Weight Stigmatization and Binge Eating in Asian Americans with Overweight and Obesity

Abstract

Weight stigma and binge eating have been found to be associated in Western populations; however, this relationship is understudied among Asian Americans. The aims of the study were to (1) investigate the prevalence of binge eating and its relationship with experienced weight stigma in higher-weight Asian Americans, and (2) examine whether the level of acculturation moderates this relationship. Data were collected from a cross-sectional study with 166 higher-weight Asian American adults living in North Carolina, United States. Demographic data, the frequency of experiencing weight stigma, the severity of binge eating, the levels of acculturation, the perceived racism against Asians, and perceived stress were assessed via self-reported questionnaires. The results indicated that experienced weight stigma was a significant independent predictor over and above the effects of other stressors, such as racism and general stress. The level of acculturation did not influence the relationship between the experienced weight stigma and binge eating after adjusting for relevant covariates. Our findings contribute to the limited literature examining weight stigma and binge eating among Asian American populations, highlighting that higher levels of experienced weight stigma are associated with a greater degree of binge eating.

Stigma, Health and Well-Being

“…In addition to the finding regarding the association between weight-related self-stigma and healthy behaviors, an association between experienced weight stigma and unhealthy behaviors has been found. Wu et al [ 12 ] recruited 166 Asian Americans, with most of them being the first generation to understand the association between experienced weight stigma and binge eating behaviors. The participants were defined as overweight according to the body mass index cutoff for Asians (i.e., body mass index > 23 kg/m 2 ).…”

“…The participants were defined as overweight according to the body mass index cutoff for Asians (i.e., body mass index > 23 kg/m 2 ). After controlling the effects of racism and general stress, Wu et al [ 12 ] concluded that experienced weight stigma is an independent factor associated with binge eating behaviors for those who may have a weight problem.…”

The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

“…A total of 20 studies were included in this review, of which 1 study comprised 4 separate studies [ 29 ], resulting in 23 studies (representing 2231 participants) evaluated in this review. A summary and detailed description of the study characteristics are shown in Table 1 and Multimedia Appendix 3 [ 25 , 26 , 29 - 47 ] and Multimedia Appendix 4 [ 25 , 26 , 29 - 37 , 39 - 47 ]. The chatbot programs included Wakamola [ 30 - 32 ], WaznApp [ 33 ], WeightMentor [ 25 ], SWITCHes [ 34 ], MobileCoach [ 35 ], PathMate2 [ 36 ], and Lark Weight Loss Health Coach AI [ 37 ].…”

“…Core functions of the chatbot were to provide personalized weight loss recommendations (20/23, 87%) [ 25 , 26 , 29 - 34 , 37 - 39 , 41 - 46 ] and motivational messages (18/23, 78%) [ 25 , 26 , 29 - 33 , 37 - 39 , 41 - 43 , 45 , 46 ] ( Multimedia Appendix 5 [ 25 , 26 , 29 - 37 , 39 - 47 ]). Only 26% (6/23) studies [ 30 - 32 , 36 , 40 , 43 ] mentioned the use of gamification to enhance user engagement, and 26% (6/23) studies [ 37 , 39 , 40 , 42 , 43 , 45 ] mentioned the use of sentiment analysis to provide emotional support through emotionally appropriate messages ( Multimedia Appendix 5 ).…”

“…These functions were generally achieved by (1) collecting various user-centric data through chatbot-based self-reports or device-detected metrics, (2) integrating collected parameters using machine learning techniques (including NLP to convert chatbot-collected information for prediction modeling) to predict and generate weight-related recommendations, (3) profiling users according to needs and preferences, and (4) providing personalized chatbot-delivered recommendations. The parameters collected included sociodemographic profiles (eg, age, race, ethnicity, education, work status, and income), food consumption, physical activity (eg, intensity, duration, frequency, and type), stress level, sleep (ie, duration), and clinical profiles (eg, presence of specific chronic diseases, medication use, smoking status, heart rate, and blood pressure; Multimedia Appendix 6 [ 25 , 26 , 29 - 37 , 39 - 47 ]). Majority of the parameters were collected through chatbots, except in 3 studies that estimated food consumption using smart refrigerator appliances [ 30 ] and nutritional information provided by retailers (scanning bar codes) [ 38 , 42 ]; 5 that estimated physical exercise type, intensity, and frequency wearable or smartphone sensors [ 33 , 37 , 38 , 42 , 43 ]; 2 that estimated stress levels using plasma cortisol and skin conductance response [ 36 ] and phone detection [ 37 ]; and 3 that measured heart rate and blood pressure [ 36 , 38 , 42 ].…”