Is time-restricted eating a healthy choice to lose weight? Investigating by qualitative analysis of Instagram posts and systematic reviews with meta-analysis

Abstract

Background

Time-restricted eating (TRE) has garnered widespread attention as a promising strategy for weight management and metabolic health improvement.

Objective

This study employs a mixed-methods approach to investigate the portrayal and effectiveness of TRE, bridging social media narratives and scientific evidence.

Methods

The first study analyzed Instagram posts qualitatively to identify prevalent TRE methods and motivations, while the second study conducted a meta-analysis of 14 randomized controlled trials (RCTs) to evaluate TRE's efficacy in weight management.

Results

Social media analysis revealed that the keto and 16:8 TRE method dominate online discussions, with fat loss and general health as primary motivators. High-frequency accounts shape public perception through grassroots narratives and consistent engagement. The meta-analysis found the 16:8 method to be significantly effective, particularly for individuals aged 40–50 and during short-term interventions (≤8 weeks). However, challenges such as reduced adherence in older adults, lower effectiveness in longer interventions, and inconsistent results from non-16:8 methods highlight the need for tailored strategies.

Conclusion

This study provides a comprehensive understanding of TRE, bridging social media narratives and scientific evidence. It identifies the mechanisms and contexts where the 16:8 TRE method is most effective and offers practical insights for individuals and professionals seeking evidence-based dietary strategies.

Keywords

Time-restricted eating health communication instagram qualitative analysis meta-analysis weight management

Introduction

Dieting is the only game where you win when you lose!

— Karl Lagerfeld

In 2022, obesity and overweight were pervasive global health challenges, affecting individuals across all age groups. One in eight people worldwide was living with obesity, including 43% of adults classified as overweight and 16% as obese. Among children and adolescents aged 5–19 years, over 390 million were overweight, with 160 million living with obesity.¹ Due to the exponential rate of increment, obesity and overweight are more than aesthetic concerns—they pose significant health risks. Excess body weight is a major risk factor for numerous chronic diseases, including cardiovascular diseases,² cancers,³ musculoskeletal disorders⁴ like bone and joint disorders, and obstructive sleep apnea.⁵ Furthermore, obesity negatively impacts mental health, contributing to low self-esteem, depression, and anxiety.^6–8 Of equally alarming concern is the financial burden associated with obesity. Purportedly, obese people incur 32% higher medical expenses compared to those with a normal weight.⁹ Straining healthcare systems worldwide with costs cause medical treatments and lost productivity.^10,11 Addressing the root causes and finding effective solutions to combat obesity is therefore a pressing priority for public health.

Amid the plethora of weight-loss strategies, Time-Restricted Eating (henceforth, TRE), also known as time-restricted feeding, namely TRF,¹² an intermittent-fasting (Intermittent fasting, by definition, refers to an eating pattern that cycles between periods of eating and fasting. It does not prescribe what foods to eat, but rather when to eat. Common methods include time-restricted eating (e.g., 16 hours fasting, 8 hours eating), alternate-day fasting, or the 5:2 diet (eating normally for 5 days, restricting calories for 2 days). The main goal is to give the body extended periods without food intake to support metabolic health, weight loss, or cellular repair processes.) regimen, has gained widespread attention in recent years. It involves limiting food intake to a specific window of time within each 24-hour period, typically ranging from 8 to 12 hours, while abstaining from caloric consumption outside this period.¹³ Unlike traditional calorie-restriction diets that often require meticulous tracking of food intake, TRE is lauded for its simplicity and flexibility, making it appealing to a wide audience.¹² Researchers have also examined that TRE has immense potential not only for weight loss but also for improving metabolic health and reducing the risk of nonalcoholic fatty liver disease.¹⁴ However, despite its popularity, questions still remain regarding its applicability, practicality, and potential long-term health effects.

To address these questions, this study adopts a mixed-methods explanatory sequential design (see Figure 1), which integrates qualitative and quantitative research in two distinct but connected phases. In the first phase, we fully leveraged the value of social media as a “crucible” for public discourse,¹⁵ as highlighted in numerous studies.¹⁶ The findings from this qualitative analysis inform the second phase—a systematic review and meta-analysis of peer-reviewed scientific literature—to quantitatively assess the health effects and efficacy of TRE methods identified in the first phase. By combining social insights with clinical evidence, this design allows us to interpret empirical findings within the broader social and cultural context in which TRE is practiced and promoted.

Figure 1.

Pipeline of this study: the Instagram posts were collected by searching the hashtag #intermittentdiet via the tool zeeschuimer (Peeters, 2024).

This work provides valuable insights into contemporary health and wellness trends, highlighting the influence of digital platforms in shaping health behaviors. Additionally, the meta-analysis offers a critical synthesis of scientific evidence, contributing to the academic and clinical understanding of TRE's role in weight management and overall health. Ultimately, this study calls for a broader discourse on sustainable and scientifically validated weight-loss practices, empowering individuals to make informed decisions about their health.

Theoretical background

In recent years, weight loss methods have gained widespread popularity online,¹⁷ with intermittent fasting emerging as a particularly prevalent approach.¹⁸ Intermittent fasting refers to a dietary strategy aimed at reducing caloric intake by extending periods of fasting to 12 hours or more. It encompasses various specific methods, each with distinct fasting and eating patterns designed to suit individual preferences and lifestyle requirements.¹⁹ One common form of intermittent fasting is TRE, which confines food consumption to a designated window each day, typically ranging from 8 to 12 hours. This approach seeks to align eating habits with circadian rhythms, potentially improving metabolic health.²⁰ Intermittent fasting can also involve longer intervals, such as 1–2 fasting days per week, interspersed with unrestricted eating on non-fasting days. This approach typically entails consuming less than 25% of baseline energy needs on fasting days, followed by unrestricted food intake on non-fasting, or “feast,” days.²¹

Despite its promising benefits, time-restricted eating presents several challenges that may hinder their widespread application and effectiveness. For instance, adherence to TRE protocols can vary significantly among participants, with reported success rates ranging from 47% to 95% in clinical trials. Factors such as lifestyle, social support, and work commitments can greatly influence an individual's ability to stick to the eating schedule.²²

As aforementioned, TRE has gained popularity in both scientific and public domains. However, debates persist regarding their overall efficacy and long-term sustainability. A typical question remaining is about whether the observed metabolic benefits of TRE are directly attributable to fasting itself or merely to reduced caloric intake.²³ While existing research has explored the physiological mechanisms and short-term outcomes of TRE, limited attention has been paid to its practical applications and perceptions in real-world contexts. By bridging the gap between social perceptions and scientific evidence, this study seeks to provide a comprehensive understanding of TRE's role in weight management. Our findings aim to contribute to the ongoing discourse by offering nuanced insights into the challenges, benefits, and limitations of this increasingly popular dietary strategy.

Research design overview

This study adopted a mixed-methods approach to explore the dietary patterns Instagram users employ for weight loss and identify which plans are more effective. In general, our research design comprises two parts (See Table 1). In Study 1, we initially scraped Instagram posts with the hashtag #intermittentdiet. Two human annotators were recruited to code the types and purposes of intermittent diet that a post has mentioned. We use the term “intermittent diet” here because the keto diet is not a form of intermittent fasting. Given the fashion of combining intermittent dieting and intermittent fasting,²⁴ and for the sake of precision and clarity, we chose to use the term “intermittent diet type.” As an exploratory research, Study 1 provided us with an overall insight into a wide variety of intermittent fasting selections for weight loss. The findings inspired us to think about which dietary pattern is popular to lose weight. Thus, we conducted our second study, where we utilized a meta-analysis to systematically evaluate and compare the effects of different dietary patterns (experimental group) versus unrestricted regular diets (control group) on body weight, body composition, and metabolic indicators. In a nutshell, our research aims to understand users’ diet preferences for losing weight and delve into the differences among these approaches. Ultimately, our research contributes to identifying evidence-backed dietary strategies, providing valuable insights for both individuals seeking effective weight-loss methods and professionals in nutrition and health sciences.

Table 1.

Materials, methods, and purposes of our study 1 and 2.

Study	Materials	Database	Keywords	Methods	Purposes
Study 1	Instagram posts with the hashtag #intermittentdiet, including metadata and captions	Instagram	#Intermittentdiet	Content analysis	Extraction of purposes and approaches mentioned on Instagram
Study 2	Relevant studies on changes in body weight, blood glucose, blood pressure, and blood lipids after dietary pattern interventions	PubMed, Web of Science, and EMBASE	Dietary pattern, dietary habit, dietary interventions, diet, and weight loss	Meta analysis	Differences in the weight loss effects of different dietary patterns

Study 1: Popular dietary patterns on Instagram

Methods

Data collection

Our data was collected via the tool Zeeschuimer²⁵ on December 29, 2024, from Instagram, a popular platform for young people to share their narratives and lifestyles.²⁶ The reason why we select Instagram is its integration of both visual and textual elements and its widespread popularity, particularly among young adults. We began by searching for hashtags such as #fasting, #diet, and #fastdiet. However, these hashtags presented two challenges: first, they contain a wide variety of diets with purposes beyond weight loss (e.g., pregnancy and rehabilitation); second, the sheer volume of data associated with these hashtags made human processing highly time-consuming. For instance, as of December 29, 2024, there were over 78, 200, 000 posts tagged with #diet. As a result, we decided to exclude these hashtags, along with other general hashtags like #loseweight and #weightlose. Upon closer inspection, we observed that these two hashtags primarily discuss dietary fasting, such as low-fat diets, the opposite of typical eating patterns.²⁷ To refine our search, we narrowed the criteria and identified a specialized hashtag highly relevant to our research topic: #intermittentdiet. Our final scraped dataset contains 868 rows.

Data annotation

To gain deeper insights from the dataset of 868 rows, a data annotation process was conducted. Two annotators were tasked with identifying and categorizing the purposes and approaches of TRE mentioned in the Instagram posts. It is worth noting that the annotation process considered both the image and the caption. Take the following post as an example: while the caption is irrelevant to TRE, the image prominently displays “16:8,” a well-known TRE approach (see Table 2).

Table 2.

Example of a post where the image is TRE-related while the caption is unrelated.

Image	Caption	Hashtag	Approach
C2WuHNrSVxJ.jpg	#FastingSuccessStory #intermittentfast #intermittentlifestyle #intermittentdiet #intermittentfastening #intermittentfastin #intermittent …	#intermittentdiet	16:8

During a preliminary annotation experiment (see Section 4.2), it was observed that many posts did not explicitly mention any specific TRE approaches. Instead, a significant portion of posts focused on the types of food that could be consumed during the designated eating window. To capture this additional context, the annotators were instructed to document these food items as part of the annotation process. This step allowed for a more nuanced analysis, accounting for not only TRE strategies but also dietary choices associated with the eating window, which has also been mentioned and tentatively explored by Parr et al. (2022).²⁸ This dual-focus annotation approach ensures that the dataset reflects both the practices and the supporting behaviors surrounding TRE, providing a richer basis for subsequent analysis. To assess the reliability of the annotations, we calculated Krippendorff's alpha²⁹ as a measure of inter-annotator agreement.

Preliminary experiments

To ensure the annotators were well-prepared for the task and to establish a clear understanding of the content, we conducted preliminary experiments using a subset of the first 100 rows from the dataset. Annotating these 100 rows served two purposes. First, it allowed the annotators to familiarize themselves with the task requirements and the nuances of the content, ensuring they were equipped to identify the purposes and approaches of TRE effectively. Second, it provided an opportunity to identify any misunderstandings or inconsistencies in their annotations. Through this process, we were able to provide targeted guidance and clarify any ambiguities, aligning their work with the objectives of the study.

Findings

As mentioned in the last section, human annotators coded two parts: the intermittent diet type that a post mentions, and the reasons for intermittent diet mentioned in a post. Besides, we calculated the most frequent accounts and summarized their types manually. The following sections illustrate results of three parts separately.

Intermittent diet type annotation

The annotation process for intermittent diet types demonstrated a high level of reliability, with a Krippendorff's alpha value of 0.99 between the two annotators, indicating excellent agreement. Out of the entire dataset, only three posts had differing judgments between the two annotators. However, through discussion and collaborative review, the annotators reached a consensus on these posts, ensuring consistency and accuracy in the final annotations.

The results have been shown in Figure 2. While keto is not a form of TRE, we included keto diet in our analysis because many users explicitly combine keto with TRE practices, reflecting common hybrid dieting behaviors observed on social media. Mentioning keto therefore does not dilute the focus of this paper; rather, it helps illustrate the real ecosystem of the dieting community, where users often blend different dietary strategies to optimize results.

Figure 2.

Frequency of different diet types mentioned in Instagram posts.

Through the frequency of different diet types, the keto method emerged as the most frequently mentioned approach (as shown in Figure 1), indicating its popularity and widespread adoption among intermittent fasting practitioners. A closer examination suggests that the keto diet's extensive media coverage, celebrity endorsements, and social media attention may account for its 192 recorded mentions. The second most common approach was “two meals a day,” which likewise garnered significant attention. Notably, “two meals a day” appears related to 16:8—the third most common method—because individuals following 16:8 often consume just two meals within the designated eating window. Other variants, such as 5:2, one meal a day, and 18:6, were mentioned only sporadically, suggesting that they are less frequently discussed or practiced. Collectively, these findings underscore the prominence of keto and 16:8 in social media discourse on intermittent dieting and highlight its perceived viability as a practical and sustainable weight-loss strategy. It highlights the perceived viability of TRE (both alone and in combination with other diets) as a practical and sustainable weight-loss strategy.

Intermittent diet reasons

The annotation process for identifying reasons behind intermittent dieting also achieved a high level of reliability, with a Krippendorff's alpha value of 0.96 between the two annotators. However, upon closer inspection, discrepancies were identified in 28 posts. In these cases, one annotator interpreted the advantages of intermittent dieting mentioned in the posts as the reasons why the user engages in intermittent dieting, while the other did not. After thorough discussion and review, the two annotators reached a consensus on these posts, ensuring consistency and clarity in the final annotations. This resolution process highlights the nuanced nature of interpreting user motivations based on social media content, where explicit reasoning is not always clearly stated.

By calculating the intermittent diet reasons annotated by human coders, we observed that the most frequently cited reason was fat loss (as shown in Figure 3), which reflects the primary motivation for many individuals engaging in intermittent dieting. This was closely followed by a focus on health in general. These posts do not mention a specific reason. Instead, being healthy and having a healthy lifestyle are the main pursuits in these posts. Meanwhile, low blood sugar management appears as a smaller but notable concern, indicating that some dieters were focused on maintaining healthy glucose levels. Similarly, bodybuilding, reversing type 2 diabetes, and detox received comparatively fewer mentions, suggesting that these goals appeal to more specialized or narrower audience segments. Overall, the data highlights the strong emphasis placed on both weight loss and broader health improvements when people choose to alter their eating patterns.

Figure 3.

Frequency of different diet reasons mentioned in Instagram posts.

High-frequency intermittent diet accounts

Social media accounts with frequent posts on a specific topic often shape public perceptions and behaviors, making them valuable focal points for understanding popular dietary patterns.

In conclusion, all top 10 accounts are personal rather than corporate or organizational (See Table 3), suggesting that the narrative around intermittent fasting is primarily driven by individual experiences and grassroots-level sharing. From the perspective of themes, these ten accounts focus on two main topics: Food (i.e., “Intermittent Dieting” and “INTERMITTENT FASTING JOURNEY”) and Fitness (i.e., “WanIsMyName&MyFatsIsKillingMe” and “ReyCreate Nation”). These accounts either emphasize specific eating patterns, such as keto and vegetarian diets, or share daily practices and personalized plans for intermittent fasting. In views of contents, these accounts are more accessible and practical for their audiences.

Table 3.

Top 10 most frequent intermittent diet accounts.

User name	Frequency	No. of followers	Account type
Intermittent dieting	115	109	Personal account sharing personalized intermittent fasting diet plan
ViKeto	76	68	Personal account sharing Keto food cooked manually
Herfoodjournal	73	20	Personal account sharing daily food
WanIsMyName & MyFatsIsKillingMe	53	66	Personal account sharing daily exercise
Lydia Davenport Nutrition Coach & EMT	40	1136	Personal account propagating group coaching programs
Ayu Baizian	30	1083	Personal account sharing food cooking
Miranda	21	28	Personal account sharing keto diet every day
INTERMITTENT FASTING JOURNEY	18	6004	Personal account sharing daily practice of Intermittent Fasting
ReyCreate Nation	17	64	Personal account sharing famous quotes to hold on to losing weights
Spice Your Life	17	317	Personal account sharing vegetarian food

Intriguingly, the accounts with the highest frequencies of posting, such as “Intermittent Dieting” (115 posts), do not necessarily have the highest number of followers (109 followers). This indicates that frequent posting alone does not guarantee widespread reach or influence, but it can signify active engagement within a niche community. On the other hand, accounts like “Lydia Davenport Nutrition Coach & EMT” and “INTERMITTENT FASTING JOURNEY” have fewer posts but significantly more followers (1136 and 6004, respectively), indicating that followers prioritize quality content, professional expertise, or targeted value over quantity.

Last but not least, a noteworthy observation is that certain accounts, such as “Intermittent Dieting,” post daily, providing a consistent stream of content. This daily engagement helps maintain audience interest and reinforces the importance of routine in intermittent fasting practices. It also allows followers to track progress, gain inspiration, and engage actively with the influencer's journey.

Study 2: Meta-analysis of scientific research

Methods

The meta review complies with the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting checklist, which is registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD420251060703). The following sections include our data collection and analysis methods.

Data collection

To conduct the meta-analysis, we collected randomized controlled trials (henceforth, RCTs) examining the impact of dietary interventions on Body Mass Index. The search was performed using keyword-based queries across several major academic databases, including Web of Science, Scopus, PubMed, and Medline. The search was concluded on December 23, 2024. The search strategy employed the following query:

(Dietary pattern OR Dietary habit OR Dietary intervention OR diet and weight loss) AND (Body mass index OR BMI).

This approach ensured the inclusion of relevant studies focusing on dietary patterns and their influence on BMI, providing a robust foundation for the subsequent meta-analysis.

Selection criteria

Studies were included in our meta-analysis if they met the following criteria: (1) RCTs compared dietary interventions for weight loss with regular diets that did not restrict caloric intake or eating times. (2) Participants were adults aged 18 years or older, regardless of nationality, gender, or ethnicity. Eligible participants included healthy volunteers or individuals with mild chronic conditions such as impaired glucose tolerance, hypertension, or hyperlipidemia, which do not significantly affect normal eating habits or study outcomes. (3) The primary outcome measure of the studies had to be body weight. (4) We explicitly excluded sources from predatory journals.

Studies were excluded if they were non-English publications, unpublished works, theses, conference abstracts, reviews, commentaries, or opinion articles. Exclusion also applied to studies that did not use body weight change as an outcome measure or failed to report body weight outcomes explicitly. Studies were further excluded if participants had severe comorbidities significantly affecting eating behavior or body weight (e.g., cancer, autoimmune diseases, chronic gastric ulcers, hyperthyroidism, diabetes, chronic pancreatitis, or postoperative conditions) or if participants were not screened for mental and behavioral disorders (e.g., depression, anorexia, or schizophrenia). Duplicate publications, animal studies, studies lacking a control group, or studies with data that could not be extracted, even after contacting the authors, were also excluded.

Selection procedure and data extraction

The literature retrieved from the database was imported into NoteExpress to remove duplicate records. Subsequently, three authors independently screened the titles and abstracts of the remaining articles to exclude irrelevant studies. Using the inclusion and exclusion criteria, the authors evaluated the eligibility of the full texts of the remaining studies. This evaluation involved analyzing key details such as the title, authors, country, publication date, study population, intervention model, intervention protocol, and outcome measures. Articles that met the eligibility criteria were included in the meta-analysis. The detailed process of literature retrieval and selection is illustrated in Figure 4.

Figure 4.

PRISMA flowchart.

Quality assessment

For the present research, three researchers evaluated the quality of the included studies using the Cochrane Risk of Bias Assessment Tool. This tool assesses several domains, including random sequence generation, allocation concealment, blinding of participants and personnel, completeness of outcome data, selective reporting, and other potential sources of bias. Each item was rated as “low risk,” “unclear risk,” or “high risk” based on the assessment results. The evaluation data were input into RevMan 5.4 software to generate a risk of bias graph. The results of the quality assessment indicated that the 14 included studies exhibited a certain level of bias.

Data analysis

The data analysis for the current study was conducted using RevMan 5.4 software, based on the 14 included studies. As the outcome measure was body weight, which is a continuous variable, the mean difference and 95% confidence interval were used as statistical measures for effect size analysis. The significance of the results was determined using the Z-test, with statistical significance set at $P \leq .05$ .

The heterogeneity among the included studies was assessed using the Q-test and I-squared statistic $(I^{2})$ . The $I^{2}$ statistic is a widely used measure in meta-analysis to quantify the degree of heterogeneity among the studies being analyzed. It helps determine how much of the observed variance in effect sizes across studies can be attributed to true heterogeneity rather than sampling error or chanc.^30,31 $P < .1$ and $I^{2} > 50 %$ indicated substantial heterogeneity. In cases where no significant heterogeneity was detected among the study results, a fixed-effects model was employed for analysis. Conversely, when substantial heterogeneity was present, a random-effects model was used, followed by sensitivity and subgroup analyses to explore the sources of heterogeneity. Ultimately, a funnel plot was utilized to assess publication bias. For heterogeneity analysis, $P < .05$ and $I^{2} > 50 %$ were considered indicative of significant heterogeneity among the included studies.

Findings

Effects of dietary intervention on body weight

An initial search identified 4522 studies. After multiple rounds of filtering, 14 studies included in the meta-analysis comprised a total of 423 participants, with 239 participants in the experimental group and 184 in the control group.

The basic information of the studies included in the meta-analysis was coded and extracted, including authors, publication year, participants’ nationality, age, total sample size, intervention duration, sample size of the experimental and control groups, gender distribution, intervention cycles, types of intervention activities, and outcome measures. In this study, dietary interventions included both Intermittent Fasting and Time-Restricted Eating. Among the included studies, one focused on Intermittent Fasting, while 13 focused on Time-Restricted Eating. Of these, nine studies employed the 16:8 dietary method, while five studies adopted other methods such as 5:2 fasting, 20:4 fasting, and 12:12 fasting. Regarding the age of participants, six studies involved samples with an age range of ≤40 years, four studies included samples aged >40 years, and four studies did not report age-specific grouping. The intervention duration varied, with seven studies lasting ≤8 weeks and another seven exceeding 8 weeks. Detailed information about these studies is presented in Table 4.

Table 4.

Basic features of included studies.

Included studies	Country	N	n		Age	Intervention protocol	Intervention duration	Outcome measures
Included studies	Country	N	E (male)	C (male)	Age	Intervention protocol	Intervention duration	Outcome measures
Moro et al. (2021)	Italy	20	10 (-)	10 (-)	-	TRE (16:8)	12 months	Body mass, defatted body weight, fat mass, total cholesterol
Ma et al. (2023)	China	107	68 (19)	39 (10)	＜65	IF (5:2)	60 days	Body mass, body fat percentage, body mass
Grant et al. (2017)	U. S.	18	10 (-)	8 (-)	-	TRE (20:4)	8 weeks	Body mass, fat mass, body fat percentage
Kelsey et al. (2018)	U. S.	46	23 (-)	23 (-)	25–65	TRE (16:8)	12 weeks	Body mass, fat mass, defatted body weight, visceral fat mass, body mass
Brady et al. (2021)	Ireland	17	10 (-)	7 (-)	36.4 ± 7.4	TRE (16:8)	8 weeks	Body mass, height, adiposity, defatted weight
Lisa et al. (2020)	U. S.	20	11 (2)	9 (1)	45.5 ± 12.1	TRE (16:8)	12 weeks	Body mass, body fat percentage, blood glucose, blood lipids HDL
Sofia et al. (2020)	U.S.	58	39 (-)	19 (-)	18–65	TRE (20:4)	10 weeks	Body mass, blood sugar, body mass
Sofia et al. (2020)	U.S.	58	39 (-)	19 (-)	18–65	TRE (18:6)	10 weeks	Lipids high density lipoprotein
Joana et al. (2021)	Portugal	18	9 (-)	9 (-)	22.4 ± 2.8	TRE (16:8)	4 weeks	Body mass, fat mass
Maha et al. (2018)	Tunisia	20	10 (10)	10 (10)	26.9 ± 1.97	TRE (12:12)	12 weeks	Body mass, body mass, lipids high density lipoprotein
Christopher et al. (2021)	U.S.	23	13 (-)	10 (-)	44 ± 7	TRE (16:8)	8 weeks	Body mass
Dylan et al. (2020)	U.S.	46	22 (-)	24 (-)	43.8 ± 11.2	TRE (16:8)	12 weeks	Body mass, fat mass, body mass
Tatiana et al. (2020)	Italy	16	8 (-)	8 (-)	19.3 ± 0.1	TRE (16:8)	4 weeks	Body mass, height, defatted weight, fat mass, total cholesterol
Kim et al. (2007)	U.S.	15	15 (5)		45.0 ± 0.7	TRE (20:4)	16 weeks	Body mass, fat mass, defatted body weight
Matthew et al. (2020)	U.S.	26	13 (-)	13 (-)	22.9 ± 3.6	TRE (16:8)	8 weeks	Body mass, blood glucose

Risk of bias assessment

The quality assessment of included studies revealed that among these 14 studies, 5 had four items rated as “high risk” or “unclear,” while 7 had three items rated as “high risk” or “unclear.” These findings indicate a certain level of bias in the studies. The primary reasons for this bias were:

Challenges in concealing the allocation scheme during the intervention.

Difficulty in achieving blinding of participants and staff.

Detailed results of the risk of bias assessment are presented in Figure 5.

Figure 5.

The left bar charts are level of bias for the included studies; the right graph summarizes the types of bias for the included studies.

Sensitivity analysis and publication bias analysis

A sensitivity analysis was conducted on the included studies to evaluate the robustness of the results. The analysis showed that excluding any single study did not alter the direction of the outcome measures, indicating that the results were reliable and stable. For publication bias, a funnel plot was used to assess the presence of bias. The plot appeared roughly symmetrical, suggesting no significant publication bias. The funnel plot is shown in Figure 6.

Figure 6.

Publication bias test for included studies.

Meta-analysis of dietary interventions on body weight

A total of 14 studies with 423 participants were included in the meta-analysis. The heterogeneity test showed $P < .1$ and $I^{2} = 86 %$ , indicating high heterogeneity among the studies. As a result, a random-effects model was employed, accompanied by subgroup analyses. The meta-analysis revealed a combined effect size of $M D = 0.09$ ( $95 % C I : - 2.26$ to $2.43$ , $P > .01$ ), indicating that the pooled effect size was not statistically significant. Furthermore, as illustrated in the forest plot, the combined effect size intersects with the null line, confirming that dietary interventions do not have a significant effect on body weight. The results are shown in Figure 7.

Figure 7.

Forest plot of the impact of dietary weight loss on body mass in meta-analysis.

To further investigate the impact of dietary interventions on body weight, subgroup analyses were conducted based on participants’ age groups, intervention duration, and intervention types. The analyses indicated heterogeneity among the three subgroups, suggesting that dietary interventions may produce varying effects on body weight across different contexts. These findings highlight the nuanced nature of dietary interventions and their potential differential impacts on body weight development.

Subgroup analysis of dietary interventions on body weight by age group: This subgroup analysis included 14 studies categorized based on the age range of participants: ≤ 40 years (115 participants), > 40 years (58 participants), and studies with unspecified age groups (250 participants). Heterogeneity testing showed P < .1 and $I^{2} = 86 %$ , and the analysis of subgroup differences revealed $P = .04$ $(P < .05)$ , indicating that age influences the effect of dietary interventions on body weight. Within the subgroups, the ≤40 years age group demonstrated a significant positive combined effect size [ $M D = - 1.89$ , $95 % C I : - 2.90$ to $- 0.88$ , $P < .01$ ], indicating a statistically significant reduction in body weight. Similarly, the >40 years age group also showed a significant positive effect [ $M D = - 4.05$ , $95 % C I : - 6.50$ to $- 1.59$ , $P = .001$ ], with a statistically significant reduction in body weight, although the effect size was smaller compared to the ≤40 years group. In contrast, the unspecified age group yielded a small effect size [ $M D = 2.38$ , $95 % C I : - 2.17$ to $6.94$ , $P = .31$ ], which, despite statistical significance, fell to the right of the null line, indicating a negative impact on body weight. These findings suggest that dietary interventions may have differential effects on body weight depending on the age group, with younger participants experiencing more substantial positive outcomes.

Subgroup analysis of dietary interventions on body weight by intervention duration: Among the 14 included studies, participants were categorized into two groups based on the intervention duration: ≤ 8 weeks (118 participants) and >8 weeks (305 participants). Heterogeneity testing showed $P < .1$ and $I^{2} = 86 %$ , indicating high heterogeneity. Analysis of subgroup differences revealed $P = .03$ , demonstrating statistically significant differences between the groups and suggesting that dietary interventions of varying durations have different effects on body weight.

In the ≤8 weeks group, the combined effect size was [ $M D = - 2.58$ , $95 % C I : - 4.06$ to $- 1.09$ , $P < .01$ ], indicating a significant positive impact on body weight reduction. Conversely, the >8 weeks group showed a combined effect size of [ $M D = 1.92$ , $95 % C I : - 1.97$ to $5.81$ , $P = .33$ ], which was not statistically significant. These results suggest that shorter dietary intervention durations (≤8 weeks) are more likely to produce a positive and statistically significant reduction in body weight, while longer interventions (>8 weeks) may not yield significant outcomes.

Subgroup analysis of dietary interventions on body weight by intervention type: The 14 included studies were divided into two groups based on the type of dietary intervention: non-16:8 diets (237 participants) and 16:8 diets (176 participants). Heterogeneity testing showed $P < .1$ and $I^{2} = 86 %$ , indicating high heterogeneity. Analysis of subgroup differences revealed $P < .01$ , demonstrating statistically significant differences between the groups and suggesting that different dietary intervention types have varying effects on body weight.

For the non-16:8 diet group, the combined effect size was [ $M D = 5.26$ , $95 % C I : 2.70$ to $7.81$ , $P < .01$ ], with the effect size falling to the right of the null line, indicating a negative impact on body weight. In contrast, the 16:8 diet group demonstrated a combined effect size of [ $M D = - 2.75$ , $95 % C I : - 4.44$ to $- 1.05$ , $P = .002$ ], with the effect size falling to the left of the null line, indicating a positive impact on body weight reduction. These suggest that the 16:8 diet is significantly more effective in reducing body weight compared to non-16:8 dietary approaches, which appear to have a negative impact.

Discussion

Theoretical contribution of study 1

The investigation into intermittent fasting through Instagram contributes significantly to the existing body of literature on dietary behaviors and health communication. The prevalence of the 16:8 approach among Instagram posts reflects broader trends observed in dietary research, where specific diets gain popularity due to their perceived effectiveness and ease of adherence. Previous studies have noted that social media platforms influence dietary choices by amplifying popular methods and creating communities around specific practice.³² The findings here underscore the role of social media in shaping public perceptions of intermittent fasting, particularly highlighting how certain methods become dominant narratives within these digital spaces.

The identification of fat loss as the primary motivation for engaging in intermittent fasting resonates with existing literature that often cites weight management as a key driver for dietary changes. This aligns with studies indicating that social media users frequently seek information on weight loss strategies, reinforcing the idea that health-related motivations are prominently featured in online discussions about diet.³³ Furthermore, the nuanced understanding of motivations—distinguishing between explicit reasons and general health aspirations—adds depth to our comprehension of user engagement with intermittent fasting content.

The analysis of high-frequency posting accounts reveals a significant aspect of social media dynamics: the relationship between content frequency and audience engagement. While frequent posting can enhance visibility within niche communities, it does not necessarily correlate with follower count or perceived influence. This observation is consistent with research indicating that quality content often trumps quantity in attracting followers, highlighting the importance of authentic engagement over mere content output.³⁴ The study's findings contribute to this discourse by emphasizing that personal narratives and relatable experiences often resonate more deeply with audiences than corporate or organizational messaging.

Theoretical contribution of study 2

The meta-analysis results indicate that the 16:8 time-restricted eating approach significantly reduces body weight. This echoes the findings of Peterson (2024).³⁵ Long-term weight loss often depends on individual adherence to dietary plans, and the 16:8 method is relatively simple and easier to sustain. Participants exhibit high compliance with this approach, and adverse events are rare. Studies have reported no significant changes in symptoms such as nausea, constipation, diarrhea, headache, fatigue, or irritability from baseline to post-treatment.^36,37 Furthermore, after 12 weeks of an 8-hour TRE regimen, neither blood cell counts, nor disordered eating behaviors showed any significant changes.³⁶

In contrast, non-16:8 methods often face challenges in adherence and report adverse events such as dizziness, nausea, headache, and diarrhea, particularly with fasting periods of 20 or 14 hours. These symptoms typically peak in the second week and subside by the third.^37,38 The 16:8-TRE model indirectly controls caloric intake by limiting the eating period. Due to the shortened time frame for food consumption, individuals are less likely to overeat. Intermittent fasting appears to mimic the effects of continuous calorie restriction, facilitating weight loss, fat reduction, lean mass preservation, and improved glucose homeostasis. It may also suppress appetite, further reducing total caloric intake.^38,39,40

Mechanistically, the extended time window of fasting in the 16:8 approach effectively lowers insulin secretion,³⁷ promoting triglyceride breakdown in adipose tissue into fatty acids. These free fatty acids are transported to the liver, where, after glycogen depletion, they undergo accelerated lipolysis, leading to increased plasma free fatty acid levels. These fatty acids are then utilized in organs such as the liver, kidneys, astrocytes, and intestinal cells, undergoing β-oxidation to produce ketone bodies. Ketone bodies are metabolized into acetyl-CoA, which enters the Krebs cycle to generate ATP, making fat breakdown a primary energy source. This prolonged lipolysis and ketone production significantly contribute to weight reduction.^41–44 Non-16:8 TRE methods showed no weight loss effects and, in some cases, led to weight gain. This could be attributed to the time window of fasting being either too short or too long. Short fasting windows may fail to initiate the fat metabolism process effectivel^y,⁴³ while excessively long fasting periods may increase hunger and food cravings, leading to higher caloric intake and potentially contributing to obesit^y.⁴⁵

Limitations and future research directions

While this study provides valuable insights into TRE, we do believe there is substantial room for improvement existing in different directions.

First, our research focuses exclusively on TRE, even though our dataset includes mentions of other dietary approaches to weight loss. As aforementioned, some posts discuss dietary patterns, such as low-fat or low-carb diets (e.g., keto diet), as alternatives or complements to TRE. While O’Connor et al. (2021)⁴⁶ has made initial comparisons between these approaches, a more comprehensive and systematic review is needed to explore their effectiveness and interactions. Future studies could integrate multiple dietary approaches to provide a holistic understanding of weight-loss strategies and their representations on social media.

Second, our meta-analysis is limited by the small number of studies included in certain subgroups, such as older adults and long-term interventions, which affects the representativeness and robustness of the findings. Additionally, the short follow-up durations in most studies leave the long-term effects and safety of TRE unclear, highlighting the need for extended follow-up in future research. Future research should focus on exploring the mechanisms of TRE, including its effects on fat metabolism, insulin sensitivity, and metabolic compensation across different age groups and intervention durations. Additionally, long-term studies are needed to evaluate TRE's sustained impact on weight loss and other metabolic markers, while optimizing fasting and eating windows, caloric control, and personalized approaches to enhance its effectiveness.

Additionally, the posts analyzed in this study were selected based on a single hashtag on Instagram, limiting the diversity and comprehensiveness of the dataset. To address this limitation, future research should expand the dataset by incorporating posts from multiple relevant hashtags and platforms. Additionally, the manual annotation process used in this study, while effective for a smaller dataset, is impractical for large-scale analysis. Future studies should employ computational methods on the basis of advanced NLP and CV techniques (e.g., Multimodal LLMs), to automatically analyze large volumes of social media content. This approach would enhance scalability and provide deeper insights into evolving trends and patterns.

By addressing these limitations, future research can build on the foundation laid by this study, contributing to a more nuanced and comprehensive understanding of weight-loss strategies.

Conclusions

Overall, Study 1 explored the popular dietary patterns associated with intermittent fasting on Instagram, providing insights into the practices and motivations of social media users. The findings revealed that the 16:8 time-restricted eating method is the most frequently mentioned dietary pattern, highlighting its prominence as a practical and sustainable approach to weight management. The primary motivations identified for engaging in intermittent fasting were fat loss and general health, emphasizing its appeal as both a fitness and wellness strategy. Additionally, the analysis of high-frequency accounts underscored the grassroots-driven nature of intermittent fasting narratives, with personal accounts sharing food, fitness routines, and personalized fasting plans. These accounts foster engagement by offering relatable, practical content and inspiring audiences through consistent posting.

Study 2 showed that TRE is an effective weight management strategy, particularly among individuals aged 40–50 and during short-term interventions (≤8 weeks), where it demonstrates significant positive effects on weight reduction. The 16:8 model emerges as the most optimal dietary time arrangement, offering a practical and sustainable approach for many individuals. However, the suitability of TRE for older adults and long-term interventions requires careful evaluation to mitigate potential negative impacts, emphasizing the importance of tailored strategies for different populations.

Footnotes

ORCID iDs

Qinghao Guan

Weishan Mai

Ziyi Qiu

Yifan Zuo

Ethical considerations

This study does not involve experiments on humans or animals. Therefore, ethical approval was not required.

Author contributions

Qinghao Guan: writing-original draft, project administration, conceptualization, data curation, methodology, and writing-review and editing. Weishan Mai: writing-original draft, validation, conceptualization, and writing-review and editing. Ziyi Qiu: data curation and methodology. Yifan Zuo: writing—original draft, validation, project administration, conceptualization, and writing-review and editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Guangdong Basic and Applied Basic Research Foundation, Education Ministry Humanities and Social Sciences Youth Fund (grant numbers 023A1515110088 and 24YJC890078).

Declaration of conflicting interests

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

Data availability statement

Data will be available on request.

Appendix

A. Why didn’t we use hashtags like #timerestrictedeating and #timerestrictedfasting?

We explored using these hashtags but found that #timerestrictedeating and #timerestrictedfasting had no posts. Additionally, #time_restricted_eating had only three posts. Given the lack of substantial content under hashtags containing “time-restricted eating,” we decided to exclude them from our search.

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