Journal of Obesity & Weight Loss Therapy
Open Access

Millions of people worldwide suffer from obesity, a chronic medical condition. Multiple factors, including genetics, environmental influences, and behavioral patterns, contribute to this complicated disease. Understanding the role of cognitive processes in obesity and its management has received more attention in recent years. The approach bias is one such cognitive process [1].

The automatic tendency to approach or seek out rewarding stimuli, such as food, that are associated with a specific behavior or situation is known as approach bias. A bias toward food can contribute to weight gain and overeating in the context of obesity. On the other hand, approach bias can be used to manage obesity.

A cognitive training method known as approach bias modification (ABM) aims to reduce approach bias toward food and encourage healthier eating habits. ABM entails teaching people to approach nonfood stimuli while avoiding food-related stimuli, which can eventually reduce approach bias toward food. Computer-based training, virtual reality, and mobile applications are all ways that ABM can be implemented [2].

The efficacy of ABM in the management of obesity has been the subject of several studies. ABM was found to be associated with a significant reduction in food approach bias and weight loss in obese individuals in a systematic review and meta-analysis of RCTs. ABM and cognitive-behavioral therapy were found to be more effective than cognitive-behavioral therapy on its own in promoting weight loss in another RCT.

Although the underlying mechanisms of ABM are not completely understood, it is believed to function by altering the brain's automatic associations between food and reward. ABM can lessen approach bias toward food by pairing reward with non-food stimuli on a regular basis [3]. This can make new associations stronger and weaker, which can reduce approach bias toward food.

Description

ABM is a promising strategy for controlling obesity, but it has some drawbacks. Individual factors, such as motivation to alter eating habits and baseline levels of approach bias, may influence ABM's efficacy. Additionally, it is unknown what the long-term effects of ABM will be on weight loss and maintenance.

Obesity and its management are influenced by a cognitive process known as the approach bias toward food. In reducing the approach bias toward food and promoting healthier eating behaviors, strategies like approach bias modification (ABM) have demonstrated promising results. Computer-based training, virtual reality, and mobile applications are all options for delivering these interventions. ABM entails teaching people to approach non-food stimuli while avoiding food-related stimuli, which can eventually reduce approach bias toward food [4,5].

A number of studies have looked into how effective ABM is for managing obesity, and the results showed that people with obesity lost weight and had less food approach bias. Additionally, when compared to cognitive-behavioral therapy on its own, ABM has been shown to be more effective.

Although the underlying mechanisms of ABM are not completely understood, it is thought to function by altering the brain's automatic associations between food and reward. ABM can lessen approach bias toward food by pairing reward with non-food stimuli on a consistent basis. This can strengthen new associations and weaken existing ones.

However, there are some drawbacks to ABM. Individual factors, such as motivation to alter eating habits and baseline levels of approach bias, may influence ABM's efficacy. Additionally, it is unknown what the long-term effects of ABM will be on weight loss and maintenance [6-8].

Conclusion

Approach bias is a cognitive process that is significant in the treatment and prevention of obesity. ABM is a promising strategy that has the potential to reduce food bias and encourage healthier eating habits. To fully comprehend ABM's underlying mechanisms and longterm effects, additional research is required. However, ABM has the potential to be a useful tool in the fight against obesity as well as in enhancing the overall health and well-being of obese people.

Acknowledgement

None

Conflict of Interest

None

1. Castagneto M, De Gaetano A, Mingrone G, Tacchino R, Nanni G, et al. (1994) Normalization of insulin sensitivity in the obese patient after stable weight reduction with biliopancreatic diversion. Obes Surg 4: 161-168.

Indexed at, Google Scholar, Crossref

2. Cooper C, Sarvey S, Collier D, Willson C, Green I, et al. (2006) For comparison: experience with a children's obesity camp. Surg Obes Relat Dis 2: 622-626.

Indexed at, Google Scholar, Crossref

3. Cowan GS Jr, Buffington CK (1998) Significant changes in blood pressure, glucose, and lipids with gastric bypass surgery. World J Surg 22: 987-992.

Indexed at, Google Scholar, Crossref

4. Fagot-Champagna A, Pettitit DJ, Engelgau MM, Burrows NR, Geiss LS, et al. (2000) Type 2 Diabetes among North American children and adolescents: An epidemiologic review and a public health perspective. J Pediatr 136: 664-672.

Indexed at, Google Scholar, Crossref

5. Fonesca H, Matos MG, Guerra A, Pedro JG (2009) Are overweight and obese adolescents different from their peers?. Int J Pediatr Obes 4: 166-174.

Indexed at, Google Scholar, Crossref

6. Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS (2001) Relationship of childhood obesity to coronary heart disease risk factors in adulthood The Bogalusa Heart Study. Pediatrics 108: 712-718.

Indexed at, Google Scholar, Crossref

7. Huelsing J, Kanafani N, Mao J, White NH (2010) Camp Jump Start: effects of a residential summer weight-loss camp for older children and adolescents. Pediatrics 125: 884-890.

Indexed at, Google Scholar, Crossref

8. l'Allemand-Jander D (2010) Clinical diagnosis of metabolic and cardiovascular risks in overweight children: early development of chronic diseases in the obese child. Int J Obes 34 Suppl 2: S32-36.

Indexed at, Google Scholar, Crossref