The impact of the first COVID-19 lockdown on weight management practices in UK adults: A self-regulation perspective

Abstract

This study aimed to identify the impact of the first UK COVID-19 lockdown on individuals’ weight management attempts (WMA). A self-regulation theoretical framework was used to identify predictors of continuing with a WMA, and weight change during the lockdown. An online retrospective cross-sectional study was conducted after the first UK COVID-19 lockdown. The sample consisted of 166 UK adults (M:31.08, SD:12.15) that were trying to manage their weight before the lockdown started. The survey assessed changes in WMA and practices, and measured perceived stress, flexible/rigid restraint, uncontrolled eating, craving control, and self-compassion. Results showed that 56% of participants reported disruption to their WMA during the lockdown. Participants with lower levels of perceived stress and higher flexible restraint were more likely to continue their WMA. Flexible restraint was a significant predictor of weight change. Interventions that promote flexibility in weight management may be beneficial for at-risk individuals under lockdown conditions.

Keywords

COVID-19 lockdown self-regulation obesity weight management energy intake physical activity

Introduction

In response to the first UK COVID-19 lockdown, all face-to-face weight management services were suspended with some adapting to digital delivery and others ceasing entirely. There was also closures to services instrumental to physical activity and dietary intake, which are two main behaviours often targeted as part of weight management attempts (Brown et al., 2021; UK. Gov., 2020). A Public Health England report showed that 60.6% of individuals in Tier 2 weight management services, and 78.3% in Tier 3 stated that their appointments had been canceled or delayed (Ells et al., 2020; Moussa et al., 2021). Additionally, there have been widespread reports that the COVID-19 lockdown had a negative impact on multiple health behaviours in general samples (e.g. Dicken et al., 2022; Naughton et al., 2021). However, there is limited evidence on how the COVID-19 lockdown impacted individuals who were attempting to manage their weight. This evidence is important because a considerable proportion of individuals attempt to manage their weight. Prior to the COVID-19 lockdown, 42% of the population reported trying to lose weight and 23% reported trying to maintain weight (Santos et al., 2017). Weight management (WM) has health benefits (Magkos et al., 2016) and small weight gain in a short period can lead to permanent substantial weight gain over time (Schoeller, 2014). Therefore, it is important to assess the impact of the COVID-19 lockdown on individuals’ WMA (either with professional support or self-led) and their ability to self-regulate weight protective behaviors.

Self-regulation of health behaviors is necessary for WM. Self-regulation refers to the ability to activate, monitor, inhibit and adapt behavior in response to internal cues, environmental stimuli, and feedback from others to attain personally relevant goals (Moilanen, 2007). There were multiple aspects about the lockdown rules that likely challenged the ability of some individuals to self-regulate weight management behaviors, such as dietary intake. For instance, the lockdown resulted in increased stocking up on comfort foods (Bhutani and Cooper, 2020). Additionally, the necessity to stay indoors resulted in constant exposure to these foods which may have consequently cued thoughts and increased food cravings (Boswell and Kober, 2016) and food intake (Nicola et al., 2020). Furthermore, the COVID-19 pandemic led to increased levels of fear, stress, sadness, and guilt (Brooks et al., 2020). Emotional distress and cue exposure can lead to dietary lapses (Forman et al., 2017; Goldstein et al., 2018). Indeed, in samples not specifically engaged in WMA, there have been reports of increased energy intake to soothe negative emotions during COVID-19 (Cherikh et al., 2020). As such, the COVID-19 lockdown may have challenged self-regulation and increased the likelihood of lapses, and been detrimental to WMA.

However, the negative effects of COVID-19 on WMA likely varied across individuals. According to self-regulation theory, various individual characteristics can help people face adversity and continue their self-regulation attempts (Teixeira et al., 2015). Some of these characteristics include self-compassion and eating behaviour traits such as flexible restraint and craving control. Self-compassion refers to a kind and understanding attitude towards oneself when faced with pain or failure (Neff, 2003). Self-compassion helps with self-regulation by fostering goal setting, taking action, evaluation of behavior, and emotional regulation (Sirois et al., 2015). In the COVID-19 situation, the extent to which individuals engaged in self-compassion may have influenced the ability to regulate stress, manage temporary relapses and therefore adhered to a WMA.

In terms of dietary restraint, flexible restraint involves a balanced approach to eating, by engaging in behaviors such as compensating at a subsequent meal for previous overconsumption (Westenhoefer et al., 1999). Flexible restraint is linked with improved weight loss outcomes (Westenhoefer et al., 2013). Rigid restraint on the other hand is described as a strict dichotomous, all-or-nothing approach to eating and WM (Westenhoefer, 1991). Therefore, individuals with higher flexible restraint may have been better able to adapt their dietary intake, engage in less uncontrolled eating and adhere to their WMA during the COVID-19 lockdown.

Additionally, flexible restraint could be beneficial to WM by supporting effective management of food cravings experienced during the WMA (Meule et al., 2011). A food craving refers to the intense desire to eat a certain food (Weingarten and Elston, 1990). Higher levels of food cravings are associated with increased BMI and disordered eating (Hill, 2007; Taetzsch et al., 2020). A low ability to control cravings (low craving control) has also been identified as a strong predictor of increased energy intake during the COVID-19 lockdown (Buckland et al., 2021; Buckland and Kemps, 2021). As such, the ability to refrain from acting on food cravings (high craving control) may have supported WM during the COVID-19 lockdown (Smithson and Hill, 2017).

Multiple studies have investigated the impact of COVID-19 on health behaviors in general samples (Bakaloudi et al., 2022; Stockwell et al., 2021). There are also emerging studies and reports on the experience of people engaged in weight loss attempts. A brief report on a commercial WM program showed that participants found it difficult to manage their weight during the COVID-19 lockdown (EASO, 2020). Furthermore, there have been some reports on changes to WMA during COVID-19 (Brown et al., 2021; Pellegrini et al., 2021). However, it remains unclear which individuals are most likely to digress from WMAs. Although some people gained weight and decreased their engagement in weight-related behaviors (Robinson et al., 2020), some used this period as an opportunity to change their lifestyle and make it healthier (Allabadi et al., 2020; Deschasaux-Tanguy et al., 2020). Furthermore, people living with obesity that were taking part in a weight management program reported that restrictions both helped and hindered their weight loss attempt (Thomson et al., 2022). This suggests there were individual differences in response to the lockdown and that further research is necessary to investigate individual differences and identify predictors of the impact of COVID-19 lockdowns on WMA.

Therefore, this study aimed to identify the impact of the first UK COVID-19 lockdown on self-regulation of weight-related behaviors from the framework of self-regulation theory. Specifically, the study aimed to identify: (i) changes in WMA and strategies; (ii) characteristics of individuals that continued their WMA; (iii) predictors of WMA continuation and weight change. It was hypothesized that the COVID-19 lockdown would have an impact on WMA, with most individuals reporting disruptions to their WMA and strategies used. Additionally, it was expected that the impact of the lockdown on WMA would vary across individuals. Specifically, it was expected that higher levels of self-compassion, craving control, flexible restraint, and lower levels of rigid restraint, stress and uncontrolled eating would be related to continued WMA. Finally, it was expected that stress, self-compassion, craving control, flexible/rigid restraint and uncontrolled eating would significantly predict changes in WMA and percentage weight change in response to the COVID-19 lockdown.

Methods

Participants and procedure

Data was collected from an online retrospective cross-sectional survey (via Qualtrics, Provo, UT) conducted after the first UK COVID-19 lockdown (September 1^st – November 9^th, 2020). Participants were recruited through social media and volunteer lists. A sample of 346 participants was targeted, based on an estimated small effect size (r = .15) and power of 0.80 (Ellis, 2010). The final sample consisted of 166 adults (M: 31.1, SD: 12.2 years). Whilst this sample size fell short of the planned sample size, Ellis (2010) suggests that this is sufficient to detect a medium effect size correlation coefficient (r = .25). To be eligible, participants needed to report engagement in a WMA (either with professional support or self-led) when the lockdown started. Respondents with a current or history of eating disorders were excluded (responded “yes” to “Do you have a current or history of an eating disorder?”). After providing informed consent, participants were asked to complete demographic information (e.g., age, gender) and screening questions (dieting status, eating disorders). Participants were then asked to complete questions about changes in WM strategies, eating behavior and physical activity in response to the COVID-19 lockdown. Participants then completed measures about stress, cognitive restraint, self-compassion, and craving control in a randomized order. Participants were then asked about their dieting history, postcode [to indicate socioeconomic status (SES)], general health (Jylhä, 2009), and COVID-19 status (e.g. infected, high-risk group; of note, participants were not excluded based on current COVID-19 infections) and impact (e.g. on income, caring responsibilities). All data in this study was self-reported. At the end of the questionnaire, participants were debriefed and had the opportunity to enter a prize draw. Two attention check questions were included for quality control, and participants that answered both incorrectly were excluded from the analysis. The study protocol was pre-registered on OSF. The study was ethically approved by the University of Sheffield ethics committee. The survey took on average 27.5 ± 11 min to complete.

Measures

Outcomes

Weight management attempt

Dieting status in response to the lockdown was measured with a single item “What happened to your weight management attempt in response to the COVID-19 lockdown?” with four response options: stopped, continued, temporarily stopped, or other.

Engagement in weight management strategies

Changes in WM strategies were measured by asking participants whether engagement in certain strategies had changed during the lockdown compared to before. The strategies used were selected from The Oxford Food and Activity Behaviors (OxFAB) taxonomy (Hartmann-Boyce et al., 2016). For this study only a selection of these strategies was assessed (Appendix SA). Engagement with each strategy was assessed using a 100-point scale (0 – extremely decreased, 100 – extremely increased) and included the option “not applicable”. Results were evaluated at the domain level and for strategies deemed essential for weight management.

Eating behavior

Changes in eating behavior during the COVID-19 lockdown were measured using three items adapted from previous COVID-19 work (Buckland et al., 2021). Participants were asked to indicate the extent to which their eating habits changed in response to the lockdown. The questions assessed changes to overall food intake, snacks, and meals. Participants first stated whether their food intake had changed, and then indicated the amount of change on a scale ranging from “0 = extremely decreased” to “100 = extremely increased”.

Physical activity

Changes to physical activity was measured using the single-item physical activity measure (Milton et al., 2011). Participants indicated the number of days they engaged in 30-min of moderate physical activity in a typical week, both before the lockdown, and during the lockdown. The Single item physical activity measure is a valid tool for measuring changes in physical activity (O’Halloran et al., 2020). Participants also reported general changes in the frequency and duration of physical activity in response to the lockdown (0 - extremely disagree to 100 - extremely agree) using questions generated for this study.

Weight change

The Dieting and Weight History Questionnaire (DWHQ) was used to assess weight changes (Witt et al., 2013). Participants were asked to report their weight before the first lockdown (weight close to 23^rd March 2020) and their current weight. Percentage weight change since the beginning of lockdown was computed by deducting current weight from weight before the lockdown. As such, a higher number represent weight gain.

Predictors (Cronbach’s α in Table S1)

Perceived stress

Perceived stress was measured using the Perceived Stress Scale (PSS) (Cohen et al., 1983). The PSS is a measure of general perceived stress and assesses the degree to which individuals find their lives unpredictable, uncontrollable, and overloading. The response scale ranged from 0 (never) to 4 (very often). Higher scores indicate higher levels of perceived stress (Cronbach’s α: .89).

Self-compassion

The Self-Compassion Scale was used to measure the main components of self-compassion as well as their negative counterparts: self-kindness/self-judgement, common humanity/isolation, mindfulness/over-identification (Neff, 2003). Responses to the items ranged from 1 (almost never) to 5 (almost always). Higher scores indicate higher levels of self-compassion. In line with previous evidence (Neff, 2003) in the current sample the scale demonstrated good internal consistency α = .93.

Flexible and rigid restraint

To measure flexible and rigid restraint, the Flexible and Rigid Control of Dietary Restraint was used (Westenhoefer et al., 1999). The questionnaire provides a score for both flexible and rigid restraint. Higher scores per scale, indicate greater flexible and rigid restraint (Flexible restraint Cronbach's α = .79; Rigid restraint Cronbach's α = .80.).

Uncontrolled eating

Uncontrolled eating was measured using the revised version of The Three-Factor Eating Questionnaire (TFEQ-R18) (Karlsson et al., 2000). Uncontrolled eating refers to the tendency to overeat, and food intake being out of control. Higher scores indicate higher levels of uncontrolled eating (Cronbach’s α: .90.).

Craving control

Craving control was assessed with the Control of Eating Questionnaire (COEQ) (Dalton et al., 2015). The scale consists of five items measuring the severity and control over food cravings that an individual experiences over the previous 7 days. Responses were assessed using a 100-point scale with higher scores indicating greater craving control (Cronbach’s α: .92.).

SES was measured to assess whether reported changes in WM practices varied according to SES (Clemmensen et al., 2020; Darmon and Drewnowski, 2008). Participants were asked to provide their postcode to determine Index of Multiple Deprivation (IMD) (Scottish Government, 2020; StatsWales, 2020; UK Government, 2019). The IMD ranks small geographical areas in the UK. Deciles are reported and scores ranged from “1 = most deprived” to “10 = least deprived”.

As data was collected retrospectively (from 1^st September to 9^th November 2020), the number of days between the start of the lockdown and survey completion was computed to be used as a covariate in the analyses where relevant.

Statistical analysis

Reported height, weight, weight change and computed BMI were screened to check for values outside of expected ranges (height between <1.40 and >2.20 m, weight <40 and >200 kg, weight change >40 kg and BMI <15 kg/m² and >60 kg/m²). Data points for incomplete surveys were retained. Averages were not computed to fill in missing data points. To compare demographic information (e.g., age, sex) of completers and non-completers, t-tests and chi-squared tests were conducted. Out of the 431 participants that expressed interest in taking part in the study (accessed the survey link) 79 did not consent to take part. A further 182 provided consent but dropped out before completing the survey. 19 were excluded due to an eating disorder and four were excluded for incorrectly answering both attention check questions or having a completion time of less than 10 min. Therefore, sample sizes vary for each variable reported.

ANOVAs were conducted to compare the characteristics of individuals that continued, temporarily stopped, or terminated their WMA. Associations between variables were explored using bivariate correlations (Pearson’s r, r < .3 = small, .3 - .5 = medium, r > .5 = large. (Cohen, 1992). Two regression models were run to identify predictors of continuing with the WMA and percentage (%) weight change. Self-compassion, flexible/rigid restraint, uncontrolled eating, stress, and craving control were entered into the model as predictors (hierarchical method). For weight change (%), SES and days passed since the start of the lockdown were entered in the first step, and all other predictors were entered at step 2. Mahalanobis, Cooks, and Leverage scores indicated that there were no outliers. There were no issues with multicollinearity as based on the variance inflation factor (VIF <10), and tolerance values (>0.2) (Tabachnick and Fidell, 2013). The criterion for significance was p < .05. For the between-subjects comparison, effect sizes are reported (Cohen’s d: small = 0.2, medium = 0.5 large = 0.8). Statistical analysis was carried out using IBM SPSS version 26.

Results

Sample

The final sample consisted of 166 adults (M: 31.1, SD: 12.2 years, range 18 to 72). Most of the sample was female, 70.5% (n = 117; male n = 45; other n = 4), from a white ethnic background (77.1%, n = 128), and had a high level of education (see Tables 1, S2, S3). Approximately 24% (n = 25) of the participants had a current BMI (M: 29.9, SD: 5.5) over 30, which is representative for this age group of UK individuals (NHS, 2019). Most participants (88%, n = 145) were following a self-led diet and (12%, n = 20) were part of a program. There were no significant differences between completers and non-completers for any sample characteristic variables measured (see Table S4).

Table 1.

Participant characteristics.

Variable (total n)	n (%) or M (SD)
Education level (166)
No formal qualifications	1 (0.6%)
1-4 GCSEs or equivalent qualifications	5 (3%)
5 GCSEs or equivalent qualifications	4 (2.4%)
Apprenticeship	1 (0.6%)
2 or more A-levels or equivalent qualifications	45 (27.1%)
Bachelor’s degree or equivalent	71 (42.8%)
Doctoral or higher education	37 (22.3%)
Other qualifications including foreign qualifications	2 (1.2%)
Ethnic group
White	128 (77.1%)
Mixed or multiple ethnic groups	4 (2.4%)
Asian or Asian British	23 (13.9%)
Black, African, Caribbean, or Black British	6 (3.6%)
Prefer not to say	2 (1.2%)
Other	3 (1.8%)

Changes in WMA and practices in response to COVID-19

Before the COVID-19 lockdown: 70.5% (n = 117) of participants were attempting to lose weight, 24.1% (n = 40) were attempting to maintain weight and 5.4% (n = 9) were trying to gain weight. In response to the lockdown, 39.8% (n = 66) of individuals reported continuing their WMA, 25.3% (n = 42) stopped and 30.7% (n = 51) stopped temporarily and then started again. Approximately 28% (n = 46) of participants reported losing weight, 35.2% (n = 58) reported gaining weight and 24.2% (n = 40) reported that their weight fluctuated during the COVID-19 lockdown.

Weight management strategies and weight change

Changes to specific WM strategies are shown in Figure 1. Approximately 42% of participants reported talking to a healthcare professional or having an online weight loss buddy. Participants also reported the highest decrease in engagement in these strategies due to the COVID-19 lockdown. Engagement in strategies related to planning meals, shopping and swapping foods increased overall. Decreased engagement in WM strategies was associated with weight gain since before the lockdown (Tables 3 and S5).

Figure 1.

Difference in changes in weight management strategies between participant that stopped/continued or temporarily stopped and restarted their WMA.

For the different strategy domains, most participants reported a decrease in strategies related to stimulus control (behaviours aimed at limiting exposure to food cues), use of WM aids, and seeking support. Changes to WM strategies significantly differed between participants that continued or had temporary disruptions and re-started to those that stopped their WMA. Participants that continued or had temporary disruptions to their WMA reported increased engagement in monitoring, information seeking, and setting rules compared to participants that stopped their WMA (Table S6).

Results showed that approximately 60% of participants reported some disruption to their WMA. Participants reported a mean weight change of −0.4 ± 8.4% (M: −0.8, SD: 7.59 kg). Around 40% of participants lost weight between the pre-COVID-19 lockdown and post-lockdown (Figure 2). As such, there was large individual variability in self-regulation ability, with some participants being more successful in self-regulating their weight during the lockdown than others.

Figure 2.

Individual variability in reported weight change (%) between pre- and post-COVID-19 first lockdown.

Characteristics of individuals that stopped, continued, or temporarily stopped their WMA

Table 2 shows the individual differences in eating behaviour traits of participants who continued, stopped, and temporarily stopped their WMA in response to the lockdown. Participants that continued their WMA reported significantly greater flexible restraint and craving control, and significantly less uncontrolled eating and perceived stress compared to participants that stopped their WMA. There were no significant differences between participants that stopped or continued their WMA in terms of self-compassion or rigid restraint. Participants that continued their WMA reported a significantly greater increase in physical activity and decrease in energy intake compared to participants that stopped their WMA (Table 3). These changes in energy intake (r = .45, p < .001) and physical activity (r = −.20, p < .05) were significantly correlated with reported percentage weight change. Specifically, participants that continued their WMA reported losing weight and those that stopped their WMA reported weight gain.

Table 2.

Individual differences between participants that stopped (n = 42), continued (n = 66), or temporarily stopped (TD; n = 51) their weight management attempt after the COVID-19 lockdown.

Variable	Group	M(SD)	F	Cohen’s d
Weight change%	Stopped^a	5.55 (6.85)	14.81**	1.40 (S/C)
	Continued^c	−4.76 (7.85)		0.74 (C/T)
	TD^ac	.94 (7.52)		0.64 (S/T)
Self-compassion	Stopped	2.68 (.73)	1.82
	Continued	2.92 (.99)	p = .15
	TD	2.55 (1.02)
Rigid control	Stopped	39.61 (6.59)	1.52
	Continued	39.32 (8.48)	p = .21
	TD	40.36 (6.58)
Flexible control	Stopped^a	29.54 (5.92)	3.36*	0.47 (S/C)
	Continued^c	32.09 (4.92)	p = .04	0.04 (C/T)
	TD^bd	31.92 (4.88)		0.44 (S/T)
Uncontrolled eating	Stopped^a	22.88 (5.26)	5.85**	0.58 (S/C)
	Continued^c	19.82 (5.25)		0.54 (C/T)
	TD^ad	22.92 (6.28)		0.01 (S/T)
Perceived stress	Stopped^a	34.95 (5.01)	9.44**	0.80 (S/C)
	Continued^c	30.54 (6.02)		0.67 (C/T)
	TD^ad	34.98 (7.25)		0.01 (S/T)
Craving control	Stopped^a	38.75 (25.92)	4.75*	0.56 (S/C)
	Continued^c	52.95 (24.46)	p = .01	0.41 (C/T)
	TD^bd	43.12 (23.8)		0.18 (S/T)

p < .001; * p < .05.

^aSignificant difference compared to participants that continued their WMA (p < .01).

^bNon-significant difference compared to participants that continued their WMA.

^cSignificant difference compared to participants that stopped their WMA (p < .01).

^dNon-significant difference compared to participants that stopped their WMA.

Table 3.

Differences in eating behaviour and physical activity changes between participants that stopped (n = 42), continued (n = 66) or temporarily stopped (n = 51) their weight management attempt after the COVID-19 lockdown.

Variable	Group	M(SD)	F
Energy intake change	Stopped^a	13.36 (17.14)	8.87**
	Continued^c	−3.11 (13.64)
	Temporary disruption^ad	9.37 (22.02)
Food amount	Stopped^a	14.74 (21.67)	6.51**
	Continued^c	−2.31 (16.75)
	Temporary disruption^ad	9.33 (23.97)
Snack amount	Stopped^a	20.69 (24.81)	6.74**
	Continued^c	−0.88 (22.02)
	Temporary disruption^bd	10.60 (28.53)
Meal change	Stopped^a	4.64 (16.86)	5.27**
	Continued^c	−6.32 (16.32)
	Temporary disruption^ad	6.66 (23.93)
Physical activity (PA) change	Stopped^a	−23.72 (23.94)	12.21**
	Continued^c	4.68 (25.88)
	Temporary disruption^ac	−9.35 (22.41)
PA day per week	Stopped^a	−1.95 (1.87)	18.16**
	Continued^c	1.79 (1.68)
	Temporary disruption^ad	−0.70 (2.28)
PA time	Stopped^a	−25.64 (28.41)	9.53**
	Continued^c	5.83 (32.08)
	Temporary disruption^ad	−14.53 (33.29)
Structured PA	Stopped^a	−24.81 (30.51)	10.87**
	Continued^c	11.03 (32.62)
	Temporary disruption^bc	−4.12 (33.23)
Incidental PA	Stopped^a	−20.71 (31.02)	3.47*
	Continued^c	−2.82 (27.42)
	Temporary disruption^bd	−9.39 (32.30)
BMI	Stopped^bd	30.64 (3.75)	1.41
	Continued	28.91 (7.22)
	Temporary disruption^bd	31.28 (4.10)

Note. ** p < .001; * p < .05.

^aSignificant difference compared to participants that continued their WMA (p < .01).

^bNon-significant difference compared to participants that continued their WMA.

^cSignificant difference compared to participants that stopped their WMA (p < .01).

^dNon-significant difference compared to participants that stopped their WMA.

Predictors of WMA continuation

Multinomial logistic regression analysis was conducted to identify predictors of changes in WMA. The model included perceived stress, self-compassion, craving control, flexible/rigid control, and uncontrolled eating as predictors of the odds of participants stopping, continuing, or re-starting their WMA.

The final model was significant, p = .008, and perceived stress (p = .01) and flexible control (p = .04) were identified as significant discriminants of whether participants stopped or continued their WMA. Participants scoring high on flexible restraint (B = .14, SE = .06, p = .02) and low on perceived stress (B = -.12, SE = .05, p = .009) were more likely to continue their WMA during the COVID-19 lockdown. Participants scoring high on flexible restraint (B = .12, SE = .06, p = .04) were more likely to restart their WMA after disruption during the COVID-19 lockdown.

The model correctly predicted participants that continued their WMA 81.4% of the time. The model was less accurate at predicting participants that stopped (41.7%) or those that had temporary disruptions to their WMA (34.8%).

Predictors of successful weight management

Regression analysis was conducted to identify predictors of weight change (%) during the COVID-19 lockdown. The regression model included perceived stress, self-compassion, craving control, flexible/rigid restraint, and uncontrolled eating as predictors and weight change as the outcome (Table 4). Number of days since the lockdown (M: 208; SD: 15 days) was added as a covariate in the regression model. The final regression model explained approximately 23% of the variance in weight change. Higher levels of flexible restraint significantly predicted greater weight loss. All other predictors and covariates were non-significant.

Table 4.

Hierarchical linear regressions for individual characteristics regressed on weight change (%).

Outcome variable	B	SE B	β
Weight change %
Step 1
Constant	6.50	12.21
SES	−0.09	0.28	−.03
Days	−0.03	0.06	−.05
Step 2
Constant	37.35	15.17
SES	−0.35	0.26	.002
Days	−0.09	0.05	−.11
Perceived stress	0.10	0.14	.08
Self-compassion	−0.36	0.92	−.04
Flexible restraint	−0.43	0.18	−.24*
Rigid restraint	−0.20	0.13	−.17
Uncontrolled eating	0.28	0.16	.20
Craving control	−0.08	0.04	−.21

Note. SES and days passed since the lockdown started were entered (enter method) as covariates in step 1, followed by all predictors in step 2 (hierarchical method).

For percentage weight change: R² = .003, p = .83 for Step 1; R² = 28, p < .001 for Step 2.

*p < .05.

B = unstandardized coefficient; B SE = unstandardized coefficient standard error; β = standardized coefficient.

SES = socioeconomic status.

Discussion

In the current study, most participants reported disruption to their WMA in response to the COVID-19 lockdown. Individuals that stopped their WMA reported a decrease in physical activity and an increase in energy intake, which corresponded with an increase in weight. Individuals that continued their WMA scored higher in flexible restraint and craving control, and lower in uncontrolled eating and perceived stress compared to those who disengaged from their WMA. Flexible restraint and perceived stress were significant predictors of continuing with a WMA. Flexible restraint was also a significant predictor of weight change during the COVID-19 lockdown. Self-compassion did not have a significant direct effect on weight change.

Reported disruptions to WMA are in line with previous research from commercial WM programs (EASO, 2020) and people living with obesity (Brown et al., 2021) and show that the COVID-19 lockdown had a negative impact on about half of the individuals that were attempting to manage their weight. Disruptions to WMA coincided with reported changes in energy intake, physical activity, and WM strategies. These results extend current knowledge by providing further evidence on changes to specific WM strategies and provide evidence on disruptions to WMA in individuals from the general population who engaged in a self-led WMA (most previous research has focused only on those engaged in structured programs).

Responses to the COVID-19 lockdown varied largely across individuals, with some individuals reporting continued WMA and weight loss. Characteristics that helped individuals continue their self-regulation attempts included greater flexible restraint and craving control, and less uncontrolled eating and perceived stress. Perceived stress and flexible restraint were significant predictors of continuation with a WMA. Individuals with a more flexible approach to eating were also more successful in managing their weight. This is in line with previous research that suggested a positive relationship between flexible restraint and success in WM (Sairanen et al., 2014). Flexible restraint may be linked with WM as flexible approaches may result in greater acceptance and ability to adapt dietary intake in response to cravings and dietary lapses. For example, individuals scoring high in flexible restraint may initiate compensatory behaviors in response to lapses, rather than generating negative emotions that can lead to disengagement from a WMA. The current findings provide novel evidence which shows that flexible restraint is important for engagement in WMA attempts, especially during challenging times such as COVID-19.

Results showing the importance of perceived stress on WM are in line with self-regulation theory stating that negative affect is a barrier to self-regulation (Wagner and Heatherton, 2014). The mechanisms through which stress is affecting self-regulation include depleting cognitive resources (Hofmann et al., 2007), increasing emotional eating (Macht, 2008), and increasing preferences for immediate rewards over larger delayed ones (Tice and Bratslavsky, 2000). Therefore, individual characteristics that help deal with stress might be beneficial for WMAs.

Self-compassion is a quality that can facilitate the self-regulation of health behavior during challenging times by supporting adaptive emotion regulation (Sirois et al., 2015). In the current research, contrary to expectations, self-compassion was not a significant predictor of continuing with a WMA or weight change during the COVID-19 lockdown. Based on the current study, there is no evidence that taking a more compassionate attitude towards oneself directly benefits weight management under lockdown conditions. This might be due to the stressors of lockdown being above the threshold for which self-compassion may be beneficial. For example, evidence shows that reappraisal strategies (e.g., self-compassion) are less effective than suppression strategies for dealing with negative emotions (Diedrich et al., 2014; Sirois et al., 2019).

There are some limitations to this research. First, the data was collected retrospectively. To minimize the influence of this, time passed since the lockdown and survey completion was accounted for in the analysis. Second, the data collected is cross-sectional and self-reported. Research shows that individuals tend to underestimate dietary intake and overestimate physical activity (Dahle et al., 2021; Silsbury et al., 2015) and we have no information on how often or well the WM strategies measured were used. However, associations between reported energy intake and physical activity and weight change (Table S7) indicate that the measures used were sensitive to detect variability in responses as they aligned with expected associations (e.g., individuals that reported increased energy intake also reported gaining weight during the lockdown r = .45), suggesting validity in the measures used. Third, this sample of participants might not be representative of the general population given that it consists of primarily of highly educated white women. This might be the result of the recruitment methods used that were limited by the lockdown rules imposed by the COVID-19 lockdown rules at the time. However, the percentages of participants with a BMI over 30 kg/m² is similar to the one reported in the general population. Finally, no pre-COVID-19 data were collected, therefore we have no baseline data to compare the current results to. An alternative explanation to the current results could be that participants responded to the WM questions on WM strategies and WMA based on the way their weight changed.

Nevertheless, the current study applied a theoretical framework to identify predictors of continuing with a WMA and weight change during a viral pandemic. This novel data provides evidence of important individual characteristics linked with the self-regulation of weight-related behaviors during challenging times. While some studies have reported on the individual characteristics of increased food intake during COVID-19 (Buckland et al., 2021; Robert et al., 2022), to our knowledge this is the first study to report on the individual characteristics associated with successful WM during COVID-19 in adults who were engaged in a WMA at the onset of the first COVID-19 lockdown. This is noteworthy because previous COVID-19 research is mainly derived from individuals not actively trying to manage their weight. Furthermore, the limited available evidence is from participants in structured WM programs (e.g. commercial or local authority commissioned) rather than self-led WMA (Ells et al., 2020; Pellegrini et al., 2021), yet most people who attempt WM adopt self-led approaches (Santos et al., 2017). The current research expands knowledge on the impact of COVID-19 lockdown on individuals following a self-led WMA. This is noteworthy as it captures a unique facet of the real-life experience of individuals dealing with overweight or obesity during a distinct historical period. Furthermore, it successfully captures the experiences of individuals with milder forms of obesity, an aspect often disregarded in existing literature.

This research also provides novel evidence on the importance of emotion regulation and a more flexible approach to eating behavior for the continuation of WMAs and weight change in the context of major disruptions to everyday life (COVID-19 lockdown). Emotional regulation and a flexible approach to eating are modifiable individual characteristics that can be trained (Rahimi-Ardabili et al., 2018; Sairanen et al., 2014). Additionally, individuals scoring low in flexible restraint can be targeted and provided with more support during risky periods. Given evidence that self-compassion interventions are effective for improving the self-regulation of health behaviors (Biber and Ellis, 2019), future research could also investigate the effects of promoting self-compassion for WM in individuals susceptible to WM lapses during stressful times. This evidence is important in developing interventions that will help individuals deal with risky periods such as the COVID-19 outbreaks and future lockdowns which are likely to happen (Xu and Li, 2020). As well as help better target support and guidance for more vulnerable people such as people living with overweight and obesity. This is noteworthy as this group is generally stigmatized and were identified as a risk group for COVID-19 and stigmatized during the pandemic (Farrell et al., 2022; Townsend et al., 2020).

Conclusions

This study provides novel findings on the impact of the COVID-19 lockdown on weight management attempts, by using self-regulation theory to identify modifiable individual characteristics that predict the continuation and success of WMA in times of added stress. The current results indicate that perceived stress and flexible control of eating behavior are significant predictors of engagement and success in self-regulation of weight during the COVID-19 lockdown. These results have important implications for the development of future interventions for weight management.

Supplemental Material

Supplemental Material - The impact of the first COVID-19 lockdown on weight management practices in UK adults: A self-regulation perspective

Supplemental Material for The impact of the first COVID-19 lockdown on weight management practices in UK adults: A self-regulation perspective by Denisa Genes, Fuschia M Sirois and Nicola J Buckland in Health Psychology Open.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical statement

ORCID iD

Denisa Genes

Data availability statement

that support the findings for this study are available on OSF upon reasonable request https://osf.io/g39hn/.

Supplemental Material

Supplemental material for this article is available online.

References

Allabadi

Dabis

Aghabekian

, et al. (2020) Impact of COVID-19 lockdown on dietary and lifestyle behaviours among adolescents in Palestine. Dynamics of Human Health 2020(2): 7.

Bakaloudi

Dimitra Rafailia

Jeyakumar

Dhanushya T

Jayawardena

Ranil

Chourdakis

Michail

(2022) The impact of COVID-19 lockdown on snacking habits, fast-food and alcohol consumption: A systematic review of the evidence. Clin Nutr 41(12): 3038–3045. doi: 10.1016/j.clnu.2021.04.020.

Bhutani

Cooper

(2020) COVID-19 related home confinement in adults: weight gain risks and opportunities. Obesity 28(9): 1576–1577. DOI: 10.1002/oby.22904.

Biber

Ellis

(2019) The effect of self-compassion on the self-regulation of health behaviors: a systematic review. Journal of Health Psychology 24(14): 2060–2071. DOI: 10.1177/1359105317713361.

Boswell

Rebecca G

Kober

Hedy

(2016) Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obes Rev 17(2): 159–177. doi: 10.1111/obr.12354.

Brooks

Webster

Smith

, et al. (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet (London, England) 395(10227): 912–920. DOI: 10.1016/S0140-6736(20)30460-8.

Brown

Flint

Kalea

, et al. (2021) Negative impact of the first COVID-19 lockdown upon health-related behaviours and psychological wellbeing in people living with severe and complex obesity in the UK. EClinicalMedicine 34: 100796. DOI: 10.1016/j.eclinm.2021.100796.

Buckland

Kemps

(2021) Low craving control predicts increased high energy density food intake during the COVID-19 lockdown: result replicated in an australian sample. Appetite 166: 105317.

Buckland

Swinnerton

, et al. (2021) Susceptibility to increased high energy dense sweet and savoury food intake in response to the COVID-19 lockdown: the role of craving control and acceptance coping strategies. Appetite 158(November): 105017. DOI: 10.1016/j.appet.2020.105017

10.

Cherikh

Frey

Bel

, et al. (2020) Behavioral food addiction during lockdown: time for awareness, time to prepare the aftermath. Obesity Surgery 30(9): 3585–3587. DOI: 10.1007/s11695-020-04649-3.

11.

Clemmensen

Petersen

Sørensen

TIA

(2020) Will the COVID-19 pandemic worsen the obesity epidemic? Nature Reviews Endocrinology 16(9): 469–470. DOI: 10.1038/s41574-020-0387-z.

12.

Cohen

(1992) Statistical power analysis. Current Directions in Psychological Science 1(3): 98–101.

13.

Cohen

Kamarck

Mermelstein

(1983) A global measure of perceived stress. Journal of Health and Social Behavior 24(4): 385–396.

14.

Dahle

Ostendorf

Zaman

, et al. (2021) Underreporting of energy intake in weight loss maintainers. The American Journal of Clinical Nutrition 114: 257–266. DOI: 10.1093/ajcn/nqab012.

15.

Dalton

Finlayson

Hill

, et al. (2015) Preliminary validation and principal components analysis of the Control of Eating Questionnaire (CoEQ) for the experience of food craving. European Journal of Clinical Nutrition 69(12): 1313–1317. DOI: 10.1038/ejcn.2015.57.

16.

Darmon

Drewnowski

(2008) Does social class predict diet quality? The American journal of clinical nutrition 87(5): 1107–1117. DOI: 10.1093/ajcn/87.5.1107.

17.

Deschasaux-Tanguy

Druesne-Pecollo

Esseddik

, et al. (2020) Diet and physical activity during the COVID-19 lockdown period (March-May 2020): results from the French NutriNet-Sante cohort study. The American Journal of Clinical Nutrition 113(4): 924–938. DOI: 10.1101/2020.06.04.20121855.

18.

Dicken

Mitchell

Newberry Le Vay

, et al. (2022) Impact of the COVID-19 pandemic on diet behaviour among UK adults: a longitudinal analysis of the HEBECO study. Frontiers in Nutrition 8: 788043. DOI: 10.3389/fnut.2021.788043

19.

Diedrich

Grant

Hofmann

, et al. (2014) Self-compassion as an emotion regulation strategy in major depressive disorder. Behaviour Research and Therapy 58: 43–51. DOI: 10.1016/j.brat.2014.05.006.

20.

EASO (2020) More than half of people struggled to manage their weight during COVID-19 lockdown. Suggests UK Survey 2.

21.

Ellis

(2010) The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results. Cambridge, MA: Cambridge University Press.

22.

Ells

Radley

Matu

, et al. (2020) Supporting Weight Management Services during the COVID-19 Pandemic Phase I Insights. London, UK: GOV.UK.

23.

Farrell

Hollmann

Roux

, et al. (2022) At home and at risk: the experiences of Irish adults living with obesity during the COVID-19 pandemic. EClinicalMedicine 51: 101568. DOI: 10.1016/j.eclinm.2022.101568.

24.

Forman

Schumacher

Crosby

, et al. (2017) Ecological momentary assessment of dietary lapses across behavioral weight loss treatment: characteristics, predictors, and relationships with weight change. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine 51(5): 741–753. DOI: 10.1007/s12160-017-9897-x.Ecological

25.

Goldstein

Zhang

Thomas

, et al. (2018) Application of machine learning to predict dietary lapses during weight loss. Journal of Diabetes Science and Technology 12(5): 1045–1052. DOI: 10.1177/1932296818775757.

26.

Government U (2019) English Indices of Deprivation 2019. London, UK: GOV.UK

27.

Government S (2020) Scottish Index of Multiple Deprivation 2020 V2 Postcode Lookup File.https://Www.Gov.Scot/Publications/Scottish-Index-of-Multiple-DeprivatIon-2020v2-Postcode-Look-Up/

28.

Government U (2020) Guidance Staying Alert and Safe (Social Distancing). London, UK: GOV.UK

29.

Hartmann-Boyce

Aveyard

Koshiaris

, et al. (2016) Development of tools to study personal weight control strategies: OxFAB taxonomy. Obesity 24(2): 314–320. DOI: 10.1002/oby.21341.

30.

Hill

(2007) The psychology of food craving: symposium on molecular mechanisms and psychology of food intake. Proceedings of the Nutrition Society 66(2): 277–285. DOI: 10.1017/S0029665107005502.

31.

Hofmann

Rauch

Gawronski

(2007) And deplete us not into temptation: automatic attitudes, dietary restraint, and self-regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology 43(3): 497–504. DOI: 10.1016/j.jesp.2006.05.004.

32.

Jylhä

(2009) What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Social Science & Medicine 69(3): 307–316. DOI: 10.1016/j.socscimed.2009.05.013.

33.

Karlsson

Persson

Sjöström

, et al. (2000) Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. International Journal of Obesity and Related Metabolic Disorders : Journal of the International Association for the Study of Obesity 24(12): 1715–1725. DOI: 10.1038/sj.ijo.0801442.

34.

Macht

(2008) How emotions affect eating: a five-way model. Appetite 50(1): 1–11. DOI: 10.1016/j.appet.2007.07.002.

35.

Magkos

Fraterrigo

Yoshino

, et al. (2016) Effects of moderate and subsequent progressive weight loss on metabolic function and adipose tissue biology in humans with obesity. Cell Metabolism 23(4): 591–601. DOI: 10.1016/j.cmet.2016.02.005.

36.

Meule

Westenhöfer

Kübler

(2011) Food cravings mediate the relationship between rigid, but not flexible control of eating behavior and dieting success. Appetite 57(3): 582–584. DOI: 10.1016/J.APPET.2011.07.013.

37.

Milton

Bull

Bauman

(2011) Reliability and validity testing of a single-item physical activity measure. British Journal of Sports Medicine 45(3): 203–208. DOI: 10.1136/bjsm.2009.068395.

38.

Moilanen

(2007) The adolescent Self-Regulatory inventory: the development and validation of a questionnaire of short-Term and long-term self-Regulation. Journal of Youth and Adolescence 36(6): 835–848. DOI: 10.1007/s10964-006-9107-9.

39.

Moussa

Zakeri

Arhi

, et al. (2021) Impact of COVID-19 on obesity management services in the United Kingdom (the COMS-UK study). Obesity Surgery 31(2): 904–908. DOI: 10.1007/s11695-020-05005-1.

40.

Naughton

Ward

Khondoker

, et al. (2021) Health behaviour change during the UK COVID-19 lockdown: findings from the first wave of the C-19 health behaviour and well-being daily tracker study. British Journal of Health Psychology 26: 624–643. DOI: 10.1111/bjhp.12500.

41.

Neff

(2003) The development and validation of a scale to measure self-compassion. Self and Identity 2(3): 223–250. DOI: 10.1080/15298860309027.

42.

NHS (2019) Statistics on obesity, physical activity and diet England 2019 statistics on obesity, physical activity and diet: England 2019. Statistics on Obesity, Physical Activity and Diet 17: 24.

43.

Nicola

Alsafi

Sohrabi

, et al. (2020) The socio-economic implications of the coronavirus pandemic (COVID-19): a review. International Journal of Surgery, January: 19: 21. DOI: 10.1007/s00134-020-05991-x.Bizzarro.

44.

O’Halloran

Kingsley

Nicholson

, et al. (2020) Responsiveness of the single item measure to detect change in physical activity. PLoS One 15(6): 1–13. DOI: 10.1371/journal.pone.0234420.

45.

Pellegrini

Webster

Hahn

, et al. (2021) Relationship between stress and weight management behaviors during the COVID-19 pandemic among those enrolled in an internet program. Obesity science & practice 7(1): 129–134. DOI: 10.1002/OSP4.465.

46.

Rahimi-Ardabili

Reynolds

Vartanian

, et al. (2018) A systematic review of the efficacy of interventions that aim to increase self-compassion on nutrition habits, eating behaviours, body weight and body image. Mindfulness 9(2): 388–400. DOI: 10.1007/s12671-017-0804-0.

47.

Robert

Deschasaux-Tanguy

Shankland

, et al. (2022) Positive psychological traits are associated with dietary behavior during the COVID-19 lockdown in a general population-based study. Appetite 169: 105507. DOI: 10.1016/J.APPET.2021.105507.

48.

Robinson

Boyland

Chisholm

, et al. (2020) Obesity, eating behavior and physical activity during COVID-19 lockdown: a study of UK adults. Appetite, 156: 104853. DOI: 10.1016/j.appet.2020.104853

49.

Sairanen

Lappalainen

Lapveteläinen

, et al. (2014) Flexibility in weight management. Eating Behaviors 15(2): 218–224. DOI: 10.1016/j.eatbeh.2014.01.008.

50.

Santos

Sniehotta

Marques

, et al. (2017) Prevalence of personal weight control attempts in adults: a systematic review and meta-analysis. Obesity Reviews : An Official Journal of the International Association for the Study of Obesity 18(1): 32–50. DOI: 10.1111/obr.12466.

51.

Schoeller

(2014) The effect of holiday weight gain on body weight. Physiology & behavior 134(C): 66–69. DOI: 10.1016/j.physbeh.2014.03.018.

52.

Silsbury

Goldsmith

Rushton

(2015) Systematic review of the measurement properties of self-report physical activity questionnaires in healthy adult populations. BMJ Open 5(9): e008430. DOI: 10.1136/bmjopen-2015-008430.

53.

Sirois

Kitner

Hirsch

(2015) Self-compassion, affect, and health-promoting behaviors. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association 34(6): 661–669. DOI: 10.1037/hea0000158.

54.

Sirois

Bögels

Emerson

(2019) Self-compassion improves parental well-being in response to challenging parenting events. Journal of Psychology 153(3): 327–341. DOI: 10.1080/00223980.2018.1523123.

55.

Smithson

Hill

(2017) It is not how much you crave but what you do with it that counts: behavioural responses to food craving during weight management. European Journal of Clinical Nutrition 71(5): 625–630. DOI: 10.1038/ejcn.2016.235.

56.

Stockwell

Stephanie

Trott

Mike

Tully

Mark

Shin

Jae

Barnett

Yvonne

Butler

Laurie

McDermott

Daragh

Schuch

Felipe

Smith

Lee

(2021) Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review. BMJ Open Sport Exerc Med 7(1): e000960. doi: 10.1136/bmjsem-2020-000960.

57.

Tabachnick

Fidell

(2013) Using Multivariate Statistics. Boston, MA: Allyn & Baco.

58.

Taetzsch

Roberts

Gilhooly

, et al. (2020) Food cravings: associations with dietary intake and metabolic health. Appetite 152: 104711. DOI: 10.1016/j.appet.2020.104711

59.

Teixeira

Carraça

Marques

, et al. (2015) Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Medicine 13(1): 16–84. DOI: 10.1186/s12916-015-0323-6.

60.

Thomson

Martin

Long

, et al. (2022) A qualitative exploration of weight management during COVID-19. Clinical Obesity 12(3): e12512. DOI: 10.1111/cob.12512.

61.

Tice

Bratslavsky

(2000) Giving in to feel good: the place of emotion regulation in the context of general self-control. Psychological Inquiry 11(3): 149–159. DOI: 10.1207/S15327965PLI1103_03.

62.

Townsend

Kyle

Stanford

(2020) Commentary: COVID-19 and obesity: exploring biologic vulnerabilities, structural disparities, and weight stigma. Metabolism: Clinical and Experimental 110: 154316. DOI: 10.1016/j.metabol.2020.154316.

63.

Wagner

Heatherton

(2014) Self-regulation and its failure: the seven deadly threats to self-regulation. APA Handbook of Personality and Social Psychology 1: 805–842. DOI: 10.1037/14341-026

64.

Wales

(2020) Welsh Index of Multiple Deprivation. Santa Monica, CA: Hentet.

65.

Weingarten

Elston

(1990) The phenomenology of food cravings. Appetite 15(3): 231–246. DOI: 10.1016/0195-6663(90)90023-2.

66.

Westenhoefer

(1991) Dietary restraint and disinhibition: is restraint a homogeneous construct? Appetite 16(1): 45–55.

67.

Westenhoefer

Stunkard

Pudel

(1999) Validation of the flexible and rigid control dimensions of dietary restraint. International Journal of Eating Disorders, 26(1), 53–64.

68.

Westenhoefer

Engel

Holst

, et al. (2013) Cognitive and weight-related correlates of flexible and rigid restrained eating behaviour. Eating Behaviors 14(1): 69–72. DOI: 10.1016/j.eatbeh.2012.10.015.

69.

Witt

Katterman

Lowe

(2013) Assessing the three types of dieting in the three-factor model of dieting. The dieting and weight history questionnaire. Appetite 63: 24–30. DOI: 10.1016/j.appet.2012.11.022.

70.

Shunqing

Yuanyuan

(2020) Beware of the second wave of COVID-19. Lancet 395(10233): 1321–1322. doi: 10.1016/S0140-6736(20)30845-X. 32277876

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.41 MB