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Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: Large-Scale, 52-Week Observational Study

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: Large-Scale,... Background: Intermittent fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile apps to help them track and adhere to their fasts and monitor changes in their weight and other biometrics. Objective: This study aimed to quantify user retention, fasting patterns, and weight loss by users of 2 IF mobile apps. We also sought to describe and model starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change. Methods: We assembled height, weight, fasting, and demographic data of adult users (ages 18-100 years) of the LIFE Fasting Tracker and LIFE Extend apps from 2018 to 2020. Retention for up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least 2 readings of weight and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as extended fasting hours (EFH; hours beyond 12 in a fast) averaged per day (EFH per day). Retention was modeled using a Cox proportional hazards regression. Weight loss was analyzed using linear regression. Results: A total of 792,692 users were followed for retention based on 26 million recorded fasts. Of these, 132,775 (16.7%) users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. The survival analysis using Cox regression indicated that retention was positively associated with age and exercise and negatively associated with stress and smoking. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH per day, which displayed a positive interaction. Users with a BMI ≥40 kg/m lost 13.9% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23 kg/m . EFH per day was an approximately linear predictor of weight loss. By week 26, users lost over 1% of their starting weight per EFH per day on average. The regression analysis using all variables was highly predictive of weight change at 26 weeks (R =0.334) with starting BMI and EFH per day as the most significant predictors. Conclusions: IF with LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Users who are obese lose substantial weight over time, with more weight loss in those who fast more. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al (JMIR Mhealth Uhealth 2022;10(10):e35896) doi: 10.2196/35896 KEYWORDS intermittent fasting; time-restricted eating; weight loss; obesity; mobile apps; diet trackers; retention individuals initially classified as overweight and obese. Many Introduction users consistently used the apps to record fasts every week for months. Users with obesity lost substantial weight over time, Background with more weight loss in avid fasters. Weight loss in users with Worldwide, 13% of adults have obesity (BMI ≥30 kg/m ) and obese or overweight BMI was sustained, on average, for up to a year with little rebound. Conversely, users with a healthy or 39% are overweight (BMI ≥25 kg/m ) [1]. In the United States, underweight BMI lost little or no weight, even with extensive obesity is >3 times higher at 42.5% of the adult population [2], fasting over 52 weeks. and by 2030, the prevalence is expected to be close to 50% [3]. Mobile health apps that incorporate practices such as intermittent fasting (IF) may be a cost-effective approach to mitigating Methods weight gain. Mobile Apps and Users IF is a set of dietary patterns commonly pursued for weight loss We assembled all fasting and weight data for users who began that limits the timing of eating without restricting food content. using either of the LIFE apps between the launch of the LFT in Studies have shown that various IF methods are effective for May 2018 and December 2020. Analyses of fasting, retention, weight loss in people who are overweight [4-9], including and weight are all relative to when the user began recording time-restricted eating, alternate day fasting, and a 5:2 diet fasts in the apps, minimizing seasonal and other calendar effects. [10-14]. However, these studies have been conducted in small Other voluntary data collected were sex, age, race, height, diet, populations (<200 completers), for short durations (a few weeks exercise frequency, stress level, smoking status, and primary up to 6 months), and with narrowly defined IF protocols health concern. For inclusion in our analyses, users had to have assigned to participants. In the real-world setting, IF patterns provided sex and date of birth and recorded at least one fast, may not be as cleanly defined, especially over longer durations the first of which had to have been started on or after their 18th during which multiple fasting patterns may be explored. birthday. Mobile apps for IF and weight tracking offer an opportunity to For the weight change analyses, we further required the user to examine IF in a less-controlled setting and investigate its have provided height and an initial weight recorded within 7 real-world efficacy for weight management. They are also a days of the first fast. Heights and weights had to have been low-cost intervention for addressing obesity in the general within validation ranges of 145-203 cm and 25-249 kg, population and may incentivize the adoption of healthy habits, respectively. Height and weight can be entered in either metric including exercise and healthy eating [15,16]. Despite their or imperial units, with subsequent conversion to metric units potential benefits, the use of mobile health apps has been limited for storage and analysis. We identified 902 users whose weight owing to low retention rates [17-19], and only a handful of change at weeks 1 to 52 was >5 SD from the average across all available health apps have been subjected to rigorous study to users for that week. Without knowing which value or values establish their efficacy. were presumably misentered, we simply excluded those users This Study entirely from the weight analysis, made feasible by the study’s large sample size. Weights were subject to a 24-hour burn-in We evaluated retention, fasting patterns, and weight change period, using the last weight recorded during that time as the among users of 2 free IF tracking apps, collectively known as baseline value. This burn-in accommodated users who may LIFE apps: LIFE Fasting Tracker (LFT), which is focused on have entered an initial weight in the app based on their fasting, and LIFE Extend (LX), which additionally supports recollection and entered an update after checking it on a scale tracking of physical activity, mindfulness, sleep, and healthy or who corrected their entry after checking units. plant intake. From 2018 to 2020, the 2 apps acquired a combined user base of 2.5 million downloads. User accounts and backend Fasting data storage for the 2 apps are shared, such that fasts could be We assembled all fasting records for the full set of nearly started in one app and stopped in the other, and all the data are 800,000 users. Although the apps allowed shorter and longer interchangeable. LFT was launched over a year earlier than LX, fasts to be tracked, we eliminated fasts under 8 hours and so only a small fraction of the data in this study was generated truncated fasts to a maximum length of 240 hours. To reduce via LX. the effects of forgotten fasts that were ended and saved in the We followed nearly 800,000 users for retention and real-world apps long after eating had resumed, we eliminated any fast that fasting behaviors. We further analyzed weight change patterns was 120 hours or longer but where a fasting goal of under 24 relative to app use and demographics in a subpopulation of over hours had been specified by the user. This yielded 25,983,817 160,000 users who used apps to track their weight over time. fasts for our analyses. We showed that practicing IF with a dedicated mobile app is We aggregated fasting statistics for each user for weeks 1 to an effective and sustainable approach to weight loss in 104 but primarily investigated weeks 1 to 52. Information https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al regarding week 53 to 104 was used, when available and Centers for Disease Control and Prevention definitions [22], applicable, to determine retention. For each week, we totaled with the further division of the healthy BMI category into the number of started fasts, the average fast length, and the sum healthy low and healthy high defined as the ranges 18.5 to 22 of hours beyond the first 12 in a fast, which we call extended and 23 to 24, respectively. Healthy low and healthy high fasting hours (EFH), and EFH per day (ie, EFH per day=sum categories had an approximately equal representation in our of EFH for all fasts started in a week/7). The 12-hour time point baseline user data. in a fast is when the body is expected to have depleted the For the weekly weight aggregates, we calculated mean weight energy from recently consumed food and may begin a metabolic and the number of weights recorded during the week. The switch to deriving energy from the breakdown of fat [20]. This baseline weights were excluded from the week 1 aggregates. shift is also referred to as entering ketosis and is thought to help drive weight loss and other health benefits [20]. EFH per day Analysis also presents a unified way to analyze fasting effects for people We performed all data analysis using Python 3.9 libraries in a with different total fasting time and frequency but similar time JupyterLab [23] notebook environment installed within in ketosis. For example, a user who performs daily 16-hour fasts LifeOmic’s Precision Health Cloud, the secure, Health Insurance will have the same 4 EFH per day as a user who performs two Portability and Accountability Act–compliant backend of the 26-hour fasts per week, even though their total recorded fasting LIFE apps. A security review process was used to ensure that time is quite different (112 vs 52 hours per week). no identifiable data were released from the precision health cloud. In addition to providing descriptive summary statistics, We also calculated the cumulative means of these measures for including means, SDs, and percentages, we used multivariate all weeks, up to and including the given week. modeling approaches. Retention was modelled using Cox Retention proportional hazards regression, as implemented in the Lifelines We assessed user retention based solely on records of completed package (version 0.27.1) [24]. Right censoring was applied to fasts and not on other user behavior such as log-ins or use of users who joined late in the study and did not have the other app features. Starting with the date of each user’s first opportunity to be retained for 52 weeks. Weight change was fast, we assessed their fasting activity for each week. The most modeled using ordinary least squares regression, as implemented restrictive definition of retention is when a user is only in Statsmodels (version 0.12.2) [25]. Graphs were generated in considered retained so long as they record a fast in each Seaborn (version 0.11.1) [26], which was also used to generate consecutive week. We refer to this definition as retention with the CIs displayed, except for the hazard ratios and regression a 0-week grace period. In contrast, the most lenient definition figures, which were generated in Plotly (version 5.0.0) [27]. of retention is where the user is considered retained the entire Data handling was managed using Pandas (version 1.3.1) [28]. time between their first and last recorded fast, regardless of how Ethical Considerations much activity they have in between. We refer to this as retention This study was exempt from institutional review board approval with an unlimited grace period. This definition is also sometimes per Indiana University’s research guidelines [29]. The study called rolling retention [21]. consisted of retrospective secondary analysis of deidentified We explored retention by varying the number of weeks in the data. The use of these data for research and aggregate reporting grace period. We looked at 0, 2, 4, 8, 13, 26, and unlimited-week is covered in the privacy policy of the LIFE apps [30]. grace periods. After considering this spectrum of retention metrics, we decided to apply the 4-week grace period retention Results definition for all subsequent analyses. For example, if the user recorded no fasts in weeks 10 to 13 but did fast in week 14, the LIFE Apps Users user was still considered retained in weeks 10 to 14, but if they A total of 792,692 users satisfied the inclusion requirements resumed fasting in week 15 or later, their retention would have for the fasting and retention analysis. The detailed demographic ended with week 9. Note that our univariate estimates of and biometric data for this population are presented in retention are conservative because many users start near the end Multimedia Appendix 1. Their mean age was 36.7 (SD 10.9, of our data collection period, thus not having the opportunity range 18-100) years, and 81.3% of users were female. Users to be counted as active in the app during the full 52 weeks (plus were located in nearly 200 different countries, with the majority the grace period) that they might otherwise have counted toward. being in the United States. Of these, 161,346 users met the In the multivariate analysis, we used right censoring to account height and weight measurement requirements and recorded at for this issue. least one post–burn-in weight. This subpopulation was demographically similar to the entire population. Weight Change and BMI Users were included in the weight change analyses for all weeks Retention for which they satisfied the 4-week grace period retention Figure 1 displays the retention patterns for the LIFE apps over definition and in which they had a recorded weight. To account the course of 52 weeks, calculated using 7 different fasting for the wide range of starting weights, weight change was activity grace periods. There was an immediate drop of 28.7% analyzed as percent change from the user’s starting weight. The of users (227,867/792,692) who never recorded a fast beyond effect of obesity was also considered in some analyses by week 1. Under the unlimited grace period, where up to 102 stratification on starting BMI. We categorized BMI using the weeks of no fasting records were permitted, 41.9%, 29.6%, https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al 21%, and 13.9% of users were retained at 13, 26, 39, and 52 retention (also known as full retention) captured a much smaller weeks, respectively. At the other extreme, 0-week grace period fraction of users (7.3%, 2.7%, 1.4%, and 0.8%, respectively). Figure 1. User retention, calculated by different grace periods of inactivity. In total, 792,692 users were tracked starting from their first recorded fast. Grace periods extended out to 104 weeks for the unlimited definition. Intermediate grace periods corresponded naturally to Figure 2). While many demographic and behavioral factors intermediate retention rates (Figure 1). For all remaining were found to correlate with retention, 4 trends were particularly analyses in this study, we opted to use the 4-week grace period notable in the Cox model. Older users had higher retention—a definition of retention because it allowed us to study the evident hazard ratio of 0.617 (95% CI 0.596-0.639) for users aged ≥60 variability of use while precluding highly prolonged inactivity. years means they are estimated to be about 38% less likely to These users recorded a fast approximately every month at a drop each week than users <30 years. Similarly, increasing minimum. Retention rates under this definition were 16.7%, levels of exercise (as reported at baseline) reflected much greater 6.9%, 3.6%, and 2.1% at 13, 26, 39, and 52 weeks, respectively. retention, with daily exercisers dropping about 28% less often While users may have slowly increased their fasting frequency, than users with a sedentary lifestyle. Conversely, stress and taken a break, or ramped down at the end, exploring such smoking conferred lower retention rates—10% and 25% higher behavioral dynamics falls outside the scope of this study. drop rates respectively for users with extreme stress or daily smoking habits relative to users who have no stress and never Demographics smoked. While losing weight was the most common primary Retention using the 4-week grace period differed substantially health concern, those users’ retention was substantially lower by several demographic criteria (Multimedia Appendix 1). The than for users whose primary concerns were healthy aging and Cox proportional hazards regression model built over the first preventing chronic disease. Sex and starting BMI appeared to 52 weeks confirmed that several factors were significant, even have only small effects on retention. after controlling for other factors (Multimedia Appendix 1; https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 2. Hazard ratios with 95% CIs for failure to retain. Based on the Cox proportional hazards model over the 52-week study. HR=1 corresponds to the reference values: female, age <30 years, primary health concern as weight loss, starting BMI in the normal low category, white, typical western diet, sedentary, and never smoker. HR<1 reflects higher retention rates. popular. We also examined the distribution of fasts per user per Fasting Practices and Patterns week over the first 26 weeks for 54,811 users retained at 26 weeks using the 4-week grace period. The mean frequency was Weekly Fasting Frequency 4.25 (SD 1.91) fasts per week. Fasting frequency was Even when retained, user fasting behavior is likely to change approximately bimodal, with a broad peak centered on 3 fasts over time. We examined fasting patterns based mainly on the per week and a sharp peak at 7. Slightly more than one-quarter first 26 weeks among users retained that long. The 26-week (13,981/54,881, 25.5%) of the users fasted 6 to 7 times per period is long enough to see what long-term use of the fasting week. In Figure 3, weekly fasting frequency is shown separately apps is like, while affording a larger sample size than looking for the 3 most common self-reported race values. The only at users who were retained at 52 weeks. It also avoids differences suggest large cultural influences on a user’s choice overweighting the first few weeks of use when we had the of fasting routine. Older users were also much more likely to largest sample but while users were still establishing their fasting fast 6 to 7 times per week than younger users (4033/11,768, routines. 34.3%, vs 1521/9572, 15.9%) for users ≥50 years versus those The most common days to start a fast were Sunday, Monday, <30 years. and Tuesday, whereas Friday and Saturday were the least https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 3. Fasting frequency statistics for users retained at 26 weeks, averaged over the first 26 weeks of use and grouped by self-reported race. Bins are half-fast width, left-inclusive, and include 7 in the highest bin. starting and ending hours were 7 PM and noon, respectively Fasting Lengths (Figure 5). A total of 93.5% (24,289,517/25,983,817) of fasts The most common fasting length of the 26 million fasts analyzed were ≤32 hours, typically spanning a single night. A pattern of over the entire length of the study was 16 hours. The mean and multiday fasts is evident when plotted on the log scale in Figure median lengths were 21.0 and 18.0 hours, respectively, while 4 (inset), with smaller peaks for each additional day and clear the lower and upper quartiles were 16.1 and 20.9 hours. Figure spikes at precise multiples of 24 hours. 4 shows the complete distribution of fasting lengths. The modal Figure 4. Histogram of fast lengths and a log scale histogram inset. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 5. Distribution of starting and ending hour of fasts in local time. We also examined the average fast length by user for the 54,881 times per week, a much larger fraction (4055/17,057, 23.7%) users who were retained at 26 weeks under the 4-week grace was in the multiday zone of >32-hour average fasts, although period definition. Figure 6 shows those 26-week averages broken the modal average was 18 hours. The average fast lengths for down by user fasting frequency. Overall, 8.2% (4506/54,881) users who fasted 6 to 7 times per week also varied greatly, of users had a mean fast length of >32 hours, indicating a pattern peaking at 19 hours. of multiday fasts. As expected, among users who fasted <3 Figure 6. Distribution of average fast lengths per user across the first 26 weeks for users still retained at 26 weeks, broken down by weekly fasting frequency. Combining fasting length and frequency, the cumulative mean age, primary health concern, starting BMI, and EFH per day EFH per day was 5.0 at 26 weeks, which would correspond to (Multimedia Appendix 2). a daily fasting routine of 17 hours. To address the correlation and confounding among variables, we conducted an ordinary least squares regression analysis of Weight Change weight change at the 26-week time point. At 26 weeks, there Demographics were 1252 users with a recorded weight and values for all input We analyzed weight change for the 161,346 users who met the variables. The result was that the only factors with P<.05 were 4-week grace period retention criteria and recorded multiple starting BMI, EFH per day, and Black or African American weights in the fasting apps. From the univariate perspective, race (Multimedia Appendix 2; Figure 7); R =0.334. Results weight change as an outcome varied by several factors, including were similar for the models built at weeks 13, 39, and 52 (data not shown). https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 7. Regression coefficients with 95% CIs for weight change at 26 weeks. The model was built using ordinary least squares linear regression for the 1252 users who had answers for all variables and a weight recorded in week 26 (R =0.334). Coefficients are shown as zero for the reference states: female, age <30 years, primary health concern as weight loss, starting BMI in the normal low category, white, typical western diet, sedentary, and never smoker. The regression coefficients reflect the difference in percent weight change at 26 weeks relative to the reference state for that category. We further graphically explored the 52-week patterns of weight 26 weeks, users with more extensive fasting lost more than 1% change relative to EFH per day and starting BMI, which of their starting weight for each additional hour of EFH per day. emerged as the main variables explaining variability in weight Within each EFH per day bin, weight change appeared to change. Figure 8 depicts weight change for users who are not eventually plateau, with weight loss continuing longer at higher categorized as underweight binned weekly based on their levels of fasting. Weight loss continued for 39 weeks for users cumulative average EFH per day. While users fasting less than with ≥8 EFH per day before plateauing. A graph of weight 2 EFH per day lost only about 2% of their starting weight by change stratified by starting BMI is shown in Figure 9. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 8. Weight change over time, stratified by users’ cumulative average extended fasting hours (EFH) per day. Excludes users with underweight starting BMI. Mean values are plotted with 95% CIs represented by shading. Figure 9. Weight change over time, stratified by user’s starting BMI category. Mean values are plotted with 95% CIs represented by shading. We examined the combined effects of starting BMI and fasting weight loss, even after accounting for the amount of fasting. quantity by plotting the EFH per day strata separately for each The evident interaction between these 2 factors was confirmed starting BMI category (Figure 10). Within each category, the by rebuilding the 26-week regression model with the addition effect of increasing EFH per day appears to be approximately of an interaction term for continuous measures of starting BMI linear, as seen previously in Figure 8, but the scale at which and EFH per day. In that analysis, the P value for the interaction extended fasting impacts weight loss increases with higher BMI. term was <.001, whereas the P values for the EFH per day bins Similarly, it is clear that the starting BMI is still predictive of increased to >.05. R increased slightly to 0.356. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 10. Weight change over time, stratified by user’s starting BMI level and cumulative average extended fasting hours (EFH) per day. Mean values are plotted with 95% CIs represented by shading. achieved in the first 13 weeks and plateaued or peaked at 26 Weight Loss Thresholds weeks. By 26 weeks, 67.2% (1475/2194) had lost at least 5% We also examined the number of users who achieved certain of their starting weight, and 38.9% (854/2194) had lost at least thresholds of weight loss. Figure 11 shows the proportion of 10% of body weight. Reaching higher weight loss thresholds users with starting BMI ≥25 kg/m (ie, overweight or obese) generally took much longer to achieve, with gradually larger who reached weight loss of 5%, 10%, 15%, and 20% over time. fractions of users reaching them in 52 weeks. Success in reaching the 5% weight loss threshold was mostly Figure 11. Percentage of users with obese or overweight starting BMI (≥25 kg/m ) who achieved 5%, 10%, 15%, and 20% weight loss by week. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al [40,41]. Interestingly, retention differences by sex, diet, and Discussion starting BMI were among the smallest. Context Fasting Patterns and Weight Change This study is the largest examination of IF conducted to date The real-world spectrum of fasting behavior documented in our and is orders of magnitude larger than any previous effort study shows variable and flexible adherence to IF regimens, [5,7,12,31-33]. Owing to the use of mobile apps to record fasting making specific idealized fasting protocols hard to discern in events and weight, we were able to document real-world the data. We did clearly see a group of 25.5% of users who behavior and results, including both retention and weight practice a daily, or nearly daily, fasting routine (≥6 days per change. Unlike most prior studies, we included people with week) averaged over the first 26 weeks, whereas the rest skip healthy weight or underweight rather than just people multiple days per week. Among users who fast, on average, categorized as overweight or obese, and our population covered fewer than 3 times per week, the majority fast under 24 hours, extensive demographic variability, including an age range of suggesting that they are more sporadic in their fasting or less 18 to 100 years. vigorous in tracking in the apps. A total of 7.5% of users had average fasts over 32 hours, likely corresponding to the extended Retention paradigms of IF such as 5:2 and alternate day fasting. The spectrum of retention metrics (Figure 1) shows that there Owing to the multidimensional gradations of fasting patterns, are many users who consistently used the apps to record fasts we proposed the concept of EFH per day, as a metric to quantify every week for months (0-week grace period). Other users took fasting across all users. EFH per day combines fasting frequency breaks lasting weeks or months, but came back to the apps and fasting length into a single measure and serves to unify the later—13.9% of users recorded a fast during weeks 52 to 104 various fasting regimens for analysis. EFH per day was (ie, retention at week 52 with an unlimited grace period), which predictive of weight loss in a nearly linear fashion (Multimedia is an underestimate because most users downloaded the app Appendix 2; Figures 8 and 10), supporting it as a relevant less than 2 years before the end of the study. Whether users are framework for quantifying fasting. We also showed that the engaged in IF during reporting gaps is unknown, but these magnitude of the fasting effect varied by starting BMI, with variable use patterns are likely typical for mobile health apps, greater weight loss in individuals with higher levels of obesity as well as health behavior in general [34,35]. practicing the same level of IF. Our findings have clear Retention statistics for mobile apps are not commonly available implications for people who wish to lose weight by using IF. for proprietary apps. An analysis by AppsFlyer found that day The daily 16:8 IF routine that is commonly promoted is a 30 retention (fraction of original users active on day 30) for minimum for those who wish to lose more than a few percent health and fitness apps in the United States in 2020 averaged of their weight. <6% [36]. The week-12 retention statistics (fraction of original To explain these results, we hypothesize that the correlation users active during week 12) for all apps in 2020 were 3.6% for between EFH per day and weight loss and the interaction with Android and 5.1% for iOS. This 12-week retention definition starting BMI can be attributed primarily to differences in caloric is more generous than our 0-week grace period definition, yet restriction. In previous studies, those who practiced alternate the retention we observed at that time point (Figure 1) was day fasting, the 5:2 diet, or time-restricted eating reduced their greater, perhaps owing to the simple core utility of the fasting daily calorie intake by 10% to 30% [42]. With shorter eating app for tracking the timing of fasts. windows, users with low starting BMI may be able to consume Age was the best predictor of retention in our study, which is sufficient calories to maintain their weight, while users with consistent with other analyses of retention predictors for lifestyle higher BMI cannot, resulting in disproportionate weight loss. interventions [37]. Older users may become more fixed in new Various IF regimens have been shown to be as effective for habits, appreciate the consistency of using apps regularly, and weight loss as intentional caloric restriction [6,8,43,44], although be less likely to try out multiple competing apps. They are also IF might be easier to adopt and follow in the long term [13,45]. more likely to have serious health concerns, such as healthy The weight loss effects of longer fasts may additionally be aging, which may increase their motivation to adhere to new driven by the metabolic switch from glucose to ketones derived interventions. Users whose stated primary health concern was from fat tissue and free fatty acids [46]. This switch has weight loss were younger and had the lowest retention rate. previously been associated with weight loss [20] and occurs Several other variables were notable in their relationship to between 12 hours and 24 hours into a fast, depending on retention in both univariate and multivariate analyses previous carbohydrate intake and energy expenditure [46,47]. (Multimedia Appendix 1). Consistent with previous reports A primary benefit of the switch from use of glucose to free fatty [37,38], 26-week retention was more than twice as high for acids and ketones is the mobilization of body fat stores while daily exercisers versus sedentary users. This was also concordant preserving muscle mass, thereby improving body composition. with previous findings of higher dietary compliance among Associated improvements in insulin sensitivity, visceral fat those who exercise regularly [39]. Stress has been shown to mass, and systemic inflammation may persist at the end of each predict poor adherence to weight loss programs [37], and users fast when the system reverts to glucose metabolism, in part of the LIFE apps reporting extreme stress had the lowest because of preserved muscle mass [19]. These effects are retention. Smoking conferred lower retention rates compared amplified in longer fasts, as insulin sensitivity reaches a nadir with nonsmoking, which is also consistent with previous reports at 54 hours. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Analysis of weight loss threshold achievement facilitates the Self-reported data in this mobile setting offered many intriguing comparison of IF with other interventions. By 26 weeks, 67.2% correlates of retention and weight loss. Meaningful factors (1475/2194) of users with overweight and obese starting BMI included diet, exercise, stress, and smoking, all of which lend lost at least 5% of their starting weight. This is comparable to themselves well to mobile tracking, including integrated the results achieved by users of the Diabetes Prevention Program wearables, for reliable measurement and analysis. through the Noom platform, 64% of whom lost over 5% of their Future Work body weight in 24 weeks [48]. In our study, 38.9% (854/2194) 2 To better understand the mechanisms and residual variability of users with BMI ≥25 kg/m lost at least 10% of their body of weight loss by app users, we would like to study the caloric weight in 26 weeks, while this threshold was achieved by only input and expenditure of users directly. This can be achieved 23% of Noom users in the same amount of time [49]. In another by asking users to record their daily dietary intake and exercise. comparison, only 25% of the participants enrolled in the Tracking exercise is amenable to passive tracking with wearable Livongo Diabetes Prevention Program lost more than 10% of technology, and many users of the LIFE apps (specifically LX) their body weight by 54 weeks, with most users achieving 5% already have regular data ingestion established with the most weight loss [50]. common fitness trackers. Such an additional study could help While it is common for people who lose weight to be subject resolve the somewhat surprising finding that the diet and to weight regain [51], overweight users sustained most of their exercise habits self-reported at the beginning of the study did weight loss at 52 weeks, with users in obese class II and III not correlate with weight change. (BMI ≥35 kg/m ) trending toward even more weight loss at 52 Although weight management is a clinically important objective, weeks. In contrast, users whose starting BMI classified them other clinically relevant outcomes could be measured and as underweight had no weight change on average at 13 weeks correlated with fasting behavior. These include assessments of and proceeded to gain weight if they continued with the apps mental and physical health, disease incidence, insulin resistance, out to 52 weeks. This is an important finding because of the medical costs, and professional and educational absenteeism. frequently raised concern that IF may promote eating disorders Advocates of IF point to studies in animal models and humans [52]. Our findings suggest that IF is generally a safe practice that suggest many of these benefits [11,14,59-61]. Facilitated even for users at the low end of BMI because even those users by mobile and digital technology, we may be able to evaluate who fasted extensively tended to lose little weight (Figure 10). real-world evidence for these promises and tease apart their etiology. Age was positively associated with greater weight loss, consistent with previous findings [53]. The effect we observed Finally, studies show that social support improves health and is explained by more fasting by older users rather than as a well-being, and that people who have strong support networks consequence of metabolic or dietary differences, according to are more likely to lose weight than those who do not [62,63]. our multivariate model. This is consistent with a 2013 study The LIFE apps have a social “Circles” feature, where users can that showed that, compared with younger participants, older communicate with other users within the app. An analysis of adults lost significantly more weight after a diet and exercise circle participation is a subject for future work, but preliminary intervention and were more successful at maintaining weight results suggest that retention is higher for users who are socially loss even after 3 years of an internet-based maintenance protocol active in the app. [54]. The lack of weight loss difference between women and men is also consistent with previous findings [55]. Limitations The primary limitation of this study was that most data were Conclusions self-reported, except for some weight values that were entered As of 2016, close to 50% of adults in the United States had tried by smart scales. This limitation is compensated for by the large to lose weight within the preceding 12 months according to sample size of the study. Centers for Disease Control and Prevention data [56]. Moreover, The observed weight change averages may be potentially as of 2018, over 40% of adults in the United States were confounded by users who stopped recording weights or even considered obese [57]. In our study of mobile app users in the stopped using the app because of a lack of progress. Conversely, real-world setting, we found that IF is an effective strategy for users who achieved success might have been less motivated to weight loss for many people. Studies in people with obesity continue recording fasts and weights. Similarly, users may have demonstrate that losing 10% of your body weight is enough to been more likely to weigh themselves and record their weight improve blood pressure and normalize cholesterol blood levels, in the app if they had lost weight, which could then exaggerate while losing just 5% is enough to improve glycemic control, the weight loss estimates in this study. These effects may be which is central to the prevention and management of diabetes challenging to untangle, but the trends and correlates of weight [58]. In just 13 weeks, among LIFE apps users with a starting change identified should be robust. classification of overweight or obese, 60% of them lost 5% or more of their starting weight and 21% lost ≥10%, reflecting the Owing to the limited observational nature of this study, users potential clinical value that is achievable. These rates of success who fasted longer may have adopted other diet-related practices were higher in our users than in users of paid apps with active more than users who fasted less without our knowledge. coaching, such as Noom and Livongo. Similarly, we did not have information about users’ previous experience with IF. Given that the largest retention losses and https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al greatest rates of weight change occurred in the earliest weeks limitation was offset by the wide range of fasting behaviors of app use, previous fasting experience or even recent weight among users. We used this natural variability as a form of changes could skew the reported progress. In an IF-naive self-directed intervention, which allowed us to contrast and population, we expect weight loss to be relatively larger. quantify the effects of different levels of fasting on a much broader scale than would be feasible for a randomized controlled Another limitation of the study is that, due to being trial. observational, it lacked explicit controls. However, this Acknowledgments The authors would like to thank Steven Bray for his close reading of the manuscript. The authors would like to appreciate the detailed and constructive feedback from the reviewers. RCD was affiliated with LifeOmic at the time of the study and is currently affiliated with Infinia ML. Authors' Contributions BAS performed most of the data collation and generation of figures and tables with substantial assistance from RCD and JPB. All major decisions were discussed and agreed upon by all authors. All authors contributed to writing the manuscript. Statistical approaches were guided by SP and executed by BAS. LT conducted most of the literature review. Conflicts of Interest BAS, LT, RCD, JPB, and SF are or were employees of LifeOmic, the makers of the apps studied in this paper, and they have ownership rights in the company. No one at LifeOmic other than the authors had any editorial oversight in performing this study or writing this paper. Multimedia Appendix 1 Baseline demographics and retention at 13-week intervals using the 4-week grace period retention definition. Hazard ratios and corresponding P values are based on the 52-week Cox proportional hazards regression model applied to retention. Hazard ratios <1.0 reflect greater rates of retention. [PDF File (Adobe PDF File), 115 KB-Multimedia Appendix 1] Multimedia Appendix 2 Weight change at weeks 13, 26, 39, and 52 using the 4-week grace period retention definition relative to user demographics. Starting BMI (mean, SD) for each demographic category is included for context. Sample sizes reflect the maximum eligible users at each time point. Regression coefficients and P values refer to an ordinary least squares model of weight change at 26 weeks (n=1252). The intercept in the model was 4.378 (P=.007). Baseline BMI values for the EFH per day groupings are based on the 26-week cohort used in the regression analysis. [PDF File (Adobe PDF File), 116 KB-Multimedia Appendix 2] References 1. Ritchie H, Roser M. Obesity. Our World in Data. 2017 Aug 11. URL: https://ourworldindata.org/obesity [accessed 2021-12-08] 2. FastStats. Centers for Disease Control and Prevention. 2021. URL: https://www.cdc.gov/nchs/fastats/obesity-overweight. htm [accessed 2021-12-08] 3. Ward ZJ, Bleich SN, Cradock AL, Barrett JL, Giles CM, Flax C, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med 2019 Dec 19;381(25):2440-2450. [doi: 10.1056/NEJMsa1909301] [Medline: 31851800] 4. 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J Med Internet Res 2017 Oct 23;19(10):e357 [FREE Full text] [doi: 10.2196/jmir.7861] [Medline: 29061555] Abbreviations EFH: extended fasting hours IF: intermittent fasting LFT: LIFE Fasting Tracker LX: LIFE Extend Edited by L Buis; submitted 22.12.21; peer-reviewed by J May, N Maglaveras; comments to author 19.02.22; revised version received 14.07.22; accepted 26.07.22; published 04.10.22 Please cite as: Torres L, Lee JL, Park S, Di Lorenzo RC, Branam JP, Fraser SA, Salisbury BA JMIR Mhealth Uhealth 2022;10(10):e35896 URL: https://mhealth.jmir.org/2022/10/e35896 doi: 10.2196/35896 PMID: ©Luisa Torres, Joy L Lee, Seho Park, R Christian Di Lorenzo, Jonathan P Branam, Shelagh A Fraser, Benjamin A Salisbury. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.10.2022. 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Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: Large-Scale, 52-Week Observational Study

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Abstract

Background: Intermittent fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile apps to help them track and adhere to their fasts and monitor changes in their weight and other biometrics. Objective: This study aimed to quantify user retention, fasting patterns, and weight loss by users of 2 IF mobile apps. We also sought to describe and model starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change. Methods: We assembled height, weight, fasting, and demographic data of adult users (ages 18-100 years) of the LIFE Fasting Tracker and LIFE Extend apps from 2018 to 2020. Retention for up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least 2 readings of weight and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as extended fasting hours (EFH; hours beyond 12 in a fast) averaged per day (EFH per day). Retention was modeled using a Cox proportional hazards regression. Weight loss was analyzed using linear regression. Results: A total of 792,692 users were followed for retention based on 26 million recorded fasts. Of these, 132,775 (16.7%) users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. The survival analysis using Cox regression indicated that retention was positively associated with age and exercise and negatively associated with stress and smoking. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH per day, which displayed a positive interaction. Users with a BMI ≥40 kg/m lost 13.9% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23 kg/m . EFH per day was an approximately linear predictor of weight loss. By week 26, users lost over 1% of their starting weight per EFH per day on average. The regression analysis using all variables was highly predictive of weight change at 26 weeks (R =0.334) with starting BMI and EFH per day as the most significant predictors. Conclusions: IF with LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Users who are obese lose substantial weight over time, with more weight loss in those who fast more. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al (JMIR Mhealth Uhealth 2022;10(10):e35896) doi: 10.2196/35896 KEYWORDS intermittent fasting; time-restricted eating; weight loss; obesity; mobile apps; diet trackers; retention individuals initially classified as overweight and obese. Many Introduction users consistently used the apps to record fasts every week for months. Users with obesity lost substantial weight over time, Background with more weight loss in avid fasters. Weight loss in users with Worldwide, 13% of adults have obesity (BMI ≥30 kg/m ) and obese or overweight BMI was sustained, on average, for up to a year with little rebound. Conversely, users with a healthy or 39% are overweight (BMI ≥25 kg/m ) [1]. In the United States, underweight BMI lost little or no weight, even with extensive obesity is >3 times higher at 42.5% of the adult population [2], fasting over 52 weeks. and by 2030, the prevalence is expected to be close to 50% [3]. Mobile health apps that incorporate practices such as intermittent fasting (IF) may be a cost-effective approach to mitigating Methods weight gain. Mobile Apps and Users IF is a set of dietary patterns commonly pursued for weight loss We assembled all fasting and weight data for users who began that limits the timing of eating without restricting food content. using either of the LIFE apps between the launch of the LFT in Studies have shown that various IF methods are effective for May 2018 and December 2020. Analyses of fasting, retention, weight loss in people who are overweight [4-9], including and weight are all relative to when the user began recording time-restricted eating, alternate day fasting, and a 5:2 diet fasts in the apps, minimizing seasonal and other calendar effects. [10-14]. However, these studies have been conducted in small Other voluntary data collected were sex, age, race, height, diet, populations (<200 completers), for short durations (a few weeks exercise frequency, stress level, smoking status, and primary up to 6 months), and with narrowly defined IF protocols health concern. For inclusion in our analyses, users had to have assigned to participants. In the real-world setting, IF patterns provided sex and date of birth and recorded at least one fast, may not be as cleanly defined, especially over longer durations the first of which had to have been started on or after their 18th during which multiple fasting patterns may be explored. birthday. Mobile apps for IF and weight tracking offer an opportunity to For the weight change analyses, we further required the user to examine IF in a less-controlled setting and investigate its have provided height and an initial weight recorded within 7 real-world efficacy for weight management. They are also a days of the first fast. Heights and weights had to have been low-cost intervention for addressing obesity in the general within validation ranges of 145-203 cm and 25-249 kg, population and may incentivize the adoption of healthy habits, respectively. Height and weight can be entered in either metric including exercise and healthy eating [15,16]. Despite their or imperial units, with subsequent conversion to metric units potential benefits, the use of mobile health apps has been limited for storage and analysis. We identified 902 users whose weight owing to low retention rates [17-19], and only a handful of change at weeks 1 to 52 was >5 SD from the average across all available health apps have been subjected to rigorous study to users for that week. Without knowing which value or values establish their efficacy. were presumably misentered, we simply excluded those users This Study entirely from the weight analysis, made feasible by the study’s large sample size. Weights were subject to a 24-hour burn-in We evaluated retention, fasting patterns, and weight change period, using the last weight recorded during that time as the among users of 2 free IF tracking apps, collectively known as baseline value. This burn-in accommodated users who may LIFE apps: LIFE Fasting Tracker (LFT), which is focused on have entered an initial weight in the app based on their fasting, and LIFE Extend (LX), which additionally supports recollection and entered an update after checking it on a scale tracking of physical activity, mindfulness, sleep, and healthy or who corrected their entry after checking units. plant intake. From 2018 to 2020, the 2 apps acquired a combined user base of 2.5 million downloads. User accounts and backend Fasting data storage for the 2 apps are shared, such that fasts could be We assembled all fasting records for the full set of nearly started in one app and stopped in the other, and all the data are 800,000 users. Although the apps allowed shorter and longer interchangeable. LFT was launched over a year earlier than LX, fasts to be tracked, we eliminated fasts under 8 hours and so only a small fraction of the data in this study was generated truncated fasts to a maximum length of 240 hours. To reduce via LX. the effects of forgotten fasts that were ended and saved in the We followed nearly 800,000 users for retention and real-world apps long after eating had resumed, we eliminated any fast that fasting behaviors. We further analyzed weight change patterns was 120 hours or longer but where a fasting goal of under 24 relative to app use and demographics in a subpopulation of over hours had been specified by the user. This yielded 25,983,817 160,000 users who used apps to track their weight over time. fasts for our analyses. We showed that practicing IF with a dedicated mobile app is We aggregated fasting statistics for each user for weeks 1 to an effective and sustainable approach to weight loss in 104 but primarily investigated weeks 1 to 52. Information https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al regarding week 53 to 104 was used, when available and Centers for Disease Control and Prevention definitions [22], applicable, to determine retention. For each week, we totaled with the further division of the healthy BMI category into the number of started fasts, the average fast length, and the sum healthy low and healthy high defined as the ranges 18.5 to 22 of hours beyond the first 12 in a fast, which we call extended and 23 to 24, respectively. Healthy low and healthy high fasting hours (EFH), and EFH per day (ie, EFH per day=sum categories had an approximately equal representation in our of EFH for all fasts started in a week/7). The 12-hour time point baseline user data. in a fast is when the body is expected to have depleted the For the weekly weight aggregates, we calculated mean weight energy from recently consumed food and may begin a metabolic and the number of weights recorded during the week. The switch to deriving energy from the breakdown of fat [20]. This baseline weights were excluded from the week 1 aggregates. shift is also referred to as entering ketosis and is thought to help drive weight loss and other health benefits [20]. EFH per day Analysis also presents a unified way to analyze fasting effects for people We performed all data analysis using Python 3.9 libraries in a with different total fasting time and frequency but similar time JupyterLab [23] notebook environment installed within in ketosis. For example, a user who performs daily 16-hour fasts LifeOmic’s Precision Health Cloud, the secure, Health Insurance will have the same 4 EFH per day as a user who performs two Portability and Accountability Act–compliant backend of the 26-hour fasts per week, even though their total recorded fasting LIFE apps. A security review process was used to ensure that time is quite different (112 vs 52 hours per week). no identifiable data were released from the precision health cloud. In addition to providing descriptive summary statistics, We also calculated the cumulative means of these measures for including means, SDs, and percentages, we used multivariate all weeks, up to and including the given week. modeling approaches. Retention was modelled using Cox Retention proportional hazards regression, as implemented in the Lifelines We assessed user retention based solely on records of completed package (version 0.27.1) [24]. Right censoring was applied to fasts and not on other user behavior such as log-ins or use of users who joined late in the study and did not have the other app features. Starting with the date of each user’s first opportunity to be retained for 52 weeks. Weight change was fast, we assessed their fasting activity for each week. The most modeled using ordinary least squares regression, as implemented restrictive definition of retention is when a user is only in Statsmodels (version 0.12.2) [25]. Graphs were generated in considered retained so long as they record a fast in each Seaborn (version 0.11.1) [26], which was also used to generate consecutive week. We refer to this definition as retention with the CIs displayed, except for the hazard ratios and regression a 0-week grace period. In contrast, the most lenient definition figures, which were generated in Plotly (version 5.0.0) [27]. of retention is where the user is considered retained the entire Data handling was managed using Pandas (version 1.3.1) [28]. time between their first and last recorded fast, regardless of how Ethical Considerations much activity they have in between. We refer to this as retention This study was exempt from institutional review board approval with an unlimited grace period. This definition is also sometimes per Indiana University’s research guidelines [29]. The study called rolling retention [21]. consisted of retrospective secondary analysis of deidentified We explored retention by varying the number of weeks in the data. The use of these data for research and aggregate reporting grace period. We looked at 0, 2, 4, 8, 13, 26, and unlimited-week is covered in the privacy policy of the LIFE apps [30]. grace periods. After considering this spectrum of retention metrics, we decided to apply the 4-week grace period retention Results definition for all subsequent analyses. For example, if the user recorded no fasts in weeks 10 to 13 but did fast in week 14, the LIFE Apps Users user was still considered retained in weeks 10 to 14, but if they A total of 792,692 users satisfied the inclusion requirements resumed fasting in week 15 or later, their retention would have for the fasting and retention analysis. The detailed demographic ended with week 9. Note that our univariate estimates of and biometric data for this population are presented in retention are conservative because many users start near the end Multimedia Appendix 1. Their mean age was 36.7 (SD 10.9, of our data collection period, thus not having the opportunity range 18-100) years, and 81.3% of users were female. Users to be counted as active in the app during the full 52 weeks (plus were located in nearly 200 different countries, with the majority the grace period) that they might otherwise have counted toward. being in the United States. Of these, 161,346 users met the In the multivariate analysis, we used right censoring to account height and weight measurement requirements and recorded at for this issue. least one post–burn-in weight. This subpopulation was demographically similar to the entire population. Weight Change and BMI Users were included in the weight change analyses for all weeks Retention for which they satisfied the 4-week grace period retention Figure 1 displays the retention patterns for the LIFE apps over definition and in which they had a recorded weight. To account the course of 52 weeks, calculated using 7 different fasting for the wide range of starting weights, weight change was activity grace periods. There was an immediate drop of 28.7% analyzed as percent change from the user’s starting weight. The of users (227,867/792,692) who never recorded a fast beyond effect of obesity was also considered in some analyses by week 1. Under the unlimited grace period, where up to 102 stratification on starting BMI. We categorized BMI using the weeks of no fasting records were permitted, 41.9%, 29.6%, https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al 21%, and 13.9% of users were retained at 13, 26, 39, and 52 retention (also known as full retention) captured a much smaller weeks, respectively. At the other extreme, 0-week grace period fraction of users (7.3%, 2.7%, 1.4%, and 0.8%, respectively). Figure 1. User retention, calculated by different grace periods of inactivity. In total, 792,692 users were tracked starting from their first recorded fast. Grace periods extended out to 104 weeks for the unlimited definition. Intermediate grace periods corresponded naturally to Figure 2). While many demographic and behavioral factors intermediate retention rates (Figure 1). For all remaining were found to correlate with retention, 4 trends were particularly analyses in this study, we opted to use the 4-week grace period notable in the Cox model. Older users had higher retention—a definition of retention because it allowed us to study the evident hazard ratio of 0.617 (95% CI 0.596-0.639) for users aged ≥60 variability of use while precluding highly prolonged inactivity. years means they are estimated to be about 38% less likely to These users recorded a fast approximately every month at a drop each week than users <30 years. Similarly, increasing minimum. Retention rates under this definition were 16.7%, levels of exercise (as reported at baseline) reflected much greater 6.9%, 3.6%, and 2.1% at 13, 26, 39, and 52 weeks, respectively. retention, with daily exercisers dropping about 28% less often While users may have slowly increased their fasting frequency, than users with a sedentary lifestyle. Conversely, stress and taken a break, or ramped down at the end, exploring such smoking conferred lower retention rates—10% and 25% higher behavioral dynamics falls outside the scope of this study. drop rates respectively for users with extreme stress or daily smoking habits relative to users who have no stress and never Demographics smoked. While losing weight was the most common primary Retention using the 4-week grace period differed substantially health concern, those users’ retention was substantially lower by several demographic criteria (Multimedia Appendix 1). The than for users whose primary concerns were healthy aging and Cox proportional hazards regression model built over the first preventing chronic disease. Sex and starting BMI appeared to 52 weeks confirmed that several factors were significant, even have only small effects on retention. after controlling for other factors (Multimedia Appendix 1; https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 2. Hazard ratios with 95% CIs for failure to retain. Based on the Cox proportional hazards model over the 52-week study. HR=1 corresponds to the reference values: female, age <30 years, primary health concern as weight loss, starting BMI in the normal low category, white, typical western diet, sedentary, and never smoker. HR<1 reflects higher retention rates. popular. We also examined the distribution of fasts per user per Fasting Practices and Patterns week over the first 26 weeks for 54,811 users retained at 26 weeks using the 4-week grace period. The mean frequency was Weekly Fasting Frequency 4.25 (SD 1.91) fasts per week. Fasting frequency was Even when retained, user fasting behavior is likely to change approximately bimodal, with a broad peak centered on 3 fasts over time. We examined fasting patterns based mainly on the per week and a sharp peak at 7. Slightly more than one-quarter first 26 weeks among users retained that long. The 26-week (13,981/54,881, 25.5%) of the users fasted 6 to 7 times per period is long enough to see what long-term use of the fasting week. In Figure 3, weekly fasting frequency is shown separately apps is like, while affording a larger sample size than looking for the 3 most common self-reported race values. The only at users who were retained at 52 weeks. It also avoids differences suggest large cultural influences on a user’s choice overweighting the first few weeks of use when we had the of fasting routine. Older users were also much more likely to largest sample but while users were still establishing their fasting fast 6 to 7 times per week than younger users (4033/11,768, routines. 34.3%, vs 1521/9572, 15.9%) for users ≥50 years versus those The most common days to start a fast were Sunday, Monday, <30 years. and Tuesday, whereas Friday and Saturday were the least https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 3. Fasting frequency statistics for users retained at 26 weeks, averaged over the first 26 weeks of use and grouped by self-reported race. Bins are half-fast width, left-inclusive, and include 7 in the highest bin. starting and ending hours were 7 PM and noon, respectively Fasting Lengths (Figure 5). A total of 93.5% (24,289,517/25,983,817) of fasts The most common fasting length of the 26 million fasts analyzed were ≤32 hours, typically spanning a single night. A pattern of over the entire length of the study was 16 hours. The mean and multiday fasts is evident when plotted on the log scale in Figure median lengths were 21.0 and 18.0 hours, respectively, while 4 (inset), with smaller peaks for each additional day and clear the lower and upper quartiles were 16.1 and 20.9 hours. Figure spikes at precise multiples of 24 hours. 4 shows the complete distribution of fasting lengths. The modal Figure 4. Histogram of fast lengths and a log scale histogram inset. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 5. Distribution of starting and ending hour of fasts in local time. We also examined the average fast length by user for the 54,881 times per week, a much larger fraction (4055/17,057, 23.7%) users who were retained at 26 weeks under the 4-week grace was in the multiday zone of >32-hour average fasts, although period definition. Figure 6 shows those 26-week averages broken the modal average was 18 hours. The average fast lengths for down by user fasting frequency. Overall, 8.2% (4506/54,881) users who fasted 6 to 7 times per week also varied greatly, of users had a mean fast length of >32 hours, indicating a pattern peaking at 19 hours. of multiday fasts. As expected, among users who fasted <3 Figure 6. Distribution of average fast lengths per user across the first 26 weeks for users still retained at 26 weeks, broken down by weekly fasting frequency. Combining fasting length and frequency, the cumulative mean age, primary health concern, starting BMI, and EFH per day EFH per day was 5.0 at 26 weeks, which would correspond to (Multimedia Appendix 2). a daily fasting routine of 17 hours. To address the correlation and confounding among variables, we conducted an ordinary least squares regression analysis of Weight Change weight change at the 26-week time point. At 26 weeks, there Demographics were 1252 users with a recorded weight and values for all input We analyzed weight change for the 161,346 users who met the variables. The result was that the only factors with P<.05 were 4-week grace period retention criteria and recorded multiple starting BMI, EFH per day, and Black or African American weights in the fasting apps. From the univariate perspective, race (Multimedia Appendix 2; Figure 7); R =0.334. Results weight change as an outcome varied by several factors, including were similar for the models built at weeks 13, 39, and 52 (data not shown). https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 7. Regression coefficients with 95% CIs for weight change at 26 weeks. The model was built using ordinary least squares linear regression for the 1252 users who had answers for all variables and a weight recorded in week 26 (R =0.334). Coefficients are shown as zero for the reference states: female, age <30 years, primary health concern as weight loss, starting BMI in the normal low category, white, typical western diet, sedentary, and never smoker. The regression coefficients reflect the difference in percent weight change at 26 weeks relative to the reference state for that category. We further graphically explored the 52-week patterns of weight 26 weeks, users with more extensive fasting lost more than 1% change relative to EFH per day and starting BMI, which of their starting weight for each additional hour of EFH per day. emerged as the main variables explaining variability in weight Within each EFH per day bin, weight change appeared to change. Figure 8 depicts weight change for users who are not eventually plateau, with weight loss continuing longer at higher categorized as underweight binned weekly based on their levels of fasting. Weight loss continued for 39 weeks for users cumulative average EFH per day. While users fasting less than with ≥8 EFH per day before plateauing. A graph of weight 2 EFH per day lost only about 2% of their starting weight by change stratified by starting BMI is shown in Figure 9. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 8. Weight change over time, stratified by users’ cumulative average extended fasting hours (EFH) per day. Excludes users with underweight starting BMI. Mean values are plotted with 95% CIs represented by shading. Figure 9. Weight change over time, stratified by user’s starting BMI category. Mean values are plotted with 95% CIs represented by shading. We examined the combined effects of starting BMI and fasting weight loss, even after accounting for the amount of fasting. quantity by plotting the EFH per day strata separately for each The evident interaction between these 2 factors was confirmed starting BMI category (Figure 10). Within each category, the by rebuilding the 26-week regression model with the addition effect of increasing EFH per day appears to be approximately of an interaction term for continuous measures of starting BMI linear, as seen previously in Figure 8, but the scale at which and EFH per day. In that analysis, the P value for the interaction extended fasting impacts weight loss increases with higher BMI. term was <.001, whereas the P values for the EFH per day bins Similarly, it is clear that the starting BMI is still predictive of increased to >.05. R increased slightly to 0.356. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Figure 10. Weight change over time, stratified by user’s starting BMI level and cumulative average extended fasting hours (EFH) per day. Mean values are plotted with 95% CIs represented by shading. achieved in the first 13 weeks and plateaued or peaked at 26 Weight Loss Thresholds weeks. By 26 weeks, 67.2% (1475/2194) had lost at least 5% We also examined the number of users who achieved certain of their starting weight, and 38.9% (854/2194) had lost at least thresholds of weight loss. Figure 11 shows the proportion of 10% of body weight. Reaching higher weight loss thresholds users with starting BMI ≥25 kg/m (ie, overweight or obese) generally took much longer to achieve, with gradually larger who reached weight loss of 5%, 10%, 15%, and 20% over time. fractions of users reaching them in 52 weeks. Success in reaching the 5% weight loss threshold was mostly Figure 11. Percentage of users with obese or overweight starting BMI (≥25 kg/m ) who achieved 5%, 10%, 15%, and 20% weight loss by week. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al [40,41]. Interestingly, retention differences by sex, diet, and Discussion starting BMI were among the smallest. Context Fasting Patterns and Weight Change This study is the largest examination of IF conducted to date The real-world spectrum of fasting behavior documented in our and is orders of magnitude larger than any previous effort study shows variable and flexible adherence to IF regimens, [5,7,12,31-33]. Owing to the use of mobile apps to record fasting making specific idealized fasting protocols hard to discern in events and weight, we were able to document real-world the data. We did clearly see a group of 25.5% of users who behavior and results, including both retention and weight practice a daily, or nearly daily, fasting routine (≥6 days per change. Unlike most prior studies, we included people with week) averaged over the first 26 weeks, whereas the rest skip healthy weight or underweight rather than just people multiple days per week. Among users who fast, on average, categorized as overweight or obese, and our population covered fewer than 3 times per week, the majority fast under 24 hours, extensive demographic variability, including an age range of suggesting that they are more sporadic in their fasting or less 18 to 100 years. vigorous in tracking in the apps. A total of 7.5% of users had average fasts over 32 hours, likely corresponding to the extended Retention paradigms of IF such as 5:2 and alternate day fasting. The spectrum of retention metrics (Figure 1) shows that there Owing to the multidimensional gradations of fasting patterns, are many users who consistently used the apps to record fasts we proposed the concept of EFH per day, as a metric to quantify every week for months (0-week grace period). Other users took fasting across all users. EFH per day combines fasting frequency breaks lasting weeks or months, but came back to the apps and fasting length into a single measure and serves to unify the later—13.9% of users recorded a fast during weeks 52 to 104 various fasting regimens for analysis. EFH per day was (ie, retention at week 52 with an unlimited grace period), which predictive of weight loss in a nearly linear fashion (Multimedia is an underestimate because most users downloaded the app Appendix 2; Figures 8 and 10), supporting it as a relevant less than 2 years before the end of the study. Whether users are framework for quantifying fasting. We also showed that the engaged in IF during reporting gaps is unknown, but these magnitude of the fasting effect varied by starting BMI, with variable use patterns are likely typical for mobile health apps, greater weight loss in individuals with higher levels of obesity as well as health behavior in general [34,35]. practicing the same level of IF. Our findings have clear Retention statistics for mobile apps are not commonly available implications for people who wish to lose weight by using IF. for proprietary apps. An analysis by AppsFlyer found that day The daily 16:8 IF routine that is commonly promoted is a 30 retention (fraction of original users active on day 30) for minimum for those who wish to lose more than a few percent health and fitness apps in the United States in 2020 averaged of their weight. <6% [36]. The week-12 retention statistics (fraction of original To explain these results, we hypothesize that the correlation users active during week 12) for all apps in 2020 were 3.6% for between EFH per day and weight loss and the interaction with Android and 5.1% for iOS. This 12-week retention definition starting BMI can be attributed primarily to differences in caloric is more generous than our 0-week grace period definition, yet restriction. In previous studies, those who practiced alternate the retention we observed at that time point (Figure 1) was day fasting, the 5:2 diet, or time-restricted eating reduced their greater, perhaps owing to the simple core utility of the fasting daily calorie intake by 10% to 30% [42]. With shorter eating app for tracking the timing of fasts. windows, users with low starting BMI may be able to consume Age was the best predictor of retention in our study, which is sufficient calories to maintain their weight, while users with consistent with other analyses of retention predictors for lifestyle higher BMI cannot, resulting in disproportionate weight loss. interventions [37]. Older users may become more fixed in new Various IF regimens have been shown to be as effective for habits, appreciate the consistency of using apps regularly, and weight loss as intentional caloric restriction [6,8,43,44], although be less likely to try out multiple competing apps. They are also IF might be easier to adopt and follow in the long term [13,45]. more likely to have serious health concerns, such as healthy The weight loss effects of longer fasts may additionally be aging, which may increase their motivation to adhere to new driven by the metabolic switch from glucose to ketones derived interventions. Users whose stated primary health concern was from fat tissue and free fatty acids [46]. This switch has weight loss were younger and had the lowest retention rate. previously been associated with weight loss [20] and occurs Several other variables were notable in their relationship to between 12 hours and 24 hours into a fast, depending on retention in both univariate and multivariate analyses previous carbohydrate intake and energy expenditure [46,47]. (Multimedia Appendix 1). Consistent with previous reports A primary benefit of the switch from use of glucose to free fatty [37,38], 26-week retention was more than twice as high for acids and ketones is the mobilization of body fat stores while daily exercisers versus sedentary users. This was also concordant preserving muscle mass, thereby improving body composition. with previous findings of higher dietary compliance among Associated improvements in insulin sensitivity, visceral fat those who exercise regularly [39]. Stress has been shown to mass, and systemic inflammation may persist at the end of each predict poor adherence to weight loss programs [37], and users fast when the system reverts to glucose metabolism, in part of the LIFE apps reporting extreme stress had the lowest because of preserved muscle mass [19]. These effects are retention. Smoking conferred lower retention rates compared amplified in longer fasts, as insulin sensitivity reaches a nadir with nonsmoking, which is also consistent with previous reports at 54 hours. https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al Analysis of weight loss threshold achievement facilitates the Self-reported data in this mobile setting offered many intriguing comparison of IF with other interventions. By 26 weeks, 67.2% correlates of retention and weight loss. Meaningful factors (1475/2194) of users with overweight and obese starting BMI included diet, exercise, stress, and smoking, all of which lend lost at least 5% of their starting weight. This is comparable to themselves well to mobile tracking, including integrated the results achieved by users of the Diabetes Prevention Program wearables, for reliable measurement and analysis. through the Noom platform, 64% of whom lost over 5% of their Future Work body weight in 24 weeks [48]. In our study, 38.9% (854/2194) 2 To better understand the mechanisms and residual variability of users with BMI ≥25 kg/m lost at least 10% of their body of weight loss by app users, we would like to study the caloric weight in 26 weeks, while this threshold was achieved by only input and expenditure of users directly. This can be achieved 23% of Noom users in the same amount of time [49]. In another by asking users to record their daily dietary intake and exercise. comparison, only 25% of the participants enrolled in the Tracking exercise is amenable to passive tracking with wearable Livongo Diabetes Prevention Program lost more than 10% of technology, and many users of the LIFE apps (specifically LX) their body weight by 54 weeks, with most users achieving 5% already have regular data ingestion established with the most weight loss [50]. common fitness trackers. Such an additional study could help While it is common for people who lose weight to be subject resolve the somewhat surprising finding that the diet and to weight regain [51], overweight users sustained most of their exercise habits self-reported at the beginning of the study did weight loss at 52 weeks, with users in obese class II and III not correlate with weight change. (BMI ≥35 kg/m ) trending toward even more weight loss at 52 Although weight management is a clinically important objective, weeks. In contrast, users whose starting BMI classified them other clinically relevant outcomes could be measured and as underweight had no weight change on average at 13 weeks correlated with fasting behavior. These include assessments of and proceeded to gain weight if they continued with the apps mental and physical health, disease incidence, insulin resistance, out to 52 weeks. This is an important finding because of the medical costs, and professional and educational absenteeism. frequently raised concern that IF may promote eating disorders Advocates of IF point to studies in animal models and humans [52]. Our findings suggest that IF is generally a safe practice that suggest many of these benefits [11,14,59-61]. Facilitated even for users at the low end of BMI because even those users by mobile and digital technology, we may be able to evaluate who fasted extensively tended to lose little weight (Figure 10). real-world evidence for these promises and tease apart their etiology. Age was positively associated with greater weight loss, consistent with previous findings [53]. The effect we observed Finally, studies show that social support improves health and is explained by more fasting by older users rather than as a well-being, and that people who have strong support networks consequence of metabolic or dietary differences, according to are more likely to lose weight than those who do not [62,63]. our multivariate model. This is consistent with a 2013 study The LIFE apps have a social “Circles” feature, where users can that showed that, compared with younger participants, older communicate with other users within the app. An analysis of adults lost significantly more weight after a diet and exercise circle participation is a subject for future work, but preliminary intervention and were more successful at maintaining weight results suggest that retention is higher for users who are socially loss even after 3 years of an internet-based maintenance protocol active in the app. [54]. The lack of weight loss difference between women and men is also consistent with previous findings [55]. Limitations The primary limitation of this study was that most data were Conclusions self-reported, except for some weight values that were entered As of 2016, close to 50% of adults in the United States had tried by smart scales. This limitation is compensated for by the large to lose weight within the preceding 12 months according to sample size of the study. Centers for Disease Control and Prevention data [56]. Moreover, The observed weight change averages may be potentially as of 2018, over 40% of adults in the United States were confounded by users who stopped recording weights or even considered obese [57]. In our study of mobile app users in the stopped using the app because of a lack of progress. Conversely, real-world setting, we found that IF is an effective strategy for users who achieved success might have been less motivated to weight loss for many people. Studies in people with obesity continue recording fasts and weights. Similarly, users may have demonstrate that losing 10% of your body weight is enough to been more likely to weigh themselves and record their weight improve blood pressure and normalize cholesterol blood levels, in the app if they had lost weight, which could then exaggerate while losing just 5% is enough to improve glycemic control, the weight loss estimates in this study. These effects may be which is central to the prevention and management of diabetes challenging to untangle, but the trends and correlates of weight [58]. In just 13 weeks, among LIFE apps users with a starting change identified should be robust. classification of overweight or obese, 60% of them lost 5% or more of their starting weight and 21% lost ≥10%, reflecting the Owing to the limited observational nature of this study, users potential clinical value that is achievable. These rates of success who fasted longer may have adopted other diet-related practices were higher in our users than in users of paid apps with active more than users who fasted less without our knowledge. coaching, such as Noom and Livongo. Similarly, we did not have information about users’ previous experience with IF. Given that the largest retention losses and https://mhealth.jmir.org/2022/10/e35896 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e35896 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Torres et al greatest rates of weight change occurred in the earliest weeks limitation was offset by the wide range of fasting behaviors of app use, previous fasting experience or even recent weight among users. We used this natural variability as a form of changes could skew the reported progress. In an IF-naive self-directed intervention, which allowed us to contrast and population, we expect weight loss to be relatively larger. quantify the effects of different levels of fasting on a much broader scale than would be feasible for a randomized controlled Another limitation of the study is that, due to being trial. observational, it lacked explicit controls. However, this Acknowledgments The authors would like to thank Steven Bray for his close reading of the manuscript. The authors would like to appreciate the detailed and constructive feedback from the reviewers. RCD was affiliated with LifeOmic at the time of the study and is currently affiliated with Infinia ML. Authors' Contributions BAS performed most of the data collation and generation of figures and tables with substantial assistance from RCD and JPB. All major decisions were discussed and agreed upon by all authors. All authors contributed to writing the manuscript. Statistical approaches were guided by SP and executed by BAS. LT conducted most of the literature review. Conflicts of Interest BAS, LT, RCD, JPB, and SF are or were employees of LifeOmic, the makers of the apps studied in this paper, and they have ownership rights in the company. No one at LifeOmic other than the authors had any editorial oversight in performing this study or writing this paper. Multimedia Appendix 1 Baseline demographics and retention at 13-week intervals using the 4-week grace period retention definition. Hazard ratios and corresponding P values are based on the 52-week Cox proportional hazards regression model applied to retention. Hazard ratios <1.0 reflect greater rates of retention. [PDF File (Adobe PDF File), 115 KB-Multimedia Appendix 1] Multimedia Appendix 2 Weight change at weeks 13, 26, 39, and 52 using the 4-week grace period retention definition relative to user demographics. Starting BMI (mean, SD) for each demographic category is included for context. Sample sizes reflect the maximum eligible users at each time point. Regression coefficients and P values refer to an ordinary least squares model of weight change at 26 weeks (n=1252). The intercept in the model was 4.378 (P=.007). Baseline BMI values for the EFH per day groupings are based on the 26-week cohort used in the regression analysis. [PDF File (Adobe PDF File), 116 KB-Multimedia Appendix 2] References 1. Ritchie H, Roser M. Obesity. Our World in Data. 2017 Aug 11. URL: https://ourworldindata.org/obesity [accessed 2021-12-08] 2. FastStats. Centers for Disease Control and Prevention. 2021. URL: https://www.cdc.gov/nchs/fastats/obesity-overweight. htm [accessed 2021-12-08] 3. Ward ZJ, Bleich SN, Cradock AL, Barrett JL, Giles CM, Flax C, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med 2019 Dec 19;381(25):2440-2450. [doi: 10.1056/NEJMsa1909301] [Medline: 31851800] 4. 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J Med Internet Res 2017 Oct 23;19(10):e357 [FREE Full text] [doi: 10.2196/jmir.7861] [Medline: 29061555] Abbreviations EFH: extended fasting hours IF: intermittent fasting LFT: LIFE Fasting Tracker LX: LIFE Extend Edited by L Buis; submitted 22.12.21; peer-reviewed by J May, N Maglaveras; comments to author 19.02.22; revised version received 14.07.22; accepted 26.07.22; published 04.10.22 Please cite as: Torres L, Lee JL, Park S, Di Lorenzo RC, Branam JP, Fraser SA, Salisbury BA JMIR Mhealth Uhealth 2022;10(10):e35896 URL: https://mhealth.jmir.org/2022/10/e35896 doi: 10.2196/35896 PMID: ©Luisa Torres, Joy L Lee, Seho Park, R Christian Di Lorenzo, Jonathan P Branam, Shelagh A Fraser, Benjamin A Salisbury. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.10.2022. 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Published: Oct 4, 2022

Keywords: intermittent fasting; time-restricted eating; weight loss; obesity; mobile apps; diet trackers; retention

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