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Despite a large volume of research on the impact of other digital screens (eg, televisions) on eating behavior, little is known about the nature and impact of mealtime smartphone use.
We investigated how smartphones are used in everyday meals, whether phone users differ according to mealtime phone use patterns, and whether specific phone functions (particularly food photography) would affect the amount and enjoyment of food eaten.
Across 2 studies, we used the experience sampling method to track 1780 meals in situ. In study 1, a total 137 young adults reported on their mealtime smartphone use 3 times per day over 7 consecutive days. This corresponded to each main meal, with participants recording whether they used their phones and what phone functions they engaged in while eating. In study 2, a total of 71 young adults were similarly tracked for 3 meals per day over 7 days. Across the week, participants’ meals were randomized to 1 of 3 smartphone conditions: food photography while eating, nonfood photography while eating, or no phone use. As the outcome measures, participants reported on the amount and enjoyment of food they ate.
During the week-long tracking, most participants (110/129, 85.3%) recorded at least one instance of mealtime smartphone use, with an average frequency of 1 in 3 meals where phones were used (27.1%; 95% CI 23.6-30.6). Unlike traditional digital screens, mealtime phone use encompassed a wide range of social and nonsocial activities. Further, specific forms of phone use behaviors influenced food intake in different ways. Specifically, in study 2, participants showed the typical pattern of increased food intake across the day when they engaged in nonfood photography during a meal (
Our findings underscore the prevalence and multifaceted nature of mealtime phone use, distinguishing mobile phones from traditional forms of digital screens.
ClinicalTrials.gov NCT03299075; https://www.clinicaltrials.gov/ct2/show/NCT03299075 and ClinicalTrials.gov NCT03346785; https://clinicaltrials.gov/ct2/show/NCT03346785
A large body of research underscores how using digital screens (eg, television, computers) can predispose an individual to obesity [
Although these guidelines have been based primarily on television research, mobile phones (or smartphones) have also been linked to weight gain [
Despite the ubiquity of mobile devices, there have been few studies on mealtime phone use, and the impact of this phenomenon remains unclear. On the one hand, a mobile phone resembles other digital screens in its sedentary usage [
Of note, one particular form of mealtime phone use has received heavy criticism, that of food photography, where the meal is photographed and the images are shared on social media. Although the capacity to take photographs pre-dates mobile phones, incorporating camera functions into the phone has transformed the extent and means by which we capture our meals. Correspondingly, food pictures now rank among the top categories of images uploaded on the photo-sharing platform Instagram.
Food photography is illustrative of how mealtime phone use can introduce new rituals to the dinner table. Unlike other forms of digital screens (or indeed other forms of phone use), taking photographs involves direct engagement with the meal—arranging the food, taking a photograph, applying a filter, and posting the photo on social media. In the public domain, restaurateurs have been so concerned that this would detract from the eating experience that they moved to ban it [
In light of these developments, there is a need for empirical research to understand the place of phones at the dinner table and how they contribute to an obesogenic environment [
Across both studies, participants were recruited from the National University of Singapore via advertisements. All participants provided informed consent at the start of the study and were reimbursed SGD $5 (study 1) or SGD $10 (study 2) for their time. Protocols were approved by the National University of Singapore’s Institutional Review Board (A-15-170) and were preregistered at ClinicalTrials.gov (NCT03299075 and NCT03346785).
In the first study, participants were 137 young adults who met the following eligibility criteria: ownership of a smartphone, aged between 18-30 years, with no history of medical or psychiatric disorders (including eating disorders), and nonsmoker status. The second study involved 71 young adults recruited using the same eligibility criteria as study 1. (For both studies,
As baseline measures, participants self-reported demographic information (age, gender, ethnicity). They also reported 3 health-related metrics: their height and weight (used to derive their BMI), and their frequency of engaging in vigorous physical activity (defined as the number of times, during a single week, they engaged in activities where they “worked up a sweat”).
Participants also completed the Dutch Eating Behavior Questionnaire (DEBQ) [
For the experience sampling component, participants were contacted daily for 7 days (Monday to Sunday) via the Facebook Messenger app (Facebook, Inc) on their mobile phones. Each day, participants received 3 prompts sent at customized schedules coinciding with their regular meal times (breakfast, lunch, and dinner). These prompts were delivered at the following median times: 9:15 AM (study 1) and 8:45 AM (study 2) for breakfast, 12:45 PM (studies 1 and 2) for lunch, and 6:45 PM (studies 1 and 2) for dinner
All messages were delivered via a Python-programmed Facebook bot, and responses were recorded through the Messenger platform with a 30-minute time-out window (median response rate for study 1: 16/21,76%; for study 2: 21/21, 100%). A full list of questions asked of participants can be found in
As study 1 was designed to understand everyday mealtime phone use, participants were asked at each prompt whether they had eaten in the last 15 minutes. In this manner, we captured 1140 meals within the day-to-day routines of 129 free-living participants.
If participants reported that they had eaten, they then indicated whether they used their phones during the meal and what phone functions they used. As a pilot for study 2, participants also recorded their consumption patterns at each prompt (
In study 2, we sought to investigate the causal impact of phone use, particularly that of mealtime food photography, on eating behaviors. As food photography involves a set of rituals in naturalistic settings (eg, arranging the food, taking photos from different angles, applying photo filters) [
To allow causal inferences, we then randomly assigned meals to 1 of 3 phone use conditions. If participants had indicated that they were about to eat, they were sent one of the following instructions: (1) take a photograph of their food as if they were doing so for the photo-sharing application Instagram (food photography condition), (2) take a photograph of their surroundings (eg, furniture, decorations) as if for Instagram (matched nonfood photography condition), (3) or refrain from phone use (no phone condition). These instructions were randomized within the week, with each participant experiencing all 3 conditions. Critically, a follow-up message was then sent 30 minutes after the instructions to verify compliance: participants were either asked to upload the photograph they took (for the food and nonfood photography conditions; see
Finally, as the primary outcome measures, we tracked participants’ enjoyment of the meal and the amount they had eaten. At the end of each prompt, participants rated how much they enjoyed the food using a 7-point scale anchored with “1 = not at all” and “7 = very much”. To assess the amount eaten, we took a relative measurement approach based on food consumption studies that had employed experience sampling [
To characterize mealtime phone use patterns (study 1), observations are summarized with counts and percentages. Characteristics of phone users themselves are summarized with means (SD) or counts and percentages, with subgroups of phone users compared using
For Study 2, we analyzed all instances where participants complied to the experimental instructions. Following the analysis strategy of screen-time studies, we planned a set of orthogonal contrasts to first assess the impact of phone use (vs no phone use) [
Across both studies, analyses were conducted using SPSS 25 (IBM Corp) and R 3.4.0 (R Foundation for Statistical Computing).
In study 1, the participants had a mean age of 21.68 years (SD 2.07), a mean BMI of 20.89 (SD 2.80), and were predominantly of Asian ethnicity: 85.3%(110/129) self-identified as Chinese, 3.8% (5/129) Indian, 3.1% (4/129) Malay, 1.6% (2/129) Eurasian, and 6.2% (8/129) as another ethnicity. Moreover, 73.6% (95/129) identified as female. In study 2, the participants had a mean age of 22.29 years (SD 2.36), a mean BMI of 21.80 (SD 3.22), and were predominantly of Asian ethnicity: 89% (62/70) self-identified as Chinese, 4% (3/70) Indian, 1% (1/70) Malay, and 6% (4/70) another ethnicity. Moreover, 57 of the 70 participants (81%) identified as female.
Across both studies, the average participant’s BMI fell within the normal range [
In the baseline phone-use questionnaire, participants self-reported that they were “likely” to use their phones during a meal (mean rating 3.74/5; 95% CI 3.58-3.91; see
In terms of how phones were used, our week-long monitoring captured a wide range of social (eg, talking on the phone) and nonsocial phone activities (eg, listening to music) that participants engaged in while eating. These patterns broadly mapped onto those outside the eating context (as reported in previous survey studies [
However, mealtime phone patterns were distinct in one important aspect. Although messaging or social networking was the top-ranked functions across all contexts, participants were far more likely to use this feature while eating than to use any other phone function. Indeed, 86.5% (313/362; 95% CI 83.5-90.4) of all mealtime phone use episodes captured involved messaging or social networking, which was 8 times the frequency of the next most widely used function, browsing websites (39/362, 11%; 95% CI 8.7-15.3). Correspondingly, mealtime phone use was more likely to be social than nonsocial in nature. Outside the eating context, however, participants self-reported that they used a range of social and nonsocial phone functions comparably (mean rating of 4.0-4.88 for the likelihood of listening to music, taking photos or videos, watching videos, browsing websites, and messaging or social networking).
In a baseline questionnaire on habitual phone use, participants reported how likely they were to use their phones in each context. A higher score corresponds to greater likelihood, and horizontal lines represent the 95% CI for the mean. When participants were then monitored for 1 week, mealtime phone use was observed in approximately 9 of 10 participants (in an average of 1 in 3 meals).
(Left) In a baseline questionnaire on habitual phone use, participants reported how likely they were to use each phone function on a regular day. A higher score corresponds to greater likelihood, and horizontal lines represent the 95% CI for the mean. (Right) Participants were then monitored closely for 1 week; the graph on the right depicts the percent of mealtime phone use episodes where each phone activity was recorded. Horizontal lines represent the 95% CI for each percentage.
The experience sampling method captured large individual differences in mealtime phone use patterns. Across the week of observation, we recorded the full range of 0%-100% of meals where phones were used (
As shown in
Box-plot depicting the distribution of mealtime phone use frequency captured across 1 week of naturalistic monitoring. The bottom, midline, and top of the box represent the 25th, 50th, and 75th percentiles, respectively, and chronic users are represented in the shaded gray area (top 15% of participants, corresponding to ≥50% of meals with phone use).
Participant characteristics as a function of mealtime phone use patternsa.
Characteristic | Chronic mealtime phone users (n=22) | Regular mealtime phone users (n=107) | Test statistic ( |
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Proportion of meals with phone use | 59.83 (11.77) | 20.31 (13.73) | –12.58 ( |
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Age (years) | 22.45 (2.44) | 21.52 (1.96) | –1.94 (.05) |
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0.18 (.67)d |
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Female | 17 (77.27) | 78 (72.90) |
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Male | 5 (22.73) | 29 (27.10) |
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2.51 (.64)d |
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Chinese | 19 (86.36) | 91 (85.05) |
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Malay | 0 (0.00) | 4 (3.74) |
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Indian | 1 (4.55) | 4 (3.74) |
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Eurasian | 1 (4.55) | 1 (0.93) |
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Others | 1 (4.55) | 7 (6.54) |
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2.63 (.62)d |
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Apple | 15 (68.18) | 74 (69.16) |
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Samsung | 4 (18.18) | 19 (17.76) |
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Others | 3 (13.64) | 14 (13.08) |
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BMI | 20.18 (2.27) | 21.04 (2.88) | 1.31 (.19) |
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0.30 (.86)d |
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0-2 times/week | 15 (0.68) | 68 (63.55) |
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3-4 times/week | 6 (27.27) | 31 (28.97) |
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5-7 times/week | 1 (4.55) | 8 (7.48) |
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Restrained eating score | 2.30 (0.66) | 2.48 (0.83) | 0.95 (.34) |
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Emotional eating score | 2.50 (0.96) | 2.61 (0.80) | 0.56 (.58) |
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External eating score | 3.39 (0.62) | 3.32 (0.54) | 0.57 (.57) |
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Messaging or social networking | 4.86 (0.35) | 4.89 (0.32) | 0.32 (.75) |
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Emailing | 3.82 (1.05) | 3.60 (1.12) | –0.85 (.40) |
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Sharing photos or videos | 3.50 (1.34) | 3.56 (1.15) | –0.19 (.85) |
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Talking on the phone | 2.73 (1.32) | 2.65 (1.30) | –0.24 (.81) |
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Browsing websites | 4.50 (0.60) | 4.36 (0.79) | –0.76 (.45) |
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Watching videos | 4.55 (0.51) | 4.01 (1.08) | 2.28 ( |
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Taking photos / videos | 3.91 (1.07) | 4.07 (0.96) | 0.68 (.50) |
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Listening to music | 3.82 (1.14) | 4.04 (1.13) | 0.83 (.41) |
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Playing games | 2.82 (1.76) | 2.58 (1.43) | 0.80 (.43) |
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Idle time | 4.82 (0.40) | 4.67 (0.47) | –1.35 (.18) |
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During commute | 4.73 (0.55) | 4.55 (0.76) | –1.04 (.30) |
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While waiting in line | 4.64 (0.49) | 4.50 (0.69) | –0.85 (.40) |
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In bed before falling asleep | 4.64 (0.73) | 4.35 (0.98) | –1.31 (.20) |
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In bed when waking up | 4.23 (1.02) | 4.09 (1.15) | –0.51 (.62) |
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During meals | 4.09 (0.61) | 3.67 (1.00) | –1.89 (.06) |
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In the toilet | 3.45 (1.57) | 3.66 (1.30) | 0.66 (.51) |
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During class time | 3.32 (1.29) | 3.18 (1.11) | –0.53 (.60) |
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In the middle of the night | 2.55 (1.54) | 2.31 (1.22) | –0.79 (.43) |
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Number of Instagram followers | 413.0 (243.81) | 534.3 (735.23) | 0.71 (.48) |
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Number of Instagram accounts followed | 598.3 (471.74) | 487.1 (261.84) | –1.45 (.15) |
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aUnless otherwise stated, the data are reported as means (SD), and the test statistic refers to the
bItalics indicate
cBased on responses to the baseline questionnaires.
dChi-square statistic reported.
In Study 2, we used linear mixed-effects models to examine the causal impact of phone use patterns, particularly mealtime food photography, on the amount and enjoyment of food eaten. For food intake, we observed a significant interaction between photography condition and meal type (
In terms of the enjoyment of food eaten, there were no significant effects involving photography condition (smallest
Mean amount and enjoyment of food eaten at each meal (breakfast, lunch, or dinner), plotted as a function of whether participants engaged in food photography or nonfood photography. A higher score corresponds to greater enjoyment or amount eaten, and vertical lines represent 1 SE of the mean.
In this series of studies, we documented how mobile phones have found a place at the modern-day dining table. During 1 week of in-depth monitoring, nearly 9 in 10 of our participants recorded at least one instance of mealtime phone use. This widespread prevalence points to new norms in eating habits, giving impetus for research on the phenomenon.
As the first step in this direction, we recorded what activities participants engaged in when they used their phones during a meal. As compared to traditional digital screens where participants perform only one function (eg, watching television), mealtime phone use entails a wide range of activities that are both social (eg, sending messages to peers) and nonsocial (eg, watching videos) in nature. This has implications for clinical guidelines and research, both of which have classified phone use as “screen time” without differentiating mobile phones from other forms of digital screens, or between various mobile phone activities [
Illustrative of this notion, in study 2, we examined in the causal impact of mealtime food photography, which is one form of phone use that has come under scrutiny in both clinical and lay circles. Relative to nonfood photography, food photography disrupted the increase in food consumption that is typically observed across a day [
More broadly, our finding that mealtime phone use is overwhelmingly social highlights another mechanism through which mobile phones may influence eating. This is particularly notable given that participants reported using more nonsocial functions outside the meal context, and because this manner of phone use distinguishes mobile phones from traditional digital screens (where little social interaction occurs). In previous research, one of the most robust determinants of food intake has been found to be the company one eats with: compared to eating alone, eating with others has been repeatedly found to increase consumption, a phenomenon termed
Finally, although the discussion has focused on what a phone user does, we also examined how much an individual engages in mealtime phone use (study 1). Here, we found no evidence that frequency altered the risk of obesity: BMIs, routine physical activities, and habitual eating behaviors did not differ significantly between participants who recorded high versus low engagement in mealtime phone use.
In presenting these various findings, we note several limitations in both variable and participant selection. First, given the current guidelines on mealtime screen use [
For the first time, we characterized the nature of mealtime phone use and its implications for eating behaviors. By using experience sampling, we tracked a large number of meals in situ (1780 meals across 2 studies) and captured phone use as it occurred in its natural environment [
Flow diagram of participant selection and dropout.
Demographic, baseline mealtime and phone use, and experience sampling questionnaires.
Validation of measures.
Sample images in the food and nonfood photography conditions.
Scatterplot of mealtime phone use frequency against number of meals captured.
Multilevel model of food eaten and enjoyment of food (study 2).
Dutch Eating Behavior Questionnaire
The authors gratefully acknowledge Alex Meyer for his assistance with the Facebook Messenger bot.
JYYY, JCJL, and EMWT designed the research; JYYY conducted the research; JYYY, JCJL, and EMWT analyzed the data; JYYY and JCJL wrote the paper; and JCJL had primary responsibility for final content. All authors read and approved the final manuscript.
None declared.