Essay on Weight Management Models

 Exploring New Insights - Weight Management

The prevalence of disorders such as obesity and eating disorders are rising globally and proving difficult to control (Backholer, 2010). No one has calculated the costs of delivery which combined may account for more than $26.6 billion (AIHW, 2017, 2018) and evaluated current models concurrently. Although public health measures such as those used in controlling other epidemics such as smoking and infectious disease are in place, the steady increase in weight management disorders indicates the need to develop a greater understanding of key drivers and consider more cost-effective and accessible self-care models using a single weight management model.

Obesity Control

Controlling obesity is one of the highest priorities for public health investment in developed countries with attention in Australia focussed on community-based approaches, social marketing campaigns, pharmaceutical, and surgical intervention. Limited evidence supports the effectiveness of such interventions with evidence supporting their potential negative effects on biological change, body image and psychological health (Wozniak, 2019).

According to the National Obesity taskforce (Australia), a range of factors contribute to a person’s ability to maintain a healthy weight. Based on current trends, this indicates two-thirds of our projected population will be overweight or obese by 2030, significantly fuelling the economic burden of eating disorders such as binge eating disorder and other specified feeding or eating disorders (Hay et al. 2017; Wade et al. 2006).

Developing public health algorithms that control weight management will reduce long term health spending on associated long-term health complaints including diabetes, cardiovascular disease, some cancers, musculoskeletal disease, disability (Peeters & Backholer 2012), impaired fertility, organ damage, and psychological interventions.

Reducing delivery cost

With more than 63.4% of Australian adults overweight or obese in 2017 (ABS, 2017) and the prevalence of eating disorders account for approximately 16% of the population (Hay et al. 2008; Hay et al. 2015; Wade et al. 2006), the cost of weight management delivery models combined may be significantly reduced by using an integrated model of accessible, patient-centered evidence-based practices, including allied health services, public health education, biological support, environmental modification, and psychological interventions.

Evaluation of current models

An approach incorporating the UK Foresight (Vandenbroeck, 2014) project and ‘obesity systems map’ provides an outline of the complexities surrounding weight management. Considering causal interdependencies and individual attributes are fundamental to the formation of an effective weight management model. To add to this model is the influence of pharmaceutical influence with studies suggesting strong associations between medication use such as antipsychotics, mood stabilizes and corticosteroids contributing to significant weight gain (Gafoor, 2018; Wharton,2018). For example, 16.8% of the Australian population) received mental health-related prescriptions subsidized by the PBS/RPBS in 2017–18 (MHSA, 2019) and a large percentage of the population using both prescribed and non-prescribed oral corticosteroids, although clear data is lacking and pharmaceutical influence is omitted from weight management algorithms.

Allied health measures

A review of algorithms for weight management utilized by allied health professions including dietitians, nutritionists, naturopaths, herbalists, personal trainers, counselors, psychotherapists, hypnotherapists, physiotherapists, chiropractors, and yoga instructors supports the further need for a uniformed inclusive and accessible approach. Although of a high standard, these approaches essentially reinforce significant opportunities for future development. A brief review of allied health professionals identified combination approaches were evident including early preventative support for key drivers to weight changes such as mental health interventions, insomnia and pain management.

Public health measures

Exploring nonpharmacological public health interventions such as allied health services and natural products such as traditional medicinal plants (Ota, 2017) is considered a favorable approach because they have lower costs or are more accessible or “natural” compared to prescribed medications with obesogenic side effects, reducing societal resource dependence (Tremmel, 2017) and concurrent increases in weight management metabolic disorders, such as type 2 diabetes and hypertension, which often require additional pharmacotherapy (Wharton, 2018).

Although high-level policy and legislative changes to alter obesogenic environments, such as incentives for healthy eating, increased levels of physical activity and increased accessibility treatment are valued considerations, without the exploration of non-pharmacological intervention the effectiveness of public health measures are considerably compromised (NPHT, 2009).

Without further research and continued reliance on algorithms based on guidelines that support reduced physical activity and increased consumption of energy-dense foods as primary causes, obesity and eating disorder epidemics will rise as one provides fuel the other. Excessive dieting contributes to further biological changes, leading to weight gain and weight loss resistance leads to disordered eating. Societal spending burden, public confusion and the rate of obesity will without a doubt continue to rise, irrespective of public and commercial health investment initiatives.

Understanding key drivers

According to self-reporting in the 2014-15 National Health Survey, an estimated 50% of Australians have at least one chronic disease (arthritis, asthma, back pain, cancer, cardiovascular disease, chronic obstructive pulmonary disease, diabetes or mental health conditions) (AIHW, 2015) linked to weight management problems and although incurable, chronic diseases can often be prevented or managed naturally avoiding the weight gain side effects of pharmaceutical interventions. It is estimated around one‐quarter of the Australian population use CAM specifically to treat chronic illness (Armstrong, 2011).

Understanding key consumer motivations to overcome impaired weight management is essential to a sustainable, long term algorithm. Research that identifies biological, environmental and psychological factors and supports a new socially inclusive approach is fundamental to controlling the current epidemic. 

According to Forbes research (2019), consumers now look for products that help them treat or prevent specific conditions which are a proactive approach and contrary to the fear-based public health information model that has been driving wellness views for decades. Although this is aligned with fresh, healthy food principles, not all ‘healthy food’ is supportive of long-term weight management highlighting the need for advanced education and complementary approaches that encompass functional food as medicine principles as affordable early intervention options.

Developing an integrated algorithm

Developing an algorithm that uses an integrated model of accessible, patient-centered evidence-based practices acknowledging patient choice and patient-centric approaches is worth exploring.   Integration between health care models and data collection for future wellness improvement may best utilize emerging technology. Intelligent health systems have the potential to harnesses holistic patient-driven, well-being. Encouraging consumers to play a central role in making decisions about their health and creating individual awareness, could significantly reduce societal resource dependence yet deliver effective long-term health and wellness solutions.

References

• ABS (Australian Bureau of Statistics) 2015. National Health Survey: First results, 2014–2015. ABS cat. no. 4364.0.55.001. Canberra: ABS. Viewed 20 September 2016, http://www.abs.gov.au/ausstats/abs@.nsf/mf/4364.0.55.001
• Armstrong, A.R., Thiébaut, S.P., Brown, L.J. and Nepal, B. (2011), Australian adults use complementary and alternative medicine in the treatment of chronic illness: a national study. Australian and New Zealand Journal of Public Health, 35: 384-390. doi:10.1111/j.1753-6405.2011.00745.x
• Australian Institute of Health and Welfare 2017. A picture of overweight and obesity in Australia 2017. Cat. no.PHE 216. Canberra: AIHW.
• Australian Institute of Health and Welfare 2018. Australia’s health 2018. Australia’s health series no. 16. AUS 221. Canberra: AIHW.
• Australian Institute of Health and Welfare 2016. Australia's health 2016. Australia's health no. 15. Cat. no. AUS 199. Canberra: AIHW.
• Backholer K, Walls H, Magliano D, Peeters A: Setting population targets for measuring successful obesity prevention. Am J Public Health. 2010, 100: 2033-7. 10.2105/AJPH.2010.200337.
• Forbes, 2019 Consumer Trends in Health and Wellness retrieved 20/1/2020 from https://www.forbes.com/sites/thehartmangroup/2015/11/19/consumer-trends-in-health-and-wellness/#3e56cb29313e
• Gafoor Rafael, Booth Helen P, Gulliford Martin C. Antidepressant utilisation and incidence of weight gain during 10 years’ follow-up: population based cohort study BMJ 2018; 361 :k1951
• Hay P, Mitchison D, Collado AEL, Gonzalez-Chica DA, Stocks N & Touyz S 2017. Burden and health-related quality of life of eating disorders, including Avoidant/Restrictive Food Intake Disorder (ARFID), in the Australian population. Journal of Eating Disorders 5:21.
• Hay PJ, Girosi F & Mond J 2015. Prevalence and sociodemographic correlates of DSM-5 eating disorders in the Australian population. Journal of Eating Disorders 3:19.
• Hay PJ, Mond J, Buttner P & Darby A 2008. Eating disorder behaviors are increasing: findings from two sequential community surveys in South Australia. PLoS ONE 3:e1541.
• Mental health services: in brief 2019 is the companion publication to the online report Mental health services in Australia (MHSA). The in brief provides an overview of data about the national response of the health and welfare system to the mental health care needs of Australians.
• Ota, A., & Ulrih, N. P. (2017). An Overview of Herbal Products and Secondary Metabolites Used for Management of Type Two Diabetes. Frontiers in pharmacology, 8, 436. doi:10.3389/fphar.2017.00436
• Peeters, A & Backholer, K 2012, ‘Is the health burden associated with obesity changing?’, American Journal of Epidemiology, vol. 176 (10), pp. 840-845
• Singh, M. (2014). Mood, food, and obesity. Frontiers in Psychology, 5, 925-925. doi:10.3389/fpsyg.2014.00925
• Tremmel, M., Gerdtham, U. G., Nilsson, P. M., & Saha, S. (2017). Economic Burden of Obesity: A Systematic Literature Review. International journal of environmental research and public health, 14(4), 435. doi:10.3390/ijerph14040435
• Technical Report 1 Obesity in Australia: a need for urgent action Including addendum for October 2008 to June 2009 Prepared for the National Preventative Health Taskforce by the Obesity Working Group.
• Vandenbroeck IP, Goossens J, M. C Foresight Tackling Obesities: Future Choices—Building the Obesity System Map. Government Office for Science, UK Government's Foresight Programme. Available: https://www.gov.uk/government/uploads/system/uploads/attachment_data/fil... obesity-building-system-map.pdf. Published October2007. Accessed 16 October 2014
• Wharton, S., Raiber, L., Serodio, K. J., Lee, J., & Christensen, R. A. (2018). Medications that cause weight gain and alternatives in Canada: a narrative review. Diabetes, metabolic syndrome and obesity : targets and therapy, 11, 427–438. doi:10.2147/DMSO.S171365
• Wozniak G, Rekleiti M, Roupa Z, Contribution of social and family factors in anorexia nervosa, Applied University of Larissa, Greece, Health Science Journal, 2017