Dos Santos EK¹, Damião JJ¹, Curioni C^*¹

1. Nutrition Institute, State of Rio University, Rio de Janeiro, Brazil. Rua São Francisco Xavier, nº524, Pavilhão João Lira Filho, 12º andar Bloco D, sala 12024, Maracanã

2. Division of Cancer Prevention, National Cancer Institute, Rockville, Maryland, United States of America

3. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America

^*Corresponding author:: Cintia Chaves Curioni, Rua São Francisco Xavier, nº524, Pavilhão João Lira Filho, 12º andar Bloco D, sala 12024, Maracanã, CEP:20559-900

Abstract

The purpose of this study was to identify, with a literature review, a set of indicators, measures, and tools (IM&T) with applicability for obesity management in adults in Primary Health Care (PHC). The data collection was performed at Medline, Lilacs, SUM Search, and Google, with a search using "obesity" and "health indicators" terms. Principal results: Most frequent IM&T within 81 documents included were: body mass index (81.5%), waist circumference (27.2%), arterial pressure (24.7%), lipid profile (21.0%), weight history (17.3%), weight loss (17.3%); glycemia (17.3%). Overall, 181 distinct IM&T were identified in categories: psychological and emotional factors (43.1%); lifestyle and health conditions (19.7%); biochemistry and blood circulation (11.7%); dietary profile (8.5%); socioeconomic, family and cultural issues (8,5); and anthropometry and physical examination (8.5%). Conclusion: The focus of research remains on anthropometry, biochemistry, psychological and emotional dimensions, with a lack of feasibility at PHC. Since the context defines the potential IM&T to monitor health status, obesity care-lines must be developed, including measurable goals and a patient-centered approach, especially in resource-limited settings.

Keywords: Obesity; Delivery of health care; Patient care management; Patient care planning; Comprehensive health care; Health status indicator.

Introduction

Obesity is a multifactorial disease, with an increasing prevalence worldwide in both high-income and low-income countries, characterized as a risk factor for other diseases, also affecting the quality of life of individuals [1,2].

Despite the recognition of obesity as a priority problem by policymakers, advances in intervention policies and actions are still modest, as initiatives focus on behavioral changes and sectoral strategies that underestimate the broader aspects of social inequities, symbolism, culture, and local diversity [3]. It is also noteworthy the complex changes in eating patterns.

Therefore, several factors may explain the ineffectiveness of obesity-related public policies, such as an excessive focus on individual approaches, the lack of socio-environmental policies and programs, the modest effects of interventions to reduce and prevent obesity at the community-level [4], the lack professional training [5,6], and inadequate focus on losing weight instead of health, which can lead to problems of body dissatisfaction, stigma, eating disorders, and even death [7].

The health care sector assumes a central task on obesity control, including the production of scientific knowledge and information, advocacy actions to insert the topic in the public policy managers agenda and development of actions aiming at prevention and comprehensive care [8]. Other responsibilities include delivering care to patients, handing out treatments and technologies considering physical, emotional, and social dimensions. And also, the food environment [9]. The health care network context and the mentioned factors should be observed to propose a useful care line to deal with this complexity.

Different countries launched some ordinances to support managers and professionals in the organization of overweight and obesity treatment [8,10,11,13-16]. However, the design, implementation and monitoring of obesity control plans within the care line at the local level include several challenges [17-21].

There are several Indicators, Measures and Tools (IM&T) to evaluate and monitor people with overweight or obesity. The clinical and outpatient diagnosis is based on the Body Mass Index (BMI) - where BMI values above 30kg/m² already constitute a diagnosis of obesity, being a quite simple and inexpensive method for evaluation at the population level [22,23]. However, other aspects may also be useful, either to identify diseases associated with obesity or to allow comprehensive care according to the specificities of each individual and location [15,24].

A literature review was conducted to identify potential IM&T for management of overweight and obesity in adults on Primary Health Care (PHC).

Materials and Methods

We carried out a literature review of scientific papers and guidelines of the treatment of obesity and overweight published between 1995 (due to diagnostic criteria) and April 8th, 2016. The following databases were searched: Medline, Lilacs, and SUMSearch using a combination of MeSH terms and text words related to obesity and overweight (body mass index; obesity; body weight; overweight) AND to the care monitoring (delivery of health care, continuity of patient care, needs assessment, patient care management, patient care planning, comprehensive health care, treatment outcome, and health status indicator).

Due to difficulty retrieving protocols and guidelines through traditional search methods and the importance and relevance of them to understand the obesity IM&T adopted worldwide, also, we searched Google, including results available until April 30th, 2017. We evaluated the 50 most relevant results.

Studies and guidelines were included if they addressed treatments, monitoring, covering factors or diseases related to obesity. Studies and guidelines focused or related to children and teenagers, other pathologies, such as depression, weight-related measures secondarily, exclusive surgical interventions for weight loss (such as bariatric surgery) and to nutrigenomics were excluded. In addition to letters, case reports, editorials, and documents not fully available.

The studies were classified according to research design (intervention, observational, protocols or guidelines, review, systematic review, or others). The IM&T were listed and organized into different dimensions: anthropometry, biochemistry and blood circulation, dietary profile, socioeconomic, familial, and cultural aspects, psychological and emotional factors, lifestyle and health conditions, resulting in a provisional set of IM&T.

Results

A total of 634 documents were found, of which 81 were included: 49 from Medline (60.5 %); 16 from Google (19.8%); 13 from Lilacs (16.0%) and 3 from SUM Search (3.7%). A wide range of study designs was observed: 38 interventional (of which only 11 were randomized controlled trials); 12 observational; 3 reviews; 2 systematic reviews; 6 studies with other designs and, 20 guidelines and protocols - of which 85.0% used systematic reviews to gather their evidence. Half of the guidelines and protocols were published by a government or in cooperation with other institutions, and the other half was published entirely by professional or researchers’ associations and societies.

The most frequently IM&T mentioned among the 81 included studies were: body mass index (81.5%); waist circumference (27.2%); blood pressure (24.7%) lipid profile (21.0%); weight loss (17.3%) and glycemia (17.3%). Table 1 describes the 39 IM&T most cited (found in 3 or more studies) according to study design, except for the category of psychological and emotional factors, in which only IM&T cited by 4 or more studies were included, due to the large quantity of IM&T identified.

Heterogeneity or absence of information regarding measuring techniques of IM&T recommended for the monitoring of obesity treatment was observed among the studies. From the total of 188 IM&T identified, 81 (43.1%) were related to psychological and emotional factors (of which 29.6% were about eating disorders and habits), 37 (19.7%) to lifestyles and health conditions, 22 (11.7%) to biochemistry and blood circulation, 16 (8.5%) to dietary profile; 16 (8.5%) to sociodemographic, familial, cultural and economic factors and 16 (8.5%) to anthropometry and physical examination. See in the supplemental material the IM&T not listed in Table 1.

Anthropometry and physical exam

Sixteen different IM&T were identified, being the body mass index (BMI) used or recommended by 81.5% of the included studies and waist circumference by 27.2%. BMI and waist circumference appeared in 95.0% and 85.0% of the protocols, respectively. It was also possible to observe that 81.6% of the interventional studies measured the BMI and showed the results in their publications.

Weight loss was a measure constantly used, but presented great measurement variability. Some studies measured weight loss only as the number of kilograms lost [49,50,84,92], while others measured the percentage of weight lost [28,34,38,49-52,88,91] or BMI changes [49,50].

Body composition was cited in some documents [68,73,77], but since they did not mention the type of body compartment evaluated nor the precise methodology of measurement, it was not possible to categorize this suggested measure. One of the protocols also mentioned that the evaluation of body composition is not essential for the treatment of obesity in the clinical routine, although it is useful to measure body fat and fat-free mass before and during treatment [82].

Biochemistry and blood circulation

A total of 22 IM&T was identified. Blood pressure measurement was the most frequently used or recommended measure, found in 24.7% of the articles or protocols included. Among the protocols, the most recommended measures were blood pressure (55.0%), blood lipids (60.0%), and glycemia (50.0%).

We found controversy results regarding the recommendation of blood lipids measurement. While some protocols did not recommend it [67,70,71,73,79,81,102], two studies [51,88] and some protocols [36,65,68,72,78,80] assessed or recommended the measurement of blood lipids, however without further detail about which serum lipid fractions were or should be evaluated. Additionally, other recommended their use detailing measurement of total cholesterol [62,64,74-76,82,94,95], triglycerides [25,64,65,74-76,82,94,95], HDL-cholesterol [60,68,74-76,82,94,95] and LDL-cholesterol [74-76,82,94,95].

Dietary profile

Sixteen IM&T were identified in this dimension, but even the most frequently mentioned IM&T, the history of the previous diet [38,48,64,89], was identified in only 4 documents.

The measurement method for most IM&T in this dimension was poorly detailed by the studies, as well as their results. Among protocols, the recommendation for evaluating food environment does not appear accompanied by the assessment methodology [64,68,74]. Although 21 intervention studies have performed and cited some kind of dietary approach within treatment [26,28,34,35,37-41,43,48-50,52-54,93-95,97,99], IM&T related to the dietary profile were performed or discussed by only 28.6% [28,34,38,41,48,94].

Psychological and emotional factors

A total of 81 IM&T related to psychological or emotional symptoms were identified, and only 11.1% were present in more than 4 studies. In addition, the measurement method for some IM&T in this dimension was poorly detailed by some studies, especially regarding the evaluation of depressive symptomatology [69,72,73,76,82]; history or general psychological state [72,74-76,78,82]; eating disorders [70,73,76]; adherence [29,39,59,83,86,94]; binge eating [74,75]; psychological stress [82]; loss of weight control [34]; number of episodes of binge eating [42,49]; history of compulsive eating [37]; history of psychiatric treatment [37]; calculated neurotic predisposition [26].

Table 1. Indicators, Measures & Tools (IM&T) most cited in the articles or protocols identified, according to dimension and study design.

IM&T identified		Intervention (n= 38)	Observational (n= 12)	Protocol (n= 20)	Systematic review (n=2)	Review (n=3)	Others (n= 6)	Total (n= 81)
Anthropometry and physical exam	Body Mass Index	31(25-55)	8(56-63)	19(64-82)	1(83)	3(84-86)	4(87-90)	66
	Waist circunference	1(25)	2(60,63)	17(64-79,81)	0	1(84)	1 (88)	22
	Weight loss	8(28,91,34,38,49-52)	0	2(65,79)	2(83,92)	1(84)	1(88)	14
	Weight history	3(38,48,51)	2(57,59)	8(67,68,72,74,79-82)	1 (83)	0	0	14
	Body fat mass	2 (25,32)	0	4(71,75,77,81)	0	1(85)	0	7
	Waist hip ratio	2 (39,40)	2(58,63)	5(36,73,75,77,79)	0	1(85)	0	8
	Weight expectations	3(51,93,94)	0	1(81)	1(83)	0	0	5
	Acantose Nigricans	0	0	3(74,80,82)	0	0	0	3
Biochemistry and blood circulation	Blood pressure	5(25,32,41,94,95)	2(60,62)	11(64,65,68,69,72,74-76,78,80,82)	0	1 (86)	1(88)	20
	Lipid profile	2(95,94)	2 (60,62)	12(36,64,65,68,69,72,74-76,78,80,82)	0	0	1(88)	17
	Glycemia	2(25,95)	1(60)	10(65,68,69,72,74-76,78,80,82)	0	0	1(8)	14
	Heart Rate	3(95,32,94)	0	4(64,68,76,80)	0	0	1 (87)	8
	Liver function	0	0	7(72,74,75,78,79,80,82)	0	0	0	7
	Glycemic Hemoglobin	0	0	4(69,72,78,80	0	0	1 (88)	5
	Insulin	3(25,95,42)	0	1(78)	0	0	0	4
	Cortisol	0	0	3(74,75,82)	0	0	0	3
	Thiroid function	0	0	3(74,75,82)	0	0	0	3
Dietary profile	Previous weight loss attempts	2(38,48)		1 (64)			1(89)	4
	Estimates of resting metabolism or energy needs	2 (28,41)	0	1 (64)	0	0	0	3
	Food records	2(51,94)	0	0	0	1(85)	0	3
	Food environment	0	0	3(64,68,74)	0	0	0	3
Psychological and emotional factors	The Structured Clinical Interview for DSM IV Axis I Disorders – SCID I	9 (29-31,35,36,43,44,47,54)	1(96)	1(79)	0	0	0	11
	Beck Depression Inventory II	9 (29,36,37,42,47-50,97)	2(61,98)	0	0	0	0	11
	Eating Disorders Examination (EDE)	7(29,36,42-44,47,54)	4(59,61,96,98)	0	0	0	0	11
	Motivation for changesa	0	0	10(68,72,73,74,76,78-82)	0	0	0	10
	Rosenberg Self-Esteem Scale	5(26,29,36,47,97)	1(59)	0	0	0	0	6
	Binge Eating Scale - BES	4(39,46,48,99)	1(100)	0	0	0	0	5
	The Structured Clinical Interview for DSM IV Axis II Disorders – SCID I	4(43,45,47,54)	0	0	0	0	0	4
	Sympton Checklist 90-Revised (SCL-90-R)	3(30,39,99)	1(100)	0	0	0	0	4
Socioeconomic, family and cultural issues	Family history of obesity	2(34,51)	0	4(72,75,78,80)	0	0	0	6
	Educational level	2(36,47)	1(57)	0	0	0	0	3
	Gender	2(36,47)	0	1(68)	0	0	0	3
	Social support	1(26)	0	3(72,78,81)	0	0	0	4
Lifestyle and health conditions	Short Form 36 (SF-36b)	2(26,99)	2(56,58)	0	0	0	0	4
	Smoking	2(51,52	1(60)	1(72)	0	0	0	4
	Alcohol and drug use	1(37)	1(60)	1(68)	0	0	0	3
	Osteoarthritis	0	0	3(69,70,78)	0	0	0	3
	Obesity-Related Well-being questionnaire – ORWELL 97	3(39,40,99)	0	0	0	0	0	3

a - Although only two documents cited the method of measurement (73,79), this indicator was maintained in this table because it was found in half of the protocols included.

b - Instrument has a validated version for the Brazilian population (101)

Otherwise, it was observed that 50.0% of the protocols recommended the evaluation of the patient's motivation for changes, but none indicates the specific instrument for measuring it [68,72-74,76,78-82]. Overall, 29.6% of the IM&T identified were directly related to eating and eating disorders.

Socioeconomic, family and cultural issues

Among the 16 IM&T identified in this dimension, the most frequent were: family history of obesity (7.4%), educational level (3.7%), gender (3.7%), and social support (4.9%). The assessment of the social history was recommended by some protocols [68,74,78,80]. The instrument for the assessment of social support was cited by a single study [26]. Among the least frequently mentioned IM&T, it was also possible to identify occupation, ethnicity or race, immigration history, and literacy to assess health skills and competencies. The last one without using a specific instrument.

Lifestyle and health conditions

A total of 37 IM&T was identified in this dimension, and the most frequently cited were quality of life by SF-36 [26,56,58,99]; Obesity-Related Well Being Questionnaire [32,39,40], some risk factors such as smoking [51,52,60], and alcohol and drugs use [60,72]. Only within the protocols, it was possible to identify the importance of evaluating specific conditions, such as osteoarthritis [69,70,78]. It was also observed the use of two other specific instruments to evaluate the quality of life for obesity: Weight Efficacy Life-Style Questionnaire - WELSQ [37,97], and Impact of Weight on Quality of Life questionnaire - IWQOL [103].

Discussion

The study identified several IM&Ts in all dimensions, but only a few accompanied by detailed assessment and evaluation criteria. It is noteworthy that many IM&T identified may present low applicability in the context of the PHC, especially in low and middle-income countries, due to the limited availability of adequate or expensive equipment for obese people (adipometer, sphygmomanometer, non-elastic tape, scale, dual-energy X-ray absorptiometry), specific and expensive blood tests or, absence of validated questionnaires and scales. In addition to financial, time, and human resource limitations at the primary health care level.

Some earlier studies showed that approaches from specific studies or clinical trials lack practical applicability for primary care [32,36,42,47,87,97], being restricted to hospitals and specialized centers with selected samples. It is a current challenge for incorporating information from clinical research into health practices [104,105], which can generate an ineffective system to control public health problems such as obesity [106,107].

Only a few articles discussed treatment models that considered factors such as large-scale applicability of the intervention [28], specificity for primary care [88], population with low income level [108,81], or evaluated outcomes of programs designed to treat obesity with minimal care [27]. Therefore, considering the sample selectivity of studies such as randomized controlled trials, with limited generalizability of results, and the discrepancy between the observed evidence concerning real life conditions [109], it is needed interventions of real-world and major data tools [110] to generate effective long-term impacts at the population level.

Although a large number of IM&T related to psychological and emotional factors were found, most of them were not mentioned by many studies. In contrast to IM&T of anthropometry and physical exam, which represented a small fraction of the total of IM&T found, however, were identified in large frequency among the included studies. This finding reflects both the focus on anthropometry as the main category for measurement of success in obesity treatment [111] and the challenge to assess parameters related to psychological and emotional issues and risk factors, despite their importance to obesity care.

Moreover, fewer IM&T related to socioeconomic, family and cultural issues and lifestyle and health conditions were identified compared to the total of IM&T identified in other dimensions. They are essential in the daily life of obese individuals and should be more carefully assessed during treatment to improve overall health and quality of life [116]. For example, difficulties in the IM&T assessment and understanding in such complex scenario could harm the actions of health professionals and obese individuals for the divergence in perceptions regarding causes, responsibility and treatment options for obesity among these groups [113]. Although we understand that individual strategies have limited impact, given the influence of the environment on the development of obesity, the inclusion of these aspects in individual care allows a joint reflection between patient and professional, handling resistance and overcoming strategies for possible changes. Therefore, a better assessment of the corresponding IM&T is needed.

The incentive for collaborative work processes integrating key actors in the development of individual care strategies [115], can lead to a cooperation between the health services, research centers and professionals responsible for obesity care [15] generating consistent improvements in health care delivery, with a potential to produce greater adaptations to the context and to the low technological complexity available.

Conclusion

We identified a significant number of indicators, measures, and tools in all the necessary dimensions listed. Most of them presented limited potential applicability by PHC, revealing the lack of compatibility between many IM&T proposed and used in the studies and the primary health care functioning structure.

Identifying an adequate and feasible subset of indicators, measures, and tools in each context, considering the local structure of primary health care could favor the organization of obesity health care delivery. Furthermore, it is necessary to include obesity specific training, management structures, and equipment provisioning.

Further research should be conducted in order to present IM&T proposals for use at PHC level, which assist professionals in individual and collective diagnosis and monitoring of people living with obesity. They should be aligned with the idea to shift the focus from interventions based on behavioral aspects, with only anthropometric measures and rapid weight loss as indicators of therapeutic success, and start to address issues such as weight stigma, food environment and social determinants of health and obesity. Besides the great difficulty to establish successful interventions for obesity, even in places with high availability of resources, it is essential to discuss long-term weight loss, weight loss maintenance, and other improvements in health and quality of life. Thus, the corresponding indicators, measures, and tools should reflect effective health care delivery.

Effective obesity care planning includes combining scientific evidence available with the current needs of individuals, population characteristics, the reality of health services, professionals, and resources available, according to regional issues and culture. This process can lead to changes in current perceptions and generate potential impact for obesity control, guaranteeing effective human-centered healthcare, instead of focusing only on the disease.

Acknowledgments

None.

Conflict of Interest

The authors declare no conflict of interest

Authorship

E.K. dos SANTOS is responsible for the project as a whole, wrote the manuscript and had primary responsibility for the final content; C. CURIONI and J.J. DAMIÃO contributed to the interpretation of results and critical review of the manuscript. All authors read and approved the final manuscript.

References

1. World Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable diseases 2013–2020, 2013.

2. World Health Organization. Comprehensive global monitoring framework, including 25 indicators and a set of nine voluntary targets for the prevention and control of noncommunicable diseases, 2012.

3. Ferreira VA. Desigualdades sociais, pobreza e obesidade. Tese (Doutorado). Rio de Janeiro: Escola Nacional de Saúde Pública Sergio Arouca. 2014.

4. Salas XR. The Ineffectiveness and Unintended Consequences of the Public Health War on Obesity (commentary). Can J Public Health. 2015; 106:79-81.

5. Lewis KH, Gudzune KA. Overcoming challenges to obesity counseling: Suggestions for the primary care provider. J Clin Outcomes Manag. 2014; 21:123-133.

6. Sebiany AM. Primary care physicians' knowledge and perceived barriers in the management of overweight and obesity. J Family Community Med. 2013; 20:147–152.

7. Almeida LM, Campos KF, Randow R, et al. Estratégias e desafios da gestão da Atenção Primária à Saúde no controle e prevenção da obesidade. Revista Gestão & Saúde (Brasília) 2017, 08(01):114-139.

8. Ministério da Saúde do Brasil. Plano de ações estratégicas para o enfrentamento das doenças crônicas não transmissíveis (DCNT) no Brasil, 2011-2022. Brasília: 2011.

9. Swinburn BA, Sacks G, Hall KD, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011; 378(9793): 804–14.

10. Scottish Government. A Healthier Future: Scotland's Diet & Healthy Weight Delivery Plan. Edinburgh, 2018.

11. Schlair S, Moore S, McMacken M, et al. How to Deliver High-Quality Obesity Counseling in Primary Care Using the 5As Framework. J Clin Outcomes Manag. 2012; 19(5):221-9.

12. Câmara Interministerial de Segurança Alimentar e Nutricional do Brasil. Estratégia Intersetorial de Prevenção e Controle da Obesidade: recomendações para estados e municípios. Brasília, 2014

13. Ministério da Saúde do Brasil. Portaria nº 424, de 19 de março de 2013. Brasília, 2013.

14. Ministério da Saúde do Brasil. Portaria nº 425, de 19 de março de 2013. Brasília, 2013.

15. Ministério da Saúde do Brasil. Departamento de Atenção Básica. Estratégias para o cuidado da pessoa com doença crônica: obesidade. Brasília, 2013.

16. Ministério da Saúde do Brasil. Organização Regional da Linha de Cuidado do Sobrepeso e da Obesidade na Rede de Atenção à Saúde das Pessoas com Doenças Crônicas. Coordenação-geral de atenção às pessoas com doenças crônicas. Brasília, 2014.

17. Blackburn M, Stathi A, Keogh E, Eccleston C. Raising the topic of weight in general practice: perspectives of GPs and primary care nurses. BMJ Open. 2015; 5:e008546.

18. STOP Obesity Alliance Research Team. Improving Obesity Management in Adult Primary Care. The George Washington University School of Public Health and Health Services. 2010.

19. Adkins, SM. Assessment and Management of Adult Obesity in a Primary Care Practice. Wright State University, Dayton, Ohio, 2011.

20. Gunther S, Guo F, Sinfield P, et al. Barriers and enablers to managing obesity in general practice: a practical approach for use in implementation activities. Quality in Primary Care. 2012; 20(3): 93–103.

21. Swinburn B, Kraak V, Rutter H, et al. Strengthening of accountability systems to create healthy food environments and reduce global obesity. Lancet. 2015; 385(9986):2534–45.

22. Ministério da Saúde do Brasil. Protocolos do Sistema de Vigilância Alimentar e Nutricional – SISVAN na assistência à saúde. Brasília, 2008.

23. World Health Organization. Physical Status: the use and interpretation of anthropometry. WHO Technical Report Series, n. 854, 1995.

24. World Health Organization Regional Office for Europe. The challenge of obesity in the WHO European Region and the strategies for response. Copenhagen, 2007.

25. Álvarez C, Ramírez R, Flores M, et al. Effect of sprint interval training and resistance exercise on metabolic markers in overweight women. Rev méd Chile. 2012; 140(10):1289-1296.

26. Canetti L, Berry EM, Elizur Y. Psychosocial predictors of weight loss and psychological adjustment following bariatric surgery and a weight-loss program: the mediating role of emotional eating. International. J Eat Disord. 2009; 42(2):109-17.

27. Carels RA, Young KM, Coit CB, et al. The failure of therapist assistance and stepped-care to improve weight loss outcomes. Obesity. 2008; 16(6):1460-2.

28. Costa AG, Fuchiwaki AC, Miranda VD, et al. O uso da internet como meio auxiliar para o tratamento do excesso de peso. Arq. bras. endocrinol. Metab. 2005; 49(2):303-07.

29. De Zwann M, Herpertz S, Zipfel S, et al. INTERBED: internet-based guided self-help for overweight and obese patients with full or subsyndromal binge eating disorder. A multicenter randomized controlled trial. Trials. 2012;13:220.

30. Deumens RA, Noorthoorn EO, Verbraak MJ. Predictors for treatment outcome of binge eating with obesity: a naturalistic study. Eat. Disord. 2012; 20(4):276-87.

31. Duchesne M, Appolinario JC, Rangé BP, et al. The use of a manual-driven group cognitive behavior therapy in a Brazilian sample of obese individuals with binge-eating disorder. Rev. bras. Psiquiatr. 2007; 29(1):23-25.

32. Dunai A, Novak M, Chung S, et al. Moderate exercise and bright light treatment in overweight and obese individuals. Obesity. 2007; 15(7):1749-57.

33. Eldredge KL, Agras WS. The relationship between perceived evaluation of weight and treatment outcome among individuals with binge eating disorder. Int J Eat Disord. 1997; 22(1):43-9.

34. Elfhag K, Rossner S. Initial weight loss is the best predictor for success in obesity treatment and sociodemographic liabilities increase risk for drop-out. Patient Education & Counseling. 2010;79(3):361-6.

35. Friederich HC, Schild S, Wild B, et al. Treatment outcome in people with subthreshold compared with full-syndrome binge eating disorder. Obesity. 2007;15(2):283-7.

36. Grilo CM, White MA, Gueorguieva R, et al. Predictive significance of the overvaluation of shape/weight in obese patients with binge eating disorder: findings from a randomized controlled trial with 12-month follow-up. Psychological Med. 2013; 43(6):1335-44.

37. King TK, Clark MM, Pera V. History of sexual abuse and obesity treatment outcome. Addictive Behaviors. 1996; 21(3):283-90.

38. Latner JD, Ciao AC. Weight-loss history as a predictor of obesity treatment outcome: prospective, long-term results from behavioral, group self-help treatment. J Health Psychol. 2014; 19(2):253-61.

39. Minniti A, Bissoli L, Di Francesco V, et al. Individual versus group therapy for obesity: comparison of dropout rate and treatment outcome. Eat Weight Disord. 2007; 12(4):161-7.

40. Minniti A, Bissoli L, Di Francesco V, et al. The relationship between body image and quality of life in treatment-seeking overweight women. Eat Weight Disord. 2004; 9(3):206-10.

41. Monego ET, Peixoto MRG, Jardim PCV, et al. Diferentes terapias no tratamento da obesidade em pacientes hipertensos. Arq. bras. Cardiol. 1996; 66(6):343-347.

42. Munsch S, Biedert E, Meyer AH, et al. CCK, ghrelin, and PYY responses in individuals with binge eating disorder before and after a cognitive behavioral treatment (CBT). Physiol Behav. 2009; 97(1):14-20.

43. Munsch S, Meyer AH, Biedert E. Efficacy and predictors of long-term treatment success for Cognitive-Behavioral Treatment and Behavioral Weight-Loss-Treatment in overweight individuals with binge eating disorder. Behav Res Ther. 2012; 50(12):775-85.

44. Ojserkis R, Sysko R, Goldfein JA, et al. Does the overvaluation of shape and weight predict initial symptom severity or treatment outcome among patients with binge eating disorder?. Int J Eat Disord. 2012; 45(4):603-8.

45. Poston WS, Ericsson M, Linder J, et al. Personality and the prediction of weight loss and relapse in the treatment of obesity. Int J Eat Disord. 1999; 25(3):301-9.

46. Rieger E, Dean HY, Steinback KS, et al. The use of motivational enhancement strategies for the maintenance of weight loss among obese individuals: a preliminary investigation. Diabetes Obes Metab. 2009; 11(6):637-40.

47. Robinson AH, Safer DL. Moderators of dialectical behavior therapy for binge eating disorder: results from a randomized controlled trial. Int J Eat Disord. 2012; 45(4):597-602.

48. Sherwood NE, Jeffery RW, Wing RR. Binge status as a predictor of weight loss treatment outcome. Int J Obes Relat Metab Disord. 1999; 23(5):485-93.

49. Smith CF, O'Neil PM, Rhodes SK. Cognitive appraisals of dietary transgressions by obese women: associations with self-reported eating behavior, depression, and actual weight loss. Int J Obes Relat Metab Disord. 1999; 23(3):231-7.

50. Tanco S, Linden W, Earle T. Well-being and morbid obesity in women: a controlled therapy evaluation. Int J Eat Disord. 1998; 23(3):325-39.

51. Toornvliet AC, Pijl H, Hopman E, et al. Predictors of weight loss during treatment with d-fenfluramine. J Intern Med. 1997; 241(5):401-6.

52. Viana LV, Paula TP, Leitão CB, et al. Fatores determinantes de perda de peso em adultos submetidos a intervenções dietoterápicas. Arq. bras. endocrinol. Metab. 2013; 57(9):717-721.

53. West DS, Harvey-Berino J, Krukowski RA, et al. Pretreatment weight change is associated with obesity treatment outcomes. Obesity. 2011; 19(9):1791-5.

54. Wilson GT, Wilfley DE, Agras WS, et al. Psychological treatments of binge eating disorder. Arch Gen Psychiatry. 2010; 67(1):94-101.

55. Wolosker N, Krutmanc M, Kaufmann P, et al. Effectiveness of oxybutynin for treatment of hyperhidrosis in overweight and obese patients. Rev Assoc Med Bras. 2013; 59(2):143-147.

56. Almeida GA, Loureiro SR, Santos JE. Obesidade mórbida em mulheres - estilos alimentares e qualidade de vida. Arch Latinoam Nutr. 2001; 51(4):359-365.

57. Amy Janke, Kozak AT. "The more pain I have, the more I want to eat": obesity in the context of chronic pain. Obesity. 2012; 20(10):2027-34.

58. De Man Lapidoth J, Ghaderi A, Norring C. Eating disorders and disordered eating among patients seeking non-surgical weight-loss treatment in Sweden. Eat Behav. 2006, 7(1):15-26.

59. Latner JD. Body checking and avoidance among behavioral weight-loss participants. Body Image. 2008; 5(1):91-8.

60. Leitão MP. Síndrome metabólica e stress em usuários de Unidades Básicas de Saúde da zona sul de São Paulo. Universidade de São Paulo, 2009.

61. Manasse SM, Forman EM, Ruocco AC, et al. Do executive functioning deficits underpin binge eating disorder? A comparison of overweight women with and without binge eating pathology. Int J Eat Disord. 2015; 48(6): 677-83.

62. Sonntag U, Esch T, Von Hagen L, et al. Locus of control, self-efficacy and attribution tendencies in obese patients - implications for primary care consultations. Medical Science Monitor. 2010; 16(7):CR330-5.

63. Wallace B, Tennant C. Nutrition and obesity in the chronic mentally ill. Aust N Z J Psychiatry. 1998; 32(1):82-5.

64. Academy of Nutrition and Dietetics. Adult weight management evidence-based nutrition practice guideline, 2014.

65. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 2014; 129(25s2):S102-38.

66. Secretaría de Salud de Mexico. Norma oficial mexicana NOM-174-SSA1-1998, para el manejo integral de la obesidad. Rev. méd. IMSS. 2000; 38(5):397-403.

67. National Health and Medical Research Council. Clinical practice guidelines for the management of overweight and obesity in adults, adolescents and children in Australia. Canberra, 2013.

68. Raynor HA, Champagne CMA. Position of the Academy of Nutrition and Dietetics: Interventions for the Treatment of Overweight and Obesity in Adults. J Acad Nutr Diet. 2016; 116(1):129-147.

69. Garvey WT, Mechanick JI, Brett EM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract. 2016; 22(s3):1-203.

70. British Columbia Guidelines & Protocols Advisory Committee. Overweight and Obese Adults: Diagnosis and Management. Victoria, 2011.

71. Brauer P, Connor GS, Shaw E, et al. Recommendations for prevention of weight gain and use of behavioural and pharmacologic interventions to manage overweight and obesity in adults in primary care. CMAJ. 2015; 187(3):184-95.

72. Department of Veterans Affairs. Clinical practice guideline for screening and management of overweight and obesity. Department of Defense. Washington, 2014.

73. Fitch A, Everling L, Fox C, Goldberg J, Heim C, Johnson K et al. Prevention and management of obesity for adults. Institute for Clinical Systems Improvement, 2013.

74. Singapore Health Promotion Board. Obesity Clinical Practice Guidelines. Singapore, 2016.

75. Malaysia Ministry of Health. Management of Obesity Clinical Practice Guideline. Malaysia, 2004.

76. Lau DCW, Douketis JD, Morrison K, et al. Canadian Clinical Practice Guidelines on the Management and Prevention of Obesity in Adults and Children. Can Med Assoc Jour. 2006; 176(9):1310.

77. LeBlanc E, O'Connor E, Whitlock EP, Patnode C, Kapka T. Screening for and Management of Obesity and Overweight in Adults. 2011.

78. National Institute for Health and Care Excellence. Obesity: identification, assessment and management. London, 2014.

79. Scottish Intercollegiate Guidelines Network. Management of Obesity: A national clinical guideline. Edinburgh, 2010.

80. University of Michigan Health System. Obesity Prevention and management. Michigan, 2013.

81. World Gastroenterology Organization. World Gastroenterology Organisation Global Guideline on Obesity. Milwaukee, 2011.

82. Yumuk V, Tsigos C, Fried M, et al. European Guidelines for Obesity Management in Adults. Obesity Facts. 2015; 8(6):402-24.

83. Crawford R, Glover L. The impact of pre-treatment weight-loss expectations on weight loss, weight regain, and attrition in people who are overweight and obese: a systematic review of the literature. Br J Health Psychol. 2012; 17(3):609-30.

84. Cuevas MA, Reyes SMS. Lo último en diagnóstico y tratamiento de la obesidad: ¿Hay lugar aún para la terapia conservadora?. Rev Méd Chile. 2005; 133(6):713-22.

85. Miller PM. Redefining success in eating disorders. Addictive Behaviors. 1996; 21(6):745-54.

86. Muller MJ, Mast M, Asbeck I, et al. Prevention of obesity-is it possible? Obesity Rev. 2001; 2(1):15-28.

87. Larsson EU, Reynisdottir S. The six-minute walk test in outpatients with obesity: reproducibility and known group validity. Physiother Res Int. 2008; 13(2):84-93.

88. Laws R. A new evidence-based model for weight management in primary care: the Counterweight. J Hum Nutr Diet. 2004; 17(3):191-208.

89. Smith CF, Williamson DA, Womble LG, et al. Psychometric development of a multidimensional measure of weight-related attitudes and behaviors. Eat Weight Disord. 2000,; 5(2)73-86.

90. Walsh JR, White AA, Kattelmann KK. Using PRECEDE to develop a weight management program for disadvantaged young adults. J Nutr Educ Behav. 2014; 46(2):S1-9.

91. DePue JD, Clark MM, Ruggiero L, et al. Maintenance of weight loss: a needs assessment. Obesity Res. 1995; 3(3):241-8.

92. Lara M, Amigo H. ¿Qué tipo de intervención tiene mejores resultados en la disminución de peso en adultos con sobrepeso u obesidad? Revisión Sistemática y Metaanálisis. Arch latinoam nutr. 2011; 61(1):45-54.

93. Linné Y, Hemmingsson E, Adolfsson B, et al. Patient expectations of obesity treatment-the experience from a day-care unit. Int J Obes Relat Metab Disord. 2002, 26(5):739-41.

94. Wadden TA, Berkowitz RI, Sarwer DB, et al. Benefits of lifestyle modification in the pharmacologic treatment of obesity: a randomized trial. Arch Intern Med. 2001; 161(2):218-27.

95. Barbato KBG, Martins RCV, Rodrigues MLG, et al. Efeitos da redução de peso superior a 5 por cento nos perfis hemodinâmico, metabólico e neuroendócrino de obesos grau I. Arq Bras Cardiol. 2006; 87(1):12-21.

96. Goldschmidt AB, Hilbert A, Manwaring JL, et al. The significance of overvaluation of shape and weight in binge eating disorder. Behav Res Ther. 2010; 48(3):187-93.

97. Riva G, Bacchetta M, Cesa G, et al. Six-month follow-up of in-patient experiential cognitive therapy for binge eating disorders. Cyberpsychol Behav. 2003; 6(3):251-8.

98. Manasse SM, Forman EM, Ruocco AC, et al. Do executive functioning deficits underpin binge eating disorder? A comparison of overweight women with and without binge eating pathology. Int J Eat Disord. 2015; 48(6):677-83.

99. Mannucci E, Petroni ML, Villanova N, et al. Clinical and psychological correlates of health-related quality of life in obese patients. Health & Quality of Life Outcomes. 2010; 8(90).

100. Dalle Grave R, Calugi S, Magri F, et al. Weight loss expectations in obese patients seeking treatment at medical centers. Obes Res. 2004; 12(12):2005-12.

101. Ciconelli RM. Tradução para o português e validação do questionário genérico de avaliação de qualidade de vida "Medical outcomes Study 36-item Short Form Health Survey (SF-36)" [tese]. São Paulo: Escola Paulista de Medicina da Universidade Federal de São Paulo, 1997.

102. National Institutes of Health. Managing Overweight and Obesity in Adults - Systematic Evidence Review From the Obesity Expert Panel. United States: National Heart, Lung, and Blood Institute, 2013.

103. Kolotkin RL, Head S, Brookhart A. Construct validity of the Impact of Weight on Quality of Life Questionnaire. Obes Res. 1997; 5(5):434-41.

104. Oelke ND, Lima MADS, Acosta AM. Translação do conhecimento: traduzindo pesquisa para uso na prática e na formulação de políticas. Revista Gaúcha de Enfermagem. 2015; 36(3): 113-117.

105. Jansen S, Desbrow B, Ball L. Obesity management by general practitioners: the unavoidable necessity. Aust J Prim Health. 2015; 21(4):366-8.

106. Grimshaw JM, Eccles MP, Lavis JN, et al. Knowledge translation of research findings. Implement Sci. 2012; 7(50).

107. Curran JA, Grimshaw JM, Hayden JA, et al. Knowledge translation research: the science of moving research into policy and practice. Educ Health Prof. 2011; 31(3):174-80.

108. Hajek P, Humphrey K, McRobbie H. Using group support to complement a task-based weight management programme in multi-ethnic localities of high deprivation. Patient Education & Counseling. 2010; 80(1):135-7.

109. Corrigan-Curay J, Sacks L, Woodcock J. Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness. JAMA. 2018; 320(9):867-868.

110. Timmins KA, Green MA, Radley D, et al. How has big data contributed to obesity research? A review of the literature. Int J Obes (Lond). 2018; 41(12):1951-1962.

111. Millstein RA. Measuring Outcomes in Adult Weight Loss Studies That Include Diet and Physical Activity: A Systematic Review. J Nutr Metab. 2014.

112. Obesity Action Coalition. Obesity action Coalition's policy Platform: beliefs and efforts supported by the OAC. Tampa. 2019.

113. Kaplan LM, Golden A, Jinnett K, et al. Perceptions of barriers to effective obesity care: results from the National ACTION Study. Obesity (Silver Spring). 2018; 26(1):61-9.

114. Ross R, Bradshaw AJ. The future of obesity reduction: beyond weight loss. Nat Rev Endocrinol. 2009; 5(6):319-25.

115. Sturgiss EA, Douglas K. A collaborative process for developing a weight management toolkit for general practitioners in Australia—an intervention development study using the Knowledge To Action framework. Pilot Feasibility Stud. 2016;2(20).

Received: January 05, 2021;
Accepted: February 02, 2021;
Published: February 04, 2021 .

To cite this article : Dos Santos EK, Damião JJ, Curioni C. Obesity and Health Care: Identifying Measures, Indicators and Tools. Health Education and Public Health. 2021; 4:1.

© 2021.Dos Santos EK, et al.