Frie et al. 2020, conducted a “think out loud” study involving overweight individuals who were tasked with recording self-weighing daily and recording their thoughts through the process. Findings revealed that participants frequently compared their weight to their goal (90% of the time) and reflected on past behaviors influencing their weight (58% of the time). Additionally, 20% of the time involved action plans, with 6% specifying these plans. Notably, the data indicated that specific action plans during self-weighing correlated with weight reduction (-2.1 kg per 1 SD increase in the predictor, 95% CI, -3.9, -0.3). Despite not all participants implementing specific action plans, many found that self-weighing influenced their actions throughout the day, serving as a constant reminder of weight loss goals [3].

Until recently, the ideal frequency of self-weighing was thought to be an unknown. Based on consistent findings from studies reviewed, a direct correlation between increased frequency of self-monitoring weight and greater weight loss was found [4,5,6,7,8,9,10,11]. The inverse relationship between weight and frequency of self-weight monitoring was depicted by Vuorinen et al. (2021), who completed a study of close to 10,000 people using data from a smart-scale users over 3 years. They found that self-monitoring of weight correlated with weight loss in normal, overweight and obese individuals (defined by BMI). They also found that over the 3 years, 72.5% of users went > 30 days period without self-weighing, which correlated with weigh gain, and was more pronounced in overweight and obese individuals (BMI > 25) compared to people in the normal range of BMI [6] Fig. 2.

Brockmann et al. (2020), completed a study on the frequency and consistency of self-monitoring of weight in weight loss maintenance. They followed 74 adults after they completed a 3-month virtual weight loss program for 9 months to track self-weighing consistency. Frequency was defined as the number of days participants self-weighed during the maintenance period vs. consistency, defined as the number of weeks that participants self-weighed at a certain frequency (i.e., 3 days vs 6 days vs 7 days of a week). They found that only a consistency of ≥ 6 days a week of self-monitoring of weight was associated with reduced weight regain [≥ 6 days r = -0.27, p = 0.019; 7 days/week r = -0.032, p = 0.005] [4]. These findings were supported by Ross et al. (2019), who found frequency of self-monitoring of weight (days per week) correlated with weight loss in that same week [Estimate -0.030, SE 0.006, p < 0.0001], as well as predicted weight loss for the following week [Estimate -0.023, SE 0.006, p < 0.001] [9].

Liampeng et al. (2023), completed a study in rural Thailand examining the impact of self-weighing and personalized counseling led by village health volunteers in adults with obesity (defined as a BMI > 27.5). They found that the intervention group which weighed twice daily had significantly greater weight loss over the 20-week periods (followed every 4 weeks) than those of the control group who did not self-weigh [mean change in BW -1.2 kg (95% CI: -2.2, -0.3) and 0.3 kg (95% CI: -0.3, 0.8) in the intervention and control groups, respectively, with p-value = 0.007] [5].

Zaremba, et al. (2023), completed a study on weight management program delivered by life coaches following attendance to a breast clinic. They set a goal of weight self-monitoring at least once per week. However, they found that participants who self-weighed once a week, more consistently achieved 3% weight loss [χ2 (d.f. = 1, n = 225) = 11.542, p = 0.001) and 5% weight loss [X 2(d.f. = 1, n = 225) = 6.321, p = 0.012] more frequently than those who did not. However, they did not quantify findings based on people who may have weighed more frequently than once a week (i.e., daily) [10].

As discussed above, an older paper by Steinberg et al. (2015), found that individuals who weigh themselves daily lost significantly more weight over a 6-month period than those who did not weigh themselves daily (-6.1 kg difference; 95% CI -10.2, -2.1; p = 0.004) [1]. These findings were supported by Vuorinen et al. (2023, mentioned above), a large retrospective cohort study in a free-living, uncontrolled environment without any specific weight loss interventions. The authors observed an inverse relationship between weight and self-monitoring of weight among participants who were normal weight, overweight and obese [r = –0.100 (P < 0.001), r = –0.125 (P < 0.001), and r = –0.148, p< 0.001, respectively] [6].

Sniehotta et al. (2019), found no impact on the relationship between self-weighing frequency and weight gain [12]. This study focused on individuals who had already achieved > 5% weight loss, and were followed for 12 months. (− 0.07 [95% CI 1.7 to − 1.9], p = 0.9 This study took place in the UK, and recruited people from one region within the UK), which might limit generalizability. Caloric intake was not tracked within this study and thus data was not stratified to control for possible confounders in the analysis [12].

Goldstein et al. (2019) conducted a secondary analysis of the Live SMART trial with the aim to further examine the effects of a multi-interventional study focused on self-monitoring dietary intake, daily weight and physical activity minutes via paper diaries and smart phones. They found that the effect of self-monitoring dietary intake became non-significant when controlling for frequency of self-monitoring of physical activity and adherence to self-weighing. This supports that weight monitoring and recording along with physical activity may be more beneficial than dietary intake tracking in weight loss [7].

Sakane et al. (2023) studied Japanese adults with obesity and hypertension using a comprehensive app focusing on achieving target weight loss, assessing healthy eating, exercise, smoking habits and self-weighing among other variables. They found that app users were more likely to adhere to daily weight-checks (mean 79.3%, SD 10.5% vs mean 68.6%, SD 22.1%; P = 0.01), wearing a pedometer (mean 75.7%, SD 13.4% vs mean 68.2%; P = 0.09), and had significantly greater weight loss (median –0.50, IQR –0.65 to 0.40 kg vs median –3.10, IQR –4.42 to –2.00; P= 0.02) compared to the control group [13].

Thomas et al. (2019) tested a smartphone-based behavioral obesity treatment program against standard group-based approaches and individualized self-monitoring via paper diaries with monthly weigh-ins. They found that the smartphone-based intervention achieved weight loss outcomes that were comparable to those obtained via the group intervention and control intervention [5.9 kg (95% CI, 4.5 to 7.4) in standard group intervention, 5.5 kg (95% CI, 3.9 to 7.0) in smart-phone group, and 6.4 kg (95% CI, 3.7 to 9.2) in control intervention] [14].

Pacanowski et al. (2019), found that using a website to facilitate self-directed learning processes in adults with a BMI > 19, resulted in increased self-observation, self-judgment and self-reactivity, thereby aiding participants in understanding the effect that behavioral actions have on overall weight, and weight loss [15]. The feedback and graphing of weight through the website was found to be a motivating factor for some participants. Additionally, studies have consistently supported electronic self-monitoring tools when compared to pen to paper log tracking, result in increased adherence to self-monitoring in behavioral weight-loss strategies [7, 16,17,18].

Goldstein et al. (2023), conducted a study to understand the effect of weight and shape concern on weight loss. They found that people who identified weight and shape concern (WSC) as very important or pretty important were much more likely to monitor their weight regularly when compared to people who rated WSC of low importance [very important; B = -3.46, SE = 0.85, p = 0.0003 and pretty important B = -2.43, SE = 0.87, p= 0.009] [19]. They also found that weight and shape concern was not related to overall weight loss, as found in previous studies (Olsen et al., 2018; Wiedemann et al., 2021) [19,20,21].

Similar to these findings, Bramante et al. (2019), found that people who were more interested in losing weight or maintaining weight were more likely to self-weigh, as well as track physical activity and caloric intake [22]. They used the PaTH Clinical Data Research network data to discover that people who reported self-weighing were more likely to be older (59 vs 54 years, p < 0.01), married (p = 0.01), college graduates (p = 0.03), white (p < 0.01) and employed (vs disabled/unemployed, p < 0.01) [22]. Thus, they concluded that socioeconomic status was a factor in regular self-weighing, and potentially could be contributing to greater health disparities in obesity rates [22].

Blackman Carr et al. (2022), explored the relationship between multiple caregivers’ role and its perceived barriers to self-care on behavioral adherence in a weight loss intervention. They found a negative correlation between multiple caregiving responsibilities and physical activity (r = 0.24, P = 0.06), daily self-weighing (r = 0.19, P = 0.10), decreased session attendance (β = 0.56 [SE = 0.27], P < 0.05) and decreased fruit/vegetable intake (β = 0.17 [SE = 0.07], P < 0.05) [23].

Fahey et al. (2020), studied self-weighing characteristics and weight changes among military personnel and found differences in self-weighing frequency in different seasons. Participants were more likely to weigh themselves in spring > summer > fall > winter (p < 0.0001), and they were more likely to gain weight from fall to winter (p < 0.001), and lose weight from winter to spring (p = 0.02) [24].