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/lp/oxford-university-press/social-environmental-predictors-of-lapse-in-dietary-behavior-an-UwaAhj6kde
- Copyright
- Copyright © 2023 Society of Behavioural Medicine
- ISSN
- 0883-6612
- eISSN
- 1532-4796
- DOI
- 10.1093/abm/kaac077
- Publisher site
- See Article on Publisher Site
Abstract
Abstract Background When losing weight, most individuals find it difficult to maintain a healthy diet. Social environmental conditions are of pivotal importance in determining dietary behavior. To prevent individuals from lapsing, insight in social environmental predictors of lapse in dietary behavior is needed. Purpose Identify social environmental predictors of lapse in dietary behavior, using ecological momentary assessment (EMA) amongst Dutch adults trying to lose weight. Methods Adults (N = 81) participated in two 7-day EMA weeks. Six times a day semi-random prompts were sent. At each prompt, participants indicated whether a lapse had occurred and responded to questions assessing social support, descriptive norm, injunctive norm, social pressure, presence of others, and current location. Generalized estimating equations were used to examine associations with lapse. Results Injunctive norm (OR = 1.07, 95% CI = 1.03–1.11), descriptive norm (OR = 1.04, 95% CI = 1.02–1.07), and social pressure (OR = 1.09, 95% CI = 1.05–1.14), all toward diverting from diet plans, predicted lapses. Social support toward sticking to diet plans and presence of others did not predict lapses. When controlling for a prior lapse, all other associations became nonsignificant. Lapses occurred most often at home and gradually occurred more often during the day. Conclusions Traditional public health perspectives have mainly focused on individual choice and responsibility for overweight related unhealthy lifestyles. This study shows that there may be opportunities to enhance intervention programs by also focusing on social norms and social pressure. The involvement of partners or housemates may create more awareness of the impact of (unintentional) social pressure on risk of lapsing, and reduce the level of exerted social pressure. Lay Summary When losing weight, most individuals find it difficult to maintain a healthy diet. As social environmental conditions are of pivotal importance in determining dietary behavior, insight in social environmental predictors of lapse in dietary behavior is needed to prevent individuals from lapsing. Therefore, this study identified social environmental predictors of lapse in dietary behavior, using ecological momentary assessment (EMA) amongst Dutch adults trying to lose weight. A total of 81 participants took part in two 7-day EMA weeks, in which six times a day semi-random prompts were sent. At each prompt, participants indicated whether a lapse had occurred and responded to questions assessing social support, descriptive norm, injunctive norm, social pressure, presence of others, and current location. The results show that injunctive norm, descriptive norm, and social pressure, all toward diverting from diet plans, predicted dietary lapses. Social support toward sticking to diet plans and presence of others did not predict dietary lapses. Additionally, lapses occurred most often at home and gradually occurred more often during the day. This study shows that there may be opportunities to enhance intervention programs by also focusing on social norms and social pressure. Lapse dietary behavior, Social environment, Ecological momentary assessment, Weight loss maintenance Introduction Being overweight or obese is associated with various health consequences, among which an increased risk for the development of non-communicable diseases, such as cardiovascular diseases, diabetes, musculoskeletal disorders, and several cancers [1, 2]. To lose weight, individuals often aim to adhere to a healthier eating pattern [3]. Yet, most people find it difficult to maintain a successful weight loss diet [4]. The majority of dieters are able to lose some weight initially; however, only a small minority manages to maintain long-term weight loss or even gains back more weight than initially lost [5]. Consequently, individuals who attempt to adhere to a healthy eating pattern are likely to experience periodic failures (i.e., slips or mistakes) whilst changing and maintaining their target behavior. This temporary return to the previous behavior that one is trying to change is also known as a lapse. Traditional public health perspectives have mainly focused on individual choice and responsibility for overweight related unhealthy lifestyles; however, social environmental conditions (e.g., social norms) are also of pivotal importance in determining eating behavior [6–9]. Therefore, to prevent individuals from lapsing, insight in these social environmental predictors of lapse in dietary behavior is needed. From a theoretical perspective, social norms, that is, implicit codes of conduct that provide a guide to appropriate action [10], and social support, that is, the perception or realization that one is accepted, cared for, and provided with assistance from certain individuals or a specific group [11], line up with more general theories of behavior change. In the Social Cognitive Theory, perceptions of other people’s behavior can influence individual behavior change [12]. Similarly, according to the Theory of Planned Behavior, subjective social norms influence behavioral intention, which may lead to behavior change [13]. In the Health Action Process Approach, the presence of social support can enable behavior change, while the lack of social support seems to be an obstacle [14]. Looking more specifically at relapse, the Relapse Prevention Model [15] provides insight into the predictors of lapse and relapse, with relapse representing a full-blown return to the previous behavior that one has been trying to change [16]. Although the Relapse Prevention Model mainly focusses on individual determinants, the determinant “high-risk situations” also comprises social pressure, that is, the influence of another individual or group who exert direct or indirect pressure [17]. Previous research has taken a (first) look into the relation between social environmental predictors and dietary behavior. In a concept mapping study amongst health professionals and adults trying to lose weight, social pressure, lack of social support, and social norms have been indicated as predictors of relapse in dietary behavior [18]. Additionally, research showed that the stronger people perceive themselves as dieters, the more often they indicate social pressure as a reason for unhealthy snacking [19]. Next to social pressure research, evidence for an association between social norms and food choices has also been shown in a meta-analysis [20]. Social norms can be distinguished into two types. The first type, descriptive norms, refers to the perception of others’ overt behaviors. The second type, injunctive norms, reflects individuals’ perceptions of which behaviors are approved or disapproved by others [21]. Last, for social support evidence for an association between social support and dietary behavior is less clear and studies are often conducted amongst specific target groups [22–24]. However, the above mentioned studies into social environmental predictors may have some limitations with regard to drawing conclusions on predicting dietary lapses amongst adults trying to lose weight. Most of the studies were mainly aimed at the general adoption and/or maintenance of dietary behavior instead of lapses. Also, not all studies were aimed at individuals’ trying to lose weight, and were often sensitive to recall bias. A useful way to prospectively gain insight into the predictors of lapse in dietary behavior is by using ecological momentary assessment (EMA). EMA comprises repeated sampling of participants’ behaviors, experiences and mood in real time and in participants’ natural environments, to minimize recall bias and maximize ecological validity. Therefore, EMA provides an unique insight into the process of behavior change and allows the researcher to not only capture the events that are associated with lapses, but the whole flow of mood, behavior, and events in the moments before and after lapsing [25, 26]. EMA has already successfully been used in previous studies on lapses in dietary behavior [27–33]. Within these studies the majority of the determinants of interest were on individual level, although some social determinants were included [30–33]. However, overall the results on social environmental determinants seem to be partially inconsistent or the determinants of interest have only been examined in one study [34]. Therefore, we aimed to identify social environmental predictors of lapse in dietary behavior, using EMA amongst Dutch adults trying to lose weight. The predictors included in this study are perceived presence of social support, perceived descriptive norms, perceived injunctive norms, perceived social pressure, and presence of others. Based on the above mentioned theories and studies, the following hypotheses are formulated. Perceived presence of social supports negatively predicts lapse in dietary behavior; perceived descriptive norms positively predicts lapse in dietary behavior; perceived injunctive norms positively predicts lapse in dietary behavior; perceived social pressure positively predicts lapse in dietary behavior; and presence of others positively predicts lapse in dietary behavior. Our findings will enrich the existing knowledge on predictors of lapse in dietary behavior, which supports the development of lapse prevention interventions. Methods An EMA study was conducted with data collection between April 2019 and August 2019, amongst participants who are currently trying to lose weight through healthy changes in physical activity and dietary behavior. This study focuses on dietary behavior and consists of two 7-day EMA weeks, separated by 3 weeks. Including two 7-day EMA weeks provides more insight into the variance of lapsing over time, and results in an extensive dataset when combining both EMA weeks. No intervention promoting the maintenance of healthy dietary behavior was provided. The LifeData smartphone application (RealLife Exp) was used to obtain data, which is a web-based application development platform designed for researchers to better understand daily experiences [35]. A Data Processing Agreement was obtained to ensure all personal data was processed in accordance with the General Data Protection Regulation. The study was conducted according to the ethical standards declared in the Declaration of Helsinki. Following the criteria of the Dutch Medical Research Involving Human Subjects Act, our study complies with the Code of Ethics of the Faculty of Science of the Vrije Universiteit Amsterdam; therefore, our study did not require further evaluation by the Research Ethics Review Committee. Informed consent was obtained from all individual participants included in the study. Study methods and results are reported following the Strengthening the Reporting of Observational Studies in Epidemiology Statement [36]. Participants and Recruitment To the best of our knowledge, there are no clear guidelines for sample size calculations for this type of study. Therefore, we based our sample size on a previous EMA study [30] and aimed for at least 80 participants. Participants were recruited via multiple strategies: a call for participation on social media (i.e., Facebook, LinkedIn), a collaboration with a health platform (i.e., Fit.nl), and flyers posted at community general practices, health centers, and dietitian practices. Additionally, we made use of snowball sampling. To provide study information in an easily accessible way, a Facebook page was designed. Remuneration included a 50-euro voucher and a factsheet with study findings and tips on weight loss maintenance. If interested, participants could respond by email, phone, or Facebook, after which they received an email with more information about the study. After agreeing to participate, one of the researchers checked whether the participant fulfilled the in- and exclusion criteria. Inclusion criteria were: ≥18 years old, sufficient knowledge of the Dutch language, being in possession of a smartphone running Android or iOS, and currently trying to lose weight through healthy changes in physical activity and dietary behavior. Exclusion criteria were: presence of an eating disorder, weight loss through bariatric surgery, and not being able to keep and use a smartphone during the day. Participants eligible for participation were invited for a briefing with one of the researchers. Procedure Prior to the first EMA week, participants were requested to download the free LifeData application on their Android or iOS smartphone [35]. A standardized briefing was conducted by trained researchers in either a video call or a face-to-face meeting, depending on the participant’s preference. During this briefing, the participant was informed about the use of the app and important aspects of the study. For example, to capture daily life the importance of not adjusting their daily rhythm was emphasized. Also, to divert participants focus on lapses, during the briefing the emphasis was laid on the interest in weight loss behaviors in general (i.e., a peek into a week of someone who is trying to lose weight) instead of lapses in weight loss behaviors. Furthermore, to ensure comprehensibility of the EMA questionnaire, the researcher, and participant ran through the EMA questionnaire together by letting the participant read the questions aloud and answer them; however, as anonymity was assured during the study, their answers were for practice purposes only. Participants received contact details they could access when they experienced problems at any point during the study. After the briefing participants completed an online informed consent, followed by an online questionnaire. Online questionnaire An online questionnaire was completed in Qualtrics [37] and consisted of several demographic and background characteristics: age, sex (male/female), educational level, height, weight, being under guidance of a health professional (yes/no), and having an exercise buddy (yes/no). Educational level was categorized into low level of education (primary education and lower general secondary education), middle level of education (higher general secondary education, pre-university education, and secondary vocational education), and high level of education (bachelor’s degree, master’s degree, and doctoral degree), based on the standard classification of the Central Bureau of Statistics Netherlands. Height and weight were used to calculate Body Mass Index (BMI) in kg/m2. EMA measurements The morning after the briefing a 7-day EMA week started. Based on prior EMA research [29], six times a day semi-random prompts were sent, anchored at 09:00 am, 11:30 am, 02:00 pm, 04:30 pm, 07:00 pm, and 9:30 pm, with a standard deviation of 30 min around the anchor time. Participants were instructed to fill in the questionnaire as soon as possible after the prompt was sent. A maximum of three reminder signals were sent every 8 min, until the questionnaire was completed. After not responding for 30 min the questionnaire expired. To lower participant burden each questionnaire did not take more than 3 min to complete [38]. Furthermore, at day two all participants were contacted by text message to check whether the prompts were received correctly. The first 7-day EMA week was followed by 3 weeks without measurements. After three weeks another 7-day EMA week was carried out, following the same procedure as the first EMA week. Note that the complete EMA questionnaire did not only consist of social environmental predictors of dietary behavior, but also individual-level predictors of physical activity and dietary behavior (emotional states, stress, intention, coping-self-efficacy, and recovery self-efficacy). However, the focus of this paper is on social environmental predictors. Outcome Variables Lapses A lapse was defined as whether the participant did not stick to their diet plans, that is, “Your own plans on what and how much you eat and/or drink” [31, 33]. Whether they lapsed was assessed daily at every second to sixth measurement by asking “Since the last measurement, did you stick to your diet plans?.” Social environmental predictors Perceived presence of social support, perceived descriptive norms, perceived injunctive norms, and perceived social pressure were based on an adapted version from Schüz et al. [39]. Perceived presence of social support was assessed at each measurement with 1 item: “At this moment I feel supported by people in my environment (e.g., partner, family, friends, colleagues) to stick to my diet plans.” Perceived descriptive norm was assessed at each measurement with 1 item: “Since the last measurement I saw others in my environment eating things that do not fit within my diet plans.” Perceived injunctive norm was measured at each measurement with 1 item: “Since the last measurement I had the feeling others expected from me to eat and/or drink something that does not fit within my diet plans.” Perceived social pressure was measured at each measurement with 1 item: “Since the last measurement, others have tried to persuade me to eat and/or drink something that does not fit within my diet plans”; all measured with an answer category ranging from 1 not at all to 10 certainly. Note that social support was framed toward the desired behavior, that is, sticking to one’s diet plan, whereas descriptive norm, injunctive norm, and social pressure were framed toward the undesired behavior, that is, not sticking to one’s diet plan. Presence of others was assessed at every measurement with one item: “With whom are you?,” measured by multiple choice: “Partner; Children; Family; Friends; Colleagues/Classmates; Acquaintances; Strangers/Others; No one.” Additionally, current location was assessed at every measurement with one item: “Where are you?,” measured by multiple choice: “At home; At others’ homes; Holiday home; Work/school; Store; Restaurant; Sports club; Outdoors; Other.” Content Validity To evaluate the content validity of the EMA questionnaire, the response options, and the instructions, a small study was conducted (N = 7). A think-aloud protocol was used, providing insight into participant’s cognitive processes [40]. During the think-aloud protocol participants were asked to read the questions aloud and mention everything that comes to mind while completing the questionnaire. Meanwhile, the researcher took notes of what the participant said and did, without interpreting the participant’s actions or words [40]. In line with the COSMIN criteria, special attention was paid to the relevance, comprehensiveness, and comprehensibility of the questionnaire [41]. The participant group consisted of individuals of different ages, educational levels, and sexes, making the results representable for our main study. Additionally, the ease of use of the EMA app was assessed. Based on the results, the questionnaire, response options, and the instructions were adapted accordingly, resulting in the EMA items as formulated above. Data Analysis IBM SPSS Statistics 26 was used to analyze the data. Lapses were identified at several time moments (i.e., before 9:00 am, between 9:00 and 11:30 am, between 11:30 am and 02:00 pm, between 02:00 and 04:30 pm, between 04:30 and 07:00 pm, between 07:00 and 09:30 pm, and after 9:30 pm). To predict a lapse at time point t, predictor variables measured at t were lagged into time point t−1. Data from participants with less than 50% overall compliance were excluded from analyses, which is in line with previous EMA research [42–44]. Study dropout was not a reason for exclusion, as long as the compliance rate of 50% was achieved. For each predictor, separate generalized estimating equations (GEE) were performed to estimate the associations with lapse in dietary behavior. The GEE analysis was performed using a binary logistic distribution with a log link function and an exchangeable matrix structure, with lapse (measured at each time moment) as outcome variable. Odds ratios (OR), 95% Confidence Interval (CI) and p-values were estimated for each predictor. Values of p < .05 were considered statistically significant. Results A total of 86 participants started the first EMA assessment period, and 84 participants completed the first EMA assessment period. Next, 83 participants started the second EMA assessment period, and 81 participants completed the second EMA assessment period. Among all participants, the mean compliance rate in the first assessment period was 85.8% (range = 11.1–100); the mean compliance rate in the second assessment period was 81.7% (range = 2.4–100), see Table 1. Data on the race/ethnicity of the participants were not collected. Of the initial 86 participants, five participants were excluded from analysis due to a compliance rate below 50%. Of these five participants, three were study dropouts. Thus, the final analysis sample size consisted of 81 participants (85.2% female; Mage = 39.7 ± 13.5 years; MBMI = 28.3 ± 4.1 kg/m2). Of these participants, 54.3% were overweight and 28.4% were obese. For participant characteristics see Table 2 and for participation flow see Supplementary Material 1. As assessment period 1 and 2 are taken together for the analysis, the final analysis sample size was 81. Table 1 Total EMA sample size, number of recordings and compliance rate by assessment point . Assessment point . Overall* . Overall** . Week 1 . Week 2 . Total number of recordings (N) 3054 2826 5880 5760 Compliance rate (% (min-max)) 85.8 (11.1–100) 81.7 (2.4–100) 83.8 (11.1–100) 86.2 (50.0–100) . Assessment point . Overall* . Overall** . Week 1 . Week 2 . Total number of recordings (N) 3054 2826 5880 5760 Compliance rate (% (min-max)) 85.8 (11.1–100) 81.7 (2.4–100) 83.8 (11.1–100) 86.2 (50.0–100) *EMA data week 1 and week 2 taken together, prior to excluding participants (N = 86). **EMA data week 1 and 2 taken together, after excluding participants (N = 81). Open in new tab Table 1 Total EMA sample size, number of recordings and compliance rate by assessment point . Assessment point . Overall* . Overall** . Week 1 . Week 2 . Total number of recordings (N) 3054 2826 5880 5760 Compliance rate (% (min-max)) 85.8 (11.1–100) 81.7 (2.4–100) 83.8 (11.1–100) 86.2 (50.0–100) . Assessment point . Overall* . Overall** . Week 1 . Week 2 . Total number of recordings (N) 3054 2826 5880 5760 Compliance rate (% (min-max)) 85.8 (11.1–100) 81.7 (2.4–100) 83.8 (11.1–100) 86.2 (50.0–100) *EMA data week 1 and week 2 taken together, prior to excluding participants (N = 86). **EMA data week 1 and 2 taken together, after excluding participants (N = 81). Open in new tab Table 2 Participant characteristics . Participants (N = 81) . Age in years (Mean (SD)) 39.7 (13.5) BMI* in kg/m2 (Mean (SD)) 28.3 (4.1) Sex (N (%)) Male 12 (14.8) Female 69 (85.2) Educational level (N(%)) Low 5 (6.2) Middle 26 (32.1) High 50 (61.7) Guidance by a health professional (N(%)) Yes 24 (29.6) No 57 (70.4) . Participants (N = 81) . Age in years (Mean (SD)) 39.7 (13.5) BMI* in kg/m2 (Mean (SD)) 28.3 (4.1) Sex (N (%)) Male 12 (14.8) Female 69 (85.2) Educational level (N(%)) Low 5 (6.2) Middle 26 (32.1) High 50 (61.7) Guidance by a health professional (N(%)) Yes 24 (29.6) No 57 (70.4) *Body Mass Index. Open in new tab Table 2 Participant characteristics . Participants (N = 81) . Age in years (Mean (SD)) 39.7 (13.5) BMI* in kg/m2 (Mean (SD)) 28.3 (4.1) Sex (N (%)) Male 12 (14.8) Female 69 (85.2) Educational level (N(%)) Low 5 (6.2) Middle 26 (32.1) High 50 (61.7) Guidance by a health professional (N(%)) Yes 24 (29.6) No 57 (70.4) . Participants (N = 81) . Age in years (Mean (SD)) 39.7 (13.5) BMI* in kg/m2 (Mean (SD)) 28.3 (4.1) Sex (N (%)) Male 12 (14.8) Female 69 (85.2) Educational level (N(%)) Low 5 (6.2) Middle 26 (32.1) High 50 (61.7) Guidance by a health professional (N(%)) Yes 24 (29.6) No 57 (70.4) *Body Mass Index. Open in new tab Lapse frequency, location, and timing Participants reported a total of 958 lapses, see Table 3. Each participant reported at least one lapse during the two assessment periods, with an average of 11.8 lapses per participant (range = 1–54). Across all locations, participants reported lapses occurring most often at home (58.1%), followed by at someone else’s home (10.9%), and at work or school (9.6%). Looking at lapse timing, results show that during the course of the day lapses gradually occur more often, with the evening (between 07:00 and 09:30 pm) being the most common time of lapse. Table 3 Characteristics lapse in dietary behavior . . Lapse (N (%)) . Total amount of lapses 958 Location of lapse At home 557 (58.1) At others’ homes 104 (10.9) Holiday home 22 (2.3) Work/school 92 (9.6) Store 11 (1.1) Restaurant 53 (5.5) Sports club 12 (1.3) Outdoors 63 (6.6) Other 44 (4.6) Time of lapse Between 9:00 and 11:30 am 104 (10.9) Between 11:30 am and 02:00 pm. 134 (14.0) Between 02:00 and 04:30 pm 192 (20.0) Between 04:30 and 07:00 pm 212 (22.1) Between 07:00 and 09:30 pm 316 (33.0) Social environmental predictors M (SD) Perceived presence of social support [1–10] 6.6 (3.0) Perceived social norm Injunctive [1–10] 2.0 (2.0) Descriptive [1–10] 4.8 (3.7) Perceived social pressure* [1–10] 1.8 (1.7) . . Lapse (N (%)) . Total amount of lapses 958 Location of lapse At home 557 (58.1) At others’ homes 104 (10.9) Holiday home 22 (2.3) Work/school 92 (9.6) Store 11 (1.1) Restaurant 53 (5.5) Sports club 12 (1.3) Outdoors 63 (6.6) Other 44 (4.6) Time of lapse Between 9:00 and 11:30 am 104 (10.9) Between 11:30 am and 02:00 pm. 134 (14.0) Between 02:00 and 04:30 pm 192 (20.0) Between 04:30 and 07:00 pm 212 (22.1) Between 07:00 and 09:30 pm 316 (33.0) Social environmental predictors M (SD) Perceived presence of social support [1–10] 6.6 (3.0) Perceived social norm Injunctive [1–10] 2.0 (2.0) Descriptive [1–10] 4.8 (3.7) Perceived social pressure* [1–10] 1.8 (1.7) N = number of times reported. *Measured reversed compared to social norm and social pressure (stick to diet plan vs. not sticking to diet plan). Open in new tab Table 3 Characteristics lapse in dietary behavior . . Lapse (N (%)) . Total amount of lapses 958 Location of lapse At home 557 (58.1) At others’ homes 104 (10.9) Holiday home 22 (2.3) Work/school 92 (9.6) Store 11 (1.1) Restaurant 53 (5.5) Sports club 12 (1.3) Outdoors 63 (6.6) Other 44 (4.6) Time of lapse Between 9:00 and 11:30 am 104 (10.9) Between 11:30 am and 02:00 pm. 134 (14.0) Between 02:00 and 04:30 pm 192 (20.0) Between 04:30 and 07:00 pm 212 (22.1) Between 07:00 and 09:30 pm 316 (33.0) Social environmental predictors M (SD) Perceived presence of social support [1–10] 6.6 (3.0) Perceived social norm Injunctive [1–10] 2.0 (2.0) Descriptive [1–10] 4.8 (3.7) Perceived social pressure* [1–10] 1.8 (1.7) . . Lapse (N (%)) . Total amount of lapses 958 Location of lapse At home 557 (58.1) At others’ homes 104 (10.9) Holiday home 22 (2.3) Work/school 92 (9.6) Store 11 (1.1) Restaurant 53 (5.5) Sports club 12 (1.3) Outdoors 63 (6.6) Other 44 (4.6) Time of lapse Between 9:00 and 11:30 am 104 (10.9) Between 11:30 am and 02:00 pm. 134 (14.0) Between 02:00 and 04:30 pm 192 (20.0) Between 04:30 and 07:00 pm 212 (22.1) Between 07:00 and 09:30 pm 316 (33.0) Social environmental predictors M (SD) Perceived presence of social support [1–10] 6.6 (3.0) Perceived social norm Injunctive [1–10] 2.0 (2.0) Descriptive [1–10] 4.8 (3.7) Perceived social pressure* [1–10] 1.8 (1.7) N = number of times reported. *Measured reversed compared to social norm and social pressure (stick to diet plan vs. not sticking to diet plan). Open in new tab Social environmental predictors For the social environmental predictors, separate GEE’s were performed. Presence of others was converted into a dichotomous variable (yes/no). In model 1, significant associations were observed for perceived injunctive norm toward diverting from diet plans (OR = 1.07, 95% CI = 1.03–1.11, p < .001), perceived descriptive norm toward diverting from diet plans (OR = 1.04, 95% CI = 1.02–1.07, p < .005), and perceived social pressure toward diverting from diet plans (OR = 1.09, 95% CI = 1.05–1.14, p < .001). This means that, for example, an individual with one unit higher perceived injunctive norm has a 1.07 times higher odds to lapse. For perceived presence of social support toward sticking to diet plans and presence of others, no significant associations were found, see Table 4. In model 2, as our results showed that lapses occurred most often at home and late in the day, we adjusted for most frequent lapse location and most frequent time of day to lapse. This did not affect the significance of the results (perceived injunctive norm OR = 1.06, 95% CI = 1.02–1.10, p < .01; perceived descriptive norm OR = 1.03, 95% CI = 1.00–1.05, p < .05; perceived social pressure OR = 1.08, 95% CI = 1.03–1.12, p < .005). In model 3, a multivariate model was run, with perceived presence of social support, perceived descriptive norm, perceived social pressure, and presence of others, whilst adjusting for most frequent lapse location and most frequent time of day to lapse. This led to perceived descriptive norm no longer showing a significant association with dietary lapse. Perceived social pressure remained significant (OR = 1.06, 95% CI = 1.02–1.11, p < .05). Due to the high correlation between perceived social pressure and perceived injunctive norm, perceived injunctive norm was not included in model 3. Finally, in model 4, building upon model 2, we added an adjustment for lapse at t−1, which led to perceived injunctive norm, perceived descriptive norm and perceived social pressure no longer being significantly associated with dietary lapse. This is statistically not surprising, as adjusting for the outcome variable at t−1, which is strongly related to the outcome variable (OR = 1.92, 95% CI = 1.45–2.55, p < .001) takes away a lot of variance from the outcome variable itself. Table 4 Associations between lapse in dietary behavior and predictors . OR . Model 1 . p . OR . Model 2 . p . OR . Model 3 . p . OR . Model 4 . p . 95% CI . 95% CI . 95% CI . 95% CI . Perceived presence of social support1 0.98 0.93–1.04 .52 0.99 0.93–1.04 .68 0.99 0.94–1.04 .73 1.02 0.96–1.08 .48 Perceived injunctive norm2 1.07 1.03–1.11 .00 1.06 1.02–1.10 .006 — — — 1.03 0.98–1.07 .23 Perceived descriptive norm 1.04 1.02–1.07 .001 1.03 1.00–1.05 .04 1.02 0.99–1.05 .19 1.00 0.97–1.03 .97 Perceived social pressure 1.09 1.05–1.14 .00 1.08 1.03–1.12 .001 1.06 1.02–1.11 .01 1.03 0.98–1.08 .23 Presence of others 1.14 0.96–1.34 .13 1.07 0.92–1.25 .37 1.03 0.88–1.21 .69 1.02 0.85–1.21 .85 . OR . Model 1 . p . OR . Model 2 . p . OR . Model 3 . p . OR . Model 4 . p . 95% CI . 95% CI . 95% CI . 95% CI . Perceived presence of social support1 0.98 0.93–1.04 .52 0.99 0.93–1.04 .68 0.99 0.94–1.04 .73 1.02 0.96–1.08 .48 Perceived injunctive norm2 1.07 1.03–1.11 .00 1.06 1.02–1.10 .006 — — — 1.03 0.98–1.07 .23 Perceived descriptive norm 1.04 1.02–1.07 .001 1.03 1.00–1.05 .04 1.02 0.99–1.05 .19 1.00 0.97–1.03 .97 Perceived social pressure 1.09 1.05–1.14 .00 1.08 1.03–1.12 .001 1.06 1.02–1.11 .01 1.03 0.98–1.08 .23 Presence of others 1.14 0.96–1.34 .13 1.07 0.92–1.25 .37 1.03 0.88–1.21 .69 1.02 0.85–1.21 .85 Note. Model 1, 2 and 4 are univariate models in which each potential predictor at t−1 was investigated in a separate GEE analysis. Model 1 shows the crude analysis. Model 2 adjusted for most frequent lapse location and most frequent time of day to lapse. Model 3 shows a multivariate model with perceived presence of social support, perceived descriptive norm, perceived social pressure and presence of others, adjusted for most frequent lapse location and most frequent time of day to lapse. Model 4 (build upon model 2) adjusted for most frequent lapse location and most frequent time of day to lapse, and lapse at t−1. 1 Measured reversed compared to social norms and social pressure (sticking to diet plan vs. not sticking to diet plan). 2 Due to multicollinearity perceived injunctive norm was removed from model 3. Open in new tab Table 4 Associations between lapse in dietary behavior and predictors . OR . Model 1 . p . OR . Model 2 . p . OR . Model 3 . p . OR . Model 4 . p . 95% CI . 95% CI . 95% CI . 95% CI . Perceived presence of social support1 0.98 0.93–1.04 .52 0.99 0.93–1.04 .68 0.99 0.94–1.04 .73 1.02 0.96–1.08 .48 Perceived injunctive norm2 1.07 1.03–1.11 .00 1.06 1.02–1.10 .006 — — — 1.03 0.98–1.07 .23 Perceived descriptive norm 1.04 1.02–1.07 .001 1.03 1.00–1.05 .04 1.02 0.99–1.05 .19 1.00 0.97–1.03 .97 Perceived social pressure 1.09 1.05–1.14 .00 1.08 1.03–1.12 .001 1.06 1.02–1.11 .01 1.03 0.98–1.08 .23 Presence of others 1.14 0.96–1.34 .13 1.07 0.92–1.25 .37 1.03 0.88–1.21 .69 1.02 0.85–1.21 .85 . OR . Model 1 . p . OR . Model 2 . p . OR . Model 3 . p . OR . Model 4 . p . 95% CI . 95% CI . 95% CI . 95% CI . Perceived presence of social support1 0.98 0.93–1.04 .52 0.99 0.93–1.04 .68 0.99 0.94–1.04 .73 1.02 0.96–1.08 .48 Perceived injunctive norm2 1.07 1.03–1.11 .00 1.06 1.02–1.10 .006 — — — 1.03 0.98–1.07 .23 Perceived descriptive norm 1.04 1.02–1.07 .001 1.03 1.00–1.05 .04 1.02 0.99–1.05 .19 1.00 0.97–1.03 .97 Perceived social pressure 1.09 1.05–1.14 .00 1.08 1.03–1.12 .001 1.06 1.02–1.11 .01 1.03 0.98–1.08 .23 Presence of others 1.14 0.96–1.34 .13 1.07 0.92–1.25 .37 1.03 0.88–1.21 .69 1.02 0.85–1.21 .85 Note. Model 1, 2 and 4 are univariate models in which each potential predictor at t−1 was investigated in a separate GEE analysis. Model 1 shows the crude analysis. Model 2 adjusted for most frequent lapse location and most frequent time of day to lapse. Model 3 shows a multivariate model with perceived presence of social support, perceived descriptive norm, perceived social pressure and presence of others, adjusted for most frequent lapse location and most frequent time of day to lapse. Model 4 (build upon model 2) adjusted for most frequent lapse location and most frequent time of day to lapse, and lapse at t−1. 1 Measured reversed compared to social norms and social pressure (sticking to diet plan vs. not sticking to diet plan). 2 Due to multicollinearity perceived injunctive norm was removed from model 3. Open in new tab Discussion With this EMA study we aimed to identify social environmental predictors of lapse in dietary behavior. Our findings revealed that perceived social pressure, perceived social injunctive norm, and perceived social descriptive norm predict lapses in dietary behavior. Thus, individuals who perceived higher levels of social pressure, injunctive norms or descriptive norms, had a higher chance of lapsing. No evidence was found for perceived presence of social support and presence of others as predictors of lapse. When adjusting for lapse at t−1, all other significant associations disappear. Furthermore, results indicated that lapses occurred most often at home, and during the course of the day lapses gradually occurred more often. Our results show that adjusting for lapse at t−1 takes away all other significant associations. This can be both statistically and theoretically explained. Statistically, the outcome variable at t−1 is strongly related to the outcome variable itself, which takes away a lot of variance from the outcome variable. Theoretically, according to the self-regulation theory there are several behaviors that may emerge after an initial failure of self-regulation, that is, a lapse [45]. These are so-called lapse-activated responses and cause a minor breakdown in self-control. This initial minor breakdown often activates factors that prevent the reassertion of self-control, resulting in an acceleration of the breakdown [45, 46]. Our finding that descriptive norms predict dietary lapses in the univariate model (model 1 and 2) adds to the existing knowledge that informational eating norms are associated with dietary behavior [20]. Yet, our finding is to the best of our knowledge the first to find this association with lapse in dietary behavior. The disappearance of the significant association in the multivariate model (model 3), suggests that the association between descriptive norms and dietary lapses runs partly through social pressure. In contrast, evidence from previous research regarding the predictive value of injunctive norms on healthy eating behavior is less convincing [47–49]. However, these studies focused on healthy eating in general, with injunctive norms framed toward the desired behavior (healthy eating). On the other hand, in a qualitative study both descriptive and injunctive norms were mentioned as relevant predictors of relapse in dietary behavior amongst individuals trying to lose weight, with statements such as “Feeling that you are being judged by others” and “If others are taking something, I will too” [18]. It is notable that the statements in this qualitative study are framed toward the undesired behavior (relapsing), which is comparable to our framing of injunctive norm. This reversed way of framing may explain the difference in predictive value of injunctive norm in our study compared to these other studies. Future research should explore if norms toward the undesired behavior may be perceived stronger and/or is stronger associated than norms toward the desired behavior. Our finding that social pressure is associated with lapse in dietary behavior, did not confirm a previous EMA study [32]. They found no association between perceived social pressure and lapses in dietary behavior, although they did find that lower weight loss maintenance was associated with lower self-efficacy to resist social pressure [32]. Our finding does support a previous Dutch cross-sectional study, which found that the more individuals perceived themselves as dieters, the more often they indicated social pressure as a reason for unhealthy snacking [19]. However, it is important to notice that the definitions of social pressure and injunctive norms partially overlap, although they are different concepts. In our study, injunctive norm was defined as the feeling that someone expected them to eat and/or drink something that does not fit within ones diet plans, whereas social pressure was defined as the actual persuasion of others to eat and/or drink something that does not fit within ones diet plans. Looking at the literature, social pressure, and injunctive norms are sometimes used interchangeably. For example, the definition used in the above mentioned Dutch cross-sectional study [19], may be more comparable to our definition used for injunctive norms. Also new comparable concepts arise, such as social sabotage [50]. Future research should focus on reaching consent on definitions of each concept, to make further progress in understanding the predictable values of each predictor on dietary lapses. We found no association between presence of social support and dietary lapses. In a more general review focusing on healthy diet studies, evidence on social support also remains inconclusive [51]. However, in a qualitative study on the predictors of relapse, lack of social support such as “Partner doesn’t cooperate” and “Feeling that you are on your own” was often mentioned as a predictor of relapse in dietary behavior [18]. Interestingly, in this qualitative study respondents seem to specifically indicate the lack of social support as a predictor, whereas we asked about the presence of social support, which might explain the inconsistent findings. Another explanation for our lack of association might be related to the way we assessed social support. We measured perceived social support, that is, a general subjective assessment without a specific time period. A distinction can be made between perceived and received support, with perceived referring to anticipating help in time of need and received referring to help provided within a given time period (51). In hindsight received social support (“since the last measurement”) may have been more fitting for an EMA study, as the overall feeling of support is a more stable construct and potentially differs more between individuals than within individuals over time. The ICCs support this: where the ICCs for perceived injunctive norms (0.40; 95% CI [0.27–0.55]), perceived descriptive norms (0.32; 95% CI [0.20–0.48]), social pressure (0.31; 95% CI [0.19–0.47]) and presence of others (0.27; 95% CI [0.16–0.42]) are relatively low, the ICC for perceived social support (0.78; 95% CI [0.69–0.86]) is relatively high. It would be of interest for future EMA research to examine if there is an association between received social support and dietary lapse. Lastly, we did not find an association between presence of others and dietary lapses. Presence of others has previously been examined in three EMA studies [30, 31, 33]; however, evidence for a relationship between the presence of others and lapse in dietary behavior remained inconsistent. It is notable that these three studies examined presence of others in different ways: McKee and colleagues [30] asked if others influenced the reaction to dietary temptation (scale 1–7), Carels and colleagues [31] asked if there were others present (yes/no) and Carels and colleagues [33] asked if they were eating with others (yes/no). Mckee and Carels [30, 33] did find a positive association between presence of others and lapse in dietary behavior, whereas Carels [31], who’s definition is most comparable with ours, did not. Additionally, the studies that did find an association specifically asked about the presence of others at the time of lapse, whereas Carels [31] and our study asked about the presence of others at the time of measurement; this may also explain the inconsistency in results. The predictive value of presence of others may also depend on who is present. Previous research on eating behavior shows that individuals have the tendency to eat more in the presence of friends and family, than in the presence of strangers [52, 53]. Interestingly, earlier research on eating behavior has shown that social norms still function when others are not physically present [54–56]. Although these studies are not specifically aimed at lapses, it may explain why we did find associations for social norms but not for presence of others. Future research should reveal to what extent presence of others, in relation to dietary lapses, is dependent of who is present. Our finding that during the course of the day lapses gradually occurred more often, with the evening being the most common time of lapse, is in accordance with previous EMA studies [29, 30]. There are various factors that may explain the higher lapse rate later in the day, as highlighted by Millar [57]. Self-regulation in relation to unhealthy foods may be more difficult later in the waking day as, for example, the availability and/or prominence of tempting foods may be relatively greater, late in the day desire might be stronger, and self-regulatory capacity may be weakened by a worsening affective state and reduced cognitive functioning (e.g., impaired by sleep-related fatigue and daily “wear and tear”) [57]. Helping individuals to prevent and/or cope with this increased chance of impaired self-regulation, may help to prevent dietary lapses. A way to do this is by forming if-then plans, also known as implementation intentions [58]. Forming implementation intentions have been shown to be a successful tool for changing behavior in several domains, among which dietary behavior [59–61]. Another suggestion is to make the environment healthier. As it is imaginable that most individuals are at home later in the day, anticipating on the lower capacity of self-control in the evening by making the home-environment less tempting could be an effective strategy to prevent lapses. A possible way to achieve this is through self-nudging, that is, designing and structuring one’s own environment in ways that make it easier to make the right choices [62]. Strengths and Limitations To our knowledge, we are one of the first using EMA to assess multiple social environmental predictors of lapse in dietary behavior. Strengths of the current study include the use of EMA, which provides an unique insight into the process of behavior change [25, 26]. EMA comprises repeated real-time assessments in participants natural environments, which contributes to minimizing recall bias and maximizing ecological validity [26]. Two 7-day EMA weeks were included, which provided more insight into the variance of lapsing over time, and resulted in an extensive dataset when combined. Furthermore, an average compliance rate of almost 84% was achieved. A compliance rate of 80% or higher indicates representativeness of the sampling, resulting in a higher generalizability and validity of the findings [38]. Nonetheless, some limitations must be considered. Our study sample included a rather high percentage of highly educated women, which makes it less generalizable for the Dutch adult population. Furthermore, although we emphasized not adjusting daily rhythm and diverted participants focus on lapses by communicating the study purpose as an interest in weight loss behaviors in general (i.e., a peek into a week of someone who is trying to lose weight), asking participants to fill in a short questionnaire six times a day could have led to an increased behavioral awareness. However, as each participant reported at least one lapse during the two assessment periods, with an average of 11.8 lapses per participant, we believe this potentially increased behavior awareness has not impacted the quality of our data. Furthermore, we assumed that an individual’s experience at measurement t−1 influences the behavioral decision (potentially eating/drinking something that does not fit within their diet plans) at measurement t. However, there is a timespan of ± 2.5 h between each measurement, which leads to a certain degree of uncertainty. Alternatively, we could have chosen an event based EMA design, that is, asking participants to fill in an EMA questionnaire every time they experience a lapse. However, an event based design also has its limitations. For example, it can bias data as individual differences, state of the individual itself or situational context can systematically impact momentary adherence [63], and one has to rely on accurate reporting of individuals when respondents fill in the questionnaire hours after the lapse occurred (memory effects) [64]. Therefore, we believe that for our study, despite the potential degree of uncertainty, a time based EMA design was most fitting. Last, it is important to note that we cannot rule out the possibility that perceived social pressure, perceived injunctive norm and perceived descriptive norm might (also) be serving as a proxy for the availability of food or drinks. Although it is common within EMA studies to measure concepts with one item (to limit participant burden), it makes it more difficult to disentangle the degree to which our findings reflect individuals’ construal of social norms and pressure, or the availability of foods that could lead to a lapse. Future research should further examine the concept of food availability, to ascertain the predictive value of social norms and social pressure. For example, combining EMA with a more objective measure of the food environment, for example, wearable egocentric cameras, could provide more insight into the interaction between social environmental predictors and food availability [65]. Future Research Future research should further investigate the relationship between social influence and lapse in dietary behavior. This study focused on perceptions of the individual who is trying to lose weight; however, it would also be of interest to investigate the perceptions of individuals in the social network of the person that is trying to lose weight. For example, our results showed that higher perceived social pressure results in a higher risk of lapsing, but what remains unknown is if the individuals who exert this social pressure are even aware of doing this. To enhance relapse prevention, more insights on dietary behavior change should be gained from the perspectives of both the individual of interest as well as their social environment, for example, a partner. A suggestion to do this is through qualitative research [66]. Besides examining a wider spectrum of potential predictors, it would also be of interest to gather information about the nature of the lapse, for example, eating a food that one was trying to avoid or eating or drinking more than one’s goal for the day [67], or the timing of the lapse, such as week- versus weekend days. Differences in dietary lapses on week- versus weekend days have been researched before in EMA, but show inconsistent results [29, 30]. Also gathering more person-level data, such as the nature of a person’s diet plan or how recently they started adopting their dietary behavior, could add to a more in depth understanding of dietary lapses. Furthermore, it is important to acknowledge that obesity is a complex construct that, among other, consists of interrelations between environmental determinants as well as individual-level determinants [68, 69]. This makes the underlying behavior lapse also a complex problem, consisting of dynamic interactions between various determinants [70]. Examining the complex construct of lapse therefore requires an approach that deals with the multilevel nature of lapses. Therefore, a system-oriented approach in evaluating predictors of lapse in dietary behavior, is required for relapse prevention [71]. To further examine the multilevel nature of lapses, we recommend future research to not only involve the social environment, but also the physical environment and its associated temptations, as well as determinants on the individual-level [72, 73]. Practical Implications Although traditional public health perspectives have mainly focused on individual choice and responsibility for overweight related unhealthy lifestyles, our study shows that the social environment is also of importance. Therefore, practitioners and intervention programs are recommended to also focus on perceived social pressure and social norms, both descriptive and injunctive, when designing new interventions regarding lapse prevention. For example, interventions aimed at making individuals more resilient against social pressure and social norms, could help individuals to cope with corresponding high risk situations. Planning coping responses on anticipated high risk situations could be an effective strategy for an individual to cope with difficult situations, such as dealing with temptations from their social environment [74]. Furthermore, for many people it is difficult to say “no” in social situations, due to perceived norms or pressure [18]. Therefore, intervention programs could aim at the development and improvement of social skills, such as such as how to say “no” whilst preserving the relationship. Besides focusing on the individual, it may also be beneficial to focus on the social environment itself. The involvement of partners or housemates in interventions might create more awareness of the impact of (unintentional) social pressure on the risk of lapsing, and reduce the level of exerted social pressure. Considering our result that most lapses occur at home, the involvement of partners or housemates may also help individuals to better cope with the tempting home-environment. Last, as we found that most lapses occur in the evening, we recommend intervention programs and practitioners to include the earlier mentioned implementation intentions and self-nudging tools to help individuals anticipate on the increased chance of impaired self-regulation in the evening [58–62, 75, 76]. Conclusion This study is one of the firsts to assess multiple social environmental predictors of dietary lapses, using an EMA design. Our findings revealed that perceived social pressure, perceived social injunctive norm, and perceived social descriptive norm predict lapses in dietary behavior. Furthermore, results showed that lapses occurred most often at home, and during the course of the day lapses gradually occurred more often. Traditional public health perspectives often focus on individual choice and responsibility for overweight related unhealthy lifestyles; however, our results indicate an opportunity to enhance intervention programs by also including social environmental aspects. Therefore, practitioners and intervention programs are recommended to also focus on perceived social pressure and social norms, both descriptive and injunctive, when designing new interventions regarding lapse prevention. Acknowledgements Not applicable. Compliance with Ethical Standards Funding: The contribution of Eline M. Roordink and Maartje M. van Stralen was supported by the Innovational Research Incentives Scheme Veni from NWO-SSH (Netherlands Organisation for Scientific Research—Division for Social Sciences and Humanities) under project number 451-16-018. Disclosure of interest: The authors report no conflict of interest. CRediT: Eline M. Roordink and Maartje M. van Stralen contributed to conceptualization equally. Eline M. Roordink served as lead for project administration, resources, supervision, formal analysis, writing-original draft and writing- review & editing. Maartje M. van Stralen served as lead for funding acquisition and served in a supporting role for writing-original draft and writing- review & editing. Ingrid H.M. Steenhuis and Willemieke Kroeze served equally in a supporting role for conceptualization, writing-original draft and writing- review & editing. 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Published by Oxford University Press on behalf of the Society of Behavioral Medicine. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Journal
Annals of Behavioral Medicine – Oxford University Press
Published: Jan 24, 2023