Obesity Management refers to the comprehensive approach to preventing and treating excessive body fat that can negatively impact health.
This field encompasses various evidence-based strategies, including dietary interventions, physical activity, behavioral modifications, and pharmacological therapies.
Effective obesity management aims to achieve and maintain a healthy weight, improve overall well-being, and reduce the risk of associated comorbidities such as type 2 diabetes, cardiovascular disease, and certain cancers.
Reserchers and healthcare providers collaborate to develop and evaluate innovative, personalized solutions that empower individuals to adopt sustainable lifestyle changes and optimize patient outcomes.
Most cited protocols related to «Obesity Management»
Practices randomised to control will continue to deliver usual diabetes and obesity management as per current clinical guidelines. Detailed information on usual care pathways will be recorded in each area. Participants recruited in these practices will be followed up for 2 years and seen on 3 occasions by the study research dietitians, for study outcome data collection (Additional file 1: Table 1). Practices randomised to intervention will continue usual guideline-based care and also deliver Counterweight Plus, which includes a Total Diet Replacement (TDR) phase followed by structured food reintroduction (FR) and a structured support programme for long term weight loss maintenance. The intervention will be delivered to each participant individually, at their general practice, by either a practice nurse or local dietitian, depending on practice preference and availability of dietetic support. Participants will attend for 35 appointments over the 2-year intervention period (Additional file 1: Table 1). Training (8 h) for the practice nurses/dietitians in delivery of Counterweight Plus and study protocol will be provided by the study research dietitians who are trained in the package developed for, and improved following, the Counterweight feasibility study in primary care [21 (link)]. A detailed description of the practical dietetic input will be published separately.
Leslie W.S., Ford I., Sattar N., Hollingsworth K.G., Adamson A., Sniehotta F.F., McCombie L., Brosnahan N., Ross H., Mathers J.C., Peters C., Thom G., Barnes A., Kean S., McIlvenna Y., Rodrigues A., Rehackova L., Zhyzhneuskaya S., Taylor R, & Lean M.E. (2016). The Diabetes Remission Clinical Trial (DiRECT): protocol for a cluster randomised trial. BMC Family Practice, 17, 20.
Aim2Be, which will be evaluated in this RCT, was built on the foundational knowledge learned in the first generation of the program called LiGHT (Living Green Healthy and Thrifty) and the second generation of the program, called Aim2Be version 1 (v1). LiGHT was an 11-module online program that integrated lifestyle behavior modification principles with environmental and financial concerns to address childhood obesity among 10- to 17-year-old children and their families [32 (link)]. Modest and nonsignificant improvements in readiness to change were initially observed for both the child and parents, likely due to ceiling effects on the measure and small sample size (N = 17 child-parent dyads). However, the qualitative feedback (N = 10 child-parent dyads) suggested that the LiGHT approach held promise. Specifically, both children and parents indicated that they related strongly to the foundational approach of the LiGHT intervention, meaning they liked having behaviors linked with health, environmental, and financial concerns. The evaluation also uncovered key areas that needed further improvement, including making the program more visually appealing, adding more interactivity, and creating a sense of community for the users. The second generation of the intervention transitioned from a web-based intervention to a mobile app (iOs and Android) and was renamed Aim2Be. For those who do not have a smartphone, Aim2Be has remained accessible as a web-based platform via the internet. Aim2Be became a gamified app that supports children and their families to initiate sustainable behaviors in four primary areas: healthy eating, active living, reducing screen time, and healthy sleeping habits. It retained its focus on linking behaviors with health and living green, as well as adding emphasis on healthy body image and strong self-esteem. While Aim2Be retained some core elements from the LiGHT program, it 1) became strongly grounded in theories of behavior change (i.e., being family-based, supporting the development of self-regulatory skills at both the individual and familial levels, and enhancing self-efficacy through graded tasks) [10 (link), 11 (link), 19 (link), 33 ]; 2) integrated principles of maintenance of health behaviors and as such it included self-regulatory processes that support intrinsic motivation [34 (link)–38 (link)]; 3) integrated gamification practices, which are designed to maximize both enjoyment and motivation [39 (link)]; 4) recognized the importance of body image and self-confidence in lifestyle management; 5) used the mHealth context to support self-regulatory processes [40 (link)]; 6) aligned with best clinical practices and guidelines in three areas (including a) the treatment of childhood obesity in Canada as it aligns with the curriculum of Canadian Weight Management programs (e.g., being family based, multi-behavioral, and focused on supporting skill development at the individual and familial levels); b) clinical guidelines for the management of childhood obesity as it integrates the central elements that need to be incorporated in these interventions (e.g., family focused, focused on improving lifestyle behaviors, skill based support) [41 (link)]; and c) Canadian health recommendations including the Canadian 24 Hour Movement Guidelines [42 (link)] and Canada’s Food Guide [43 ]); and 7) was designed specifically with and for children and their families (see details below). Aim2Be was iteratively and incrementally developed. The first version of Aim2Be (v1) was field tested for 4.5 months among 301 teens between 14- and 17-years old and 315 parents. The quantitative evaluation revealed that teens who were moderately and/or highly engaged in the app (> 30 min of app usage) as compared to those with low engagement (≤ 30 min of app usage) significantly increased their motivation and self-efficacy to improve their dietary habits (i.e., limit sugar-sweetened beverages and increase intake of fruits and vegetables) and sedentary behaviors (i.e., limit screen time). At 4.5 months, children significantly increased their previous day’s intake of fruits and vegetables, decreased their consumption of 100% fruit juice, and reduced their screen time. In addition, parents who were more engaged with Aim2Be v1 reported significant improvements in the children’s dietary behaviors (i.e., increased intake of fruits and vegetables and decreased intake of sugar-sweetened beverages). Additionally, multiple rounds of qualitative evaluations were conducted among 36 teens/preteens and 24 parents. The qualitative evaluations included both focus groups and 2-week prototype testing, followed by semi-structured interviews. The results of these evaluations led to numerous improvements in Aim2Be including clarifying the overall purpose of Aim2Be, supplementing the tracking and check-in sections, adding more engaging features, and syncing the app with physical activity monitoring (i.e., Fitbit). The conceptual framework of Aim2Be is shown in Fig. 2. At its core, the behavior change techniques (BCT) incorporated in Aim2Be are rooted in 1) Social Cognitive Theory (SCT) [44 (link)]; 2) the Player Experience and Need Satisfaction (PENS) model, which is an extension of the Self Determination Theory model for the gamified context and incorporates enjoyment to support intrinsic motivation and the basic psychological needs that promote intrinsic motivation (autonomy, relatedness, and competence/self-efficacy) [39 (link), 45 (link)]; and 3) the ACUDO framework [46 ]. The ACUDO framework, is a best practice framework developed by Ayogo Health Inc. [46 ] to promote engagement and enjoyment by: a) supporting Agency, b) incorporating Challenges, c) infusing Uncertainty, d) supporting self-Discovery and exploration, and e) adding fun Outcomes such as rewards. Specifically, Aim2Be includes strategies that target 1) gamified mediators of behavior change to ensure the experience is both enjoyable and engaging; 2) behavioral mediators that aim to activate self-regulatory skills and support the development of intrinsic motivation and increase self-efficacy as a way to support behavior change and ultimately support healthy body weights; and 3) environmental mediators because it is recognized that behavior change needs to be physically and emotionally supported in the familial environment and that social support from both peers and/or coach is also important to changing health behaviors (see Fig. 2).
Conceptual framework of Aim2Be
Mâsse L.C., Vlaar J., Macdonald J., Bradbury J., Warshawski T., Buckler E.J., Hamilton J., Ho J., Buchholz A., Morrison K.M, & Ball G.D. (2020). Aim2Be mHealth intervention for children with overweight and obesity: study protocol for a randomized controlled trial. Trials, 21, 132.
ARID1A protein, human Behavior Therapy Body Image Body Weight Child Diet Food Fruit Fruit Juices Light Mobile Health Motivation Movement Obesity Management Parent Pediatric Obesity Pleasure Satisfaction Self-Management Self Esteem Social Control, Informal Sugar-Sweetened Beverages Teens Vegetables
The GestationaL Obesity Weight management: Implementation of National Guidelines (GLOWING) pilot study aims to test whether it is feasible and acceptable to deliver a theory-based behaviour change intervention to support midwives in overcoming barriers to practice and to facilitate the implementation of weight management guidelines. The specific pilot study objectives are to:
Pilot the intervention delivery, data collection and analysis methods to assess feasibility and acceptability thereof
Explore the intervention’s active ingredients (in success or failure) through process evaluation
Collect baseline and outcome data required to inform sample size estimations and scope data collection procedures for economic evaluation within the definitive trial
Heslehurst N., Rankin J., McParlin C., Sniehotta F.F., Howel D., Rice S, & McColl E. (2018). GestationaL Obesity Weight management: Implementation of National Guidelines (GLOWING): a pilot cluster randomised controlled trial of a guideline implementation intervention for the management of maternal obesity by midwives. Pilot and Feasibility Studies, 4, 47.
Trained staff measured participant height and body weight for anthropometric assessment. We calculated body mass index (BMI) by dividing body weight (kg) by the square of the height (m). We defined obesity as a BMI ≥25 kg/m2, according to the Asia-Pacific criteria of the World Health Organization guidelines. Obesity classes were defined using the 2018 KSSO Obesity Guideline for the Management of Obesity as follows, class I obesity, BMI 25.0–29.9 kg/m2; class II obesity, 30.0–34.9 kg/m2; class III obesity, ≥35.0 kg/m2.5 (link)
Nam G.E., Kim Y.H., Han K., Jung J.H., Rhee E.J, & Lee W.Y. (2021). Obesity Fact Sheet in Korea, 2020: Prevalence of Obesity by Obesity Class from 2009 to 2018. Journal of Obesity & Metabolic Syndrome, 30(2), 141-148.
This study is a secondary analysis of data collected during the GestationaL Obesity Weight management: Implementation of National Guidelines (GLOWING) pilot trial. The published protocol reports the description of the intervention and pilot trial methods [26 (link)]. GLOWING was developed using social cognitive theory in order to support midwives’ implementation of national guidelines for weight management during pregnancy, and delivered as an intensive midwife training day plus provision of resources for routine practice [26 (link)]. Primary intervention outcomes related to change in midwifery practice. The intervention was piloted as a cluster RCT in four National Health Service (NHS) Trusts in the North East of England, UK, a region which includes some of the most deprived localities in England [27 ] and has a significantly higher than national average prevalence of maternal obesity [2 (link)]. Pregnant women were recruited to GLOWING for data collection purposes only and did not directly receive any intervention. Recruitment took place during routine ultrasound scan appointments in the four participating NHS Trusts. Women were eligible if they had a booking BMI ≥ 30.0 kg/m2, were aged 18 or over, and could speak and read English (due to the lack of translated validated questionnaires). All recruited women were asked to complete questionnaires with components on their socio-demographics; the types of discussions they had with their midwives about weight, diet and PA; their diet and PA behaviours; and psychosocial questions relating to their weight. Different time points and recruitment strategies were used pre- and post-intervention delivery to test trial data collection procedures for a future definitive trial. Prior to delivering the GLOWING intervention to midwives, the recruitment strategy involved randomising women who had their first antenatal appointment at a GLOWING NHS Trust and approaching women at their 20 week scan appointment to complete a one-off questionnaire including all components; we aimed to recruit 15 women per cluster (60 in total) at this stage (sample 1). After delivering the intervention to midwives, the recruitment strategy involved convenience sampling, where any women who were booked for a 12 week scan who met the inclusion criteria were approached for recruitment (sample 2). For sample 2, we aimed to recruit 60 women at 12 weeks gestation to provide questionnaire data on the types of discussions they had with their midwife during their first antenatal contact, and to follow up these women in their third trimester, at approximately 36 weeks gestation, to complete a second questionnaire on their diet and PA behaviours. Sample size was determined based on recommendations for pilot trials [26 (link)]. Women received a £10 gift voucher for each questionnaire they returned. The pilot trial was not powered to detect any between group differences, and analysis exploring any potential intervention effect confirmed this (Table S1). Therefore, the STROBE guidelines for reporting observational studies [28 (link)] have been used (Table S2).
Heslehurst N., Flynn A.C., Ngongalah L., McParlin C., Dalrymple K.V., Best K.E., Rankin J, & McColl E. (2021). Diet, Physical Activity and Gestational Weight Gain Patterns among Pregnant Women Living with Obesity in the North East of England: The GLOWING Pilot Trial. Nutrients, 13(6), 1981.
The baseline questionnaires were used to evaluate some potential confounding variables: sociodemographic factors (age, sex, ethnic background and household income), socioeconomic status (Townsend Deprivation Index), lifestyle habits (smoking status, alcohol consumption, tea intake, processed meat intake obesity, dietary intake (fruits and vegetables). Furthermore, medication use (blood pressure drugs and cholesterol-lowering drugs use), vitamin supplements (vitamin A, vitamin B, vitamin C, vitamin D, vitamin E, multivitamins, or folic acid), minerals supplements (calcium, iron, zinc or selenium) and comorbidities (hypertension, diabetes, high cholesterol and long-term illness) were evaluated. The Townsend Deprivation Index was provided in the UK Biobank. The information for medical history (hypertension, diabetes, high cholesterol and long-term illness) was obtained through self-report at baseline. The body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. In this study, obesity was considered as BMI ≥ 30, which consists with the European guidelines for obesity management in adults [19 (link)]. Details of these evaluations could be obtained from UK Biobank (www.ukbiobank.ac.uk).
Zhou J., Wu Z., Lin Z., Wang W., Wan R, & Liu T. (2023). Association of milk consumption with all-cause mortality and cardiovascular outcomes: a UK Biobank based large population cohort study. Journal of Translational Medicine, 21, 130.
In 2014–2018 twenty-five Caucasian men and women patients with a BMI of 30–35 kg/m2 and diagnosed type 2 diabetes (duration <10 years) were qualified for the study. They underwent surgery and were subjected to postoperative follow-up 12 months after the operation. No control group was formed due to the nature of the study and the parameters evaluated. To exclude latent autoimmune diabetes of adults (LADA) type diabetes an anti-glutamic acid decarboxylase (anti-GAD) antibody test was performed as well as a C-peptide level determination on fasting (>1 ng/ml) and after glucagon loading to define pancreatic reserves of insulin. Polish recommendations in bariatric and metabolic surgery have been developed based on the guidelines American Diabetes Federation (ADA), European Guidelines for Obesity Management in Adults, American Association of Clinical Endocrinologist, The Obesity Society, American Society for Metabolic and Bariatric Surgery (AACE, TOS, ASMBS), International Diabetes Federation (IDF) and European Association for Endoscopic Surgery (EAES) guidelines. [13 ]. Qualification for surgical bariatric/metabolic treatment is one of the key factors influencing its outcomes. Before offering the patient a surgical treatment, we analyze the previous history of attempts to reduce weight and prequalify the patient for surgery. The final qualification of the patient for surgery is multidisciplinary: it is a decision of a team consisting of specialists experienced in obesity treatment, namely, a surgeon, internist, anaesthesiologist, psychologist or psychiatrist , dietician, physiotherapist, and, if necessary, a cardiologist, pulmonologist, gastroenterologist and neurologist. Recommendations also define the optimal time of patient preparation for surgery, it should not be shorter than 3 months or longer than 6–12 months.
Jaworski P., Binda A., Barski K., Wawiernia K., Kudlicka E., Wąsowski M., Jankowski P, & Tarnowski W. (2023). OAGB with shortened excluded ileal loop as an effective treatment for type 2 diabetes mellitus in the cases of Caucasian men and women with obesity of the first degree (BMI 30–35 kg/m2). Langenbeck's Archives of Surgery, 408(1), 84.
The reporting of this study conforms to the STROBE guidelines.11Retrospective cohort study of anonymized secondary data, whose follow‐up begins from the indication of the surgical procedure to 1 year postoperatively. A population of patients undergoing MBS was taken from a highly complex institution specialized in bariatric surgery in the city of Bucaramanga and its metropolitan area, Colombia, between January 2015 and December 2019. Inclusion criteria: Patients between 18 and 80 years old, BMI > 30 kg/m2, strict follow‐up at 1 year of the clinical variables already exposed. Exclusion criteria: Patient with previous bariatric surgeries. In addition, the type of procedure to be performed was taken from the management guidelines of the Colombian Association of Obesity and Bariatric Surgery.
Domínguez Alvarado G.A., Otero Rosales M.C., Cala Duran J.C., Serrano‐Gómez S., Carrero Barragan T.Y., Domínguez Alvarado P.N., Ramírez M.L., Serrano‐Mesa K., Lozada‐Martinez I.D., Narvaez‐Rojas A.R, & López Gómez L.E. (2023). The effect of bariatric surgery on metabolic syndrome: A retrospective cohort study in Colombia. Health Science Reports, 6(2), e1090.
Our study will be conducted at two clinical sites in Canada (Alberta Children’s Hospital in Calgary, AB; Trillium Health Partners in Mississauga, ON). Children will be assigned to an experimental (FN + Usual Care) or control (Usual Care) group after they enroll in publicly funded, multidisciplinary, pediatric obesity management clinics at our two sites.
Ball G.D., O’Neill M.G., Noor R., Alberga A., Azar R., Buchholz A., Enright M., Geller J., Ho J., Holt N.L., Lebel T., Rosychuk R.J., Tarride J.E, & Zenlea I. (2023). A multi-center, randomized, 12-month, parallel-group, feasibility study to assess the acceptability and preliminary impact of family navigation plus usual care versus usual care on attrition in managing pediatric obesity: a study protocol. Pilot and Feasibility Studies, 9, 14.
To inform any modifications before our definitive RCT, we will compare study data to pre-set feasibility criteria (Table 2). For objective 1 (measuring acceptability), we will perform a theoretically informed analysis of interview data using the Theoretical Framework of Acceptability (TFA; [96 (link)]). Our theoretically informed analysis situates the professional knowledge of the researcher, allows transparent examination of the research by the reader, and has two main characteristics: how data are structured and how data are interpreted [97 (link)]. The structure of our analytic process will be anchored in the theoretical framework. We will interpret the meaning of participants’ words vis-à-vis the theory while allowing new themes to develop. Our analysis will begin after the first interview and be ongoing during the study. RCs at each site will conduct initial coding, using TFA as a guide to structure themes while remaining open to identifying new themes, with line-by-line analysis. Initial findings will be shared with our SSC for review and discussion, then applied to the full data set. In analyzing the full data set, we will remain open to identifying new themes not accounted for in the TFA, reflecting our sensitive use of theory to guide analysis. For rigor and transparency, we will complete the Consolidated Criteria for Reporting Qualitative Research (COREQ) [98 (link)].
Acceptability and study rigor and conduct data, with thresholds for assessing feasibility of future definitive randomized controlled trial
Methodological issues
Commentsa
Feasibility dataa
Criteriab
Feasible? (Y/N)
Need to modify pre-RCT? (Y/N)
1.Was the Family Navigation intervention acceptable to children and caregivers based on the 7 domains from the Theoretical Framework of Acceptability (TFA), including:
a. Affective attitude
b. Burden
c. Perceived effectiveness
d. Ethicality
e. Intervention coherence
f. Opportunity costs
g. Self-efficacy
(e.g., data from 1-on-1 interviews)
(e.g., data will be analyzed using thematic analysis, which precludes quantification; 7 themes, plus additional sub-themes)
For each of the 7 domains, findings will be reviewed, discussed, and interpreted with our Stakeholder Committee and research team to determine whether parts of the Family Navigation intervention should remain unchanged or if changes are needed before implementing in our definitive RCT
(e.g., “yes” for all 7 domains)
(e.g., “yes” for 1 domain; “no” for 6 domains)
2.What proportion of participants approached were eligible?
Equal numbers of participants randomized to experimental and control groups
7.Were blinding procedures effective?
100% analysts remained blinded to group assignment
8.Were outcome assessments completed?
≥ 95% participants retained in the study completed outcome assessments
9.How complete were outcome assessments at all study measurement intervals?
≥ 95% outcome assessments were complete
10. Were outcome assessments burdensome for families?
≥ 90% children and caregivers disagreed that outcome assessments were burdensome
11. Was study protocol acceptable to children and caregivers?
≥ 90% children and caregivers agreed study protocol was acceptable
12. Was the level of attrition adequate within experimental (Family Navigation + Usual Care) and control (Usual Care only) groups at 12 months?
Experimental (FN + UC): 15–25% attrition
Control (UC): 30–40% attrition
13. Was collection of attrition data adequate to calculate sample size for definitive RCT?
≥ 95% participants had attrition data collected by 12 months post-baseline
14. Were logistics of running a multi-center trial assessed?
Review procedures (ongoing and end-of-grant) with investigators, research staff, navigators
15. Did all components of the protocol work together adequately?
Review procedures (ongoing and end-of-grant) with investigators, research staff, navigators
aComments and Feasibility Data columns will be populated with study data collected during our study; example provided for context
bCriteria column thresholds based on objective criteria (when possible) and experience gained through study implementation as well as data analysis and interpretation with Stakeholder Committee and research team
For objective 2 (measuring attrition; study rigor and conduct; clinical, health services, economic outcomes), we will describe continuous data by summaries (means, medians, ranges) and categorical variables with frequency distributions. Data will be described for each group (e.g., sex, gender), stratification (e.g., age [6–9 years, 10–13 years, 14–17 years]), and clinic (Calgary, Mississauga). Group differences in outcomes will be calculated with 95% CIs. To complete power calculations for our definitive RCT, the 95% CI for the primary outcome (attrition yes/no at 12 months post-baseline) will be used. R [99 ] will be used for statistical analysis by a data analyst blinded to group assignments. Our SSC will discuss and contextualize study findings. REDCap will house quantitative data and generate data files for analyses. Consistent with feasibility studies [56 (link)], our analyses will be descriptive. Uncertainty exists on what elements(s) of FN are essential to optimize intervention effects [43 (link)]. In response, the participatory nature of our research, inclusion of qualitative and quantitative data sources, and heavy participation of our SSC will provide a full assessment of FN, revealing vital insights into how our experimental intervention can optimize treatment impact for managing pediatric obesity in our future definitive RCT. Qualitative data analysis will occur throughout the study. Quantitative data analysis will occur at study completion only. Based on literature reviews of attrition and pediatric obesity management [16 (link), 17 (link)], subgroup analyses will be exploratory and descriptive. We will describe attrition and other outcomes (e.g., HRQoL, intervention dose received) in experimental and control groups to explore potential differences by age, sex, gender, clinic, and changes within and between these subgroups over time.
Ball G.D., O’Neill M.G., Noor R., Alberga A., Azar R., Buchholz A., Enright M., Geller J., Ho J., Holt N.L., Lebel T., Rosychuk R.J., Tarride J.E, & Zenlea I. (2023). A multi-center, randomized, 12-month, parallel-group, feasibility study to assess the acceptability and preliminary impact of family navigation plus usual care versus usual care on attrition in managing pediatric obesity: a study protocol. Pilot and Feasibility Studies, 9, 14.
SPSS ver. 20.0 is a software package for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS is to enable users to perform a variety of statistical tests and analyses on data sets.
SPSS Statistics for Mac, version 28 is a statistical software package designed for data analysis and management. It provides a comprehensive set of tools for advanced statistical analysis, data visualization, and data management.
Sourced in Germany, United Kingdom, United States, China, France, Switzerland
A digital scale is a weighing device that measures the mass or weight of an object using electronic sensors. It provides a precise and digital display of the measured value.
Sourced in United States, United Kingdom, New Zealand
The QDR 4500A is a dual-energy X-ray absorptiometry (DXA) system designed for bone density measurements. It is a diagnostic medical device used to assess bone mineral density (BMD) and evaluate the risk of osteoporosis.
The Human Genome U133A Array is a microarray platform used for the analysis of gene expression. It contains approximately 22,000 probe sets that target human transcripts. The array is designed to provide a comprehensive and unbiased representation of the human transcriptome, enabling researchers to study gene expression patterns across the human genome.
The HBF-306 is a body composition monitor developed by Omron. It measures body weight, body fat percentage, and body mass index (BMI) through bioelectrical impedance analysis. The device provides accurate and reliable measurements to support health and fitness monitoring.
Stata V.13 is a comprehensive, integrated statistical software package. It provides a wide range of data management, statistical analysis, and graphical tools to support various research and analytical needs. Stata V.13 offers a user-friendly interface and a powerful programming language for advanced data manipulation and statistical modeling.
SPSS Statistics is a comprehensive statistical software package for data analysis, management, and reporting. Version 22.0 provides a range of statistical procedures and analytical tools for various types of data, including quantitative and qualitative. The software supports data manipulation, visualization, and modeling capabilities. SPSS Statistics 22.0 is designed to help organizations and researchers analyze and interpret their data effectively.
STATA V.11 is a data analysis and statistical software package developed by StataCorp. It is designed to help users manage, analyze, and visualize data. The software provides a wide range of tools for data manipulation, statistical modeling, and graphical presentation.
The Tanita BC-418 is a body composition analyser that measures various body metrics, including weight, body fat percentage, muscle mass, and bone mass. It uses bioelectrical impedance analysis technology to provide these measurements.
Obesity management can be challenging due to the complex interplay of factors such as diet, physical activity, genetics, and lifestyle. Common challenges include finding a sustainable dietary approach, maintaining long-term behavioral changes, overcoming barriers to regular exercise, and managing comorbidities like type 2 diabetes or cardiovascular disease. Personalized, multidisciplinary care is often required to address the unique needs of each individual.
PubCompare.ai can revolutionize obesity management research by empowering researchers to more efficiently screen protocol literature and leverage AI to pinplint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to identify the most effective protocols related to obesity management for their specific research goals. This can help researchers choose the best options for reproducibility and accuracy, ultimately improving patient outcomes.
Obesity management encompasses a range of evidence-based strategies, including dietary interventions (e.g., calorie-controlled diets, low-carb diets, intermittent fasting), physical activity programs (e.g., aerobic exercise, strength training, lifestyle activity), behavioral modifications (e.g., cognitive-behavioral therapy, habit-based approaches), and pharmacological therapies (e.g., weight-loss medications, bariatric surgery). The optimal approach often involves a combination of these methods, tailored to the individual's needs and preferences to achieve sustainable weight loss and improved health.
PubCompare.ai's AI-enhanced platform allows researchers to quickly compare the effectiveness of different obesity management protocols from the literature, pre-prints, and patents. By leveraging machine learning algorithms, the platform can identify key differences in protocol characteristics, such as intervention components, patient populations, and outcomes measured. This empowers researchers to make more informed decisions about which protocols to replicate or build upon, ultimately leading to more reproducible and impactful obesity management research.
Obesity management strategies have a wide range of practical applications, from improving cardiovascular health and reducing the risk of type 2 diabetes to enhancing physical functioning and boosting self-esteem. Effective obesity management can also help mitigate the risk of certain cancers, improve fertility outcomes, and reduce the severity of obesity-related sleep disorders. By adopting a comprehensive, personalized approach, individuals can achieve and maintain a healthy weight, improve their overall well-being, and reduce the burden of obesity-related comorbidities.
PubCompare.ai's AI-driven protocol comparison capabilities can help researchers optimize their obesity management studies in several ways. By rapidlly screening and identifying the most effective protocols from the literature, researchers can build upon proven strategies and avoid reinventing the wheel. The platform's ability to pinpoint criitical insights can also reveal unique protocol variations or underexplored interventions that may lead to breakthroughs in the field. Additionally, the platform's comparison features empower researchers to make more informed decisions about study design, patient selection, and outcome measures, ultimately enhancing the quality and impact of their obesity management research.
More about "Obesity Management"
Obesity Management encompasses a comprehensive approach to preventing and treating excessive body fat, which can negatively impact an individual's health.
This multifaceted field utilizes various evidence-based strategies, including dietary interventions, physical activity promotion, behavioral modifications, and pharmacological therapies.
The primary goal of Obesity Management is to achieve and maintain a healthy weight, improve overall well-being, and reduce the risk of associated comorbidities such as type 2 diabetes, cardiovascular disease, and certain cancers.
Researchers and healthcare providers collaborate to develop and evaluate innovative, personalized solutions that empower individuals to adopt sustainable lifestyle changes and optimize patient outcomes.
Cutting-edge technologies, such as SPSS ver. 20.0, SPSS Statistics for Mac, version 28, Digital scale, QDR 4500A, Human Genome U133A Array, HBF-306, Stata V.13, SPSS statistical software version 22.0, STATA V.11, and BC-418 body composition analyser, play a crucial role in the assessment, monitoring, and evaluation of obesity management interventions.
PubComapre.ai, an AI-driven platform, revolutionizes obesity management research by facilitating the identification of the best protocols and products from literature, pre-prints, and patents.
This intuitive tool empowers researchers to optimize their studies and improve patient outcomes through data-driven, AI-enhanced decision-making.
With its comprehensive approach and innovative technologies, Obesity Management strives to provide personalized, sustainable solutions that empower individuals to achieve and maintain a healthy weight, ultimately enhancing their overall health and well-being.