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Digital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes.
The aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change.
This was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change.
Among women, users of self-weighing technology had a mean BMI of 1.62 kg/m2 (95% CI 1.03-2.22) lower than the general population (of the same age and height) (
Users of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics.
Self-monitoring of weight has a long history rooted in consumer demand for weight control, reinforced in recent decades by public concern over rising obesity levels [
Data from connected health technologies have potential for adoption in clinical practice and research; however, there are at least 2 concerns with their use. The first concern is that, on an individual level, the accuracy of the data may be considered inferior to that recorded by a health professional. Generally, self-measured height is overestimated and weight is underestimated [
The aim of this study was to explore the possibility of using data collected from contemporary self-weighing smart scales for epidemiological research. Our first objective was to compare the population of people using smart scales in England with the wider population to get an idea of the selection bias. Our second objective was to understand how engagement with the smart scales varies between participants and how this engagement affects (or is affected by) weight change.
There were 2 sources of data used in this study. The first dataset was the 2011 wave of the Health Survey for England (HSE), used to obtain a representation of the distribution of height, weight, and body mass index (BMI) in England. The HSE is a series of annual cross-sectional surveys carried out in England. First piloted in 1991, it has been fully running since 1992. Weight is measured by a nurse to the nearest 100 g using an electronic scale after removal of shoes or bulky clothing (participants were not weighed if they were pregnant, unsteady on their feet, or chair-bound). Height, to the nearest millimeter, is measured by a nurse using a portable stadiometer. Previous surveys reported, on average, 70% of households agreed to an interview and BMI was available from approximately 90% of those interviewed (with some variation by year and region) [
The second data source was a random sample of Withings Smart Scale users based in England, representing the population engaged with self-weighing. A
The anonymized HSE is publicly available for research purposes. The Withings Smart Scale users consented to their data being used for research purposes as part of the Terms and Conditions when setting up a user account (see [
We restricted analysis a priori to persons aged 16 or older. BMI measurements below 15 and above 70 were assumed to be erroneous and were removed. BMI was used as a continuous variable as well as a categorical variable using the World Health Organization cut-offs [
Description of the self-weighing process and data storage for Withings Smart Scale.
Descriptive statistics were produced using standard methods. We compared these between the 2 datasets (HSE and Withings Smart Scale) and additionally stratified this comparison by gender. Continuous variables that were not expected a priori to have substantial skew (age, height, weight, and BMI) were summarized using means and standard deviations, and compared using
We compared the BMI of the smart scale users with the HSE participants using linear regression, with BMI as the response and an indicator of smart scale user as the predictor of primary interest.
Withings Smart Scale data were investigated in more detail to explore the association between engagement with self-weighing and BMI. First, determinants of BMI change over the follow-up period were examined using linear regression. BMI change (the response) was calculated as a single measurement for each individual as the difference between the first and last BMI measures reported divided by the time (in months) between them, with negative change representing overall BMI loss. Individuals required at least 2 measurements to be included in this model. Primary predictors of interest were number of measurements per month, total follow-up time, and initial weight. Second, a multilevel Cox proportional hazard model was used to assess determinants of a weighing event occurring. This was treated as a recurrent event with frailty terms used to account for within-person correlation. The primary covariates of interest for this model were BMI at the previous reading and a measure of the recent change in BMI. Recent change in BMI was considered in 2 ways in 2 separate analyses. The “current” incremental change was defined as the difference in BMI between the previous weighing and the current weighing. This may represent an individual’s perception of recent weight change when making the current weighing. The “previous” incremental change was defined as the difference in BMI between the 2 previous weighings. Therefore, this represents a BMI change that has already been observed before the current weighing. For both measures of change, we recorded whether this was a gain or loss and this was represented in 2 separate variables. For example, if BMI at weighing
For all the preceding models, height, age, and age squared (age2) were included as confounders because they are all known to be associated with BMI [
For the Withings Smart Scale data, there were 975 users in the sample; for the HSE data there were 7035 individuals. A data exclusion flowchart is given in
The baseline characteristics of the 2 populations are given in
Data exclusion flowchart for Health Survey for England data (left) and Withings Smart Scale data (right).
Comparison of baseline characteristics between Withings Smart Scale and Health Survey for England (HSE) participants (N=8010).
Variable | Smart scale |
HSE |
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Men | Women | Overall | Men | Women | Overall | Men | Women | Overall | |
Participants, n (%) | 591 (60.6) | 384 (39.4) |
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3164 (44.98) | 3871 (55.02) |
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<.001 | |
Age (years), mean (SD) | 39.00 (10.52) | 39.34 (12.55) | 39.13 (11.36) | 49.30 (18.11) | 48.86 (18.36) | 49.05 (18.25) | <.001 | <.001 | <.001 | |
Measurements per person, median (IQR) | 87 (30-188) | 50 (15-123) |
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Follow-up days, median (IQR) | 377 (187-700) | 351 (143-655) |
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Measurements per person per month, median (IQR) | 7.6 (3.7-16.1) | 5.5 (2.2-14.1) |
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BMI at first measurement (kg/m2), mean (SD) | 28.32 (5.42) | 25.17 (5.34) | 27.08 (5.60) | 27.51 (4.79) | 27.30 (5.77) | 27.39 (5.35) | <.001 | <.001 | .09 | |
Height (cm), mean (SD) | 178.91 (7.77) | 165.19 (6.47) | 173.51 (9.90) | 175.05 (7.42) | 161.62 (6.81) | 167.66 (9.74) | <.001 | <.001 | <.001 | |
Weight (kg), mean (SD) | 90.65 (18.27) | 68.77 (15.62) | 82.03 (20.31) | 84.37 (15.80) | 71.29 (15.58) | 77.17 (16.98) | <.001 | .003 | <.001 | |
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Underweight (<18.5) | 4 (0.7) | 14 (3.6) | 18 (1.8) | 31 (0.98) | 77 (1.99) | 108 (1.54) | .01 | <.001 | .12 |
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Normal (18.5-24.9) | 160 (27.1) | 213 (55.5) | 373 (38.3) | 966 (30.53) | 1474 (38.08) | 2440 (34.68) |
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Overweight (25.0-29.9) | 241 (40.8) | 99 (25.8) | 340 (34.9) | 1373 (43.39) | 1286 (33.22) | 2659 (37.80) |
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Obese (≥30) | 186 (31.5) | 58 (15.1) | 244 (25.0) | 794 (25.09) | 1034 (26.71) | 1828 (25.98) |
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a Based on Fisher exact test or
The regression model for Withings Smart Scale user status on BMI is given in
Example BMI trajectories of the first 100 men and 100 women in the Withings Smart Scale data over time (January 1, 2010 to January 1, 2014).
Results of regression model comparing BMI between Withings Smart Scale and Health Survey for England (HSE) data.
Variable | Men, n=3755 | Women, n=4255 | ||
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Coef (95% CI) |
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Coef (95% CI) |
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Smart scale cohort indicator | 1.26 (0.84, 1.69) | <.001 | –1.62 (–2.22, 1.03) | <.001 |
Age | 0.34 (0.29, 0.39) | <.001 | 0.27 (0.22, 0.32) | <.001 |
Age2 | –0.0028 (–0.0033, –0.0023) | <.001 | –0.0022 (–0.0027, –0.0017) | <.001 |
Height | –0.03 (–0.05, –0.01) | <.001 | –0.07 (–0.094, –0.04) | <.001 |
Intercept | 23.42 (19.60, 27.65) | <.001 | 30.94 (26.55, 35.34) | <.001 |
Model-estimated BMI for Health Survey for England data for men of average height (175 cm; blue line) and women of average height (162 cm; pink line).
We then looked in more detail at the Withings Smart Scale data to understand how engagement with the smart scale technology related to BMI change over time. First, in the regression of BMI change against measurement intensity, we found that more frequent measurement over the entire period was associated with greater weight loss per month in both women (regression coefficient 0.03, 95% CI 0.02-0.05 kg/m2 per measurement per month,
Results of regression model for weight loss versus measurement intensity.
Variable | Men, n=586 | Women, n=376 | ||
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Coef (95% CI) |
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Coef (95% CI) |
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Measurements per month | –0.03 (–0.05, –0.02) | <.001 | –0.03 (–0.05, –0.01) | .01 |
Time observed (months) | 0.006 (–0.006, 0.018) | .30 | 0.01 (–0.004, 0.031) | .12 |
BMI at start | –0.12 (–0.15, –0.09) | <.001 | –0.05 (–0.09, –0.02) | .005 |
Intercept | –5.71 (–9.89, –1.54) | .007 | –3.07 (–8.65, 2.50) | .28 |
Age | 0.02 (–0.07, 0.11) | .65 | 0.08 (–0.004, 0.17) | .06 |
Age2 | –0.0002 (–0.0012, 0.0009) | .76 | –0.0010 (–0.0020, 0.0000) | .049 |
Height (m) | 2.75 (0.66, 4.85) | .01 | 0.77 (–2.43, 3.96) | .64 |
We then considered longitudinal patterns of subsequent weighings based on recent weight change. The results of these analyses are summarized in
Hazard ratios (HR) calculated from the Cox proportional hazards model.
Variable | Men |
Women |
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HR (95% CI) |
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HR (95% CI) |
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BMI | 0.99 (0.98-0.99) | <.001 | 1.02 (1.01-1.02) | <.001 |
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Time since first weighing (months) | 0.98 (0.97-0.98) | <.001 | 0.98 (0.98-0.98) | <.001 |
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Indicates BMI lost | 1.20 (1.18-1.22) | <.001 | 1.06 (1.03-1.09) | <.001 |
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BMI change (gain) | 0.09 (0.09-0.10) | <.001 | 0.10 (0.09-0.10) | <.001 |
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BMI change (loss) | 7.38 (7.03-7.75) | <.001 | 5.86 (5.50-6.25) | <.001 |
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Age | 1.03 (1.03-1.04) | <.001 | 1.01 (1.01-1.02) | <.001 |
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Age2 | 0.9997 (0.9996-0.9997) | <.001 | 0.9999 (0.9999-1.0000) | .28 |
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Height (m) | 1.79 (1.63-1.96) | <.001 | 1.08 (0.93-1.26) | .30 |
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BMI | 0.97 (0.97-0.97) | <.001 | 1.00 (1.00-1.00) | .30 |
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Time since first weighing (months) | 0.97 (0.97-0.97) | <.001 | 0.97 (0.97-0.97) | .002 |
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Indicates BMI lost | 1.15 (1.12-1.17) | <.001 | 0.98 (0.95-1.001) | .12 |
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BMI change where BMI gained | 0.41 (0.40-0.43) | <.001 | 0.40 (0.38-0.42) | <.001 |
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BMI change where BMI lost | 2.88 (2.74-3.02) | <.001 | 2.44 (2.28-2.60) | <.001 |
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Age | 1.05 (1.04-1.05) | <.001 | 1.02 (1.02-1.03) | <.001 |
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Age2 | 0.9996 (0.9995-0.9996) | <.001 | 0.9999 (0.9998-0.9999) | <.001 |
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Height (m) | 1.47 (1.34-1.61) | <.001 | 1.18 (1.01-1.37) | .04 |
This study compared English users of Withings smart scales connected to consumer health records to the general population in England. We found that Withings Smart Scale users are younger and more likely to be male than the general population in England. Among women, we found Withings Smart Scale users had, after correction for confounding, a BMI 1.62 kg/m2 lower than the general population; for a woman of average height (162 cm), this is a weight difference of 4.25 kg. Among men, we found Withings Smart Scale users had, after correction, a BMI 1.26 kg/m2 higher than the general population; for a man of average height (175 cm), this is a weight difference of 3.86 kg. Looking in more detail at Withings Smart Scale users, we found that more frequent measurement was associated with greater weight loss; again considering average height, each additional weighing per month was associated with further weight loss over the entire follow-up period of 1.13 kg for men and 0.92 kg for women. A positive feedback loop was identified in which a recent observed decrease in weight encourages further weighing.
A strength of the study is that we used data from large, robust sources for both the general population and the randomly selected population of individuals who use a popular brand of smart scales to monitor their weight. We employed advanced modeling techniques, including multilevel Cox regression, to exploit the longitudinal richness of the data.
A limitation is that the BMI comparison is based on standardized measurement in HSE, whereas readings in the Withings Smart Scale data were not standardized to such things as the amount of clothing worn. However, even self-reported height and weight without automated data capture from one type of instrument are generally accepted to be sufficiently accurate for such comparisons to be made [
The HSE is a cross-sectional study and the Withings Smart Scale data are longitudinal. Therefore, there is a difference in timeframe, although this was minimized by using the 2011 wave of HSE, which is within the Withings Smart Scale data timeframe. Although changes in BMI in the English population are likely to be small over the Withings Smart Scale data timeframe (2010-2013) [
A further limitation is that this is an observational study, so propensity to use self-weighing technology is subject to confounding. We have mitigated this by correcting our comparative models for age, gender, and height. However, we could not consider unmeasured potential confounding factors. An important unmeasured confounder is baseline engagement with weight or BMI; it is likely that individuals with more interest in BMI monitoring are more likely to purchase self-weighing technology, which would amplify the association of smart scale use with BMI control. Therefore, the results of our study should not be interpreted causally and further studies are needed to isolate the causal effect of self-weighing.
Our findings reinforce those of others that found increased engagement with self-weighing is associated with greater weight loss or reduced weight gain [
Unlike other studies, our observations suggest that women engaging with self-weighing technology tend to be lighter than average, whereas men tend to be heavier. A possible hypothesis for this finding could be that men who engage may be fit with high muscle mass.
Users of Withings Smart Scale devices are not representative of the general population. Any inferences about the general population should be corrected for at least age and gender by regression analysis or reweighting. In addition, even after correction for age and gender, BMI measures differ between the smart scales and the general population. Because this difference is in the opposite direction for men and women, there may be complementary reasons for engagement with smart scales between the genders. Further qualitative research into these drivers may allow for transfer across the genders and improve uptake of such devices.
Connected health technologies incorporating self-weighing can provide richer data than those from infrequent contact with health professionals. In particular, much higher longitudinal resolution of BMI can be captured for individuals and populations. However, these data are complex: the relation between the frequency of self-weighing and the underlying level and change in the weight itself needs careful consideration. Usefully, self-weighing is associated with better weight control; however, more research is needed to examine potential mediators and confounders of this relationship.
As personal health records start to gather data from a wider ecosystem of frequent measurement, the links between health observations and behaviors will become more tightly coupled. For example, physical activity monitoring from smart watches linked to weight measures from smart scales brings together information on weight control interventions and outcomes in a potentially persuasive ensemble. The statistical challenges of harnessing linked observation, intervention, and outcome processes should not be underestimated.
Connected health ecosystems are being driven by the consumer health/wellness market, but they also have the potential to support clinical interventions and research [
The use of connected health technologies is a promising area for clinical research and practice as well as consumer health markets. Their real potential may be realized through their linkage with each other and with more conventional sources such as electronic health records.
In this paper, we have demonstrated that current engagement with smart scale technology involves a selected population. Therefore, use of the associated data needs to correct for this selection. We have also demonstrated an opposing selection effect between men and women, with male users being heavier than average and female users being lighter, as well as a positive feedback loop with more frequent weighings following greater weight loss. The drivers behind these findings need to be explored in more detail to understand how engagement with smart scale technology drives, and is driven by, healthy behavior.
body mass index
hazard ratio
Health Survey for England
This work was supported by the University of Manchester’s Health eResearch Center (HeRC) funded by the Medical Research Council Grant MR/K006665/1. WGD was supported by an MRC Clinician Scientist Fellowship (G0902272).
MS, WGD, AN, and IB conceived and designed the study. MS and HR carried out the statistical analysis. All authors contributed to the drafting and revision of the manuscript, and approved the final version for submission.
AN, JV, and AC are employees of Withings, who develop self-weighing and other self-monitoring equipment. MS, HR, WGD, and IB have no conflicts to declare.