Eating behavior tendencies among Finnish adults in relation to previous weight loss attempts
Author names
Faranak Halalia, Anja Lapveteläinena, Leila Karhunena, Teuvo Kantanenb
Author affiliations
aInstitute of Public Health and Clinical Nutrition, University of Eastern Finland (UEF), Kuopio, Finland
bDepartment of Business, Faculty of Social Sciences and Business Studies, University of Eastern Finland (UEF), Kuopio, Finland
Name, Mailing address, email address and telephone number of the corresponding author
Name: Faranak Halali
Mailing address: Institute of Public Health and Clinical Nutrition, University of Eastern Finland,
PO Box 1627, FI-70211 Kuopio, Finland Email address: Faranak.halali@uef.fi Telephone No: 00358465244592
1 Introduction 1
Despite obesity rates leveling off during the past decade in developed countries, global levels 2
of obesity are still increasing (Chooi, Ding, & Magkos, 2019). In 2016, about 39% of adults 3
worldwide were overweight and 13% were obese (WHO, 2018). However, most of the 4
individuals who lose weight regain the lost weight within the next few years (Byrne, Cooper, 5
& Fairburn, 2003). Nevertheless, trying to lose weight is common; a recent systematic review 6
reported that 42% of adults from general populations had reported trying to lose weight in the 7
past year (Santos, Sniehotta, Marques, Carraça, & Teixeira, 2017). In Finland, 35% of women 8
and 24% of men aged between 15 and 64 have reported trying to lose weight during the past 9
year (Helldán & Helakorpi, 2015). Existing evidence has shown a greater number of weight 10
loss attempts is associated with unfavorable health behaviors (Blake et al., 2013) and higher 11
body mass index (BMI) (Pietiläinen, Saarni, Kaprio, & Rissanen, 2012; Raynor, Jeffery, 12
Ruggiero, Clark, & Delahanty, 2008; Sainsbury et al., 2019). Prevalence of binge eating 13
disorder (BED) among women has been reported to increase with increasing number of 14
previous intentional weight loss attempts (Giusti, Héraïef, Gaillard, & Burckhardt, 2004).
15
Better understanding of different eating behavior tendencies related to weight management 16
could contribute to more successful prevention and treatment of obesity (Jáuregui-Lobera, 17
García-Cruz, Carbonero-Carreño, Magallares, & Ruiz-Prieto, 2014). Weight gain and failure 18
in weight loss maintenance have been associated with maladaptive eating-related behaviors 19
(Hou et al., 2011; Koenders & Van Strien, 2011). In a study among healthy young adults, 20
uncontrolled eating was higher in obese than in lean individuals (Calvo, Galioto, Gunstad, &
21
Spitznagel, 2014). Uncontrolled eating has been positively associated with energy intake and 22
central adiposity (Jaakkola, Hakala, Isolauri, Poussa, & Laitinen, 2013). Associations of 23
emotional eating with higher snacking frequency (O’Connor, Jones, Conner, McMillan, &
24
Ferguson, 2008), higher consumption of sweet foods (Konttinen, Männistö, Sarlio- 25
Lähteenkorva, Silventoinen, & Haukkala, 2010) and more depressive symptoms (Clum, Rice, 26
Broussard, Johnson, & Webber, 2014) have also been reported. On the other hand, cognitive 27
eating restraint has yielded contradictory results and has been associated with both healthy 28
behaviors such as higher consumption of low-fat products and green vegetables (Jaakkola et 29
al., 2013) and unhealthy characteristics such as weight gain (De Lauzon-Guillain et al., 2006) 30
and greater number of attempts to lose weight (Walsh, White, & Greaney, 2009).
31
Thus, the aim of this study was to investigate whether the eating behavior tendencies differ 32
among Finnish adults who have made a different number of attempts to lose weight during 33
their lifetime, i.e., 1-2 attempts, ≥ 3 attempts, continuous attempts, compared to those with no 34
previous weight loss attempts. Our hypothesis was that increasing number of lifetime attempts 35
to lose weight would be associated with more cognitive restraint, uncontrolled eating and 36
emotional eating compared to those with no previous weight loss attempts.
37
2 Material and methods 38
2.1 Participants 39
This study was a part of the KULUMA project (Consumers at the weight management market), 40
the details of which are reported elsewhere (Halali et al., 2018). For the present study, 20,000 41
customers of the consumer register of K Group (www.kesko.fi) received an invitation email 42
asking them to take part in a web-based survey. Individuals were eligible to participate in the 43
survey if they shopped for groceries at least once a month and neither they nor their family 44
members were studying/working in a food-related sector. The research was conducted 45
according to the general guidelines of the Committee on Research Ethics of the University of 46
Eastern Finland and participants were informed that the data would be confidential and used 47
only for research purposes. Participants completed a background questionnaire and the Three- 48
Factor Eating Questionnaire (TFEQ-R18) (see details below). Ten 20-euro gift cards were 49
raffled among the respondents as incentive. A total of 1985 individuals completed the survey 50
within a given 2-week time period. Overall, 1679 participants had complete data for all our 51
variables of interest (i.e., background information and eating behaviors) and were included in 52
the analyses.
53
2.2 Measures 54
2.2.1 Background information 55
A self-report questionnaire collected information on participants’ age, gender, education, 56
occupation, weight, height, current weight satisfaction, number of lifetime attempts to lose 57
weight, and current intention to lose weight. BMI was calculated as weight (kg) divided by 58
height in meters squared. Data on education level were categorized into three categories of 59
basic, middle and high education levels. Occupation was categorized into two categories of 60
working/studying and house mom or house dad/unemployed/retired. Satisfaction with current 61
weight was measured by asking participants ‘Are you satisfied with your current weight?
62
(Yes/No, I wish to lose weight/No, I wish to gain weight)’. Due to the aims of the present study, 63
those who had wished to gain weight (n=21) were excluded. Number of lifetime attempts to 64
lose weight was determined by the question ‘Have you tried to lose weight during your 65
lifetime? (No/No, but I have been trying to keep my weight stable/Yes, 1-2 times/Yes, ≥ 3 66
times/Yes, continuously)’. Participants declared their current intention to lose weight by 67
answering the question “Are you currently trying to lose weight? (Yes/No).
68
2.2.2 Eating behavior tendencies 69
We used TFEQ-R18 to measure eating behavior tendencies cognitive restraint, uncontrolled 70
eating and emotional eating (Karlsson, Persson, Sjöström, & Sullivan, 2000). Cognitive 71
restraint refers to relying on conscious restriction of food intake for weight control purposes.
72
Uncontrolled eating refers to loss of control over eating and emotional eating refers to the 73
tendency to eat as a response to negative emotions (Karlsson et al., 2000). In the TFEQ-R18, 74
6 items measure cognitive restraint, 9 items measure uncontrolled eating and 3 items measure 75
emotional eating. Item 1 (related to the uncontrolled eating subscale) was modified from 76
‘When I smell a sizzling steak or a juicy piece of meat …’ to a more general version ‘When 77
I smell a delicious food …’ since steak and meat are not necessarily the most tempting foods 78
to all respondents (Kosonen et al., 2005; Pentikäinen, Arvola, Karhunen, & Pennanen, 2018).
79
All items of the questionnaire employed a 4-point scale. Item 18 (related to the cognitive 80
restraint subscale) was an exception and employed an 8-point scale. However, the scores of 81
this item was then recoded into a 4-point scale, i.e., the 1-2 scores were coded 1, 3-4 scores 82
were coded 2, 5-6 scores were coded 3 and 7-8 scores were coded 4 (Karlsson et al., 2000). To 83
calculate the final scores of the subscales, items 1-13 were reverse-coded so that higher scores 84
indicated a higher level of eating behavior tendencies. The raw score for each of the three scales 85
of eating behaviors was the sum of its items (Karlsson et al., 2000; Stunkard & Messick, 1985).
86
These raw scores were then transformed to a 0-100 scale [((raw score _ lowest possible raw 87
score)/possible raw score range)*100] (de Lauzon et al., 2004).
88
2.3 Reliability analysis 89
For the TFEQ-R18, Cronbach's alpha was calculated to evaluate its internal consistency.
90
Values higher than 0.7 were considered acceptable (Cortina, 1993). Calculation of the 91
Cronbach’s alpha suggested three items of the questionnaire (all related to the cognitive 92
restraint subscale) be deleted in order to improve the estimate value. These items were: ‘How 93
frequently do you avoid ‘stocking up’ on tempting foods?’ (item 15), ‘How likely are you to 94
consciously eat less than you want?’ (item 16), and ‘On a scale of 1 to 8, where 1 means no 95
restraint in eating (eating whatever you want, whenever you want it) and 8 means total 96
restraint (constantly limiting food intake and never ‘giving in’), what number would you give 97
yourself?’ (item 18). After deleting these items, the Cronbach’s alpha value of the cognitive 98
restraint subscale improved from 0.50 to 0.73. The original uncontrolled eating and emotional 99
eating subscales had a Cronbach’s alpha of 0.83 and 0.89, respectively. The revised version of 100
the TFEQ-R18 was designated as TFEQ-R15.
101
2.4 Confirmatory factor analysis (CFA) 102
We performed confirmatory factor analysis (CFA) using maximum likelihood estimation to 103
study fit of the TFEQ-R18 as well as the revised TFEQ-R15 in our study population (IBM 104
SPSS AMOS 26). Relative chi-square (X2/df) (Wheaton, Muthen, Alwin, & Summers, 1977), 105
comparative fit index (CFI) (Hu & Bentler, 1999), root mean square error of approximation 106
(RMSEA) (Hu & Bentler, 1999) and standardized root mean square residual (SRMR) were 107
used to determine the model fit (Hu & Bentler, 1999). Cut-off points for these indices are 108
presented in Table 1. Our results were in line with the previous studies (Cappelleri et al., 2009;
109
Chong et al., 2016; Pentikäinen et al., 2018) and supported the fit of the TFEQ-R15. Table 1 110
presents the fit indices for both the TFEQ-R18 and the TFEQ-R15.
111
Table 1. Fit indices of the confirmatory factor analysis of the TFEQ 112
X2/dfa CFIb RMSEAc SRMRd
TFEQ-R18 7.95 0.92 0.06 0.05
TFEQ-R15 4.6 0.97 0.04 0.02
Cut-off points ≤ 5 ≥ 0.95 ≤ 0.06 ≤ 0.08
TFEQ: Three-Factor Eating Questionnaire 113
aRelative Chi-square; bComparative fit index; cRoot mean square error of approximation; dStandardised 114
root mean square residual.
115 116
2.5 Statistical analyses 117
We used SPSS version 25 (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY). A 118
p-value < 0.05 was considered statistically significant. Although according to normality tests 119
data was not normally distributed, we used parametric analyses because our sample size was 120
large enough to apply the central limit theorem (Moran, Sui, Cramp, & Dodd, 2012; Upton &
121
Cook, 2014). In order to examine the differences in background and weight-related 122
characteristics across the categories of weight loss attempts, we used Analysis of Variance 123
(ANOVA) for continuous variables and Chi-squared test for categorical variables. Difference 124
in BMI was assessed by Analysis of Covariance (ANCOVA) as well, in which age and gender 125
were adjusted for as the confounding variables. In the subsequent follow-up tests for pairwise 126
comparisons, the category ‘No weight loss attempts’ was considered as the reference category 127
and all other categories were compared against it. We used Multivariate Analysis of Covariance 128
(MANCOVA) in order to assess the difference in eating behavior tendencies, i.e., cognitive 129
restraint, uncontrolled eating and emotional eating among the categories of weight loss 130
attempts. In MANCOVA the variables age, gender and BMI were considered as the covariates 131
and the category ‘No weight loss attempts’ was considered as the reference category in pairwise 132
comparisons.
133
3 Results 134
3.1 Background and weight-related characteristics 135
Table 2 represents the background and weight-related characteristics of study participants 136
across the five categories of lifetime attempts to lose weight. Significant differences in gender 137
distribution, BMI, weight satisfaction, and current intention to lose weight were observed 138
between the categories. Differences in BMI remained the same after adjusting for confounding 139
variables, i.e., age and gender. Pairwise comparisons showed that compared to the individuals 140
with no lifetime attempts to lose weight, all four categories of weight loss attempts were less 141
satisfied with their current weight and had more current intentions to lose weight. Individuals 142
who had attempted weight loss 1-2 times, ≥ 3 times or continuously had significantly higher 143
BMI than those with no previous weight loss attempts. Among women, most of them (32%) 144
had attempted weight loss for ≥ 3 times whereas most of men (25.7%) had been trying to keep 145
their weight stable during their lifetime.
146
147
Table 2. Background and weight-related characteristics of study participants in five 148
categories of number of weight loss attempts (n=1679) 149
Weight loss attempts attemptsNo a
(n=132)
Keep stable
(n=318) 1-2 times
(n=449) ≥ 3 times (n=499)
Continuous attempts
(n=281) P* Age (years),
mean (sd§) 46.4 (12.8) 45.7 (14.0)
NS 43.1 (12.1)
NS 46.6 (11.8)
NS 47.1 (13.9)
NS <0.001 Gender, n (%)
Females
Males 71 (5.6)
61 (14.7) 211 (16.7) 107 (25.8)
*
347 (27.5) 102 (24.6)
**
406 (32.1) 93 (22.4)
**
229 (18.1) 52 (12.5)
** <0.001 BMI¶ (kg/m2),
mean (sd) 24.0 (3.8) 24.6 (3.8)
NS 26.3 (4.1)
** 27.8 (4.9)
** 29.6 (5.7)
** <0.001 Weight
satisfaction, n (%) Yes No, wish to lose weight
89 (19.1)
43 (3.5) 167 (35.9) 151 (12.4)
*
110 (23.7) 339 (27.9)
**
86 (18.5) 413 (34.0)
**
13 (2.8) 268 (22.1)
** <0.001 Current intention
to lose weight, n (%)
No Yes 115 (16.9)
17 (1.7) 232 (34.1) 86 (8.6)
*
182 (26.7) 267 (26.8)
**
128 (18.8) 371 (37.2)
**
24 (3.5) 257 (25.8)
** <0.001 Education, n (%)
Basic Middle High
60 (10.6) 40 (6.8) 32 (6.1)
106 (18.7) 111 (18.8) 101 (19.3)
152 (26.9) 154 (26.1) 143 (27.3)
146 (25.8) 189 (32.1) 164 (31.3)
102 (18.0) 95 (16.1)
84 (16.0) 0.086
Occupation, n (%) Working/student House mom/dad, unemployed, retired
93 (7.6)
39 (8.7) 236 (19.2)
82 (18.2) 326 (26.5)
123 (27.3) 377 (30.7)
122 (27.1) 197 (16.0)
84 (18.7) 0.47
*Analysis of variance (ANOVA) for continuous variables and Chi-squared test for categorical variables 150 §Standard deviation
151 ¶Body Mass Index. Difference in BMI is adjusted for age and gender using Analysis of Covariance 152 (ANCOVA)
153 Pairwise comparisons: a=reference category; NS: Not Significant; *P<0.05; **P<0.001 154 155
156
3.2 Eating behavior tendencies 157
Table 3 presents the means for eating behavior tendencies across five categories of lifetime 158
attempts to lose weight. The categories differed significantly in cognitive restraint and 159
uncontrolled eating. Cognitive restraint was significantly higher among individuals who had 160
attempted to lose weight ≥ 3 times than those in the reference category. In addition, individuals 161
in the former category reported more emotional eating that those with no attempts to lose 162
weight. Uncontrolled eating increased linearly across the categories of weight loss attempts, 163
although none of the categories differed significantly from the reference category.
164 165
Table 3. Eating behavior tendencies (TFEQ-R15) of study participants in five categories of 166
lifetime attempts to lose weight (n=1679) 167
Weight loss attempts No attemptsa
(n=132) Keep stable
(n=318) 1-2 times
(n=449) ≥ 3 times (n=499)
Continuous attempts
(n=281) P* Cognitive
restraint 45.9 (25.8) 47.2 (22.7)
NS 48.4 (22.6)
NS 52.3 (23.1)
** 48.6 (23.6)
NS 0.005
Uncontrolled
eating 32.8 (15.1) 32.4 (16.8)
NS 35.1 (16.5)
NS 36.5 (17.1)
NS 37.7 (18.6)
NS 0.027
Emotional
eating 34.4 (28.3) 35.7 (27.4)
NS 39.6 (27.3)
NS 41.0 (27.6)
* 40.0 (29.2)
NS 0.133
Values are reported as mean (standard deviation)
168 *Multivariate Analysis of Covariance (MANCOVA). Age, gender and BMI were the covariates.
169 Pairwise comparisons: a=reference category; NS: Not Significant; *P<0.05; **P<0.01 170 171
4 Discussion 172
The present study investigated eating behavior tendencies among a sample of Finnish adults to 173
find out whether there is a relationship between number of lifetime attempts to lose weight and 174
eating behavior tendencies, i.e., cognitive restraint, uncontrolled eating and emotional eating.
175
Findings of this study supported our hypothesis in terms of more cognitive restraint, 176
uncontrolled eating and emotional eating among those with either ≥ 3 times or continuous 177
weight loss attempts compared to those with no or less previous weight loss attempts. In 178
addition, BMI increased linearly along with the number of weight loss attempts, being the 179
highest among those who reported continuous attempts to lose weight. Because of the cross- 180
sectional nature of our study, we cannot determine any causal relationship between the number 181
of lifetime attempts to lose weight and BMI. However, it is likely that individuals with a higher 182
BMI would intend to lose weight more frequently. On the other hand, frequent weight loss 183
attempts can lead to a higher BMI. Pietiläinen et al. (2012) reported BMI as a significant 184
determinant of future intentional weight loss attempts. They also reported a longitudinal causal 185
relationship, i.e., normal-weight individuals with greater number of future weight loss episodes 186
were more likely to become overweight later (Pietiläinen et al., 2012). Furthermore, 187
preoccupation with weight could encourage using unhealthy behaviors for weight loss such as 188
meal skipping, cigarette smoking or taking diet pills (Sharpe et al., 2018). Using these 189
unhealthy behaviors could further increase the likelihood of weight regain over time (Neumark- 190
Sztainer, Wall, Story, & Standish, 2012).
191
The trend of more uncontrolled eating and emotional eating as well as higher BMI among those 192
with more weight loss attempts than those with no or less previous attempts to lose weight was 193
supported by previous studies. Obese individuals have reported more uncontrolled eating and 194
emotional eating compared to normal-weight individuals (Brytek-Matera, Rogoza, &
195
Czepczor-Bernat, 2017). Moreover, uncontrolled eating and emotional eating have been 196
associated with several unhealthy features such as more central obesity and greater energy and 197
sucrose intake (Jaakkola et al., 2013), greater consumption of sweet foods among adults 198
(Konttinen et al., 2010), larger meal portion sizes (Spence et al., 2016) and less success in 199
weight loss attempts (Canetti, Berry, & Elizur, 2009). Similarly, disinhibition of eating, which 200
includes both uncontrolled eating and emotional eating (Stunkard & Messick, 1985), has been 201
associated with less weight satisfaction (Walsh et al., 2009) and is likely to increase with 202
frequent dieting (Lowe, 1993).
203
Previous literature concerning the association between cognitive restraint and weight 204
loss/weight loss maintenance is contradictory. Greater cognitive restraint has been associated 205
with higher BMI among adult women (Anglé et al., 2009), and with higher fat mass among 206
normal-weight individuals (De Lauzon-Guillain et al., 2006). On the other hand, intensive 207
lifestyle intervention increased cognitive restraint and this increment was associated with 208
greater weight loss (Karhunen et al., 2012; Keränen, Strengell, Savolainen, & Laitinen, 2011;
209
Neve, Morgan, & Collins, 2012; Nurkkala et al., 2015). It has been suggested that different 210
forms of eating restraint, i.e. rigid and flexible restraint, could at least partially explain the 211
contradictory findings (Nurkkala et al., 2015). It seems that interventions enhancing flexible 212
eating restraint could lead to better weight management (Karhunen et al., 2012; Nurkkala et 213
al., 2015; Teixeira et al., 2010), whereas rigid restraint has been associated with less weight 214
loss, higher BMI and more body fat (Provencher, Drapeau, Tremblay, Després, & Lemieux, 215
2003; Teixeira et al., 2010).
216
Because the TFEQ-R18 does not measure separately the rigid and flexible forms of cognitive 217
restraint, we could not ascertain either an unfavorable or a favorable role for cognitive restraint 218
in weight management. However, given the greater BMI among those with greater number of 219
weight loss attempts in the present study, and considering previously reported associations 220
between greater number of weight loss attempts and higher BMI (Anglé et al., 2009; Halali et 221
al., 2018), it seems that our finding refers to a more rigid rather than flexible restraint of eating 222
among those with more lifetime attempts to lose weight.
223
In addition, interaction of cognitive restraint with other eating behavior tendencies, i.e., 224
uncontrolled eating and emotional eating should be taken into account (Pentikäinen et al., 225
2018). There is evidence that cognitive restraint could benefit weight management if 226
accompanied by low uncontrolled eating and low emotional eating (Keränen et al., 2009). As 227
a further support for this, in a lifestyle intervention study (Nurkkala et al., 2015), uncontrolled 228
eating decreased and cognitive restraint increased among successful dieters (who had 229
maintained ≥5% of their original weight) (Nurkkala et al., 2015). Although in our study we 230
cannot conclude specific causal relationship between the eating behaviour tendencies and 231
repeated weight loss attempts, these findings support the view that lifestyle counseling and 232
weight management programs should concentrate on improving eating behavior, i.e., 233
enhancing flexible cognitive restraint and reducing uncontrolled eating and emotional eating 234
in order to increase the chance of achieving sustained outcomes. This goal could be achieved 235
by helping individuals in finding effective ways of coping with every-day life circumstances 236
and negative emotions.
237
The Three-Factor Eating Questionnaire (TFEQ-51) (Stunkard & Messick, 1985) and its revised 238
shortened version, the TFEQ-R18 (Karlsson et al., 2000) are the two most frequently used tools 239
for measuring eating behavior. Although the TFEQ-R18 was primarily developed for studying 240
eating behavior among obese individuals, its validity in other populations such as general 241
populations and young females has been confirmed (Anglé et al., 2009; de Lauzon et al., 2004).
242
However, we found that the cognitive restraint subscale of the TFEQ-R18 did not achieve 243
acceptable internal consistency and needed refinement. This finding was consistent with the 244
previous studies reporting a similar problem (Bryant et al., 2018; Cappelleri et al., 2009; Chong 245
et al., 2016; Pentikäinen et al., 2018). Since the reliability of the restraint subscale of the TFEQ 246
has varied between populations (de Lauzon et al., 2004; Elfhag & Linné, 2005; Karlsson et al., 247
2000; Tholin, Rasmussen, Tynelius, & Karlsson, 2005), cultural differences might play a 248
contributing role (Cappelleri et al., 2009).
249
This study has some limitations. First, all information collected were self-reported and are 250
subject to bias. Second, due to the cross-sectional design of the study we cannot draw any 251
cause-effect conclusions. Moreover, as already mentioned, women comprised 75% of the 252
sample, which could have affected the findings, although this was taken into account in the 253
statistical analyses. Further research should address these issues in more depth, especially in a 254
prospective setting.
255
5 Conclusions 256
Individuals with greater number of lifetime attempts to lose weight had more cognitive 257
restraint, uncontrolled eating, emotional eating and higher BMI than those who had not tried 258
to lose weight during their lifetime. Therefore, decreasing uncontrolled eating, decreasing 259
emotional eating when experiencing negative emotions, and enhancing flexible restraint of 260
eating should receive more emphasis to support successful weight management.
261
Acknowledgments 262
We are thankful to MSc Kaisa Pulkkinen for her great contribution to data management and 263
analysis.
264
Funding 265
This research was supported by ‘The Finnish Funding Agency for Innovation’ (decision 266
numbers 40182/09 and 457/09) and Academy of Finland (decision number 286028). The 267
funding sources had no role in study design, data collection, data analysis and interpretation, 268
writing of the report, and in the decision to submit the article for publication.
269
Conflicts of interest 270
The authors declare that they have no conflicts of interest.
271 272
References 273
Anglé, S., Engblom, J., Eriksson, T., Kautiainen, S., Saha, M. T., Lindfors, P., … Rimpelä, A.
274
(2009). Three factor eating questionnaire-R18 as a measure of cognitive restraint, 275
uncontrolled eating and emotional eating in a sample of young Finnish females.
276
International Journal of Behavioral Nutrition and Physical Activity, 6.
277
https://doi.org/10.1186/1479-5868-6-41 278
Bryant, E. J., Thivel, D., Chaput, J. P., Drapeau, V., Blundell, J. E., & King, N. A. (2018).
279
Development and validation of the Child Three-Factor Eating Questionnaire 280
(CTFEQr17). Public Health Nutrition, 21(14), 2558–2567.
281
https://doi.org/10.1017/S1368980018001210 282
Brytek-Matera, A., Rogoza, R., & Czepczor-Bernat, K. (2017). The Three-Factor Eating 283
Questionnaire-R18 Polish version: factor structure analysis among normal weight and 284
obese adult women. Archives of Psychiatry and Psychotherapy, 19(3), 81–90.
285
https://doi.org/10.12740/app/76342 286
Byrne, S., Cooper, Z., & Fairburn, C. (2003). Weight maintenance and relapse in obesity: A 287
qualitative study. International Journal of Obesity, 27(8), 955–962.
288
https://doi.org/10.1038/sj.ijo.0802305 289
Calvo, D., Galioto, R., Gunstad, J., & Spitznagel, M. B. (2014). Uncontrolled eating is 290
associated with reduced executive functioning. Clinical Obesity, 4(3), 172–179.
291
https://doi.org/10.1111/cob.12058 292
Canetti, L., Berry, E. M., & Elizur, Y. (2009). Psychosocial predictors of weight loss and 293
psychological adjustment following bariatric surgery and a weight-loss program: The 294
mediating role of emotional eating. International Journal of Eating Disorders, 42(2), 295
109–117. https://doi.org/10.1002/eat.20592 296
Cappelleri, J. C., Bushmakin, A. G., Gerber, R. A., Leidy, N. K., Sexton, C. C., Lowe, M. R., 297
& Karlsson, J. (2009). Psychometric analysis of the Three-Factor Eating Questionnaire- 298
R21: Results from a large diverse sample of obese and non-obese participants.
299
International Journal of Obesity, 33(6), 611–620. https://doi.org/10.1038/ijo.2009.74 300
Chong, M. F. F., Ayob, M. N. im M., Chong, K. J., Tai, E. S., Khoo, C. M., Leow, M. K. S., 301
… Khoo, E. Y. H. (2016). Psychometric analysis of an eating behaviour questionnaire for 302
an overweight and obese Chinese population in Singapore. Appetite, 101, 119–124.
303
https://doi.org/10.1016/j.appet.2016.03.005 304
Chooi, Y. C., Ding, C., & Magkos, F. (2019). The epidemiology of obesity. Metabolism, 92, 305
6–10. https://doi.org/10.1016/J.METABOL.2018.09.005 306
Clum, G. A., Rice, J. C., Broussard, M., Johnson, C. C., & Webber, L. S. (2014). Associations 307
between depressive symptoms, self-efficacy, eating styles, exercise and body mass index 308
in women. Journal of Behavioral Medicine, 37(4), 577–586.
309
https://doi.org/10.1007/s10865-013-9526-5 310
Cortina, J. M. (1993). What Is Coefficient Alpha? An Examination of Theory and Applications.
311
Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021- 312
9010.78.1.98 313
De Lauzon-Guillain, B., Basdevant, A., Romon, M., Karlsson, J., Borys, J. M., & Charles, M.
314
A. (2006). Is restrained eating a risk factor for weight gain in a general population?
315
American Journal of Clinical Nutrition, 83(1), 132–138.
316
de Lauzon, B., Romon, M., Deschamps, V., Lafay, L., Borys, J.-M., Karlsson, J., … Fleurbaix 317
Laventie Ville Sante Study Group. (2004). The Three-Factor Eating Questionnaire-R18 is 318
able to distinguish among different eating patterns in a general population. The Journal of 319
Nutrition, 134(9), 2372–80. https://doi.org/10.1093/jn/134.9.2372 320
Elfhag, K., & Linné, Y. (2005). Gender differences in associations of eating pathology between 321
mothers and their adolescent offspring. Obesity Research, 13(6), 1070–1076.
322
https://doi.org/10.1038/oby.2005.125 323
Giusti, V., Héraïef, E., Gaillard, R. C., & Burckhardt, P. (2004). Predictive factors of binge 324
eating disorder in women searching to lose weight. Eating and Weight Disorders, 9(1), 325
44–49. https://doi.org/10.1007/BF03325044 326
Halali, F., Lapveteläinen, A., Karhunen, L., Saarela, A. M., Lappalainen, R., & Kantanen, T.
327
(2018). Motivators, barriers and strategies of weight management: A cross-sectional study 328
among Finnish adults. Eating Behaviors, 31, 80–87.
329
https://doi.org/10.1016/j.eatbeh.2018.08.009 330
Helldán, A., & Helakorpi, S. (2015). Suomalaisen aikuisväestön terveyskäyttäytyminen ja 331
terveys, kevät 2014.
332
Hou, R., Mogg, K., Bradley, B. P., Moss-Morris, R., Peveler, R., & Roefs, A. (2011). External 333
eating, impulsivity and attentional bias to food cues. Appetite, 56(2), 424–427.
334
https://doi.org/10.1016/j.appet.2011.01.019 335
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure 336
analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 337
6(1), 1–55. https://doi.org/10.1080/10705519909540118 338
Jaakkola, J., Hakala, P., Isolauri, E., Poussa, T., & Laitinen, K. (2013). Eating behavior 339
influences diet, weight, and central obesity in women after pregnancy. Nutrition, 29(10), 340
1209–1213. https://doi.org/10.1016/j.nut.2013.03.008 341
Jáuregui-Lobera, I., García-Cruz, P., Carbonero-Carreño, R., Magallares, A., & Ruiz-Prieto, I.
342
(2014). Psychometric properties of Spanish version of the three-factor eating 343
questionnaire-R18 (Tfeq-Sp) and its relationship with some eating- and body image- 344
related variables. Nutrients, 6(12), 5619–5635. https://doi.org/10.3390/nu6125619 345
Karhunen, L., Lyly, M., Lapveteläinen, A., Kolehmainen, M., Laaksonen, D. E., Lähteenmäki, 346
L., & Poutanen, K. (2012). Psychobehavioural factors are more strongly associated with 347
successful weight management than predetermined satiety effect or other characteristics 348
of diet. Journal of Obesity, 2012. https://doi.org/10.1155/2012/274068 349
Karlsson, J., Persson, L. O., Sjöström, L., & Sullivan, M. (2000). Psychometric properties and 350
factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and 351
women. Results from the Swedish Obese Subjects (SOS) study. International Journal of 352
Obesity, 24(12), 1715–1725. https://doi.org/10.1038/sj.ijo.0801442 353
Keränen, A. M., Savolainen, M. J., Reponen, A. H., Kujari, M. L., Lindeman, S. M., Bloigu, 354
R. S., & Laitinen, J. H. (2009). The effect of eating behavior on weight loss and 355
maintenance during a lifestyle intervention. Preventive Medicine, 49(1), 32–38.
356
https://doi.org/10.1016/j.ypmed.2009.04.011 357
Keränen, A. M., Strengell, K., Savolainen, M. J., & Laitinen, J. H. (2011). Effect of weight 358
loss intervention on the association between eating behaviour measured by TFEQ-18 and 359
dietary intake in adults. Appetite, 56(1), 156–162.
360
https://doi.org/10.1016/j.appet.2010.10.004 361
Koenders, P. G., & Van Strien, T. (2011). Emotional eating, rather than lifestyle behavior, 362
drives weight gain in a prospective study in 1562 employees. Journal of Occupational 363
and Environmental Medicine, 53(11), 1287–1293.
364
https://doi.org/10.1097/JOM.0b013e31823078a2 365
Konttinen, H., Männistö, S., Sarlio-Lähteenkorva, S., Silventoinen, K., & Haukkala, A. (2010).
366
Emotional eating, depressive symptoms and self-reported food consumption. A 367
population-based study. Appetite, 54(3), 473–479.
368
https://doi.org/10.1016/j.appet.2010.01.014 369
Kosonen, H., Rimpelä, A., Rauma, A. L., Väisänen, P., Pere, L., Virtanen, S. M., & Rimpelä, 370
M. (2005). Consumption of special diet among Finnish adolescents in 1979-2001:
371
Repeated national cross-sectional surveys. Sozial- Und Praventivmedizin, 50(3), 142–150.
372
https://doi.org/10.1007/s00038-005-3167-6 373
Lowe, M. R. (1993). The effects of dieting on eating behavior: A three-factor model.
374
Psychological Bulletin, 114(1), 100–121. https://doi.org/10.1037/0033-2909.114.1.100 375
Moran, L., Sui, Z., Cramp, C., & Dodd, J. (2012). A decrease in diet quality occurs during 376
pregnancy in overweight and obese women which is maintained post-partum. Obesity 377
Research & Clinical Practice, 6, 84. https://doi.org/10.1016/j.orcp.2012.08.173 378
Neumark-Sztainer, D., Wall, M., Story, M., & Standish, A. R. (2012). Dieting and unhealthy 379
weight control behaviors during adolescence: Associations with 10-year changes in body 380
mass index. Journal of Adolescent Health, 50(1), 80–86.
381
https://doi.org/10.1016/j.jadohealth.2011.05.010 382
Neve, M. J., Morgan, P. J., & Collins, C. E. (2012). Behavioural factors related with successful 383
weight loss 15 months post-enrolment in a commercial web-based weight-loss 384
programme. Public Health Nutrition, 15(7), 1299–1309.
385
https://doi.org/10.1017/S1368980011003090 386
Nurkkala, M., Kaikkonen, K., Vanhala, M. L., Karhunen, L., Keränen, A. M., & Korpelainen, 387
R. (2015). Lifestyle intervention has a beneficial effect on eating behavior and long-term 388
weight loss in obese adults. Eating Behaviors, 18, 179–185.
389
https://doi.org/10.1016/j.eatbeh.2015.05.009 390
O’Connor, D. B., Jones, F., Conner, M., McMillan, B., & Ferguson, E. (2008). Effects of Daily 391
Hassles and Eating Style on Eating Behavior. Health Psychology, 27(1 SUPPL.).
392
https://doi.org/10.1037/0278-6133.27.1.S20 393
Pentikäinen, S., Arvola, A., Karhunen, L., & Pennanen, K. (2018). Easy-going, rational, 394
susceptible and struggling eaters: A segmentation study based on eating behaviour 395
tendencies. Appetite, 120, 212–221. https://doi.org/10.1016/j.appet.2017.09.001 396
Pietiläinen, K. H., Saarni, S. E., Kaprio, J., & Rissanen, a. (2012). Does dieting make you fat?
397
A twin study. International Journal of Obesity, 36(3), 456–464.
398
https://doi.org/10.1038/ijo.2011.160 399
Provencher, V., Drapeau, V., Tremblay, A., Després, J. P., & Lemieux, S. (2003). Eating 400
behaviors and indexes of body composition in men and women from the Québec family 401
study. Obesity Research, 11(6), 783–792. https://doi.org/10.1038/oby.2003.109 402
Raynor, H. A., Jeffery, R. W., Ruggiero, A. M., Clark, J. M., & Delahanty, L. M. (2008).
403
Weight loss strategies associated with BMI in overweight adults with type 2 diabetes at 404
entry into the look ahead (action for health in diabetes) trial. Diabetes Care, 31(7), 1299–
405
1304. https://doi.org/10.2337/dc07-2295 406
Sainsbury, K., Evans, E. H., Pedersen, S., Marques, M. M., Teixeira, P. J., Lähteenmäki, L., 407
… Sniehotta, F. F. (2019). Attribution of weight regain to emotional reasons amongst 408
European adults with overweight and obesity who regained weight following a weight 409
loss attempt. Eating and Weight Disorders, 24(2), 351–361.
410
https://doi.org/10.1007/s40519-018-0487-0 411
Santos, I., Sniehotta, F. F., Marques, M. M., Carraça, E. V., & Teixeira, P. J. (2017). Prevalence 412
of personal weight control attempts in adults: a systematic review and meta-analysis.
413
Obesity Reviews. https://doi.org/10.1111/obr.12466 414
Sharpe, H., Griffiths, S., Choo, T. H., Eisenberg, M. E., Mitchison, D., Wall, M., & Neumark- 415
Sztainer, D. (2018). The relative importance of dissatisfaction, overvaluation and 416
preoccupation with weight and shape for predicting onset of disordered eating behaviors 417
and depressive symptoms over 15 years. International Journal of Eating Disorders, 418
51(10), 1168–1175. https://doi.org/10.1002/eat.22936 419
Spence, M., Stancu, V., Dean, M., Livingstone, M. B. E., Gibney, E. R., & Lähteenmäki, L.
420
(2016). Are food-related perceptions associated with meal portion size decisions? A cross- 421
sectional study. Appetite, 103, 377–385. https://doi.org/10.1016/j.appet.2016.04.039 422
Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure dietary 423
restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29(1), 71–83.
424
https://doi.org/10.1016/0022-3999(85)90010-8 425
Teixeira, P. J., Silva, M. N., Coutinho, S. R., Palmeira, A. L., Mata, J., Vieira, P. N., … 426
Sardinha, L. B. (2010). Mediators of weight loss and weight loss maintenance in middle- 427
aged women. Obesity (Silver Spring, Md.), 18(4), 725–735.
428
https://doi.org/10.1038/oby.2009.281 429
Tholin, S., Rasmussen, F., Tynelius, P., & Karlsson, J. (2005). Genetic and environmental 430
influences on eating behavior: The Swedish Young Male Twins Study. American Journal 431
of Clinical Nutrition, 81(3), 564–569.
432
Upton, G., & Cook, I. (2014). A Dictionary of Statistics.
433
https://doi.org/10.1093/acref/9780199679188.001.0001 434
Walsh, J. R., White, A. A., & Greaney, M. L. (2009). Using focus groups to identify factors 435
affecting healthy weight maintenance in college men. Nutrition Research, 29(6), 371–
436
378. https://doi.org/10.1016/j.nutres.2009.04.002 437
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing Reliability and 438
Stability in Panel Models. Sociological Methodology, 8, 84.
439
https://doi.org/10.2307/270754 440
WHO. (2018). Obesity and overweight. Retrieved May 10, 2019, from 441
https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight 442
443
