Effect and factors associated with weight and waist circumference reductions in information and communication technology-based specific health guidance

Abstract

Objectives: Specific health guidance (SHG) has served as a preventive intervention for metabolic syndrome in Japan since 2008. For SHG, health professionals guide diet and physical activity to achieve body weight (BW) and waist circumference (WC) reductions. Since 2013, SHG intervention using information and communication technology (ICT-based SHG) has also been available. Therefore, in this study, we examined the effects of ICT-based SHG, and identified factors associated with BW and WC reductions in response to this intervention. Methods: Our intervention was performed using a smartphone application with videophone guidance and message exchanges provided by health professionals. We analysed 1,994 participants. Primary outcomes included changes in BW and WC after versus before the intervention. We used multiple linear regression analyses to identify factors associated with reductions in BW and WC due to the intervention. Results: The mean ages were 49.3 (standard deviation [SD], 5.8) years for males and 50.5 (SD, 5.8) years for females. The mean BW change was −1.37 kg for both sexes. The mean WC changes were −1.05 for males and −2.05 cm for females. For males, baseline body mass index, pre-intervention action history, and the numbers of videophone communications and messages were significantly associated with larger changes in BW and WC. For females, no factors were significant for BW reduction, while baseline WC and pre-intervention action history were associated with WC reduction. Conclusions: ICT-based SHG reduces BW and WC. Videophone communication and messaging are associated with reductions in BW and WC in males. These results may help to improve the efficacy of ICT-based SHG.

Introduction

Metabolic syndrome (MetS) is a metabolic disorder that includes hypertension, dyslipidemia, insulin resistance, visceral fat accumulation, and excess body weight (BW)^1,2). This syndrome is a substantial risk factor for atherosclerotic cardiovascular disease²⁾. Since the prevalence of MetS is 15% in Europe³⁾ and 7.8% in Japan⁴⁾, MetS is considered to be a common disease⁵⁾. In addition, the aforementioned elements of MetS can be improved by lifestyle interventions⁶⁾. For example, exercise training and energy restriction ameliorate obesity, hyperlipidemia, diabetes, and hypertension⁷⁾. Energy restriction also reduces BW⁶⁾ and hypertension⁸⁾. Therefore, lifestyle interventions are a fundamental approach to managing MetS.

To prevent MetS, specific health guidance (SHG) was implemented by the Japanese Ministry of Health, Labour and Welfare (MHLW) in April 2008 for people between the ages of 40 and 74 years^9,10). Participants underwent annual health check-ups and were screened for enrollment in the SHG program using the following steps¹¹⁾:

• Step 1: Classification of obesity

 ○ Case 1: excessive waist circumstance (WC) (male ≥85 cm; female ≥90 cm)

 ○ Case 2: being overweight (body mass index [BMI] ≥25 kg/m² with WC <85 cm in males and <90 cm in females).

• Step 2: Classification using additional risk factors

 ○ In case 1, if ≥2 additional risk factors, the patients are indicated for intensive health guidance

 ○ In case 2, if ≥3 additional risk factors, the patients are indicated for intensive health guidance

Additional risk factors:

• Hyperglycemia (fasting blood glucose levels ≥100 mg/dL or hemoglobin A1c [HbA1c] ≥5.6%)

• Dyslipidemia (plasma high-density lipoprotein cholesterol levels [<40 mg/dL], plasma triglycerides levels [≥150 mg/dL])

• Hypertension (systolic blood pressure ≥130 mm Hg and/or diastolic blood pressure ≥85 mm Hg)

• Smoking (only used if above additional risk factors applicable)

SHG is carried out for 3–6 months, and health professionals provide participants with dietary and physical activity guidance¹⁰⁾. Previous studies have indicated that SHG improves BW, WC, and blood biochemical parameters¹¹⁾. Also, on-site SHG, provided for 6 months, improves BW, WC, HbA1c, plasma high-density lipoprotein cholesterol levels, plasma triglycerides levels, and blood pressure¹²⁾. Therefore, SHG contributes to amelioration of MetS in those receiving this intervention.

For the convenience of SHG participants, in 2013 the MHLW permitted performing SHG using information and communication technology (ICT-based SHG)¹³⁾. ICT-based SHG is performed using a videophone and message application and has benefits regarding location and time. Previous studies have indicated that on-site SHG and ICT-based SHG had the same effects on the reductions of WC and BMI¹⁴⁾. Another study suggested that online-based interventions are effective for the promotion of health^15,16,17). Moreover, ICT-based interventions (telehealth) are needed due to circumstances, such as those created by the COVID-19 pandemic¹⁸⁾. However, no study has assessed the effects of the ICT-based SHG on BW and WC, nor has there been research on the factors associated with BW and WC reductions. Therefore, in this study, we examined the effects of ICT-based SHG, and identified factors associated with BW and WC reductions in response to this intervention.

Methods

Study design

This was a retrospective study using data obtained from intensive health guidance intervention for ICT based-SHG. The intervention was carried out by MEDCARE Inc. (Tokyo, Japan), and we analyzed data collected from April 1, 2019, until October 22, 2020.

ICT-based intensive health guidance intervention

ICT-based intensive health guidance interventions were conducted nationwide in Japan, at the request of the health insurance association to which the participants belonged. Those participating in specific health check-ups (SHC) were categorized into the intensive SHG group according to their risk of metabolic syndrome (Figure 1)¹¹⁾. The SHG participants downloaded the SHG application “Medically” (MEDCARE Inc.), which has a message function and a videophone function, into their smartphones, and registered their personal information in this application (app). The general participation rate in this program was 68%. After registration, participants read the conditions for participating in the intervention and agreed to them by tapping an agreement button on the app. The SHG was composed of multiple forms of guidance. Initial guidance had to be at least 30 minutes using the app videophone. Subsequent guidance (at least four) sessions had to be at least 10 minutes by videophone or involve one full round of message exchanges. Final guidance also had to be more than 10 minutes by videophone or the one full round of message exchanges. In the initial guidance, health professionals (registered dietitians or public health nurses) provided guidance on diet, physical activity, and smoking cessation, with the aim of achieving an overall healthier lifestyle. Those providing support tailored goals to the desired lifestyle improvements. In the subsequent sessions, including the final one, they checked the degree of success in achieving these tailored goals and provided additional advice, as needed. In the final guidance session, they also evaluated behavior changes relevant to diet and physical activity. The planned intervention duration was 3 or 6 months. All health professionals providing guidance had undertaken a training program given by MEDCARE Inc. and had passed the exam of this program.

Fig. 1. ICT-based intensive health guidance intervention. In this intervention, SHC participants were designated to receive the intensive SHG. This group was at risk of metabolic syndrome. The intervention consisted of 3 guidance sessions: initial, subsequent, and final. Initial guidance was performed by videophone (≥30 min), and the others by videophone (≥10 min) or one full round of messaging using the app. SHG, specific health guidance; SHC, specific health check-up.

Participants

Intervention participants (n=2,491) completed the initial guidance during the period from April 1, 2019 until October 22, 2020. Since most of the participants (n=2,400) received the 3-month intervention, those who received the 6-month intervention were excluded from the final analysis (n=91). Drop-outs (n=136) were also excluded from the analysis. Exclusion criteria were as follows: taking medication (n=16); non-Japanese (n=2); special treatment (unusual intervention) required (n=247); refusal to allow data use for research (n=5). Ultimately, 1,994 participants were analyzed (Figure 2). This study was approved in advance by the Hospital Ethics Committee of Juntendo University Hospital for all procedures (approval number: 21-030). In addition, all 1,994 participants consented to the secondary use of their individual data when registering for the Medically application.

Fig. 2. Flow chart of the analysis. ^a Intervention participants took medications for various conditions. These medications were: antihypertensive drugs, hypoglycaemics, insulin and hypolipidemic agents. ^b special treatment indicates any medical treatment differing from routine interventions for MetS.

Data source

From medical records, the following information was obtained: baseline data, progress, outcome evaluations. The baseline data included sex, age, BW, BMI, WC, smoking history (smoker or non-smoker), and pre-intervention action history. We used the age at the start of the intervention. BW, BMI, and WC were measured at the SHC. Smoking history was obtained employing a questionnaire at the SHC. Pre-intervention action history indicates the actions taken to achieve lifestyle improvements before the intervention and was acquired at the initial guidance session by conducting an interview.

The progress information included the number of videophone communications and/or messages in the subsequent guidance sessions and the number of days that completion was delayed. Interventions are usually designed to be completed 3 or 6 months after the initial guidance. In some cases, the end of the intervention was delayed beyond the planned completion date due to participants occasionally forgetting their scheduled intervention dates. In such instances, the number of days that completion had been delayed was subtracted from the actual date of finishing the intervention. The evaluated information included BW and WC, both of which were self-reported at completion of the intervention.

Health outcomes

Our primary outcomes were changes in BW and WC after versus before the intervention. The amounts of these changes were calculated by subtracting the values at SHC from the values at the completion of the intervention.

Statistical analysis

Multiple linear regression analysis was performed to identify factors associated with changes in BW and WC during the intervention. In the models applied for analyzing BW and WC reductions, explanatory variables included sex, age, baseline BMI and WC, pre-intervention action history, smoking history, the number of subsequent guidance sessions (by videophone or messaging), and the number of days that completion was delayed, because these factors have all been shown to correlate with BW reduction^{19,20,21,22,23,24)}.

Possible violations of the assumptions of normality were examined using visual inspection of the distribution of residuals through both histograms and normal quantile-quantile plots. We further checked for deviations from linearity and homoscedasticity by visually inspecting scatterplots of standardized residuals using standardized predicted values. In addition, we assessed variance inflation factors to examine the possibility of multicollinearity; values >2.5 were considered to indicate collinearity. The p-values were obtained using the t-test as in multiple linear regressions. R 4.0.4 version software (R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses. p-values <0.05 were considered to indicate statistical significance for all analyses.

Results

Table 1 shows the characteristics at baseline. There were approximately six times more males (n=1,699) than females (n=295). The mean age was 49.3 (standard deviation [SD], 5.8) years for males and 50.5 (SD, 5.8) years for females. Obese participants (BMI ≥25 kg/m²) accounted for 69.9% of males and 91.9% of females. Nearly all of the participants (98.7% of males, 92.9% of females) had abdominal obesity (WC ≥85 cm for males, ≥90 cm for females).

Table 1. Baseline information on the participants

Sample characteristics		Male (n=1,699)	Female (n=295)
Age, yearsa		49.3 (5.8)	50.5 (5.8)
Baseline BMI, kg/m2 a		26.6 (3.0)	29.2 (3.9)
Obesity,b %		69.9	91.9
Baseline WC, cm a		92.5 (6.7)	96.7 (7.5)
Abdominal obesity,c %		98.7	92.9
Smoking history, %
	Yes	51.0	26.4
	No	49.0	73.6
Pre-intervention action history, %
	Yes	28.4	28.1
	No	71.6	71.9

BMI, body mass index; BW reduction history, body weight reduction history; WC, waist circumference.

^a   Mean (standard deviation)

^b   Obesity: BMI ≥25 kg/m².

^c   Abdominal obesity: WC ≥85 cm in males or ≥90 cm in females.

SHG results obtained in this study are shown in Table 2. Changes in BW, BMI, and WC were smaller than pre-SHG, in both males and females, with the ICT-based SHG. BW change, BMI change, numbers of videophone communications and messages, and the number of days that completion was delayed did not differ between the sexes. Females showed greater WC change than males. Histograms of BW change and WC change are shown in eFigure 1 and eFigure 2, respectively.

Table 2. Intervention information on the participants

Sample Characteristics	Male (n=1,699)	Female (n=295)
BW change, kg a	−1.37 (−1.51 to −1.22)	−1.37 (−1.74 to −1.01)
BMI change, kg/m2 a	−0.46 (−0.50 to −0.41)	−0.54 (−0.68 to −0.39)
WC change, cm a	−1.05 (−1.24 to −0.86)	−2.05 (−2.60 to −1.51)
Videophone communications, n a	1.52 (1.48–1.56)	1.53 (1.45–1.61)
Messages, n a	3.01 (2.98–3.04)	2.88 (2.81–2.96)
Number of days delayed a	16.8 (15.0–18.7)	15.7 (11.8–19.6)

BMI, body mass index; BW, body weight; WC, waist circumference.

^a   Mean (95% confidence interval)

The results of linear regression models for BW are shown in Table 3. For males, baseline BMI, pre-intervention action history, number of days that completion was delayed, and the numbers of videophone communications and messages were significantly associated with larger changes in BW. In contrast, no factors were found to be significantly associated with BW in females.

Table 3. Multiple linear regression analysis for the associations of BW change with each parameter

			β coefficient	95% CI	p-value
Male (n=1,699)
	Age		0.001	−0.025 to 0.026	0.964
	Baseline BMI		−0.100	−0.149 to −0.052	<0.001*
	Pre-intervention action history	Yes	−0.682	−0.997 to −0.367	<0.001*
		No	Ref.	Ref.	Ref.
	Number of days delayed		0.007	0.003–0.011	<0.001*
	Videophone communications, n		−0.595	−0.882 to −0.308	<0.001*
	Messages, n		−0.387	−0.726 to −0.048	0.025*
	Smoking history	Yes	0.137	−0.151 to 0.425	0.351
		No	Ref.	Ref.	Ref.
Female (n=295)
	Age		0.035	−0.035 to 0.104	0.327
	Baseline BMI		−0.079	−0.183 to 0.024	0.132
	Pre-intervention action history	Yes	−0.633	−1.441 to 0.175	0.124
		No	Ref.	Ref.	Ref.
	Number of days delayed		0.006	−0.005 to 0.017	0.276
	Videophone communications, n		0.122	−0.653 to 0.897	0.757
	Messages, n		0.673	−0.129 to 1.475	0.100
	Smoking history	Yes	0.158	−0.718 to 1.034	0.722
		No	Ref.	Ref.	Ref.

BMI, body mass index; BW, body weight; CI, confidence interval.

Adjusted R²=0.040 (male), 0.020 (female) for BW change in multiple linear models. Reference (Ref.) indicates the reference of each value (β coefficient and corresponding 95% confidence intervals). Asterisk (*) indicates statistical significance (p<0.05).

Results of the linear regression model for WC are shown in Table 4. For males, baseline WC, pre-intervention action history, and the numbers of videophone communications and messages were significantly associated with WC changes. For females, age, baseline WC, and pre-intervention action history were related to WC changes.

Table 4. Multiple linear regression analysis for the associations of WC change with each parameter

			β coefficient	95% CI	p-value
Male (n=1,699)
	Age		−0.021	−0.053 to 0.012	0.211
	Baseline WC		−0.116	−0.144 to −0.089	<0.001*
	Pre-intervention action history	Yes	−0.760	−1.168 to −0.351	<0.001*
		No	Ref.	Ref.	Ref.
	Number of days delayed		−0.0002	−0.005 to 0.005	0.949
	Videophone communications, n		−0.680	−1.051 to −0.308	<0.001*
	Messages, n		−0.585	−1.024 to −0.146	0.009*
	Smoking history	Yes	−0.113	−0.484 to 0.257	0.549
		No	Ref.	Ref.	Ref.
Female (n=295)
	Age		0.095	0.001–0.189	0.048*
	Baseline WC		−0.138	−0.211 to −0.066	<0.001*
	Pre-intervention action history	Yes	−1.906	−3.068 to −0.744	0.001*
		No	Ref.	Ref.	Ref.
	Number of days delayed		−0.008	−0.024 to 0.008	0.320
	Videophone communications, n		−0.035	−1.156 to 1.086	0.951
	Messages, n		0.731	−0.427 to 1.889	0.215
	Smoking history	Yes	0.131	−1.110 to 1.373	0.835
		No	Ref.	Ref.	Ref.

BMI, body mass index; BW, body weight; CI, confidence interval; WC, waist circumference.

Adjusted R²=0.057 (total), 0.047 (male), 0.086 (female) for WC change in multiple linear models. Reference (Ref.) indicates the reference for each value (β coefficient and corresponding 95% confidence intervals). Asterisk (*) indicates statistical significance (p<0.05).

In the outcome evaluations of SHG starting in 2024, WC reduction of 2 cm or more will be required²⁵⁾. Consequently, logistic regression analysis was performed in this study. The results indicated that, for each sexes, both the baseline WC and pre-intervention action history were associated with a WC reduction of 2 cm or more (eTable 1).

Discussion

Herein, we retrospectively investigated the effects of and factors associated with BW and WC reductions in response to ICT-based SHG. Indeed, ICT based-SHG was associated with decreased BW and WC in both sexes, while the numbers of videophone communications and messages were significantly associated with lowering of BW and WC only in males.

Previous studies have suggested the positive effects of SHG. Intensive SHG resulted in a 0.26 kg reduction in BW over 3 months²⁶⁾. Another study found that 6 months of intensive SHG led to a BMI decrease of 0.38 kg/m² for males and 0.53 kg/m² for females²⁷⁾. It also resulted in a WC reduction of 1.54 cm in males and 1.92 cm in females when the intervention was provided for 6 months²⁷⁾. On the other hand, in our study, 3 months of intensive SHG led to a 1.37 kg decrease in BW for both sexes, and a BMI reduction of 0.46 kg/m² for males and 0.54 kg/m² for females. WC reductions were 1.05 cm and 2.05 cm in males and females, respectively. Thus, this 3-month ICT-based SHG may provide more beneficial outcomes than conventional SHG for 6 months.

In this study, pre-intervention action history was found to be linked to lower BW (males) and WC (both sexes). According to the transtheoretical model developed by Prochaska and DiClemente, behavior change consists of five sequential stages: precontemplation, contemplation, preparation, action, and maintenance²⁸⁾. Individuals in the action stage generally adopt a healthier lifestyle, including lower fat intake²⁹⁾, higher vegetable and fruit intakes³⁰⁾, and increased physical activity³¹⁾. Consistently, the participants in our present study who began lifestyle improvements before the SHG intervention experienced greater reductions in BW and WC.

Multiple regression analysis showed the number of videophone communications to be associated with small BW and WC reductions in males. In fact, 10 phone-based interventions were reportedly associated with weight loss amounts³²⁾. Another study indicated videophone intervention to be effective for achieving dietetic compliance and improving blood pressure, HbA1c, and low-density lipoprotein cholesterol levels³²⁾. Herein, we confirmed videophone communication to be effective for reducing BW and WC in males.

Numbers of videophone communications and messages were associated with reductions in BW and WC only in males (ie, not in females). Females are reportedly more health consciousness and have healthier eating habits than males^33,34). They also show more interest in lifestyle changes³⁵⁾ and exhibit higher motivational variables than males³⁶⁾. We speculate that this accounts for the numbers of videophone communications and messages not being associated with BW and WC reductions in females.

This study has limitations. First, SHG performance was evaluated several months after SHC, and thus may not have yielded accurate BW and WC reduction amounts. Second, WC was self-measured. Females tend to undermeasure their WC more than males³⁷⁾. Although a 1 kg reduction in BW usually corresponds to 1 cm reduction in WC³⁸⁾, we observed larger WC than BW reductions in females. Third, we did not include participants who dropped out of the study in the analysis. Finally, the study was observational and lacked a control group. The results of this study do not reveal whether BW and/or WC reductions are a consequence of SHG.

In conclusion, we assessed the effects of ICT-based SHG, and identified factors associated with BW and WC reductions in response to this intervention. Our results might prove to be useful for improving SHG intervention strategies. MetS is a widespread disease with major public health and socioeconomic implications. The knowledge obtained in this study may be useful for planning an approach to reducing the serious clinical risks associated with MetS.

Acknowledgments

This research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors. We are indebted to the study participants. We appreciate the professional staff members of MEDCARE Inc. for providing the SHG. We also thank Ms. Haruna Morimoto for data acquisition assistance.

Data profile

Due to the sensitive nature of the data, information obtained and/or analyzed during the current study is available from the corresponding author [Yuki Shimba. Email: [e-mail]] upon reasonable request to bona fide researchers.

Sources of funding

None.

Conflicts of interest

YI, YS, and CV were employees of MEDCARE Inc. when this research was performed and the manuscript was prepared. YY has no competing interests to declare.

Author contributions

Research conception and design: YI, YS and YY; statistical analysis of the data: YI and CV; interpretation of the data: YI, YS, YY; writing of the manuscript: YI, YS, YY. YI and YS contributed equally to this work.

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Appendixes

eFigure 1. Histogram of BW change

eFigure 2. Histogram of WC change

eTable 1. Logistic regression analysis of WC change (≥2 cm)

			Odds ratio	95% CI	p-value
Male (n=1,699)
	Age		1.005	0.987–1.022	0.592
	Baseline WC		1.050	1.035–1.066	<0.001*
	Pre-intervention action history	Yes	1.352	1.088–1.68	0.007*
		No	Ref.	Ref.	Ref.
	Number of days delayed		1.002	0.999–1.004	0.200
	Videophone communications, n		1.208	0.990–1.476	0.064
	Messages, n		1.176	0.930–1.490	0.177
	Smoking history	Yes	1.156	0.948–1.412	0.153
		No	Ref.	Ref.	Ref.
Female (n=295)
	Age		0.985	0.944–1.028	0.495
	Baseline WC		1.055	1.020–1.094	0.002*
	Pre-intervention action history	Yes	1.768	1.046–3.020	0.035*
		No	Ref.	Ref.	Ref.
	Number of days delayed		1.002	0.995–1.010	0.558
	Videophone communications, n		0.849	0.509–1.413	0.528
	Messages, n		0.674	0.395–1.139	0.143
	Smoking history	Yes	1.097	0.626–1.928	0.747
		No	Ref.	Ref.	Ref.

CI, confidence interval; WC, waist circumference.

Reference (Ref.) indicates the reference for each value (odds ratio and 95% confidence intervals). Asterisk (*) indicates statistical significance (p<0.05).