What Is A Negative Effect Of Society's Increasing Emphasis On Losing Weight?

Health Psychol Open up. 2022 Jul-Dec; 5(two): 2055102918816606.

Health and appearance reasons for weight loss as predictors of long-term weight change

Abstract

This study investigated whether women'southward initial reasons (health, appearance to others, or advent to self) for wanting to lose weight influenced their weight change over a 30-calendar month web-based intervention. Multilevel modeling with 1416 observations revealed that only appearance in relation to one's cocky was a pregnant (negative) predictor. Women highly motivated to lose weight to improve their advent in relation to themselves gained weight at 30 months, whereas those not motivated for this reason achieved clinically significant weight loss. Results propose examining participants' initial reasons for weight loss equally an important component of intervention failure or success.

Keywords: appearance, body image, females, wellness behavior, weight loss, women'south health

Obesity is a challenging public wellness issue, as it is associated with chronic affliction, reduced quality of life, higher rates of disability, and poor psychological health outcomes (Jensen et al., 2022). Despite the pressing public wellness concerns and amount of resources devoted toward reducing the rate of obesity, the proportion of obese adults in the United states population continues to increase (Hales et al., 2022). Although information technology is important to foster good for you lifestyles and behavior change amongst overweight and obese individuals who practice not have plans for losing weight, involvement must also center on factors that inhibit success in people who are actively trying to reduce their weight. Because many individuals fail in their weight loss efforts (MacLean et al., 2022), it is important to gain insight into some of the factors responsible for the failure.

Populations are differentially affected by obesity, and women from rural areas in the Us are medically underserved, with an obesity rate of over 75 percent for older women (Folta et al., 2009). Obesity-related behaviors among rural women are attributed to a college prevalence of tardily-life inability compared to men (Befort et al., 2022; Leveille et al., 2000). One reason for the loftier obesity rate among this population may be that women in rural communities ofttimes lack access to in-person weight loss or good for you eating preventive services such every bit professional person nutrition and physical activity counseling (Befort et al., 2022). Partially in response to the limited in-person services, researchers have recently turned toward the use of spider web-based behavioral change interventions with weight loss and weight maintenance as targets (Raaijmakers et al., 2022; Wieland et al., 2022). Despite the prevalence of web-based weight loss interventions, many studies employ a combination approach whereby the web-based component supplements an in-person intervention (Brindal et al., 2022). Inside rural locations, it can exist difficult to conduct in-person interventions as both the facilities and number of available participants present logistical challenges (Hageman et al., 2022). The effectiveness of web-only interventions is less well known, although some testify suggests that spider web-only interventions result in less weight loss and less weight maintenance when compared to in-person interventions (Raaijmakers et al., 2022; Wieland et al., 2022).

Whereas there are many physiological and psychological benefits associated with weight loss, individuals may experience either positive or negative consequences during the process depending on the reasons or motives of why they initially decided to lose weight. Literature findings advise that motivational factors are fundamental psychosocial variables that influence long-term success in weight loss (Teixeira et al., 2004, 2022). For case, intrinsic reasons or motivation, such equally an private's interest in do, predicts long-term weight maintenance and the adoption of healthy behaviors (Santos et al., 2022; Silva et al., 2022). Reasons cited as extrinsic, such as appearance (Teixeira et al., 2022), are associated with binge eating and linked to negative weight loss outcomes (Vartanian et al., 2022).

Relatively few studies have explored the effect of the initial reason for weight loss on intervention outcomes such equally actual change in weight, especially as observed over an extended catamenia of time or within a population of midlife or older rural women (Elfhag and Rossner, 2005; Teixeira et al., 2022). As noted by Teixeira et al. (2012), behavioral interventions for weight loss typically focus on increasing or maintaining participants' level of motivation, with little attention paid to the nature or quality of that motivation. An appreciation of participants' initial reasons for considering weight loss interventions is of import considering in situations when an intervention fails or individuals experience poor outcomes, lack of participant readiness or motivation are often cited explanatory factors (Meyer et al., 2010).

A growing trunk of piece of work examines the consequences of ii primary types of goals for engaging in weight loss strategies: to improve one's health or to improve one'due south advent. Wellness is a frequently cited reason for weight loss because obesity is largely seen as a health risk (Meyer et al., 2010; Putterman and Linden, 2004). Appearance goals take been related to a variety of negative outcomes such as high body dissatisfaction and low self-esteem (Thome and Espelage, 2007), heightened anxiety apropos their physiques (Strelan et al., 2003), and decreased psychological well-being (Maltby and 24-hour interval, 2001). Indeed, individuals motivated to lose weight for appearance reasons versus health reasons were constitute to be more probable to use unhealthy eating strategies (Putterman and Linden, 2004), have higher concerns about body prototype (Vartanian et al., 2022), be more likely to binge consume (Schelling et al., 2022), and exist more probable to engage in fat talk and unhealthy eating (Guertin et al., 2022). These negative effects are particularly concerning given that torso dissatisfaction may be among the primary factors motivating women and young adults to attempt weight loss (Holley et al., 2022; Vandervoort et al., 2022).

Meyer et al. (2010) recently adult a valid and reliable assessment of overweight and obese individual's reasons for weight loss across three factors. Wellness, being the desire to be healthier and live longer, was one factor. A second factor was the want to improve ane'due south appearance to oneself, and individuals motivated by this factor want to lose weight to improve their torso image. The third factor is to improve one'due south advent in relation to others, interpreted as losing weight in club to have more than friends, then that other people volition be more accepting, and so on. The Meyer et al. (2010) scale is unique from other conceptualizations of health versus appearance reasons for weight loss because it distinguishes the appearance gene farther into appearance in relation to one's cocky and appearance in relation to others. Findings from a cross-sectional written report that used this scale suggest that appearance-based motives for losing weight can exist associated with negative outcomes.

The primary purpose of this article was to explore whether rural women'south initial reasons for wanting to lose weight (wellness, advent to others, or advent to cocky), influenced their change in weight over a thirty-month spider web-based intervention. No previous study of weight loss motivations has focused solely on this population, which has high rates of obesity-related diseases and disability (Hageman et al., 2022). Furthermore, the spider web-based, longitudinal nature of the report is unique because, if successful, it may provide one avenue through which to combat obesity among rural women. Given previous work related to reasons for weight loss and various outcomes among women, we anticipate that women motivated for health reasons volition lose more weight than those motivated for appearance reasons, but the consequence of self- versus other-directed appearance motives is unclear.

The present report expands upon the existing literature in that it is 1 of few studies detailing the level and nature of the motivation amid rural women to lose weight at the offset of a clinical weight loss intervention equally a predictor of their long-term weight change. This written report contributes to the growing literature on web-based weight loss interventions, which is a context wherein participant motivation may be specially important to success. And, this written report explores the relative bear on of a participant'due south initial reason for wanting to lose weight on actual weight loss as opposed to self-reported weight loss, while examining appearance to others split up from appearance to self.

Method

Participants and procedure

This study included 301 women between the ages of 40 and 69 (M = 53.94, standard divergence (SD) = 6.88). Participants were primarily White (99%) and well-educated (41% with bachelor'south caste or higher), and 53 percent reported almanac household income of over Us $sixty,000. To be included in the study, participants were required to (a) live in a rural community, based on population density and working commuting patterns, (b) take a body mass index (BMI) of 28–45 kg/g², (c) be not taking whatever medications that impact weight, (d) be able to speak and read English, (east) be able to apply a computer and have access to the Net and a DVD role player, and (f) be willing to drive equally many as lxx miles one-way to the enquiry office. From the recruited sample, participants were excluded if they had diabetes type 1, or diabetes type two that required insulin, experienced weight loss of ten percentage or more within the prior 6 months, or if they were currently participating in any other weight loss intervention or research study (Hageman et al., 2022).

This report was a secondary analysis of clinical trial information collected as role of the Women Counterbalance-in for Wellness project that focused on three unlike web-based interventions promoting lifestyle modification for weight loss and weight maintenance for rural women. Informed consent was obtained from all participants, and we followed all ethical procedures necessary for human subjects inquiry. The clinical trial is registered on clinicaltrials.gov with trial identifier: {"type":"clinical-trial","attrs":{"text":"NCT01307644","term_id":"NCT01307644"}}NCT01307644. More information on the project protocol and main effects tin can be institute in other papers (Hageman et al., 2022, 2022).

Data were nerveless over a period of thirty months, with in-person measurements taken at baseline, 3, 6, 12, xviii, 24, and 30 months. At the end of the 30 months, 236 participants had complete data and were included in this analysis. The intervention included three phases: Phase ane: baseline–6 months, guided weight loss; Phase 2: 6–18 months, guided weight management; and Stage 3: 18–30 months, cocky-directed weight maintenance. In general, the intensity of each intervention decreased every bit time progressed.

Measures

Motivation for weight loss

At baseline, participants completed the 24-item motivation for weight loss scale (Meyer et al., 2010). Participants read a series of statements with the stem "I want to lose weight …" followed past a reason associated with ane of three factors: health-related reasons, advent in relation to others, and appearance in relation to self. Then, using a 4-betoken Likert-type scale, participants indicated how much they identified with each statement from one (absolutely not) to 4 (strongly). The wellness subscale (α = 0.70) consisted of seven items such as "To be healthier" and "To decrease my health risks." Appearance in relation to others (α = 0.86) was 10 items and included items such as "So I will exist accepted by order," "So that other people will think better of me," and "To take more than friends." Appearance in relation to one's cocky (α = 0.90) was 7 items, examples of which are "Because I want to like myself more than," "To feel more self-confident," and "Considering I want to be more bonny."

Percentage weight alter

At 3, six, 12, xviii, 24, and 30 months, participants traveled to the research role where trained medical staff recorded their weight (kg). Percentage weight change was calculated at each point as the difference betwixt the only-measured weight and the baseline weight.

Effects of time

Change over time was coded to allow usa to model both the linear and curvilinear effects. The linear effect relates to an overall decrease or increase in weight across the study. However, given previous research on weight loss interventions, and the three-phase (loss, management, and cocky-directed maintenance) design of the current study, nosotros did not anticipate that participants would, on average, experience an equal amount of weight loss or gain as a function of a given time unit. Rather, participants in weight loss interventions typically lose weight rapidly, regain weight, and so, if the intervention is successful, end the report at weight lower than the baseline weight but higher than the everyman weight achieved early on (Jeffery et al., 2000). As such, we likewise coded weight to examine this curvilinear effect. Time was coded every bit 3 months: 0, six months: 0.13, 12 months: 0.25, 18 months: 0.50, 24 months: 0.75, and 30 months: 1. The curvilinear issue was the square of the linear time codes.

Covariates

Baseline age, BMI, and intervention grouping of participants were used every bit covariates. Two contrast codes were created to test the furnishings of the three intervention groups. One code compared web-only (–1) versus email (one), and the other code compared web-only (–1) versus discussion (1). Age and BMI are frequently controlled for in studies examining weight change because of the strong associations among the variables (Hageman et al., 2022; Hennecke and Freund, 2022).

Analytic approach

All hypotheses were tested using multilevel modeling (Scott et al., 2022) in the program HLM version vii.03 (HLM, 2022). Multilevel modeling was appropriate for these analyses as the weight change data were inherently nested within participants considering participants were measured multiple times. Multilevel modeling, therefore, accounts for this information nesting and allows analyses to focus on how individuals changed over time, in addition to examining differences between participants.

Several multilevel models were estimated. Model 1 was an unconditional model that included just the dependent variable (percent alter in weight). This model provided the proportion of variability, or the intra-form correlation, of percent change in weight within individuals and between individuals. Model 2 added the linear and curvilinear furnishings of time to the prediction of percent weight alter. In Model iii, we added age, BMI, and intervention group every bit covariates equally between-participants factors of the intercept, linear alter over time, and curvilinear change over time. The curvilinear outcome of time and the intercept were treated as random factors, significant that the curvilinear result of time and the terminal percent weight change were allowed to vary between participants. In Model 4, the effect of each reason for initial weight loss was added to all inside-participant predictors. After each model was estimated, we compared model fit and the proportional reduction in prediction mistake to the previously estimated model (Snijders and Bosker, 1999). Earlier any models were estimated, we examined the data to ensure that assumptions of regression were not violated.

Results

Descriptive statistics and correlations among study variables are displayed in Table 1. Model ane (the unconditional model) indicated that 27.78 per centum of the variability in percentage of weight loss was at the within-participant level, whereas 72.22 per centum of the variance was between individuals. The level 1 (within-participant) variability represented a significant portion of the total variability, χ ⁱⁱ ₍₂₁₁₎ = 3213.98, p < 0.001. Given the big amount of level-1 and level-2 variability, and the finding that the variability at level-1 was significant, the use of multilevel modeling was justified (Table 2).

Table 1.

Descriptive statistics and correlations of between-participant variables.

Variable	M	SD	1	2	3	iv	5	6
1. Age	53.94	half-dozen.88	–
2. BMI	34.84	4.21	–0.06	–
three. Group	–	–	–0.07	0.03	–
four. Health reason	iii.69	0.34	0.20^*	0.02	–0.02	0.70
5. Appearance to others	2.94	0.64	–0.xi	–0.11	0.06	0.26^**	0.86
6. Appearance to cocky	1.68	0.63	–0.10	0.11	–0.03	0.23^**	0.66^**	0.ninety

Tabular array 2.

Multilevel model estimates.

Predictors	Model ii (effects of time)		Model 3 (covariates)		Model 4 (final)
Predictors	b (SE)	t (df)	b (SE)	t (df)	b (SE)	t (df)
Intercept	0.05 (0.01)	fifteen.27 (224)*	0.05 (0.01)	fifteen.01 (220)*	0.05 (0.01)	15.33 (217)*
Net versus email group			<0.01 (0.01)	0.75 (220)	<0.01 (0.01)	0.63 (217)
Internet versus discussion group			<–0.01 (0.01)	–0.66 (220)	<–0.01 (0.01)	–0.47 (217)
BMI			–0.01 (0.01)	–ane.56 (220)	<–0.01 (0.01)	–1.37 (217)
Historic period			<0.01 (0.01)	one.02 (220)	<0.01 (0.01)	1.00 (217)
Wellness reason					<0.01 (0.01)	0.53 (217)
Appearance to others					<–0.01 (0.01)	–0.06 (217)
Appearance to cocky					–0.01 (0.01)	–ane.48 (217)
–PRPE %: χ ² _{(∆ df)}	67.94%	567.02 (five)	0.45%	14.29 (4)*	0.46%	33.75 (3)*
Alter over time (linear)	–0.03 (0.01)	–ii.31 (224)*	–0.03 (0.01)	–2.60 (220)*	–0.03 (0.01)	–2.91 (217)*
Cyberspace versus e-mail group			0.04 (0.02)	2.13 (220)*	0.03 (0.02)	1.90 (217)
Internet versus word group			–0.03 (0.02)	–1.83 (220)	–0.03 (0.02)	–ane.57 (217)
BMI			<0.01 (0.01)	0.06 (220)	0.01 (0.01)	0.67 (217)
Age			–0.03 (0.01)	2.29 (220)*	0.04 (0.01)	iii.00 (217)*
Health reason					–0.02 (0.01)	–1.60 (217)
Appearance to others					0.02 (0.02)	1.07 (217)
Appearance to self					–0.04 (0.02)	–2.31 (217)*
–PRPE %: χ ^two _{(∆ df)}			iii.20%	17.55 (4)*	3.49%	18.63 (3)*
Modify over time (curvilinear)	<0.01 (0.01)	0.xx (224)	<0.01 (0.01)	0.48 (220)	0.01 (0.01)	0.66 (217)
Net versus electronic mail group			–0.03 (0.02)	–1.84 (220)	–0.03 (0.01)	–1.67 (217)
Cyberspace versus word group			0.03 (0.02)	–1.84 (220)	0.02 (0.01)	1.60 (217)
BMI			<0.01 (0.01)	0.48 (220)	<–0.01 (0.01)	–0.06 (217)
Age			–0.02 (0.01)	–2.22 (220)*	–0.03 (0.01)	–2.80 (217)
Wellness reason					0.03 (0.01)	1.29 (217)
Appearance to others					–0.01 (0.01)	–0.83 (217)
Advent to self					0.04 (0.01)	2.04 (217)*
–PRPE %: χ ² _{(∆ df)}			2.94%	ix.89 (4)	3.02%	13.28 (3)*

In Model 2, the linear and curvilinear effects of change over fourth dimension were added as random effects. The linear effect was significant (b = –0.03, standard error (SE) = 0.01, t ₍₂₂₄₎ = –2.31, p = 0.02), but the curvilinear outcome was not (b < 0.01, SE = 0.01, t ₍₂₂₄₎ = 0.19, p = 0.85). At the finish of the study, participants weighed significantly less than their baseline weights. The linear and curvilinear change over time proportionally reduced prediction mistake (proportional reduction in prediction error [PRPE] = 67.94%), which represented a significant comeback to the model of pct weight loss (Δχ ² ₍₅₎ = 567.02, p < 0.001).

In Model 3, we began calculation between-participant variables. First, we included the covariates every bit predictors of three-month weight change (intercept) forth with the linear and curvilinear effects of fourth dimension. There was no consequence of BMI or spider web versus give-and-take lath, just age predicted both the linear (b = 0.03, SE = 0.01, t ₍₂₂₀₎ = 2.xxx, p = 0.02) and curvilinear (b = –0.02, SE = 0.01, t ₍₂₂₂₎ = –2.22, p = 0.03) effects of time, and spider web versus email predicted the linear effect of fourth dimension (b = 0.03, SE = 0.02, t ₍₂₂₀₎ = 2.13, p = 0.04). For the linear effect, this indicates that older individuals tended to lose weight less quickly than younger individuals, while participants in the electronic mail counseling group lost weight more than rapidly than those in the web-only grouping. Adding the covariates slightly reduced prediction error of the intercept (PRPE = 0.45%), linear modify over time (PRPE = 3.20%), and curvilinear change over time (PRPE = ii.94%). The model of the intercept (χ ² _(iv) = xiv.29, p = 0.006) and of the linear fourth dimension effect was improved (χ ² ₍₄₎ = 17.55, p < 0.001), but the model of the curvilinear effect of fourth dimension was non significantly improved (χ ⁱⁱ ₍₄₎ = ix.89, p = 0.07).

Model 4 added the iii reasons of motivation to lose weight as predictors of the intercept and the two furnishings of fourth dimension. The "appearance to cocky" as an initial reason for weight loss was a pregnant predictor of linear modify over time (b = 0.04, SE = 0.02, t ₍₂₁₇₎ = –2.17, p = 0.02) and the curvilinear event of time (b = 0.03, SE = 0.02, t ₍₂₁₇₎ = 2.04, p = 0.04). Also, PRPE was reduced somewhat for the intercept (PRPE = 0.46%) and moderately for the linear (PRPE = 3.49%) and curvilinear furnishings of time (PRPE = 3.02%). Participants who were highly motivated for cocky-advent reasons (1 SD above the hateful) lost weight less apace (and actually gained weight on average) than moderately (mean) or less (1 SD below the mean) motivated participants. Results are depicted in Figure 1. The models for the iii effects in the prediction of percentage weight change were improved, χ ² _(three) = 33.75, xviii.63, and 13.28, p < 0.05.

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The effect of baseline motivation to lose weight for self-appearance reasons on weight loss at 3 months through 30 months. Women who were highly motivated on this factor at baseline gained weight, on average, 30 months later, whereas women minimally motivated by this gene lost 6 percent of their baseline weight. Effects are controlling for baseline BMI, age, other reasons for wanting to lose weight, and intervention group. Negative weight loss indicates weight gain relative to baseline weight.

Give-and-take

Our study examined the association between women's initial reason for weight loss and their alter in weight later 30 months of participation in a purely web-based intervention. Nosotros establish that simply one factor, appearance in relation to oneself, was a unique predictor of weight change. Women who most strongly endorsed wanting to lose weight to improve their appearance to themselves gained weight, on boilerplate, after thirty months of participating in a weight loss intervention. Those who were moderately motivated for self-advent reasons lost a marginal amount of weight (around 2%). Importantly, participants least motivated for this reason obtained clinically meaningful weight loss of at least 5 percentage, which is necessary for obtaining wellness benefits of weight reduction (Stevens et al., 2001). Our findings are in line with other work suggesting that appearance-based motives for losing weight are associated with negative outcomes (Vartanian et al., 2022), and the results support and extend previous cross-sectional, retrospective, and cocky-report studies.

Implications and future directions

A number of implications and future directions stem from our findings. Starting time, the initial reason a adult female has for losing weight is predictive of her bodily weight change xxx months later. When because a woman'due south initial reason for weight loss discussed in the literature—health reasons, appearance to one's self, and appearance to others—only appearance to one's self predicted weight alter. Although a robust corporeality of inquiry examines factors that influence individuals to lose weight for appearance reasons, such as low self-esteem (Thome and Espelage, 2007) and poor body image (Vartanian et al., 2022), additional work should explore interventions to promote more positive reasons for weight loss prior to start weight loss interventions.

2d, our findings indicate that amid our rural sample of older women, wellness-related reasons for weight loss were non independent of appearance reasons. Indeed, there were positive associations betwixt the two appearance factors and health, suggesting that women are highly likely to be motivated by a variety of reasons when deciding to pursue weight loss. This is important considering the effect of even a moderately high endorsement of cocky-appearance as an initial reason for weight loss buffered the contribution of all other reasons on actual attainment of weight loss. The mean of the self-appearance factor was ane.68, meaning that women classified every bit "highly motivated" in this report (1 SD above the mean) scored but 2.31 out of 4.00 on the measure. Although our sample had few women who reported high or very high endorsement of cocky-appearance equally an initial reason for weight loss, we postulate that individuals who highly place with self-advent as a reason for weight loss would be most likely to proceeds weight, as high identification weight self-appearance may indicate greater body dissatisfaction and a negative body image (Thome and Espelage, 2007). For example, it is possible that there is an inverted-u curvilinear effect such that a moderate corporeality of motivation for weight loss for self-appearance reasons is detrimental, but as motivation increases beyond that signal, the negative effect diminishes. Future research might farther explore the nature of this human relationship.

Relatedly, one potentially fruitful area for time to come research could be to explore factors that influence women's reasons for standing a weight loss intervention, rather than focusing solely on why women began the intervention. Continued interaction with components of the web-based intervention messaging may take influenced why women wanted to lose weight equally they progressed through the report. Nosotros measured reasons for weight loss only at the beginning of the present intervention, merely future piece of work could measure it throughout, and examine whether a change in weight loss reasons may be especially benign for participants initially spurred to improve their ain appearance via weight loss.

3rd, our finding that women highly motivated for weight loss due to self-appearance reasons were the least successful in the intervention suggests that the reason for the motivation may exist more important in general than the intensity of the motivation, as has been suggested by others (Teixeira et al., 2022). It is possible that loftier self-appearance motivation pb to an initial boost of engagement in the intervention among participants, merely that slow changes in appearance, or less change than anticipated, contributed to lower engagement and disillusionment after iii months. Similarly, found that a greater discrepancy between perceived and platonic body size was positively related to weight proceeds virtually 2 years later. In this written report, we did not include measures of women'due south' satisfaction with their appearance throughout, simply time to come investigations should do so to more fully explore the mechanisms by which initial advent for weight loss affects actual weight loss. Ane explanatory mechanism may be that participants motivated for self-appearance reasons are less autonomously motivated to exercise, which has been demonstrated to be a very of import factor in long-term weight maintenance (Santos et al., 2022; Silva et al., 2022).

Limitations

Although we uncovered a robust series of relationships, some limitations must be considered before drawing any house conclusions from our findings. Given the nature of the study, our sample was limited to rural, midlife, or older, largely White women. Potentially as a result of the inclusion criteria the sample was largely high income. As such, how well these findings generalize to other populations is unknown. There is some evidence that women are more likely to want to lose weight for advent reasons compared to men (Crane et al., 2022; Tylka and Homan, 2022), and that younger adults may differ in the reasons for losing weight when compared to older adults (Lanoye et al., 2022; Rancourt et al., 2022). Although we propose some possible explanations for our findings, additional data are necessary to provide insight. For instance, information technology is possible that women who were highly motived at baseline to lose weight for self-appearance reasons were enthusiastic at 3 months; yet their enthusiasm for the report dwindled as time progressed and appearance changes were not rapid or of the desired nature. Unfortunately, women in this study did not rate their satisfaction with their appearance, and this could exist an outcome of involvement in hereafter studies.

Another limitation is that we are unable to account for other, confounding variables that may explain our results. For example, given the ofttimes individual and personal nature of weight loss, personality factors non captured in our piece of work may jointly influence advent for weight loss and, concurrently, other health-related behaviors. Besides, a woman'south human relationship status (married, single, divorced, etc.) at the start of the intervention may influence the reason why she wants to lose weight. Perhaps, women are more than motivated for appearance reasons when not married compared to when they are married. Relationship status may be an important factor to consider in time to come piece of work. Another factor that may affect initial reasons for weight loss is whether someone has participated in previous weight loss interventions, specifically those with promoting weight loss as a ways of achieving wellness benefits. We practise not have this information for participants, but, given the rural location, it is unlikely many women participated in other studies. In improver, although we collected women'south' initial reasons for wanting to lose weight, we did not tailor the intervention to business relationship for these factors, nor did we explore how specific features of the intervention may have differentially affected participants with varying motivational profiles.

Conclusion

This study investigated the clan between women's' initial reasons for wanting to lose weight with weight change over 30 months. Of three initial reasons for weight loss—health, advent to others, and advent to self—only advent to self uniquely predicted weight modify in our longitudinal, multilevel modeling analysis. Women more highly motivated by appearance to self gained weight at the stop of the intervention, whereas women who identified very footling with this reason achieved clinically significant weight loss of at least 5 percent. Based on these findings, which contribute to a growing body of piece of work on the impact of initial motivational factors on weight loss success, researchers should seek to incorporate and business relationship for participants' reasons for trying to lose weight when designing and analyzing weight loss interventions. Furthermore, this written report is unique in our focus on rural women who are midlife or older, and results are particularly relevant to improving wellness outcomes in this population.

Footnotes

Annunciation of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following fiscal support for the research, authorship, and/or publication of this article: This research was supported by funds from the (1) National Found of Nursing Research, National Institutes of Wellness Grant (Grant no. R01 NR010589) awarded to C.H.P., (2) Inquiry Support Fund Grant from The Nebraska Medical Centre and the University of Nebraska Medical Centre awarded to C.H.P., and (three) The Hygenic Corporation Thera-Band University awarded to C.H.P. These funding source sponsors were not involved with the study design, implementation, or dissemination of this commodity.

References

Befort CA, Nazir N, Perri MG. (2012) Prevalence of obesity among adults from rural and urban areas of the United States: Findings from NHANES (2005–2008). The Journal of Rural Health 28: 392–397. [PMC complimentary article] [PubMed] [Google Scholar]
Brindal E, Freyne I, Saunders Southward, et al. (2012) Features predicting weight loss in overweight or obese participants in a web-based intervention: Randomized trial. Journal of Medical Cyberspace Research 14: e173. [PMC complimentary article] [PubMed] [Google Scholar]
Crane MM, Jeffery RW, Sherwood NE. (2016) Exploring gender differences in a randomized trial of weight loss maintenance. American Journal of Men's Health eleven: 369–375. [PMC gratuitous article] [PubMed] [Google Scholar]
Elfhag K, Rossner S. (2005) Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain. Obesity Reviews 6: 67–85. [PubMed] [Google Scholar]
Folta SC, Lichtenstein AH, Seguin RA, et al. (2009) The Strong Women-Salubrious Hearts Program: Reducing cardiovascular disease run a risk factors in rural, sedentary, overweight, and obese midlife and older women. American Journal of Public Wellness 99: 1271–1277. [PMC complimentary article] [PubMed] [Google Scholar]
Guertin C, Barbeau K, Pelletier Fifty. (2018) Examining fat talk and self-pity equally distinct motivational processes in women'due south eating regulation: A self-determination theory perspective. Journal of Health Psychology. Epub ahead of print 26 June 2022. DOI: 10.1177/1359105318781943. [PubMed] [CrossRef] [Google Scholar]
Hageman PA, Pullen CH, Yoerger M. (2018) Physical function and health-related quality of life in overweight and obese rural women who meet physical activity recommendation. Periodical of Crumbling and Physical Activity 26: 438–444. [PubMed] [Google Scholar]
Hageman PA, Pullen CH, Hertzog Thou, et al. (2011) Web-based interventions for weight loss and weight maintenance among rural midlife and older women: Protocol for a randomized controlled trial. BMC Public Health 11: 521. [PMC free article] [PubMed] [Google Scholar]
Hageman PA, Pullen CH, Hertzog M, et al. (2017) Web-based interventions alone or supplemented with peer-led support or professional person e-mail counseling for weight loss and weight maintenance in women from rural communities: Results of a clinical trial. Journal of Obesity 2017: 1602627. [PMC free article] [PubMed] [Google Scholar]
Hales CM, Carroll Md, Fryar CD, et al. (2017) Prevalence of obesity among adults and youth: United States, 2022–2016. NCHS Data Brief, No 288, October. Hyattsville, Md: National Center for Wellness Statistics. [PubMed] [Google Scholar]
Hennecke Yard, Freund AM. (2016) Age, activeness orientation, and self-regulation during the pursuit of a dieting goal. Applied Psychology: Health and Well-Beingness viii: 19–43. [PubMed] [Google Scholar]
HLM (2018). Computer software. Available at: http://world wide web.ssicentral.com/hlm/new.html
Holley TJ, Collins CE, Morgan PJ, et al. (2016) Weight expectations, motivations for weight change and perceived factors influencing weight management in young Australian women: A cross-sectional study. Public Health Diet 19: 275–286. [PubMed] [Google Scholar]
Jeffery RW, Epstein LH, Wilson GT, et al. (2000) Long-term maintenance of weight loss: Current condition. Wellness Psychology 19: 5–16. [PubMed] [Google Scholar]
Jensen MD, Ryan DH, Apovian CM, et al. (2013) 2013 AHA/ACC/TOS guideline for the direction of overweight and obesity in adults. Circulation 129(25 Suppl. 2): S102–S138. [PMC free article] [PubMed] [Google Scholar]
Lanoye A, Gorin AA, LaRose JG. (2016) Young adults' attitudes and perceptions of obesity and weight direction: Implications for treatment practice. Electric current Obesity Reports 5: 14–22. [PMC gratuitous article] [PubMed] [Google Scholar]
Leveille Southward, Resnick H, Balfour J. (2000) Gender differences in disability: Evidence and underlying reasons. Aging: Clinical and Experimental Research 12: 106–112. [PubMed] [Google Scholar]
MacLean PS, Wing RR, Davidson T, et al. (2015) NIH working group report: Innovative inquiry to amend maintenance of weight loss. Obesity 23: seven–15. [PMC free article] [PubMed] [Google Scholar]
Maltby J, Twenty-four hours L. (2001) The relationship between exercise motives and psychological well-being. The Journal of Psychology 135: 651–660. [PubMed] [Google Scholar]
Meyer AH, Weissen-Schelling South, Munsch S, et al. (2010) Initial development and reliability of a motivation for weight loss scale. Obesity Facts three: 205–211. [PMC gratuitous article] [PubMed] [Google Scholar]
Putterman E, Linden Due west. (2004) Appearance versus health: Does the reason for dieting affect dieting behavior? Periodical of Behavioral Medicine 27: 185–204. [PubMed] [Google Scholar]
Raaijmakers LCH, Pouwels Due south, Berghuis KA, et al. (2015) Technology-based interventions in the treatment of overweight and obesity: A systematic review. Appetite 95: 138–151. [PubMed] [Google Scholar]
Rancourt D, Jensen CD, Duraccio JM, et al. (2018) Successful weight loss initiation and maintenance among adolescents with overweight and obesity: Does historic period matter? Clinical Obesity 8: 176–183. [PMC free article] [PubMed] [Google Scholar]
Santos I, Mata H, Silva M, et al. (2015) Predicting long-term weight loss maintenance in previously overweight women: A signal detection approach. Obesity 23: 957–964. [PubMed] [Google Scholar]
Schelling Due south, Munsch Southward, Meyer AH, et al. (2011) Relationship between motivation for weight loss and dieting and binge eating in a representative population survey. International Journal of Eating Disorders 44: 39–43. [PubMed] [Google Scholar]
Scott JS, Simonoff MA, Marx BD. (2013) The Sage Handbook of Multilevel Modeling. Thousand Oaks, CA: SAGE. [Google Scholar]
Silva MN, Markland D, Carraca EV, et al. (2011) Practise democratic motivation predicts three-year weight loss in women. Medicine & Science in Sports & Do 43: 728–737. [PubMed] [Google Scholar]
Snijders T, Bosker R. (1999) Multilevel Analysis: An Introduction to Bones and Advanced Multilevel Modeling. London: SAGE. [Google Scholar]
Stevens VJ, Obarzanek E, Cook NR, et al. (2001) Long-term weight loss and changes in blood pressure: Results of the trials of hypertension prevention, phase 2. Annals of Internal Medicine 134: ane–11. [PubMed] [Google Scholar]
Strelan P, Mehaffey SJ, Tiggemann Grand. (2003) Cursory report: Self-objectification and esteem in young women: The mediating role of reasons for do. Sex Roles 48: 89–95. [Google Scholar]
Teixeira PJ, Carraça EV, Markland D, et al. (2012) Exercise, concrete activity, and self-determination theory: A systematic review. International Journal of Behavioral Nutrition and Concrete Activity 9: 78. [PMC free commodity] [PubMed] [Google Scholar]
Teixeira PJ, Going SB, Houtkooper LB, et al. (2004) Pretreatment predictors of attrition and successful weight direction in women. International Journal of Obesity 28: 1124–1133. [PubMed] [Google Scholar]
Thome JL, Espelage DL. (2007) Obligatory practice and eating pathology in college females: Replication and evolution of a structural model. Eating Behaviors eight: 334–349. [PubMed] [Google Scholar]
Tylka TL, Homan KJ. (2015) Practise motives and positive body image in physically active higher women and men: Exploring an expanded acceptance model of intuitive eating. Body Image 15: 90–97. [PubMed] [Google Scholar]
Vandervoort J, Aimé A, Dark-green-Demers I. (2015) The monster in the mirror: Reasons for wanting to change appearance. Eating and Weight Disorders 20: 99–107. [PubMed] [Google Scholar]
Vartanian LR, Wharton CM, Green EB. (2012) Appearance vs. wellness motives for exercise and for weight loss. Psychology of Sport and Exercise thirteen: 251–256. [Google Scholar]
Wieland LS, Falzon Fifty, Sciamanna CN, et al. (2012) Interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people. Cochrane Database of Systematic Reviews viii: CD007675. [PMC free article] [PubMed] [Google Scholar]

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