Skip to main content
NCBI home page
Preventive Medicine Reports logoLink to Preventive Medicine Reports
. 2025 Jun 28;56:103153. doi: 10.1016/j.pmedr.2025.103153

Heterogeneous effects on body mass index in the “checkup championship”: A behavioral science-based health promotion program by health interest level

Takuya Yamada a,b,⁎, Kumi Sugimoto a,b, Hanae Nagata b, Yoshiharu Fukuda a, Koryu Sato b,c, Naoki Kondo b
PMCID: PMC12272467  PMID: 40687055

Abstract

Objective

Behavioral science principles, including approaches such as gamification, commitment strategies, and nudges, are widely used in health promotion programs to prevent non-communicable diseases. These approaches are expected to influence behavior change regardless of health interest; however, their effectiveness remains unclear. This study evaluated the impact of a behavioral science-based health promotion program on body mass index (BMI) reduction across different levels of health interest.

Methods

This study evaluated the “Checkup Championship,” a program that applies various behavioral science strategies to improve health checkup results for employees at Hakuhodo DY Group in Japan. Participants in the program in 2020 were compared with non-participants. Health interest was classified as low, middle, or high based on a single-question assessment. A linear regression model analyzed BMI changes between 2019 and 2020, using the inverse probability weighting of propensity scores to adjust for background differences between groups.

Results

A total of 410 participants and 390 non-participants were included in the study. BMI reduction was greater among participants than non-participants (−0.36 kg/m2 vs. −0.12 kg/m2). A significant BMI reduction was observed in the middle (average treatment effect [ATE]: −0.30 kg/m2, 95 % confidence interval [CI]: −0.55, −0.06) and low health interest groups (ATE: −0.34 kg/m2, 95 % CI: −0.61, −0.08); however, no clear BMI reduction was seen in the high health interest group.

Conclusions

The “Checkup Championship” demonstrated effectiveness, particularly among individuals with a lower health interest. Health programs incorporating diverse behavioral science strategies may help reduce health disparities.

Keywords: Behavioral science, Nudge, Health education, Weight loss, Health concerns, Health disparities, Occupational health

Highlights

  • •

    Behavioral science program lowers body mass index in low- and mid-health interest groups.

  • •

    No clear body mass index reduction observed in the high-health interest group.

  • •

    The study compares participants and non-participants in a workplace health program.

  • •

    A linear regression model analyzed body mass index changes from 2019 to 2020.

  • •

    The Behavioral science-based health programs can reduce health disparities.

1. Introduction

Non-communicable diseases among workers represent a global health problem (World Health Organization and Burton, 2010). Obesity, in particular, contributes to a higher incidence of diabetes, cardiovascular disease, cancer (Safaei et al., 2021), and increased mortality risk (Flegal et al., 2013; Global BMI Mortality Collaboration, 2016). To support weight control, collaboration among various sectors is recommended to promote healthy lifestyles (World Health Organization, 2023). Many countries have established guidelines for weight control (Semlitsch et al., 2019). Programs focused on diet and exercise for weight loss (Johns et al., 2014; Johnston et al., 2014; Joseph et al., 2019; Ma et al., 2017) and weight gain prevention (Peirson et al., 2014) have shown some effectiveness.

However, the effectiveness of healthy lifestyle practices and weight control programs depends on the characteristics of the target population. Individuals with lower health consciousness and motivation often show reluctance toward accessing health information (Iversen and Kraft, 2006) and are less likely to adopt health behaviors such as regular exercise (Iversen and Kraft, 2006; Pu et al., 2020) and fruit and vegetable consumption (Iversen and Kraft, 2006) in daily life (Nishizawa et al., 2024). Furthermore, lower subjective health scores have been reported in this population (Xue et al., 2020). Socioeconomic factors influence health interest (Nishizawa et al., 2024), with lower socioeconomic status (SES) associated with reduced motivation for weight control (Siu et al., 2011), infrequent weight management efforts (Wardle and Griffith, 2001), and lower perceived program effectiveness (Beauchamp et al., 2014). Therefore, expanding the development and dissemination of effective health programs targeting individuals with low health consciousness and motivation remains essential for addressing health disparities.

In the development and dissemination of health programs, integrating behavioral science theories and techniques is recommended (Glanz and Bishop, 2010; Bach Habersaat and Altieri, 2023). Approaches such as gamification, commitment strategies, incentivization, and insights from behavioral economics (Thaler and Sunstein, 2008) have recently been applied to health program development. Programs applying these diverse behavioral science principles have been implemented and evaluated for effectiveness in weight control (Kurtzman et al., 2018; Patel et al., 2021; Nagata et al., 2022; Takebayashi et al., 2022), smoking cessation (Littman et al., 2022; Nurchis et al., 2023), physical activity promotion (Agarwal et al., 2021; Boonmanunt et al., 2023; Takebayashi et al., 2024), and dietary behavior and food selection guidance (Arno and Thomas, 2016; Boonmanunt et al., 2023; Fergus et al., 2023; Gibson et al., 2024; Harbers et al., 2020; Nagatomo et al., 2019; Vermeer et al., 2014).

The “Checkup Championship” (Hakuhodo Inc, 2025), examined in this study, represents a workplace health promotion program in Japan. Designed as a workplace-based health promotion initiative, this program sets the health checkup day as a goal date and begins approximately one and a half months in advance. The purpose is to encourage participants to improve their health checkup results compared to the previous year (Hakuhodo Inc, 2025; Nagata et al., 2022). This program incorporates a range of behavioral science principles, including “ease of initiation” (related to choice architecture), “commitment,” “gamification,” “use of social norms,” and “timely program implementation,” drawing from the Easy, Attractive, Social, and Timely framework (The Behavioural Insights Team, 2024). Participation in the “Checkup Championship” has been associated with significant improvements in weight, body mass index (BMI), and abdominal circumference, with particularly notable benefits for individuals eligible for health guidance (Nagata et al., 2022).

These behavioral science approaches are expected to promote health behaviors and ultimately reduce health disparities, regardless of SES or health interest level (Roberto and Kawachi, 2015). Although previous studies have shown that programs applying nudge theory are effective for individuals and populations with low SES and contribute to reducing health disparities, others have reported no effect on reducing health disparities (Harbers et al., 2020; Schüz et al., 2021). Furthermore, the effectiveness of these behavioral science interventions for individuals with low health interests remains unclear.

This study aimed to evaluate the impact of participation in the “Checkup Championship” on weight loss based on varying levels of health interest. Because the “Checkup Championship” applies multiple behavioral science strategies, a weight loss effect is expected across all health interest levels. However, differences in effectiveness based on health interests have not been verified.

2. Methods

2.1. Participants and recruitment

The study targeted 3172 employees of the Hakuhodo DY Group (Hakuhodo DY Holdings, Hakuhodo, and Hakuhodo DY Media Partners) with available health checkup data from both 2019 and 2020. Recruitment began one and a half months before the 2020 regular health checkups. Invitations were sent via e-mail, and posters were displayed within the company. Employees could register through the company intranet system before their scheduled health checkup. Eligibility criteria included all employees with 2019 health checkup data, regardless of position, age, or employment status. The participation fee was free of charge.

2.2. Intervention: Program components of the “Checkup Championship”

The intervention group included employees who participated in the 2020 “Checkup Championship” program, held from July 21 to September 29, 2020. This workplace health promotion program was based on multiple behavioral science principles, primarily incorporating elements of gamification, commitment strategies, and incentivization, rather than relying solely on nudge theory. Weekly e-mails were sent to all employees, with the content varying each week. Messages included encouragement to register (leveraging timeliness and ease of participation), information on metabolic syndrome and lifestyle improvements (providing knowledge), examples of employees engaging in healthy behaviors (social norm/modeling), health declarations (commitment element), and insights from program planners.

Participation required employees to declare their involvement via the company's intranet, serving as a soft commitment technique. Individual results were provided online to participants 3 months after the health checkups. Feedback included radar charts with enhanced visual elements highlighting improvement rates across various health indicators, serving as a key gamification element that visualized their progress against the prior year's results and calculated improvement scores. A proprietary algorithm generated scores based on nine parameters: BMI, metabolic syndrome, blood pressure, lipids, uric acid, blood glucose, liver function, urine protein, and visual acuity. As an incentive, the top eight employees demonstrating the most significant improvements or maintenance of health from 2019 to 2020 received rewards and prizes (incentivization). Furthermore, to promote early registration and participation, 100 rewards were distributed by lottery to participants who signed up during the initial phase of the program. Notably, the program did not provide personalized health goals or individual counseling.

The control group consisted of non-participants in the 2020 “Checkup Championship” who underwent health checkups in both 2019 and 2020, with available health checkup data.

2.3. Outcome

BMI (kg/m2) was selected as the primary outcome of this study for several reasons. First, it is a globally recognized, standardized measure for classifying overweight and obesity (World Health Organization, 2023), and changes in this established health risk indicator are valuable to evaluate. Second, BMI allows comparison with a large body of public health literature, including workplace wellness programs, which commonly report outcomes using BMI or its categories (Asia Pacific Cohort Studies Collaboration, 2006; Flegal et al., 2013; Global BMI Mortality Collaboration et al. 2016; Jayedi et al., 2018; Safaei et al., 2021). BMI is also a simple, non-invasive method widely used in interventions targeting obesity among employees (Anderson et al., 2009; Bezzina et al., 2024) and other populations (Hillier-Brown et al., 2014).

BMI was calculated from height and weight measured during routine annual health checkups. Trained medical staff followed standardized procedures. Body weight (kg) was measured using calibrated medical scales, with participants in light clothing and no shoes. Height (m) was measured with a stadiometer, with participants standing upright without shoes. BMI was calculated as weight divided by height squared. Changes in BMI from 2019 to 2020 were compared between participants and non-participants of the “Checkup Championship.” To confirm robustness and address the use of weight change in other studies, we conducted sensitivity analyses using absolute weight change (kg) as an outcome.

2.4. Level of health interest

Health interest was measured in 2020 using the question, “How interested are you in your own health?” Responses were recorded on a 10-point scale (0: not at all interested, 10: very interested). Participants were categorized into three groups based on the tertiles of the health interest score distribution: low health interest (scores below 8), middle health interest (scores of 8 and 9), and high health interest (score of 10).

2.5. Covariates

Adjustment variables were extracted from the interview data collected during the 2020 medical examination. Socioeconomic characteristics included sex, age, and household composition. Working condition variables encompassed average monthly working hours and management position. Lifestyle habits, such as smoking, alcohol consumption, exercise habits, and sleep quality, were obtained from the 2019 medical examination to reflect pre-program lifestyle behaviors.

2.6. Statistical analysis

Summary statistics for each variable were presented as numbers and percentages (%) stratified by health interest levels. A χ-square test was performed for differences in percentages by health interest groups.

The association between participation in the “Checkup Championship” and BMI changes between 2019 and 2020 was assessed by comparing pre- and post-implementation BMI values. Analyses were conducted using a linear regression model stratified into three groups: low, middle, and high health interest groups. The dependent variable was BMI change from 2019 to 2020, whereas the independent variable was participation in the “Checkup Championship.” Adjustments were made for smoking, alcohol consumption, exercise habits, and sleep quality in 2019, as well as sex, age, household composition, average monthly working hours, and management position in 2020. To examine the interaction effect between program participation and health interest level on BMI, we employed a generalized linear model that included main effects for Time (2019, 2020), Program Participation, and Health Interest Level, as well as their two-way interactions. This model allowed us to estimate BMI at each time point as well as assess differences in BMI trajectories across subgroups.

Inverse probability weighting (IPW) of propensity scores (Austin and Stuart, 2015; Chesnaye et al., 2022) was used to adjust for background factors for participation in the “Checkup Championship.” Propensity scores were derived using logistic regression model. Based on prior literature (Lerman and Shemer, 1996; Nagata et al., 2022) suggesting potential influences on both participation and BMI change, the model included the following pre-participation variables from 2019 and 2020: sex, age, management position, household composition, smoking status, alcohol consumption, quality of sleep, exercise habits, average monthly working hours, health interest score, baseline BMI, baseline waist circumference, baseline blood pressure, baseline LDL cholesterol level, and baseline HbA1c level. We assessed covariate balance before and after IPW by examining standardized differences for all included covariates, with values less than 0.1 indicating adequate balance (Austin and Stuart, 2015) (see eTable 1 for details). The Average Treatment Effect (ATE) of program participation on one-year BMI change was estimated using a weighted linear regression model adjusted for baseline covariates.

To assess the robustness of our findings and enhance comparability with studies focusing specifically on weight change, we conducted a sensitivity analysis using the change in absolute weight (kg) between 2019 and 2020 as an alternative outcome. This sensitivity analysis employed the same IPW-weighted linear regression approach with identical covariate adjustments as the primary BMI analysis.

Missing values were identified primarily in lifestyle covariates (e.g., smoking habits, alcohol consumption, exercise habits, quality of sleep, and average working hours). To address this, multiple imputations were conducted using the fully conditional specification method (n = 20). The imputation model included variables with missing data as well as complete variables (such as BMI) as auxiliary to improve imputation accuracy.

All analyses were conducted using R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria), with a two-tailed significance level of <0.05.

All individuals involved in this study provided informed consent. This study was approved by the Ethics Committee of Medical Ethics of Kyoto University Graduate School of Medicine and School of Medicine, Kyoto, Japan (approval number: R3057–1).

3. Results

Among the 3172 employees, 800 responded to the 2020 health interest question. Of these, 206 (25.8 %) belonged to the low health interest group, 309 (38.6 %) to the middle health interest group, and 285 (35.6 %) to the high health interest group. Table 1 presents the characteristics of participants based on health interest levels. A higher percentage of smokers was observed in the lower health interest group. Individuals without exercise habits also tended to report lower health interest levels. Moreover, participation in the “Checkup Championship” was less frequent among those with lower health interests.

Table 1.

Characteristics of adult employee participants in Japan (2020) included in the analysis, by health interest level.

 All
(800)
 Health interest level⁎
 p-value
by
χ2-test
Low
(206)
 Middle
(309)
 High
(285)
n n % n % N %
Sex
Women 180 52 25.2 67 21.7 61 21.4 0.55
Men 620 154 74.8 242 78.3 224 78.6
Age
20's 116 34 16.5 49 15.9 33 11.6 0.09
30's 183 57 27.7 66 21.4 60 21.1
40's 200 54 26.2 75 24.3 71 24.9
>50's 301 61 29.6 119 38.5 121 42.5
Management position
No 607 163 79.1 235 76.1 209 73.3 0.33
Yes 193 43 20.9 74 24.0 76 26.7
Household composition
Single or with siblings or friends 207 65 31.6 75 24.3 67 23.5 0.22
Only two members 192 41 19.9 76 24.6 75 26.3
Two or more generations 401 100 48.5 158 51.1 143 50.2
Smoking
No smoking 430 102 49.5 172 56.0 156 54.9 < 0.01
Quit smoking 129 22 10.7 48 15.6 59 20.8
Smoking 237 81 39.5 87 28.3 69 24.3
Alcohol consumption
Rarely drink 125 34 16.6 49 16.0 42 14.8 0.92
Sometimes drink 375 100 48.8 141 45.9 134 47.2
Almost daily drink 296 71 34.6 117 38.1 108 38.0
Exercise habits
Weekly 345 47 22.9 140 45.6 158 55.6 < 0.01
Monthly 213 64 31.2 84 27.4 65 22.9
Rarely 238 94 45.9 83 27.0 61 21.5
Quality of sleep
Good 422 116 56.6 156 50.8 150 52.8 0.44
Poor 374 89 43.4 151 49.2 75 47.2
Average working hours per month
Short 191 42 26.9 64 27.2 85 37.9 0.04
Medium 256 66 42.3 99 42.1 91 40.6
Long 168 48 30.8 72 30.6 48 21.4
“Checkup Championship”
Non-participants 390 122 59.2 140 45.3 128 44.9 < 0.01
Participants 410 84 40.8 169 54.7 157 55.1
Open in a new tab

Low: Score < 8, Moderate: Score = 8 or 9, High: Score = 10.

⁎

Health Interest Level: Defined by responses to a 10-point scale (0 = not at all interested, 10 = very interested).

Changes in BMI from 2019 to 2020 are shown in Table 2. Overall, BMI reduction was greater among participants than non-participants (−0.12 vs. -0.36). Furthermore, greater BMI reductions were observed among program participants in the low and middle health interest groups.

Table 2.

Change in body mass index from 2019 to 2020 among adult employees in Japan, by health interest level⁎ and participation in the “Checkup Championship” program.

2019 2020 Amount of change from 2019 to 2020
Mean (SD†) Mean (SD†) Mean (SD†)
All subjects
Non-participants 23.4 (3.6) 23.3 (3.7) -0.1 (1.0)
Participants 23.6 (3.4) 23.2 (3.2) −0.4 (1.2)
Low health interest
Non-participants 23.4 (3.7) 23.4 (3.8) −0.1 (0.9)
Participants 23.1 (3.3) 22.8 (3.2) −0.3 (1.0)
Middle health interest
Non-participants 23.7 (3.8) 23.7 (3.8) −0.0 (0.9)
Participants 23.5 (3.6) 23.2 (3.4) −0.3 (1.2)
High health interest
Non-participants 23.1 (3.4) 22.8 (3.4) −0.3 (1.0)
Participants 23.9 (3.1) 23.5 (3.0) −0.4 (1.3)
Open in a new tab

Low: Score < 8, Moderate: Score = 8 or 9, High: Score = 10.

⁎

Health Interest Level: Defined by responses to a 10-point scale (0 = not at all interested, 10 = very interested).

†

SD: Standard deviation.

Table 3 summarizes the estimated effects of “Checkup Championship” on BMI change by health interest level from 2019 to 2020. In the low (estimate: −0.34, 95 % confidence interval [CI]: −0.61, −0.08) and middle (estimate: -0.30, 95 % CI: −0.55, −0.06) health interest groups, BMI reduction was greater in the “Checkup Championship” participation group. No significant BMI reduction was found among participants with high health interests (estimate: -0.16, 95 % CI: −0.44, 0.12). Sensitivity analyses using the change in absolute weight (kg) as the outcome yielded results consistent with these findings for BMI change across all health interest levels (see eTable 2 for details).

Table 3.

Effects of participation in the “Checkup Championship” program on changes in body mass index from 2019 to 2020 among adult employees in Japan, by health interest level⁎.

 Non-IPW†
 IPW†
Univariate analysis Multivariable analysis‡ Multivariable analysis‡
Coefficient 95 % CI Coefficient 95 % CI ATE§ 95 % CI
All subjects
Non-participants 1.00 1.00 1.00
Participants −0.24 −0.39 −0.09 −0.23 −0.38 −0.08 −0.22 −0.37 −0.07
Low health interest
Non-participants 1.00 1.00 1.00
Participants −0.27 −0.33 −0.21 −0.32 −0.59 −0.04 −0.34 −0.61 −0.08
Middle health interest
Non-participants 1.00 1.00 1.00
Participants −0.27 −0.32 −0.21 −0.31 −0.56 −0.06 −0.30 −0.55 −0.06
High health interest
Non-participants 1.00 1.00 1.00
Participants −0.17 −0.23 −0.11 −0.15 −0.43 0.14 −0.16 −0.44 0.12
Open in a new tab
⁎

Health Interest Level: Defined by responses to a 10-point scale (0 = not at all interested, 10 = very interested). Low: Score < 8, Moderate: Score = 8 or 9, High: Score = 10.

†

IPW: Inverse probability weighting.

‡

Models were adjusted for smoking, alcohol consumption, exercise habits, and quality of sleep in 2019; and sex, age, household composition, average monthly working hours, and management position in 2020.

§

ATE: Average treatment effect.

CI: Confidence interval.

Table 4 presents the results from the generalized linear model examining the interaction between program participation and health interest level. Compared to the high health interest group (reference), program participation was associated with significantly lower BMI in both the low (interaction estimate: −1.41, 95 % CI: −2.22, −0.59) and middle (interaction estimate: −1.01, 95 % CI: −1.75, −0.28) health interest groups. Fig. 1 illustrates the estimated marginal mean BMI values in 2019 and 2020 by program participation and health interest level. Program participants exhibited a trajectory of BMI reduction or maintenance across all groups, particularly in the low and middle health interest groups.

Table 4.

Results from the generalized linear model⁎ examining the interaction between program participation and health interest level† on body mass index among adult employees in Japan.

Coefficient 95 % CI‡
Time: Post-Program (2020) −0.27 −0.88 0.33
“Checkup Championship” participation 0.75 0.14 1.36
Low health interest 0.53 −0.17 1.24
Middle health interest 0.65 0.01 1.29
Post-Program (2020) × “Checkup Championship” participation −0.22 −0.84 0.40
Post-Program (2020) × Low health interest 0.19 −0.61 0.99
Post-Program (2020) × Middle health interest 0.21 −0.50 0.93
“Checkup Championship” participation × Low health interest −1.41 −2.22 −0.59
“Checkup Championship” participation × Middle health interest −1.01 −1.75 −0.28
Open in a new tab

Low: Score < 8, Moderate: Score = 8 or 9, High: Score = 10.

⁎

The model included main effects for time point (pre-program 2019 vs. post-program 2020), program participation (yes vs. no), health interest level (low, middle vs. high [reference]), and key two-way interaction terms (Time × Participation, Time × Health Interest, Participation × Health Interest). Models were weighted by inverse probability of program participation and adjusted for smoking, alcohol consumption, exercise habits, and quality of sleep in 2019 and sex, age, household composition, average monthly working hours, and management position in 2020.

†

Health Interest Level:Defined by responses to a 10-point scale (0 = not at all interested, 10 = very interested).

‡

CI: Confidence interval.

Fig. 1.

Fig. 1

Open in a new tab

Estimated mean body mass index (BMI) at baseline (2019) and post-intervention (2020) among adult employees in Japan, by health interest level and participation in the “Checkup Championship” program.Estimates were derived from a generalized linear model with BMI at each time point as the dependent variable. The model included main effects for time point (2019 vs. 2020), program participation (yes vs. no), health interest level (low, middle vs. high [reference]), and key two-way interaction terms (Time × Participation, Time × Health Interest, Participation × Health Interest). Models were weighted by inverse probability of program participation and adjusted for smoking, alcohol consumption, exercise habits, and quality of sleep in 2019 and sex, age, household composition, average monthly working hours, and management position in 2020. Error bars represent 95 % confidence intervals (CIs).Health Interest Level:Defined by responses to a 10-point scale (0 = not at all interested, 10 = very interested).Low: Score < 8, Moderate: Score = 8 or 9, High: Score = 10.

4. Discussion

This study examined the effects of a health promotion program incorporating behavioral science on BMI, considering different levels of health interest. Overall, participation in the program contributed to BMI reduction, with a stronger effect observed in the low and middle health interest groups. Although the average reduction in BMI was modest, its clinical relevance should be considered from several perspectives. First, even small changes in BMI at a population level can contribute to a reduction in the prevalence of chronic conditions (Kearns et al., 2014), such as hypertension (Jayedi et al., 2018) and diabetes (Asia Pacific Cohort Studies Collaboration, 2006). Second, this widely accessible and low-intensity program design, which does not include personalized counseling, positions it as a practical approach to primary prevention. In this context, the observed effects, particularly in groups with initially lower health interest, may represent an important first step toward healthier behaviors and potentially greater, sustained improvements over time.

The suppression of BMI increase among participants in the low and middle health interest groups may be attributed to the program's design, which incorporated principles from behavioral science and nudge theory. Several factors, including ease of choice, gamification, commitment, synchronization effect, and timely program implementation (The Behavioural Insights Team, 2024), likely played a role in promoting weight loss behavior. Employees with low health interests often worked long hours, and the simple one-click participation feature may have facilitated engagement among individuals facing time constraints that hinder healthy choices. The program was introduced one and a half months before the scheduled health checkup, aligning with a period of heightened health awareness. Implementing the program within a shorter timeframe may have helped sustain motivation and encouraged behavioral changes. Commitment-driven psychological reinforcement, entertainment elements, and the synchronization effect with social norms—enhanced by the company-wide nature of the program—likely contributed to sustained weight loss efforts. These combined nudging strategies may explain the greater BMI reduction observed among participants compared to non-participants.

However, in the high health interest group, participation in the program had no effect on weight loss. Regardless of participation, this group demonstrated some ability to control BMI increases. Individuals with high health interest likely engaged in healthy behaviors voluntarily, without requiring specific interventions such as the study program. Small stimuli, including program recruitment efforts and the workplace environment associated with the “Checkup Championship,” may have been sufficient to promote their health improvements.

The study also highlighted key characteristics of individuals with low health interests. A greater likelihood of working long hours and negative associations with health behaviors—such as smoking, lack of exercise, and non-participation in the program—were observed in this group. Although the classification of health interest was relative, negative associations with health behaviors aligned with findings from previous studies (Iversen and Kraft, 2006; Nishizawa et al., 2024; Pu et al., 2020). These findings suggest that individuals with low health interests should be prioritized as the main target for the “Checkup Championship.” As the majority of the study population exhibited high health interest levels (705 respondents, 88.1 % scored 7 or higher), a relative classification was applied to assess the program's effects. Therefore, stronger negative associations with health behaviors may emerge in individuals with even lower absolute health interests. Recently, the interest in health scale has been developed for the Japanese population (Ozawa C, Ishikawa H, Kato M, Fukuda Y, 2021), with a proposed cutoff value (Nishizawa et al., 2024). Applying this scale could facilitate the identification of individuals with absolute health indifference. To better understand the characteristics of this population and evaluate program effectiveness, modifications to the “Checkup Championship” and new strategies for encouraging participation should be considered.

This study suggests that a nudge-applied health promotion program is an effective intervention for the health-indifferent population and helps reduce health disparities. Several studies on the effects of nudge-based health programs on health disparities have been reported in countries other than Japan (Harbers et al., 2020; Schüz et al., 2021). In Japan, nudge-based health promotion programs have also been implemented (Nagatomo et al., 2019; Takebayashi et al., 2022, Takebayashi et al., 2024), although their impact on health disparities has been limited. For example, financial incentives may increase vegetable intake more effectively among non-fulltime workers with lower incomes (Nagatomo et al., 2019). Evaluating the effectiveness of health promotion programs on health disparities is challenging. However, examining the effects of various programs, with or without nudge theories, on health disparities while understanding the characteristics of the target population, such as health interests and SES, is essential, as demonstrated in this study.

This study had some limitations. First, the generalizability of the results was limited because the study was conducted within a single firm, and the number and demographics of the participants were restricted. Second, the non-randomized controlled trial design introduced a risk of selection bias and made it difficult to eliminate the influence of unmeasured confounders when estimating causal relationships. However, efforts were made to reduce this limitation by simulating a quasi-randomized controlled trial using IPW with propensity scores to align background factors as closely as possible. Despite these efforts using measured covariates, the potential influence of unmeasured confounders cannot be fully eliminated. As a result, it remains inherently difficult to determine whether the observed differences are attributable to participants' health interest or to other measured and unmeasured factors. Furthermore, although the “Checkup Championship” began in 2019, the analysis did not adjust for participation status in 2019, which may have led to an underestimation of the actual impact. The effects of continuous participation were not fully explored, highlighting the need for further analyses that consider changes in health interest over time. Future research should examine the long-term impact of sustained participation to provide a more comprehensive evaluation. Third, the single-item question used to assess health interest may have led to variations in how respondents interpreted “health” and “interest,” potentially influencing the results. Furthermore, this measure has not been formally evaluated for its validity or reliability, adding further uncertainty to the findings related to health interest. Future research would benefit from using established multi-item scales with demonstrated psychometric properties, such as the interest in health scale (Ozawa et al., 2021), to more reliably assess this construct. Additionally, the 2020 intervention occurred during the COVID-19 pandemic. The shift to remote work and other pandemic-related societal changes may have independently influenced employees' lifestyle and health outcomes. Finally, due to insufficient data on lifestyle and environmental factors, we could not determine the specific changes that contributed to successful weight loss from participation in the “Checkup Championship.”

5. Conclusion

The “Checkup Championship” program, designed based on behavioral science and nudge theory, contributed to BMI reduction in the middle and low health interest groups. This study suggests that the program may be effective for improving BMI in populations with these health interest levels, which are considered to have a high need for health promotion interventions. Based on this study results, future research is necessary to examine the long-term impact of sustained participation while clarifying the absolute health indifference group.

CRediT authorship contribution statement

Takuya Yamada: Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Kumi Sugimoto: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hanae Nagata: Writing – review & editing, Validation, Software, Project administration, Methodology, Investigation, Formal analysis. Yoshiharu Fukuda: Writing – review & editing, Supervision, Methodology, Conceptualization. Koryu Sato: Writing – review & editing, Validation, Software, Methodology, Formal analysis. Naoki Kondo: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

All authors have reviewed the final version of the manuscript and approved it for submission.

Funding

This work was supported by JSPS KAKENHI Grant Numbers 18H04071 and 19H01076; Grants-in-Aid for Scientific Research from the Japanese Ministry of Health, Labour and Welfare (Grant Numbers 19FA1011 and 22FA1001); Hakuhodo DY Holdings Inc. and Hakuhodo DY Media Partners Inc.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Naoki Kondo received research funding from Hakuhodo DY Holdings Inc. and Hakuhodo DY Media Partners Inc. The funds were allocated for conducting epidemiologic studies using the data provided by the company. The funding agreement imposed conditions on data availability (e.g., prohibiting the publication of data related to the company's salary structure) but did not impose any restrictions on selecting study topics, study designs, analysis, interpretation, writing of the article, or submission decisions. Additionally, NK advised the company on evaluating the studied program after its implementation and had no role in designing the program.

Acknowledgments

We thank the Hakuhodo DY Group for providing the data and research funding. We also thank Yuri Matsumoto (Hakuhodo DY Media Partners Inc.) for assistance with data provision. We would like to thank Editage (www.editage.jp) for English language editing.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2025.103153.

Appendix A. Supplementary data

Supplementary material

mmc1.docx (33.9KB, docx)

Data availability

The authors do not have permission to share data.

References

  1. Agarwal A.K., Waddell K.J., Small D.S., Evans C., Harrington T.O., Djaraher R., et al. Effect of gamification with and without financial incentives to increase physical activity among veterans classified as having obesity or overweight: a randomized clinical trial. JAMA Netw. Open. 2021;4(7) doi: 10.1001/jamanetworkopen.2021.16256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anderson L.M., Quinn T.A., Glanz K., Ramirez G., Kahwati L.C., Johnson D.B., et al. The effectiveness of worksite nutrition and physical activity interventions for controlling employee overweight and obesity: a systematic review. Am. J. Prev. Med. 2009;37(4):340–357. doi: 10.1016/j.amepre.2009.07.003. [DOI] [PubMed] [Google Scholar]
  3. Arno A., Thomas S. The efficacy of nudge theory strategies in influencing adult dietary behaviour: a systematic review and meta-analysis. BMC Public Health. 2016;16:676. doi: 10.1186/s12889-016-3272-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Asia Pacific Cohort Studies Collaboration Body mass index and risk of diabetes mellitus in the Asia-Pacific region. Asia Pac. J. Clin. Nutr. 2006;15(2):127–133. [PubMed] [Google Scholar]
  5. Austin P.C., Stuart E.A. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat. Med. 2015;34(28):3661–3679. doi: 10.1002/sim.6607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bach Habersaat K., Altieri E. Behavioural sciences for better health: WHO resolution and action framework. Eur. J. Pub. Health. 2023;33(Suppl. 2) doi: 10.1093/eurpub/ckad160.463. [DOI] [Google Scholar]
  7. Beauchamp A., Backholer K., Magliano D., Peeters A. The effect of obesity prevention interventions according to socioeconomic position: a systematic review. Obes. Rev. 2014;15(7):541–554. doi: 10.1111/obr.12161. [DOI] [PubMed] [Google Scholar]
  8. Bezzina A., Clarke E.D., Ashton L., Watson T., James C.L. Workplace health promotion programs targeting smoking, nutrition, physical activity, and obesity in men: a systematic review and meta-analysis of randomized controlled trials. Health Educ. Behav. 2024;51(1):113–127. doi: 10.1177/10901981231208396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boonmanunt S., Pattanaprateep O., Ongphiphadhanakul B., McKay G., Attia J., Vlaev I., Thakkinstian A. Evaluation of the effectiveness of behavioral economic incentive programs for goal achievement on healthy diet, weight control and physical activity: a systematic review and network meta-analysis. Ann. Behav. Med. 2023;57(4):277–287. doi: 10.1093/abm/kaac066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chesnaye N.C., Stel V.S., Tripepi G., Dekker F.W., Fu E.L., Zoccali C., Jager K.J. An introduction to inverse probability of treatment weighting in observational research. Clin. Kidney J. 2022;15(1):14–20. doi: 10.1093/ckj/sfab158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fergus L., Long A.R., Holston D. Modeling behavioral economics strategies in social marketing messages to promote vegetable consumption to low-resource Louisiana residents: a conjoint analysis. J. Acad. Nutr. Diet. 2023;123(6):876–888.e1. doi: 10.1016/j.jand.2023.01.011. [DOI] [PubMed] [Google Scholar]
  12. Flegal K.M., Kit B.K., Orpana H., Graubard B.I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82. doi: 10.1001/jama.2012.113905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gibson L.A., Stephens-Shields A.J., Hua S.V., Orr J.A., Lawman H.G., Bleich S.N., et al. Comparison of sales from vending machines with 4 different food and beverage messages: a randomized trial. JAMA Netw. Open. 2024;7(5) doi: 10.1001/jamanetworkopen.2024.9438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Glanz K., Bishop D.B. The role of behavioral science theory in development and implementation of public health interventions. Annu. Rev. Public Health. 2010;31:399–418. doi: 10.1146/annurev.publhealth.012809.103604. [DOI] [PubMed] [Google Scholar]
  15. Global BMI Mortality Collaboration Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776–786. doi: 10.1016/S0140-6736(16)30175-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hakuhodo Inc Checkup Championship. 2025. https://www.hakuhodo-global.com/work/checkup-championship.html Available from. (Accessed 25 February 2025)
  17. Harbers M.C., Beulens J.W.J., Rutters F., de Boer F., Gillebaart M., Sluijs I., van der Schouw Y.T. The effects of nudges on purchases, food choice, and energy intake or content of purchases in real-life food purchasing environments: a systematic review and evidence synthesis. Nutr. J. 2020;19(1):103. doi: 10.1186/s12937-020-00623-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hillier-Brown F.C., Bambra C.L., Cairns J.M., Kasim A., Moore H.J., Summerbell C.D. A systematic review of the effectiveness of individual, community and societal-level interventions at reducing socio-economic inequalities in obesity among adults. Int. J. Obes. 2014;38(12):1483–1490. doi: 10.1038/ijo.2014.75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Iversen A.C., Kraft P. Does socio-economic status and health consciousness influence how women respond to health related messages in media? Health Educ. Res. 2006;21(5):601–610. doi: 10.1093/her/cyl014. [DOI] [PubMed] [Google Scholar]
  20. Jayedi A., Rashidy-Pour A., Khorshidi M., Shab-Bidar S. Body mass index, abdominal adiposity, weight gain and risk of developing hypertension: a systematic review and dose-response meta-analysis of more than 2.3 million participants. Obes. Rev. 2018;19(5):654–667. doi: 10.1111/obr.12656. [DOI] [PubMed] [Google Scholar]
  21. Johns D.J., Hartmann-Boyce J., Jebb S.A., Aveyard P., Behavioural Weight Management Review Group Diet or exercise interventions vs combined behavioral weight management programs: a systematic review and meta-analysis of direct comparisons. J. Acad. Nutr. Diet. 2014;114(10):1557–1568. doi: 10.1016/j.jand.2014.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Johnston B.C., Kanters S., Bandayrel K., Wu P., Naji F., Siemieniuk R.A., et al. Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis. JAMA. 2014;312(9):923–933. doi: 10.1001/jama.2014.10397. [DOI] [PubMed] [Google Scholar]
  23. Joseph M.S., Tincopa M.A., Walden P., Jackson E., Conte M.L., Rubenfire M. The impact of structured exercise programs on metabolic syndrome and its components: a systematic review. Diabetes Metab. Syndr. Obes. 2019;12:2395–2404. doi: 10.2147/DMSO.S211776. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kearns K., Dee A., Fitzgerald A.P., Doherty E., Perry I.J. Chronic disease burden associated with overweight and obesity in Ireland: the effects of a Small BMI reduction at population level. BMC Public Health. 2014;14(1):143. doi: 10.1186/1471-2458-14-143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kurtzman G.W., Day S.C., Small D.S., Lynch M., Zhu J., Wang W., et al. Social incentives and gamification to promote weight loss: the LOSE IT randomized, controlled trial. J. Gen. Intern. Med. 2018;33(10):1669–1675. doi: 10.1007/s11606-018-4552-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lerman Y., Shemer J. Epidemiologic characteristics of participants and nonparticipants in health-promotion programs. J. Occup. Environ. Med. 1996;38(5):535–538. doi: 10.1097/00043764-199605000-00015. [DOI] [PubMed] [Google Scholar]
  27. Littman D., Sherman S.E., Troxel A.B., Stevens E.R. Behavioral economics and tobacco control: current practices and future opportunities. Int. J. Environ. Res. Public Health. 2022;19(13):8174. doi: 10.3390/ijerph19138174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ma C., Avenell A., Bolland M., Hudson J., Stewart F., Robertson C., et al. Effects of weight loss interventions for adults who are obese on mortality, cardiovascular disease, and cancer: systematic review and meta-analysis. BMJ. 2017;359 doi: 10.1136/bmj.j4849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Nagata H., Sato K., Haseda M., Kobayashi Y., Kondo N. A novel behavioral science-based health checkup program and subsequent metabolic risk reductions in a workplace: checkup championship. Prev. Med. 2022;164 doi: 10.1016/j.ypmed.2022.107271. [DOI] [PubMed] [Google Scholar]
  30. Nagatomo W., Saito J., Kondo N. Effectiveness of a low-value financial-incentive program for increasing vegetable-rich restaurant meal selection and reducing socioeconomic inequality: a cluster crossover trial. Int. J. Behav. Nutr. Phys. Act. 2019;16(1):81. doi: 10.1186/s12966-019-0830-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nishizawa Y., Yamada T., Sugimoto K., Ozawa C., Tabuchi T., Ishikawa H., Fukuda Y. Quantitative definition of low-health-interest populations by using regression trees: a nationwide internet survey in Japan. Int. J. Environ. Res. Public Health. 2024;21(8):1049. doi: 10.3390/ijerph21081049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nurchis M.C., Di Pumpo M., Perilli A., Greco G., Damiani G. Nudging interventions on alcohol and tobacco consumption in adults: a scoping review of the literature. Int. J. Environ. Res. Public Health. 2023;20(3):1675. doi: 10.3390/ijerph20031675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ozawa C., Ishikawa H., Kato M., Fukuda Y. Development of the interest in health scale to understand the ‘population indifferent to health. Jpn. J. Health Educ. 2021;29(3):266–277. [Google Scholar]
  34. Patel M.S., Small D.S., Harrison J.D., Hilbert V., Fortunato M.P., Oon A.L., et al. Effect of behaviorally designed gamification with social incentives on lifestyle modification among adults with uncontrolled diabetes: a randomized clinical trial. JAMA Netw. Open. 2021;4(5) doi: 10.1001/jamanetworkopen.2021.10255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Peirson L., Douketis J., Ciliska D., Fitzpatrick-Lewis D., Ali M.U., Raina P. Prevention of overweight and obesity in adult populations: a systematic review. CMAJ Open. 2014;2(4):E268–E272. doi: 10.9778/cmajo.20140019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pu B., Zhang L., Tang Z., Qiu Y. The relationship between health consciousness and home-based exercise in China during the COVID-19 pandemic. Int. J. Environ. Res. Public Health. 2020;17(16):5693. doi: 10.3390/ijerph17165693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Roberto C.A., Kawachi I., editors. Behavioral Economics and Public Health. Oxford University Press; New York: 2015. [Google Scholar]
  38. Safaei M., Sundararajan E.A., Driss M., Boulila W., Shapi’i A. A systematic literature review on obesity: understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Comput. Biol. Med. 2021;136 doi: 10.1016/j.compbiomed.2021.104754. [DOI] [PubMed] [Google Scholar]
  39. Schüz B., Meyerhof H., Hilz L.K., Mata J. Equity effects of dietary nudging field experiments: systematic review. Front. Public Health. 2021;9 doi: 10.3389/fpubh.2021.668998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Semlitsch T., Stigler F.L., Jeitler K., Horvath K., Siebenhofer A. Management of overweight and obesity in primary care-a systematic overview of international evidence-based guidelines. Obes. Rev. 2019;20(9):1218–1230. doi: 10.1111/obr.12889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Siu J., Giskes K., Turrell G. Socio-economic differences in weight-control behaviours and barriers to weight control. Public Health Nutr. 2011;14(10):1768–1778. doi: 10.1017/S1368980011000644. [DOI] [PubMed] [Google Scholar]
  42. Takebayashi M., Yoshiike N., Koyama T., Toriyabe M., Nakamura H., Takebayashi K. Validation of the most cost-effective nudge to promote workers' regular self-weighing: a cluster randomized controlled trial. Sci. Rep. 2022;12(1) doi: 10.1038/s41598-022-18916-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Takebayashi M., Namba M., Koyama T., Kaneda Y., Kawaguchi H., Uemura C., et al. Impact on step count by commitment-based health application. PLoS One. 2024;19(8) doi: 10.1371/journal.pone.0305765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Thaler R.H., Sunstein C.R. Yale University Press; 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. [Google Scholar]
  45. The Behavioural Insights Team . updated edition. 2024. EAST Four Simple Ways to Apply Behavioural Insights; Revised and.https://www.bi.team/wp-content/uploads/2014/04/BIT-EAST.pdf October. Available from. [Google Scholar]
  46. Vermeer W.M., Steenhuis I.H.M., Poelman M.P. Small, medium, large or supersize? The development and evaluation of interventions targeted at portion size. Int. J. Obes. 2014;38(suppl. 1 S1):S13–S18. doi: 10.1038/ijo.2014.84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wardle J., Griffith J. Socioeconomic status and weight control practices in British adults. J. Epidemiol. Community Health. 2001;55(3):185–190. doi: 10.1136/jech.55.3.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. World Health Organization . World Health Organization; Geneva: 2023. WHO Acceleration Plan to Stop Obesity.https://www.who.int/publications/i/item/9789240075634> Available from: [Google Scholar]
  49. World Health Organization, Burton J. World Health Organization; Geneva: 2010. WHO Healthy Workplace Framework Model: Background Supporting Literature Practices.https://apps.who.int/iris/handle/10665/113144> Available from: [Google Scholar]
  50. Xue Y., Liu G., Feng Y., Xu M., Jiang L., Lin Y., Xu J. Mediating effect of health consciousness in the relationship of lifestyle and suboptimal health status: a cross-sectional study involving Chinese urban residents. BMJ Open. 2020;10(10) doi: 10.1136/bmjopen-2020-039701. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.docx (33.9KB, docx)

Data Availability Statement

The authors do not have permission to share data.

Articles from Preventive Medicine Reports are provided here courtesy of Elsevier

RESOURCES