Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives

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Abstract

Obesity is a chronic condition associated with risk factors for many medical complications and comorbidities such as cardiovascular diseases, some types of cancer, osteoarthritis, hypertension, dyslipidemia, hypercholesterolemia, type-2 diabetes, obstructive sleep apnea syndrome, and different psychosocial issues and psychopathological disorders. Obesity is a highly complex, multifactorial disease: genetic, biological, psychological, behavioral, familial, social, cultural, and environmental factors can influence in different ways. Evidence-based strategies to improve weight loss, maintain a healthy weight, and reduce related comorbidities typically integrate different interventions: dietetic, nutritional, physical, behavioral, psychological, and if necessary, pharmacological and surgical ones. Such treatments are implemented in a multidisciplinary context with a clinical team composed of endocrinologists, nutritionists, dietitians, physiotherapists, psychiatrists, psychologists, and sometimes surgeons. Cognitive behavioral therapy (CBT) is traditionally recognized as the best established treatment for binge eating disorder and the most preferred intervention for obesity, and could be considered as the first-line treatment among psychological approaches, especially in a long-term perspective; however, it does not necessarily produce a successful weight loss. Traditional CBT for weight loss and other protocols, such as enhanced CBT, enhanced focused CBT, behavioral weight loss treatment, therapeutic education, acceptance and commitment therapy, and sequential binge, are discussed in this review. The issue of long-term weight management of obesity, the real challenge in outpatient settings and in lifestyle modification, is discussed taking into account the possible contribution of mHealth and the stepped-care approach in health care.

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Excess deaths associated with underweight, overweight, and obesity.

Katherine Flegal (2005)

As the prevalence of obesity increases in the United States, concern over the association of body weight with excess mortality has also increased. To estimate deaths associated with underweight (body mass index [BMI] or =30) in the United States in 2000. We estimated relative risks of mortality associated with different levels of BMI (calculated as weight in kilograms divided by the square of height in meters) from the nationally representative National Health and Nutrition Examination Survey (NHANES) I (1971-1975) and NHANES II (1976-1980), with follow-up through 1992, and from NHANES III (1988-1994), with follow-up through 2000. These relative risks were applied to the distribution of BMI and other covariates from NHANES 1999-2002 to estimate attributable fractions and number of excess deaths, adjusted for confounding factors and for effect modification by age. Number of excess deaths in 2000 associated with given BMI levels. Relative to the normal weight category (BMI 18.5 to or =30) was associated with 111,909 excess deaths (95% confidence interval [CI], 53,754-170,064) and underweight with 33,746 excess deaths (95% CI, 15,726-51,766). Overweight was not associated with excess mortality (-86,094 deaths; 95% CI, -161,223 to -10,966). The relative risks of mortality associated with obesity were lower in NHANES II and NHANES III than in NHANES I. Underweight and obesity, particularly higher levels of obesity, were associated with increased mortality relative to the normal weight category. The impact of obesity on mortality may have decreased over time, perhaps because of improvements in public health and medical care. These findings are consistent with the increases in life expectancy in the United States and the declining mortality rates from ischemic heart disease.

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Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

Gunther Eysenbach (2011)

Background Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.

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Mapping mHealth Research: A Decade of Evolution

Maddalena Fiordelli, Nicola Diviani, Peter J. Schulz (2013)

Background For the last decade, mHealth has constantly expanded as a part of eHealth. Mobile applications for health have the potential to target heterogeneous audiences and address specific needs in different situations, with diverse outcomes, and to complement highly developed health care technologies. The market is rapidly evolving, making countless new mobile technologies potentially available to the health care system; however, systematic research on the impact of these technologies on health outcomes remains scarce. Objective To provide a comprehensive view of the field of mHealth research to date and to understand whether and how the new generation of smartphones has triggered research, since their introduction 5 years ago. Specifically, we focused on studies aiming to evaluate the impact of mobile phones on health, and we sought to identify the main areas of health care delivery where mobile technologies can have an impact. Methods A systematic literature review was conducted on the impact of mobile phones and smartphones in health care. Abstracts and articles were categorized using typologies that were partly adapted from existing literature and partly created inductively from publications included in the review. Results The final sample consisted of 117 articles published between 2002 and 2012. The majority of them were published in the second half of our observation period, with a clear upsurge between 2007 and 2008, when the number of articles almost doubled. The articles were published in 77 different journals, mostly from the field of medicine or technology and medicine. Although the range of health conditions addressed was very wide, a clear focus on chronic conditions was noted. The research methodology of these studies was mostly clinical trials and pilot studies, but new designs were introduced in the second half of our observation period. The size of the samples drawn to test mobile health applications also increased over time. The majority of the studies tested basic mobile phone features (eg, text messaging), while only a few assessed the impact of smartphone apps. Regarding the investigated outcomes, we observed a shift from assessment of the technology itself to assessment of its impact. The outcome measures used in the studies were mostly clinical, including both self-reported and objective measures. Conclusions Research interest in mHealth is growing, together with an increasing complexity in research designs and aim specifications, as well as a diversification of the impact areas. However, new opportunities offered by new mobile technologies do not seem to have been explored thus far. Mapping the evolution of the field allows a better understanding of its strengths and weaknesses and can inform future developments.

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Author and article information

Journal

Journal ID (nlm-ta): Psychol Res Behav Manag

Journal ID (iso-abbrev): Psychol Res Behav Manag

Journal ID (publisher-id): Psychology Research and Behavior Management

Title: Psychology Research and Behavior Management

Publisher: Dove Medical Press

ISSN (Electronic): 1179-1578

Publication date Collection: 2017

Publication date (Electronic): 06 June 2017

Volume: 10

Pages: 165-173

Affiliations

[1 ]Psychology Research Laboratory, Istituto Auxologico Italiano IRCCS, San Giuseppe Hospital, Verbania

[2 ]Department of Psychology, Catholic University of Milan, Milan

[3 ]Faculty of Psychology, eCampus University, Novedrate, Italy

Author notes

Correspondence: Gianluca Castelnuovo, Clinical Psychology Lab, Istituto Auxologico Italiano IRCCS, San Giuseppe Hospital, Strada Cadorna 90 – 28824 Piancavallo di Oggebbio (VB), Italy, Email gianluca.castelnuovo@ 123456auxologico.it

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Publisher ID: prbm-10-165

DOI: 10.2147/PRBM.S113278

PMC ID: 5476722

PubMed ID: 28652832

SO-VID: 55542071-d25e-436a-9dfb-63eb8d44ecb4

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The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives

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Most cited references 121

Excess deaths associated with underweight, overweight, and obesity.

Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

Mapping mHealth Research: A Decade of Evolution

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