Does Health At Every Size® fit all? A group-based trajectory modeling of a non-diet intervention
Introduction
Obesity is widely known as a public health issue that accounts for quality of life impairments and many serious health conditions, such as cardiovascular diseases (GBD 2015 Obesity Collaborators, 2017; World Health Organization, 2018). Meanwhile, it is also associated with psychological distress, body dissatisfaction, stigmatization and depression, another leading cause of disease burden worldwide (Luppino et al., 2010; Petry, Barry, Pietrzak, & Wagner, 2008; Puhl & Heuer, 2010; Schwartz & Brownell, 2004). Among the several strategies developed in obesity management, the Health At Every Size® (HAES®) movement stands out as a non-diet approach, with an emphasis on self-acceptance and healthy day-to-day practices. While they acknowledge health risks associated with higher BMI, HAES® practitioners advocate the idea that health relies on a wide and more complex range of factors, not solely on body mass index (BMI), and can be improved without necessarily losing weight Bacon & Aphramor, 2011, Gaesser, Angadi, & Sawyer, 2011, Tomiyama et al., 2013. HAES® interventions aim to reduce weight stigma, which threatens both psychological and physical health (Puhl & Heuer, 2010), while also promoting healthy eating habits, such as intuitive eating and physical activity (Burgard, 2009, pp. 42–53; Robison, 2007a; 2007b). In doing so, the HAES® movement addresses simultaneously health issues associated with high BMI as well as the excessive preoccupations towards food, weight and body image among women, who are significantly targeted by societal ideals of thinness (Burgard, 2009, pp. 42–53; Saguy, 2012, Robison, 2007b). According to a recent systematic review, non-diet interventions seem to display rather positive outcomes on many levels, including psychological well-being, eating behaviors and physical health (Clifford et al., 2015; Ulian et al., 2018). They also seem to stabilize weight or even produce weight loss, though results are still inconsistent (Clifford et al., 2015). As such, it has been suggested to implement this approach as a public health policy since it shows promising results while doing no harm (Bombak, 2014; Thomas, 2006).
It is well established that obesity interventions, regardless of the approach, produce great variability in responses to treatment (Brownell & Wadden, 1991; Field, Camargo, & Ogino, 2013; Gordon-larsen, 2018; Yang, Ginsburg, & Simmons, 2013). This leads evidently to significant clinical challenges for practitioners. Several studies have pointed out the heterogeneity of obese/overweight individuals as a factor that should be acknowledged to tailor interventions to the patients’ needs and, as a result, have identified different subtypes based on either personality, psychological symptoms, eating patterns, metabolic health or genetics (Claes, Vandereycken, Vandeputte, & Braet, 2013; Field et al., 2018; Gagnon-Girouard, Bégin, Provencher, Tremblay, Boivin, et al., 2010a; Gullo, Lo; Jansen, Havermans, Nederkoorn, & Roefs, 2008; Müller, Claes, Wilderjans, & de Zwaan, 2014; Wang et al., 2009). For instance, evidence suggests that individuals with high depressive symptoms profile would display more body concerns and problematic eating behaviors despite having no significant difference in BMI with their non-depressive counterparts (Gagnon-Girouard et al., 2010a; Jansen et al., 2008). Heterogeneity in psychological functioning could also reflect on distinctive metabolic profiles, as some recent studies have suggested the existence of a mood-metabolic syndrome that could represent a more severe subtype of obesity (Mansur, Brietzke, & Mcintyre, 2015; Phillips & Perry, 2015). These findings suggest the unlikelihood of achieving similar outcomes from all in response to a given treatment. A better understanding of individual characteristics could certainly improve outcomes by enhancing personalized healthcare (Brownell & Wadden, 1991; Field et al., 2013; Kiernan, 2012; van der Merwe, 2007; Yang et al., 2013).
Nevertheless, little attention has been given to exploring the different types of responses among participants. Up until now, most research emphasizes on assessing the efficacy or effectiveness of interventions by comparing group means over time, thus adopting a one-size-fits-all philosophy in which interventions are compared to each other using strictly mean group outcomes (Field et al., 2013; Yang et al., 2013). Since this can lead to biased conclusions – either underestimating or overestimating effect sizes, it is thus important not only to examine the overall outcomes of an intervention, but also to look how individuals respond to it.
Notwithstanding this need for more individual-oriented research, it should be mentioned that many researchers have put efforts to identify pre-treatment predictors of outcomes with the intent of matching individuals to different treatments (Teixeira, Going, Sardinha, & Lohman, 2005). However, these studies have several statistical and methodological shortcomings that have been raised (Stubbs et al., 2011; Teixeira et al., 2005). Among them, the potentially large number of variables involved that interact with each other in complex ways and the use of too simple statistical analyses have been suggested as potential limitations (Teixeira, Going, Sardihna & Lohman, 2005). In this regard, the use of mixture modeling (e.g. latent class analysis, latent profile analysis, latent class growth modeling) comes in handy for identifying subtypes with similar characteristics within a heterogeneous population.
So far, very few studies have proceeded to both identify subtypes and associate them with treatment outcomes. To our knowledge, only one large-scale study in the field of obesity was conducted using such an analytic approach with a broad range of variables, although it was performed in a context of weight loss after a bariatric surgery (Field et al., 2018). In her commentary article, Gordon-larsen (2018) clearly points out the need to address heterogeneity in a variety of obesity-related treatments. Along the same lines, it has been suggested that outcomes other than weight loss should be examined in obesity management, as many variables account for health-related effects (Teixeira, Going, Sardihna & Lohman, 2005). In the more specific case of the HAES® approach, no study has yet explored these research questions and it remains unknown whether these types of intervention produce benefits in a similar manner across all treatment-seeking overweight/obese individuals or not. As well, no study has yet provided a thorough characterization of these individuals in such a way that could help better understand for whom the HAES® approach could be most appropriate.
This study first aims to unravel heterogeneity in response to core outcomes of a community-based HAES® intervention for women by identifying several types of responders using group-based trajectories. As its effectiveness has been assessed several times by comparing outcomes of the experimental group to a control group, both in controlled and natural environments (specifically in the cohort of women we are studying here),(Bégin et al., 2018; Carbonneau et al., 2016; Gagnon-Girouard, Bégin, Provencher, Tremblay, Mongeau, et al., 2010b; Provencher et al., 2007; Provencher et al., 2009), this research focuses exclusively on exploring types of responses within the intervention group. Secondly, it aims to profile the given types of responders on sociodemographic, weight, health, psychological, eating and lifestyle variables. It is expected that women would neither respond nor maintain their gains in the same way. Similarly, we also expected different starting points on outcomes that reflects on the heterogeneity of the obese/overweight population. Thus, the existence of multiple trajectories differing in intercepts and slope magnitude or direction was hypothesized. More particularly, we expected to identify at least two types of responders, comprising a more responsive and a less responsive subtype. Given the literature revealing the existence of more severe profiles with poorer psychological functioning and more disordered eating (Gagnon-Girouard et al., 2010a; Jansen et al., 2008), it is expected that such profiles would be associated with a less responsive type of responders to the non-diet intervention.
Section snippets
Research design and overview of procedures
Data were extracted from a multicentric quasi-experimental study aiming to evaluate a non-diet intervention “Choisir de maigrir?” (CdM?) (What about losing weight?) compared to a waiting-list control group. Conducted in a real-world setting, twenty-one Health and Social Services Centres (HSSC) from different areas of the province of Quebec (Canada) were entirely in charge of recruitment and data collection. Participants were recruited during the fall or winter sessions of the program in 2010
Descriptive statistics
Participants were aged from 21 to 83 years-old (M = 51.26, SD = 11.17). The sample consisted of individuals presenting a wide range of socioeconomic status, from which a considerable percentage came from a lower socioeconomic environment. Participants’ sociodemographic characteristics at baseline are presented in Table 1. Differences between completers and dropouts were found only on education levels.
Group-based trajectory modeling on outcomes
The LCGM revealed a significant 4-trajectory model (p < .001). The 4 trajectories were
Discussion
This study investigated the heterogeneity of responses among women participating in a HAES® intervention by modeling group-based trajectories, thus allowing the categorization of participants into types of responders, which were then profiled on sociodemographic, weight, health, lifestyle, psychological and eating variables. To our knowledge, this is the first study to investigate for whom the non-dieting framework of intervention works best when considering individual characteristics, using an
Conclusions
The current study is, to our knowledge, the first to examine for whom a community-based non-diet intervention fits. This research has successfully identified several trajectories that were categorized as four types of responders. While most participants displayed some improvement on either or both intuitive eating and body esteem, a non-responder group was identified. The latter displayed the more negative profile of all on psychological, eating and health variables, being markedly more
Submission declaration
The manuscript is original and not under any consideration for publication at another journal. Its publication is approved by all contributing authors.
Sources of funding
This research project was funded by the Canadian Institutes of Health Research (CIHR) GIR 99712 operating grant (Population Health Intervention Research) and by the Heart and Stroke Foundation.
Conflicts of interest
The authors of this manuscript do not have any conflicts of interest to declare.
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