nu12061657 449 35. Rodríguez-Pérez C, Molina-Montes E, Verardo V, Artacho R, García-Villanova 450 B, Guerra-Hernández EJ, et al. Changes in dietary behaviours during the COVID-19 451 outbreak confinement in the Spanish COVIDiet study. Nutrients. 2020;12:1730. 452 https://doi.org/10.3390/nu12061730 453 36. Abouzid M, El-Sherif DM, Eltewacy NK, Dahman N, Okasha S, Ghozy S, et al. 454 Influence of COVID-19 on lifestyle behaviors in the Middle East and North Africa 455 Region: a survey of 5896 individuals. J Transl Med. 2021;19:129. 456 https://doi.org/10.1186/s12967-021-02767-9 457 37. Wang X, Lei SM, Le S, Yang Y, Zhang B, Yao W, et al. Bidirectional influence 458 of the COVID-19 pandemic lockdowns on health behaviors and quality of life among 459 Chinese adults. Int J Environ Res Public Health. 2020;17:5575. 460 https://doi.org/10.3390/ijerph17155575 461 38. FAO and WHO. COVID-19 and Food Safety: Guidance for food businesses: 462 Interim guidance. 2020. https://doi.org/10.4060/ca8660en 463 39. Ministerio de Agricultura P y A. Análisis de consumo alimentario del 27 de abril 464 al 3 de mayo (datos provisionales). 2020. https://www.mapa.gob.es/es/prensa/ultimas- 465 noticias/los-hogares-espa%C3%B1oles-estabilizan-el-consumo-de-alimentos-con- 466 respecto-a-la-semana-anterior/tcm:30-537932. Accessed 20 Jan 2021. 467 20 40. Ministerio de Agricultura P y A. Análisis de consumo en el hogar del 20 al 26 de 468 abril. 2020. https://www.mapa.gob.es/es/prensa/ultimas-noticias/los-hogares- 469 espa%C3%B1oles-muestran-su-preferencia-por-el-consumo-de-productos-frescos-- 470 /tcm:30-537733. Accessed 20 Jan 2021. 471 41. Costi S, Paltrinieri S, Bressi B, Fugazzaro S, Rossi PG, Mazzini E. Poor sleep 472 during the first peak of the SARS-CoV-2 pandemic: a cross-sectional study. Int J 473 Environ Res Public Health. 2021;18:306. https://doi.org/10.3390/ijerph18010306 474 42. Suso–Ribera C, Martín–Brufau R. How much support is there for the 475 recommendations made to the general population during confinement? A study during 476 the first three days of the covid–19 quarantine in Spain. Int J Environ Res Public 477 Health. 2020;17:4382. https://doi.org/10.3390/ijerph17124382 478 43. López-Moreno M, López MTI, Miguel M, Garcés-Rimón M. Physical and 479 psychological effects related to food habits and lifestyle changes derived from covid-19 480 home confinement in the Spanish population. Nutrients. 2020;12:3445. 481 https://doi.org/10.3390/nu12113445 482 44. Mandelkorn U, Genzer S, Choshen-Hillel S, Reiter J, Meira C, Hochner H, et al. 483 Escalation of sleep disturbances amid the COVID-19 pandemic: a cross-sectional 484 international study. J Clin Sleep Med. 2021;17:45-53. https://doi.org/10.5664/jcsm.8800 485 45. Mattioli AV, Sciomer S, Cocchi C, Maffei S, Gallina S. Quarantine during 486 COVID-19 outbreak: Changes in diet and physical activity increase the risk of 487 cardiovascular disease. Nutr Metab Cardiovasc Dis. 2020;30:1409-17. 488 https://doi.org/10.1016/j.numecd.2020.05.020 489 46. Pérez-Rodrigo C, Gianzo Citores M, Hervás Bárbara G, Ruíz-Litago F, Casis 490 Sáenz L, Arija V, et al. Patterns of change in dietary habits and physical activity during 491 21 lockdown in Spain due to the COVID-19 pandemic. Nutrients. 2021;13:300. 492 https://doi.org/10.3390/nu13020300 493 47. Cheikh Ismail L, Osaili TM, Mohamad MN, Al Marzouqi A, Jarrar A, Zampelas 494 A, et al. Assessment of eating habits and lifestyle during the coronavirus 2019 pandemic 495 in the Middle East and North Africa region: a cross-sectional study. Br J Nutr. 496 2021;126:757-66. https://doi.org/10.1017/S0007114520004547 497 48. Cheikh Ismail L, Osaili TM, Mohamad MN, Al Marzouqi A, Jarrar A, Abu 498 Jamous D, et al. Eating habits and lifestyle during COVID-19 lockdown in the United 499 Arab Emirates: a cross-sectional study. Nutrients. 2020;12:3314. 500 https://doi.org/10.3390/nu12113314 501 49. Flanagan EW, Beyl RA, Fearnbach SN, Altazan AD, Martin CK, Redman LM. 502 The impact of COVID‐19 stay‐at‐home orders on health behaviors in adults. Obesity. 503 2021;29:438-45. https://doi.org/10.1002/oby.23066 504 505 22 Tables 506 Table 1. Sociodemographic characteristics of the participants by age group 507 508 Variables 18-35 years 36-65 years >65 years Total Days of lockdown (mean ± SD) 39.4±7.10 37.7±7.58 41.7±5.87 38.7±7.35 Sex1 p=0.311 Woman 471 (55.8%) 362 (42.9%) 11 (1.3%) 844 Man 143 (49.7%) 138 (47.9%) 7 (2.4%) 288 Studies2 p=0.283 Not higher 260 (51.8%) 233 (46.4%) 9 (1.8%) 502 Higher 360 (56.4%) 269 (42.2%) 9 (1.4%) 638 No. of people living together p<0.001 Alone 42 (35%) 75 (62.5%) 3 (2.5%) 120 With others 578 (56.7%) 427 (41.9%) 15 (1.5%) 1020 House m² p=0.741 ⋖60 46 (58.2%) 33 (41.8%) 0 79 61-90 227 (54.4%) 184 (44.1%) 6 (1.4%) 417 ⋗91 347 (53.9%) 285 (44.3%) 12 (1.9%) 644 During lockdown, income has been reduced3 p<0.001 Yes 316 (57.0%) 236 (42.6%) 2 (0.4%) 554 No 276 (51.4%) 248 (46.2%) 13 (2.4%) 537 Due to your working situation, do you have to leave home to go to work? p<0.001 Yes 108 (43.5%) 139 (56.0%) 1 (0.4%) 248 No 512 (57.4%) 363 (40.7%) 17 (1.9%) 892 509 1The figures do not coincide because 8 respondents preferred not to answer the sex 510 question. 2Higher (diploma, degree, university degree, Master's degree, and doctorate) 511 and not higher (from primary studies to baccalaureate, including vocational training). 512 3The figures do not coincide because there were 49 respondents who preferred not to 513 answer the income reduction question. 514 515 23 Table 2. Habits before and during lockdown by age group 516 517 Variables 18-35 years 36-65 years >65 years Total Weight change p=0.001 Yes 321 (55.2%) 255 (43.9%) 5 (0.9%) 581 No 160 (47.8%) 164 (49.0%) 11 (3.3%) 335 Has your sleep pattern changed? p<0.001 Yes 492 (60.1%) 319 (39.0%) 7 (0.9%) 818 No 128 (39.8%) 183 (56.8%) 11 (3.4%) 322 Do you find it more difficult to fall asleep? p<0.05 Yes 409 (58.1%) 288 (40.9%) 7 (1.0%) 704 No 211 (49.1%) 214 (48.4%) 11 (2.5%) 436 Assessment of your diet p<0.001 Better 206 (63.4%) 119 (36.6%) 0 325 Worse 229 (58.3%) 164 (41.7%) 0 393 The same 185 (43.8%) 219 (51.9%) 18 (4.3%) 422 Level of physical activity p<0.001 Better 273 (71.1%) 109 (28.4%) 2 (0.5%) 384 Worse 167 (45.3%) 196 (53.1%) 6 (1.6%) 369 The same 180 (46.5%) 197 (50.9%) 10 (2.6%) 387 Amount of food p<0.001 More 286 (55.2%) 231 (44.6%) 1 (0.2%) 518 Less 94 (64.4%) 50 (34.2%) 2 (1.4%) 146 The same 240 (50.4%) 221 (46.4%) 15 (3.2%) 476 Variety of food p=0.004 More 271 (60.4%) 174 (38.8%) 4 (0.9%) 449 Less 116 (55.8%) 88 (42.3%) 4 (1.9%) 208 The same 233 (48.2%) 240 (49.7%) 10 (2.1%) 483 Regularity of schedules p<0.001 More 172 (54.3%) 143 (45.1%) 2 (0.6%) 317 Less 194 (67.1%) 95 (32.9%) 0 289 The same 254 (47.6%) 264 (49.4%) 16 (3.0%) 534 Number of meals per day p<0.001 More 201 (58.4%) 141 (41.0%) 2 (0.6%) 344 Less 107 (74.3%) 36 (25.0%) 1 (0.7%) 144 The same 312 (47.9%) 325 (49.8%) 15 (2.3%) 652 Snacking between meals p<0.001 More 263 (52.7%) 233 (46.7%) 3 (0.6%) 499 Less 147 (66.8%) 71 (32.3%) 2 (0.9%) 220 The same 210 (49.9%) 198 (47.0%) 13 (3.1%) 421 Fresh food consumption p=0.001 More 213 (62.3%) 129 (37.7%) 0 342 Less 102 (51.8%) 90 (45.7%) 5 (2.5%) 197 The same 305 (50.7%) 283 (47.1%) 13 (2.2%) 601 Packaged food consumption p=0.036 More 124 (55.9%) 93 (41.9%) 5 (2.3%) 222 Less 195 (58.0%) 141 (42.0%) 0 336 The same 301 (51.7%) 268 (46.0%) 13 (2.3%) 582 Fast food consumption at home p<0.001 More 24 (63.2%) 14 (36.8%) 0 38 Less 457 (58.6%) 319 (40.9%) 4 (0.5%) 780 The same 139 (43.2%) 169 (52.5%) 14 (4.3%) 322 24 Table 3. Frequency of consumption of drinks and carbohydrate-containing foods 518 according to age group 519 520 18-35 years 36-65 years >65 years Total Cereals and derivatives (bread, pasta, rice) p<0.001 Not consumed 12 (33.3%) 20 (55.6%) 4 (11.1%) 36 More 182 (58.1%) 127 (40.6%) 4 (1.3%) 313 Less 91 (66.4%) 45 (32.8%) 1 (0.7%) 137 The same 335 (51.2%) 310 (47.4%) 9 (1.4%) 654 Cereals and whole-grain derivatives p<0.001 Not consumed 112 (47.3%) 116 (48.9%) 9 (3.8%) 237 More 107 (59.8%) 72 (40.2%) 0 179 Less 121 (63.0%) 70 (36.5%) 1 (0.5%) 192 The same 280 (52.6%) 244 (45.9%) 8 (1.5%) 532 Sweets and pastries (including breakfast cereals, chocolate and biscuits) p<0.05 Not consumed 103 (50.7%) 94 (46.3%) 6 (3.0%) 203 More 236 (54.5%) 194 (44.8%) 3 (0.7%) 433 Less 148 (64.3%) 78 (33.9%) 4 (1.7%) 230 The same 133 (48.5%) 136 (49.6%) 5 (1.8%) 274 Fruit juices (including natural) p=0.062 Not consumed 164 (54.8%) 127 (42.5%) 8 (2.7%) 299 More 158 (59.6%) 105 (39.6%) 2 (0.8%) 265 Less 87 (56.5%) 67 (43.5%) 0 154 The same 211 (50.0%) 203 (48.1%) 8 (1.9%) 422 Vegetables and salad p=0.104 Not consumed 13 (72.2%) 5 (27.8%) 0 18 More 233 (59.1%) 157 (39.8%) 4 (1.0%) 394 Less 77 (53.1%) 64 (44.1%) 4 (2.8%) 145 The same 297 (50.9%) 276 (47.3%) 10 (1.7%) 583 Potatoes p<0.001 Not consumed 8 (21.6%) 29 (79.4%) 0 37 More 224 (60.1%) 146 (39.1%) 3 (0.8%) 373 Less 56 (55.4%) 44 (43.6%) 1 (1.0%) 101 The same 332 (52.8%) 283 (45.0%) 14 (2.2%) 629 Pulses p=0.018 Not consumed 30 (61.2%) 19 (38.8%) 0 49 More 197 (59.3%) 130 (39.2%) 5 (1.5%) 332 Less 72 (64.3%) 39 (34.8%) 1 (0.9%) 112 The same 321 (49.6%) 314 (48.5%) 12 (1.9%) 647 Soft drinks p=0.077 Not consumed 241 (55.4%) 183 (42.1%) 11 (2.5%) 435 More 112 (57.7%) 81 (41.8%) 1 (0.5%) 194 Less 118 (56.7%) 86 (41.3%) 4 (2.0%) 208 The same 149 (49.2%) 152 (50.2%) 2 (0.6%) 303 Stimulant drinks (with caffeine, theine, and ginseng) p<0.05 Not consumed 330 (57.5%) 231 (40.2%) 13 (2.3%) 574 More 58 (47.5%) 64 (52.5%) 0 122 Less 109 (59.3%) 74 (39.6%) 4 (2.1%) 187 The same 123 (47.9%) 133 (51.8%) 1 (0.4%) 257 Alcoholic beverages p<0.001 Not consumed 193 (56.8%) 138 (40.6%) 9 (2.6%) 340 More 72 (38.9%) 112 (60.5%) 1 (0.5%) 185 Less 265 (73.2%) 93 (25.7%) 4 (1.1%) 362 The same 90 (35.6%) 159 (62.8%) 4 (1.6%) 253 25 Table 4. Frequency of consumption of protein-based food according to age group 521 522 18-35 years 36-65 years >65 years Total Milk and dairy products (yoghurt, cheese) p<0.05 Not consumed 46 (55.4%) 36 (43.4%) 1 (1.2%) 83 More 185 (64.7%) 99 (34.6%) 2 (0.7%) 286 Less 55 (58.5%) 38 (40.4%) 1 (1.1%) 94 The same 334 (49.3%) 329 (48.6%) 14 (2.1%) 677 Dairy-type beverages (soy, almond, oatmeal) p=0.401 Not consumed 310 (54.4%) 247 (43.3%) 13 (2.3%) 570 More 65 (59.1%) 45 (40.9%) 0 110 Less 49 (57.6%) 35 (41.2%) 1 (1.2%) 85 The same 196 (52.3%) 175 (46.7%) 4 (1.1%) 375 Dairy desserts (custard, flan, rice pudding, etc.) p<0.05 Not consumed 220 (50.6%) 166 (42.2%) 7 (1.8%) 393 More 147 (58.6%) 103 (41.0%) 1 (0.4%) 251 Less 72 (60.0%) 45 (37.5%) 3 (2.5%) 120 The same 181 (48.1%) 188 (50.0%) 7 (1.9%) 376 Fatty meats (pork, beef) p<0.05 Not consumed 77 (63.1%) 45 (36.9%) 0 122 More 121 (60.5%) 76 (38.0%) 3 (1.5%) 200 Less 96 (57.5%) 69 (41.3%) 2 (1.2%) 167 The same 326 (50.1%) 312 (47.9%) 13 (2.0%) 651 Lean meats (chicken, turkey, rabbit) p<0.05 Not consumed 41 (74.5%) 14 (25.5%) 0 55 More 156 (58.9%) 106 (40.0%) 3 (1.1%) 265 Less 67 (65.7%) 34 (33.3%) 1 (1.0%) 102 The same 356 (49.6%) 348 (48.5%) 14 (1.9%) 718 Sausages and cold cuts p<0.05 Not consumed 119 (63.6%) 61 (32.6%) 7 (3.7%) 187 More 132 (54.1%) 110 (45.1%) 2 (0.8%) 244 Less 113 (58.9%) 76 (39.6%) 3 (1.6%) 192 The same 256 (49.5%) 255 (49.3%) 6 (1.2%) 517 Oily fish (sardines, salmon, tuna, mackerel) p<0.001 Not consumed 79 (75.2%) 25 (23.8%) 1 (1.0%) 105 More 143 (62.4%) 85 (37.1%) 1 (0.4%) 229 Less 117 (50.2%) 110 (47.2%) 6 (2.6%) 233 The same 281 (49.0%) 282 (49.2%) 10 (1.7%) 573 Lean and semi-fatty fish (hake, cod, monkfish, gilt-head bream, sole) p<0.001 Not consumed 80 (82.5%) 17 (17.5%) 0 97 More 159 (62.8%) 91 (36.0%) 3 (1.2%) 253 Less 113 (48.1%) 116 (49.4%) 6 (2.6%) 235 The same 268 (48.3%) 278 (50.1%) 9 (1.6%) 555 Seafood p<0.001 Not consumed 194 (68.1%) 87 (30.5%) 4 (1.4%) 285 More 56 (54.9%) 44 (43.1%) 2 (2.0%) 102 Less 140 (42.3%) 186 (56.2%) 5 (1.5%) 331 The same 230 (54.5%) 185 (43.8%) 7 (1.7%) 422 Eggs p=0.594 Not consumed 16 (57.1%) 12 (42.9%) 0 28 More 220 (55.0%) 175 (43.8%) 5 (1.2%) 400 Less 46 (63.0%) 25 (34.2%) 2 (2.8%) 73 The same 338 (52.9%) 290 (45.4%) 11 (1.7%) 639 523