Heavy drinking into older age adds 4 cm to waistline

More than half of drinkers aged 59 and over have been heavy drinkers and this is linked to a significantly larger waistline and increased stroke risk, according to a new UCL study.

The study, published in the journal Addiction, examined the association between heavy drinking over a lifetime and a range of health indicators including cardiovascular disease.

The researchers used data from the Whitehall II cohort, which collected information from UK civil servants, aged 34-56 years at study outset, since 1985-88. The final sample for this study was made up of 4,820 older adults, aged between 59 and 83 years. The mean (average) age was 69, and 75% were male.

It found that heavy alcohol consumption over a lifetime is associated with higher blood pressure, poorer liver function, increased stroke risk, larger waist circumferences and body mass index (BMI) in later life, even if you stop drinking heavily before age 50. However, stopping heavy drinking at any point in life is likely to be beneficial for overall health.

Dr Linda Ng Fat (UCL Institute of Epidemiology & Health Care), first author on the study, said: “Alcohol misuse, despite the common perception of young people binge drinking, is common among older adults, with alcohol related hospital admissions in England being the highest among adults aged over 50.

“Previous studies have focused on single snapshots of consumption, which has the potential to mask the cumulative effects of drinking. This study raises awareness of the effect of alcohol consumption over the life-course.”

A heavy drinker was identified using the Alcohol Use Disorders Identification Test for Consumption (AUDIT-C), a standard screening tool for GPs. The screening tool consists of just three questions, and assesses how often you drink, how much you drink, and how often you binge (have six or more drinks). To provide an example a person who has three or four drinks, four or more times a week, would score positive as a hazardous drinker on the AUDIT-C.

Participants were asked on a single occasion to complete the AUDIT-C retrospectively for each decade of their life, from 16-19 to 80 and over. This information was used to categorise their life-time drinking pattern: never hazardous drinker, former early hazardous drinker (stopped before age 50), former later hazardous drinker (stopped at age 50 or after), current hazardous drinker, and consistent hazardous drinker (during every decade of their life).

More than half of drinkers (56%) had been hazardous drinkers at some point in their life, with 21% being current hazardous drinkers and 5% being consistent hazardous drinkers.

Current and consistent heavy drinkers were mainly male (80% and 82%, respectively), predominately white, and likely to be in senior level jobs (61% compared with 52% in the total sample).

Former later, current and consistent hazardous drinkers had significantly higher systolic blood pressure and poorer liver function, than never hazardous drinkers, after adjusting for lifestyle factors. Among current hazardous drinkers, systolic blood pressure was 2.44 mmHG higher and gamma-glutamyl transferase (GGT), a marker of liver disease, was elevated by 22.64 IU/l, compared with never hazardous drinkers.

Current hazardous drinkers had three times greater risk of stroke and former later hazardous drinkers had approximately two times higher risk of non-cardiovascular disease mortality compared with never hazardous drinkers.

Lifetime hazardous drinkers had significantly larger waist circumferences and BMI than never hazardous drinkers, with the magnitude increasing with more current and consistent hazardous drinking.

Former early hazardous drinkers on average had a 1.17 cm larger waist than never hazardous drinkers, whereas former later hazardous drinkers, current hazardous drinkers and consistent hazardous drinkers had a waist circumference that was 1.88 cm, 2.44 cm and 3.85cm larger respectively.

Dr Ng Fat added: “This suggests that the longer adults engage in heavy drinking the larger their waistline in older age. That is why it is beneficial, along with other health benefits, that adults reduce heavy drinking earlier rather than later.”

Professor Annie Britton (UCL Institute of Epidemiology & Health Care), senior author on the study, said: “Despite high prevalence of stroke and liver disease steadily increasing in the United Kingdom, heavy drinking remains common among older adults.

“Early intervention and screening for alcohol consumption, as part of regular check-ups, could help reduce hazardous drinking among this demographic.”

Source

How we perceive close relationships with others determines our willingness to share food

In the midst of a global pandemic, a lot of people rightly will be reluctant to offer food from their plate to another person, or accept such an offer due to the fear of contamination.

But for people with “attachment avoidance,” a psychological term for reluctance to form close personal relationships, this was true long before anybody had heard the term “COVID-19.”

“When we share food, it shows trust — it shows we’re willing to give up some of our resources, and it shows we want to get close with someone,” said Omri Gillath, professor of psychology at the University of Kansas. “Think about the psychological aspect of comfort food. When people are feeling down, when they’re upset, when they’re stressed, food is a source of comfort. We think in part this is because of the connection between food and love. If you think about breastfeeding, babies are getting both the food and the nutrition they need, and the warmth and love from their mom, this creates a strong connection between food and love.”

Gillath is a co-author of a new paper, led by KU doctoral student Sabrina Gregersen, that examines the links between food-sharing and styles of attachment. It was just published in the peer-reviewed journal Appetite.

“‘Attachment’ is a theory that explains how people bond to each other and how they regulate their emotions,” Gillath said. “People have an attachment style based on early interactions with their primary caregivers — parents usually. The three main styles are secure, anxious and avoidant. If you have parents that were supportive and sensitive and find a good balance between helping you on the one hand and providing autonomy on the other, you’re more likely to be secure. If you had parents that were insensitive and intrusive and weren’t consistent about the help they provided, you’re more likely to be anxious. And then if you had parents that were cold and rejecting, you are more likely to develop an avoidant attachment style. These differences that people develop pretty early predict a lot of relational behaviors and outcomes.”

To see how these attachment styles affected people’s food-sharing behaviors, the KU researchers conducted several studies. In one study, participants answered a battery of questions, many of which touched on how food preferences might be tied to romantic or dating behavior for people with various attachment styles. In a different study, participants were placed in a situation where they interacted with another person while one of them had a pack of fruit snacks.

“We brought people to the lab and had them fill out a few questionnaires, then we exposed them to either attachment-security-related cues or control cues,” Gillath said. “For example, we asked them to think about a secure relationship, which activated their security-related models. Then we asked them to wait outside in a waiting area. In both studies they happened to meet another ‘participant’ in that area. In one study, we gave the participant a bag of treats and wanted to see if they would share it, and in the other study, we gave our confederate, who was supposedly another participant, a bag of treats and they offered to share. We wanted to see whether participants would accept the food offering. Many participants were reluctant to take food or give it. However, some people — those who were exposed to security-related cues — were more likely to share their treat with a stranger.”

From the first study, the researchers found people high on attachment avoidance were less likely to share food or date a potential partner who had dissimilar food preferences. In the second study, they found enhancing attachment security increased the tendency to offer one’s food to a fellow participant. In the final study, the team found the tendency to accept food from a fellow participant was positively associated with attachment anxiety, but security priming did not affect this tendency.

Gillath said a better understanding of the links between attachment and food could potentially help inform efforts to extend help to people during the current coronavirus pandemic — particularly among people with high attachment avoidance, who, the authors wrote, “were less likely to engage in food sharing behaviors with current romantic partners and less likely to cook and eat meals with their partners.”

“We could also use the findings to better understand people’s tendencies when it comes to prosocial behavior,” he said. “Right now, with the coronavirus crisis, trust — or the lack of — is a major obstacle. On the one hand, some people don’t have food, they don’t have a job, they don’t have the means to support themselves. On the other hand, there are people who want to help others by giving them food or other resources but are afraid of the consequences. Fear and confusion — of contamination or starvation — may lead people to hoard, preventing them from sharing resources — not giving to those who need them, or buying too much and not leaving to people who also might need — see the rush to buy toilet paper. How can we fix that? If people aren’t taking food because of trust issues, we should ask, ‘Are you guys willing to starve (or not getting the right treatment because of your fears)?’ I’m not saying we want to make people trust anyone and everyone; however, we do want to reduce anxiety, increase security and facilitate food — and resource sharing — in a safe way.”

According to Gillath, in a crisis situation sharing and accepting food and other resources could have psychological benefits beyond just making sure people have enough to eat.

“Not having food and feeling insecure about the whole situation is definitely going to increase mental health issues — whereas having food and having people looking out for you can not only keep people from starving but also potentially help with their mental health and anxieties,” he said. “It is in times like these when we need to find a way to come together, reduce anxiety and help each other out. Making people feel secure can help with that.”

Source

Relationship between different levels of the Mexican food environment and dietary intake: a qualitative systematic review

1.Hawkes, C (2006) Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Global Health 2, 4.

2.Popkin, BM (2001) The nutrition transition and obesity in the developing world. J Nutr 131, 871S873S.

3.Malik, VS, Popkin, BM, Bray, GAet al. (2010) Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes a meta-analysis. Diabetes Care 33, 24772483.

4.Rodríguez-Ramírez, S, Mundo-Rosas, V, García-Guerra, Aet al. (2011) Dietary patterns are associated with overweight and obesity in Mexican school-age children. Arch Latinoam Nutr 61, 270278.

5.Rivera, JA, Barquera, S, Campirano, Fet al. (2002) Epidemiological and nutritional transition in Mexico: rapid increase of non-communicable chronic diseases and obesity. Public Health Nutr 5, 113122.

6.Rivera, JA, Barquera, S, Gonzalez-Cossio, Tet al. (2004) Nutrition transition in Mexico and in other Latin American countries. Nutr Rev Suppl 62, S149S157.

7.Clark, SE, Hawkes, C, Murphy, SMet al. (2012) Exporting obesity: US farm and trade policy and the transformation of the Mexican consumer food environment. Int J Occup Environ Health 18, 5364.

8.Roodenburg, A, Popkin, B & Seidell, J (2011) Development of international criteria for a front of package food labelling system: the International Choices Programme. Eur J Clin Nutr 65, 1190.

9.Guiterrez, J, Rivera-Dommarco, J, Shamah-Levy, Tet al. (2012) Encuesta Nacional de Salud y Nutricion 2012. Resultados Nacionales (National Survey of Health and Nutrition 2012. National Results). Cuernavaca, Mexico: Instituto Nacional de Salud Publica. https://ensanut.insp.mx/informes/ENSANUT2012ResultadosNacionales.pdf (accessed February 2018).

10.Barquera, S, Campos-Nonato, I, Hernández-Barrera, Let al. (2013) Prevalencia de obesidad en adultos mexicanos, 2000–2012 (Prevalence of obesity in Mexican adults, 2000–2012). Salud Publica Mex 55, S151S160.

11.Fantuzzi, G (2005) Adipose tissue, adipokines, and inflammation. J Allergy Clin Immunol 115, 911919.

12.Wellen, KE & Hotamisligil, GS (2003) Obesity-induced inflammatory changes in adipose tissue. J Clin Invest 112, 17851788.

13.Flynn, J (2013) The changing face of pediatric hypertension in the era of the childhood obesity epidemic. Pediatr Nephrol 28, 10591066.

14.Sorof, JM, Lai, D, Turner, Jet al. (2004) Overweight, ethnicity, and the prevalence of hypertension in school-aged children. Pediatrics 113, 475482.

15.Tu, W, Eckert, GJ, DiMeglio, LAet al. (2011) Intensified effect of adiposity on blood pressure in overweight and obese children. Hypertension 58, 818824.

16.Franks, PW, Hanson, RL, Knowler, WCet al. (2010) Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med 362, 485493.

17.Mokdad, AH, Ford, ES, Bowman, BAet al. (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289, 7679.

18.Chan, JM, Rimm, EB, Colditz, GAet al. (1994) Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 17, 961969.

19.Bell, JA, Kivimaki, M & Hamer, M (2014) Metabolically healthy obesity and risk of incident type 2 diabetes: a meta‐analysis of prospective cohort studies. Obes Rev 15, 504515.

22.Barquera, S, Campirano, F, Bonvecchio, Aet al. (2010) Caloric beverage consumption patterns in Mexican children. Nutr J 9, 47.

23.Barquera, S, Hernandez-Barrera, L, Tolentino, MLet al. (2008) Energy intake from beverages is increasing among Mexican adolescents and adults. J Nutr 138, 24542461.

24.Stuckler, D, McKee, M, Ebrahim, Set al. (2012) Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PLoS Med 9, e1001235.

25.Cullen, KW & Zakeri, I (2004) Fruits, vegetables, milk, and sweetened beverages consumption and access to a la carte/snack bar meals at school. Am J Public Health 94, 463467.

26.Story, M, Nanney, MS & Schwartz, MB (2009) Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q 87, 71100.

27.Davis, B & Carpenter, C (2009) Proximity of fast-food restaurants to schools and adolescent obesity. Am J Public Health 99, 505510.

28.Borradaile, KE, Sherman, S, Vander Veur, SSet al. (2009) Snacking in children: the role of urban corner stores. Pediatrics 124, 12931298.

29.Glanz, K, Sallis, JF, Saelens, BEet al. (2005) Healthy nutrition environments: concepts and measures. Am J Health Promot 19, 330333.

30.Rummo, PE, Meyer, KA, Boone-Heinonen, Jet al. (2015) Neighborhood availability of convenience stores and diet quality: findings from 20 years of follow-up in the coronary artery risk development in young adults study. Am J Public Health 105, e65e73.

31.Drewnowski, A, Moudon, AV, Jiao, Jet al. (2014) Food environment and socioeconomic status influence obesity rates in Seattle and in Paris. Int J Obes 38, 306314.

32.Viola, D, Arno, PS, Maroko, ARet al. (2013) Overweight and obesity: can we reconcile evidence about supermarkets and fast food retailers for public health policy? J Public Health Policy 34, 424438.

33.Jilcott, SB, Keyserling, T, Crawford, Tet al. (2011) Examining associations among obesity and per capita farmers’ markets, grocery stores/supermarkets, and supercenters in US counties. J Am Diet Assoc 111, 567572.

34.Walker, RE, Keane, CR & Burke, JG (2010) Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place 16, 876884.

35.Caspi, CE, Sorensen, G, Subramanian, Set al. (2012) The local food environment and diet: a systematic review. Health Place 18, 11721187.

36.Charreire, H, Casey, R, Salze, Pet al. (2010) Measuring the food environment using geographical information systems: a methodological review. Public Health Nutr 13, 17731785.

37.Feng, J, Glass, TA, Curriero, FCet al. (2010) The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place 16, 175190.

38.Aceves-Martins, M, Llauradó, E, Tarro, Let al. (2016) Obesity-promoting factors in Mexican children and adolescents: challenges and opportunities. Glob Health Action 9, 113.

39.Ohri-Vachaspati, P & Leviton, LC (2010) Measuring food environments: a guide to available instruments. Am J Health Promot 24, 410426.

40.Oldenburg, B, Sallis, JF, Harris, Det al. (2002) Checklist of Health Promotion Environments at Worksites (CHEW): development and measurement characteristics. Am J Health Promot 16, 288299.

41.Block, D & Kouba, J (2006) A comparison of the availability and affordability of a market basket in two communities in the Chicago area. Public Health Nutr 9, 837845.

42.McKinnon, RA, Reedy, J, Morrissette, MAet al. (2009) Measures of the food environment: a compilation of the literature, 1990–2007. Am J Prev Med 36, Suppl. 4, S124S133.

43.Fulkerson, JA, Nelson, MC, Lytle, Let al. (2008) The validation of a home food inventory. Int J Behav Nutr Phy 5, 55.

46.Aguirre-Arenas, J, Escobar-Perez, M & Chavez-Villasana, A (1998) Evaluation of food consumption patterns and nutrition in 4 rural communities. Salud Publica Mex 40, 398407.

47.Kaiser, LL & Dewey, KG (1991) Household economic strategies, food resource allocation, and intrahousehold patterns of dietary intake in rural Mexico. Ecol Food Nutr 25, 123145.

48.Leatherman, TL & Goodman, A (2005) Coca-colonization of diets in the Yucatan. Soc Sci Med 61, 833846.

49.Chaudhari, LS, Begay, R & Schulz, LO (2013) Fifteen years of change in the food environment in a rural Mexican community: the Maycoba project. Rural Remote Health 13, 2404.

50.Beaton, GH, Calloway, D & Murphy, SP (1992) Estimated protein intakes of toddlers: predicted prevalence of inadequate intakes in village populations in Egypt, Kenya, and Mexico. Am J Clin Nutr 55, 902911.

51.Cerqueira, MT, Fry, MM & Connor, WE (1979) The food and nutrient intakes of the Tarahumara Indians of Mexico. Am J Clin Nutr 32, 905915.

52.Eastwood Garcia, S, Kaiser, L & Dewey, K (1990) Self-regulation of food intake among rural Mexican preschool children. Eur J Clin Nutr 44, 371380.

53.Monárrez-Espino, J, Béjar-Lío, GI & Vázquez-Mendoza, G (2010) Adequacy of the diet served to Tarahumara children in indigenous boarding schools of northern Mexico. Salud Publica Mex 52, 2329.

54.Moor, MA, Fraga, MA, Garfein, RSet al. (2017) Individual and community factors contributing to anemia among women in rural Baja California, Mexico. PLoS One 12, e0188590.

55.López-Barrón, RG, Jiménez-Cruz, A & Bacardí-Gascón, M (2015) Modifiable environmental obesity risk factors among elementary school children in a Mexico-US Border City. Nutr Hosp 31, 20472053.

56.Alvear-Galindo, MG, Yamamoto-Kimura, LT, Morán-Álvarez, Cet al. (2013) Consumo alimentario dentro y fuera de la escuela. Rev Med Inst Mex Seguro Soc 51, 450454.

57.Lozada, M, Sanchez-Castillo, CP, Cabrera, GAet al. (2007) School food in Mexican children. Public Health Nutr 11, 924933.

58.Pérez-Lizaur, AB, Kaufer-Horwitz, M & Plazas, M (2008) Environmental and personal correlates of fruit and vegetable consumption in low income, urban Mexican children. J Hum Nutr Diet 21, 6371.

59.Vargas, L, Jiménez-Cruz, A & Bacardí-Gascón, M (2013) Unhealthy and healthy food consumption inside and outside of the school by pre-school and elementary school Mexican children in Tijuana, Mexico. J Community Health 38, 11661174.

60.Bridle-Fitzpatrick, S (2015) Food deserts or food swamps? A mixed-methods study of local food environments in a Mexican city. Soc Sci Med 142, 202213.

61.Batis, C, Rivera, JA, Popkin, BMet al. (2016) First-year evaluation of Mexico’s tax on nonessential energy-dense foods: an observational study. PLoS Med 13, e1002057.

62.Bonvecchio, A, Théodore, FL, Safdie, Met al. (2014) Contribution of formative research to design an environmental program for obesity prevention in schools in Mexico City. Salud Publica Mex 56, 139147.

63.Batis, C, Rodríguez-Ramírez, S, Ariza, ACet al. (2016) Intakes of energy and discretionary food in Mexico are associated with the context of eating: mealtime, activity, and place. J Nutr 146, 1907S1915S.

64.Jiménez-Aguilar, A, del Carmen Morales-Ruán, M, López-Olmedo, Net al. (2017) The fight against overweight and obesity in school children: public policy in Mexico. J Public Health Pol 38, 407428.

65.Shamah-Levy, T, Cuevas-Nasu, L, Mendez-Gomez-Humaran, Iet al. (2011) Obesity in Mexican school age children is associated with out-of-home food consumption: in the journey from home to school. Arch Latinoam Nutr 61, 288295.

66.Glanz, K, Rimer, BK & Viswanath, K (editors) (2008) Health Behavior and Health Education: Theory, Research, and Practice, 4th ed.San Francisco, CA: Jossey-Bass.

67.Lopez-Barron, RG, Jimenez-Cruz, A & Bacardi-Gascon, M (2015) Modifiable environmental obesity risk factors among elementary school children in a Mexico–US Border City. Nutr Hosp 31, 20472053.

68.Theodore, F, Bonvecchio, A, Blanco, Iet al. (2011) Culturally constructed meanings for consumption of sweetened beverages among schoolchildren in Mexico City. Rev Panam Salud Publ 30, 327334.

69.French, SA (2003) Pricing effects on food choices. J Nutr 133, 841S843S.

70.Waterlander, WE, de Boer, MR, Schuit, AJet al. (2013) Price discounts significantly enhance fruit and vegetable purchases when combined with nutrition education: a randomized controlled supermarket trial. Am J Clin Nutr 97, 886895.

71.Haws, KL & Winterich, KP (2013) When value trumps health in a supersized world. J Marketing 77, 4864.

72.Turner, LR & Chaloupka, FJ (2012) Student access to competitive foods in elementary schools: trends over time and regional differences. Arch Pediatr Adolesc Med 166, 164169.

73.Soltero, EG, Ortiz Hernández, L, Jauregui, Eet al. (2017) Characterization of the school neighborhood food environment in three Mexican cities. Ecol Food Nutr 56, 139151.

74.Hernandez Barrera, L, Rothenberg, SJ, Barquera, Set al. (2016) The toxic food environment around elementary schools and childhood obesity in Mexican cities. Am J Prev Med 51, 264270.

75.Wojcicki, JM, Jimenez-Cruz, A, Bacardi-Gascon, Met al. (2012) Bimodal distribution of risk for childhood obesity in urban Baja California, Mexico. J Urban Health 89, 628638.

76.Austin, SB, Melly, SJ, Sanchez, BNet al. (2005) Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments. Am J Public Health 95, 15751581.

78.Drewnowski, A (2004) Obesity and the food environment: dietary energy density and diet costs. Am J Prev Med 27, 154162.

79.Drewnowski, A & Specter, S (2004) Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr 79, 616.

80.Kroker-Lobos, MF, Pedroza-Tobías, A, Pedraza, LSet al. (2014) The double burden of undernutrition and excess body weight in Mexico. Am J Clin Nutr 100, 1652S1658S.

81.Barquera, S, Campos, I & Rivera, JA (2013) Mexico attempts to tackle obesity: the process, results, push backs and future challenges. Obes Rev 14, 6978.

82.Kelly, B, Flood, VM & Yeatman, H (2011) Measuring local food environments: an overview of available methods and measures. Health Place 17, 12841293.

83.Glanz, K (2009) Measuring food environments: a historical perspective. Am J Prev Med 36, S93S98.

84.Gibson, RS (2005) Principles of Nutritional Assessment, 2nd ed. New York, NY: Oxford University Press.

Source

Higher daily step count linked with lower all-cause mortality

In a new study, higher daily step counts were associated with lower mortality risk from all causes. The research team, which included investigators from the National Cancer Institute (NCI) and the National Institute on Aging (NIA), both parts of the National Institutes of Health, as well as from the Centers for Disease Control and Prevention (CDC), also found that the number of steps a person takes each day, but not the intensity of stepping, had a strong association with mortality.

The findings were published March 24, 2020, in the Journal of the American Medical Association.

“While we knew physical activity is good for you, we didn’t know how many steps per day you need to take to lower your mortality risk or whether stepping at a higher intensity makes a difference,” said Pedro Saint-Maurice, Ph.D., of NCI’s Division of Cancer Epidemiology and Genetics, first author of the study. “We wanted to investigate this question to provide new insights that could help people better understand the health implications of the step counts they get from fitness trackers and phone apps.”

Previous studies have been done on step counts and mortality. However, they were conducted primarily with older adults or among people with debilitating chronic conditions. This study tracked a representative sample of U.S. adults aged 40 and over; approximately 4,800 participants wore accelerometers for up to seven days between 2003 and 2006. The participants were then followed for mortality through 2015 via the National Death Index. The researchers calculated associations between mortality and step number and intensity after adjustment for demographic and behavioral risk factors, body mass index, and health status at the start of the study.

They found that, compared with taking 4,000 steps per day, a number considered to be low for adults, taking 8,000 steps per day was associated with a 51% lower risk for all-cause mortality (or death from all causes). Taking 12,000 steps per day was associated with a 65% lower risk compared with taking 4,000 steps. In contrast, the authors saw no association between step intensity and risk of death after accounting for the total number of steps taken per day.

“At NIA, we’ve long studied how exercise is important for older adults, and it’s good to see further evidence from a large study with a broad sample that the main thing is to get moving for better overall health as we age,” said Eric Shiroma, Ph.D., a co-author and NIA Intramural Research Program scientist.

In analyses by subgroups of participants, the authors found that higher step counts were associated with lower all-cause death rates among both men and women; among both younger and older adults; and among white, black, and Mexican-American adults. In secondary outcomes of the study, higher step counts were also associated with lower rates of death from cardiovascular disease and cancer.

Data collection was conducted through the CDC’s National Health and Nutrition Examination Survey (NHANES), a program of studies designed to assess a nationally representative sample of the health and nutritional status of adults and children in the United States.

The researchers were surprised they didn’t find an association between higher stepping intensity and all-cause mortality after adjusting for the total number of steps per day. Because few studies have investigated an association between mortality and intensity among adults going about their daily lives, the study authors wrote that future studies of walking intensity and mortality are warranted.

Although the study authors controlled for factors that could have affected the results, the study is observational and cannot prove causality. Nevertheless, their findings are consistent with current recommendations that adults should move more and sit less throughout the day. Adults who do any amount of physical activity gain some health benefits. For even greater health benefits, adults are recommended to get at least 150 minutes of moderate-intensity physical activity per week.

“Being physically active has many benefits, including reducing a person’s risk of obesity, heart disease, type 2 diabetes, and some cancers. And on a daily basis, it can help people feel better and sleep better,” said Janet Fulton, Ph.D., of CDC’s Division of Nutrition, Physical Activity, and Obesity. “CDC is working with communities and partners across the country, as part of the Active People, Healthy Nation initiative, to make it easier, safer, and more convenient for people to be active in their own communities.”

Source

Orthorexia nervosa: examining the Eating Habits Questionnaire’s reliability and validity, and its links to dietary adequacy among adult women

1.Bratman, S & Knight, D (2001) Health Food Junkies: Orthorexia Nervosa Overcoming the Obsession with Healthful Eating. New York: Broadway Books.

2.American Psychiatric Association (2013) Feeding and eating disorders. In Diagnostic and Statistical Manual of Mental Disorders, 5th ed.Arlington: American Psychiatric Association, doi: 10.1176/appi.books.9780890425596.dsm10.

3.Dunn, TM & Bratman, S (2016) On orthorexia nervosa: a review of the literature and proposed diagnostic criteria. Eating Behav 21, 1117.10.1016/j.eatbeh.2015.12.006

4.Cena, H, Barthels, F, Cuzzolaro, Metal. (2019) Definition and diagnostic criteria for orthorexia nervosa: a narrative review of the literature. Eat Weight Disord 24, 209246.

5.Moroze, RM, Dunn, TM, Holland, JCetal. (2015) Microthinking about micronutrients: a case of transition from obsessions about healthy eating to near-fatal “Orthorexia Nervosa” and proposed diagnostic criteria. Psychosomatics 56, 397403.

6.Mathieu, J (2005) What is orthorexia? J Am Dietetic Assoc 105, 15101512.

7.Donini, LM, Marsili, D, Graziani, MPetal. (2005) Orthorexia nervosa: validation of a diagnosis questionnaire. Eat Weight Disord 10, e28e32.

8.Ramacciotti, CE, Perrone, P, Coli, Eetal. (2011) Orthorexia nervosa in the general population: a preliminary screening using a self-administered questionnaire (ORTO-15). Eat Weight Disord 16, e127e130.

9.Herranz Valera, J, Acuña Ruiz, P, Romero Valdespino, Betal. (2014) Prevalence of orthorexia nervosa among ashtanga yoga practitioners: a pilot study. Eat Weight Disord 19, 469472.

10.Alvarenga, MS, Martins, MC, Sato, KSetal. (2012) Orthorexia nervosa behavior in a sample of Brazilian dietitians assessed by the Portuguese version of ORTO-15. Eat Weight Disord 17, e29e35.

11.Smink, REF, Van Hoeken, WD, Hoek, WH (2013) Epidemiology, course, and outcome of eating disorders. Curr Opin Psychiatry 26, 543548.

12.Roncero, M, Barrada, JR & Perpiñá, C (2017) Measuring orthorexia nervosa: psychometric limitations of the ORTO-15. Span J Psychol 20, 19.

13.Segura-García, C, Papaianni, MC, Caglioti, Fetal. (2012) Orthorexia nervosa: a frequent eating disordered behavior in athletes. Eat Weight Disord 17, e226e233.

14.Koven, NS & Senbonmatsu, R (2013) A neuropsychological evaluation of orthorexia nervosa. Open J Psychiatry 3, 214222.10.4236/ojpsych.2013.32019

15.Brytek-Matera, A, Krupa, M, Poggiogalle, Eetal. (2014) Adaptation of the ORTHO-15 test to Polish women and men. Eat Weight Disord 19, 6976.10.1007/s40519-014-0100-0

16.Missbach, B, Hinterbuchinger, B, Dreiseitl, Vetal. (2015) When eating right, is measured wrong! A validation and critical examination of the ORTO-15 questionnaire in German. PLoS One 10, e0135772.

17.Varga, M, Thege, BK, Dukay-Szabó, Setal. (2014) When eating healthy is not healthy: orthorexia nervosa and its measurement with the ORTO-15 in Hungary. BMC Psychiatry 14, 5959.10.1186/1471-244X-14-59

18.Arusoglu, G, Kabakci, E, Koksal, Getal. (2008) Orthorexia nervosa and adaptation of ORTO-11 into Turkish. Turk Psikiyatri Derg 19, 283291.

19.Barnes, MA & Caltabiano, ML (2017) The interrelationship between orthorexia nervosa, perfectionism, body image and attachment style. Eat Weight Disord 22, 177184.

20.Gleaves, DH, Graham, EC & Ambwani, S (2013) Measuring “orthorexia”: development of the eating habits questionnaire. Int J Educ Psych Assess 12, 118.

21.Oberle, CD, Samaghabadi, RO, Hughes, EM (2017) Orthorexia nervosa: assessment and correlates with gender, BMI, and personality. Appetite 108, 303310.

22.Oberle, CD, Watkins, RS & Burkot, AJ (2018) Orthorexic eating behaviors related to exercise addiction and internal motivations in a sample of university students. Eat Weight Disord 23, 6774.

23.Oberle, CD & Lipschuetz, SL (2018) Orthorexia symptoms correlate with perceived muscularity and body fat, not BMI. Eat Weight Disord 23, 363368.

25.Hendrie, GA, Rebuli, MA & Golley, RK (2017) Reliability and relative validity of a diet index score for adults derived from a self-reported short food survey. Nutr Diet 74, 291297.

26.Kyriazos, TA (2018) Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology 9, 22072230.

27.Cohen, J (1992) A power primer. Psychol Bull 112, 155159.

28.Cohen, J (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates.

29.Burkhardt, K, Loxton, H, Kagee, Aetal. (2012) Construction and validation of the South African version of the fear survey schedule for children: an exploratory factor analysis. Behav Ther 43, 570582.

30.Ho, PM, Cooper, AJ, Hall, PJetal. (2015) Factor structure and construct validity of the temporal experience of pleasure scales. J Pers Assess 97, 200208.

31.Costello, AB & Osborne, J (2005) Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess 10, 19.

32.DeVellis, RF (2012) Scale Development: Theory and Applications, 3rd ed. USA: SAGE Publications.

33.Lake, AA, Hyland, RM, Rugg-Gunn, AJetal. (2007) Healthy eating: perceptions and practice (the ASH30 study). Appetite 48, 176182.

34.Paquette, M-C (2005) Perceptions of healthy eating: state of knowledge and research gaps. Can J Public Health 96, Suppl. 3, S15S19, S16–S21.

36.Barthels, F, Poerschke, S, Müller, Retal. (2019) Orthorexic eating behavior in vegans is linked to health, not to animal welfare. Eat Weight Disord, doi: 10.1007/s40519-019-00679-8.

37.Koven, NS & Abry, AW (2015) The clinical basis of orthorexia nervosa: emerging perspectives. Neuropsychiatr Dis 11, 385394.

38.Bethlehem, J (2010) Selection bias in web surveys. Int Stat Rev 78, 161188.

39.Hebert, JR, Clemow, L, Pbert, Letal. (1995) Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. Int J Epidemiol 24, 389398.

40.Cade, JE, Burley, VJ, Warm, DLetal. (2004) Food-frequency questionnaires: a review of their design, validation and utilisation. Nutr Res Rev 17, 522.

41.Wong, JE, Parnell, WR, Black, KEetal. (2012) Reliability and relative validity of a food frequency questionnaire to assess food group intakes in New Zealand adolescents. Nutr J 11, 65.

43.Dell’Osso, L, Abelli, M, Carpita, Betal. (2016) Historical evolution of the concept of anorexia nervosa and relationships with orthorexia nervosa, autism, and obsessive-compulsive spectrum. Neuropsychiatr Dis Treat 12, 16511660.

44.Vandereycken, W (2011) Media hype, diagnostic fad or genuine disorder? Professionals’ opinions about night eating syndrome, orthorexia, muscle dysmorphia, and emetophobia. Eat Disord 19, 145155.

Source