Stages Of A Transtheoretical Model As Predictors Of The Decline in Estimated Glomerular Filtration Rate: A Retrospective Cohort Study Ⅱ

RESULTS

A total of 253,673 employees were enrolled and fulfilled the inclusion criteria; 12,593 (4.9%) were excluded due to missing data and 1,392 due to the prevalence of kidney disease. We analyzed the remaining 239,755 employees (Figure 1). By the end of the follow-up, there were 1,836 persons (0.8%) whose eGFR decreased 30% or more, and the mean follow-up was 2.9 (standard deviation, 1.2) years.

Supportive Service Of Wecistanche-The largest cistanche exporter in the China:

Email:wallence.suen@wecistanche.com

Whatsapp/Tel:+86 15292862950

Shop For More Specifications Details:

https://www.xjcistanche.com/cistanche-shop

CLICK HERE TO GET NATURAL ORGANIC CISTANCHE EXTRACT WITH 25% ECHINACOSIDE AND 9% ACTEOSIDE FOR KIDNEY FUNCTION

The characteristics of each stage are shown in Table 1. The group of stage 5 tended to have a higher serum creatinine and a higher proportion of prescription drugs, including for diabetes and dyslipidemia. The proportion of physical activity changes at 1 year after the first health check-up tended to be higher in stages 3–5 than in stages 1–2. In particular, the proportion of undertaking physical activity was 8.0% in Stage 3; 12.0% in Stage 4; and 8.6% in Stage 5, compared with 5.3% in Stage 1; and 5.2% in Stage 2.

cistanche

Compared with the stage 1 group, the risk of decreasing renal function was significantly lower in the stage 3 group (HR 0.77; 95% CI, 0.65–0.91); in the stage 4 group (HR 0.80; 95% CI, 0.65–0.98); and the stage 5 group (HR 0.79; 95% CI, 0.66– 0.95), after adjusting for age, sex, eGFR, body mass index, blood pressure, blood sugar, dyslipidemia, uric acid, urinary protein (Table 2). The forest plots of the HRs of other covariates are shown in Figure 2, which shows that urinary protein, diabetes, blood pressure, age, and lower eGFR were associated with decreasing renal function.

The major results of the subgroup analysis are shown in Figure 3. When we included 226,667 employees whose eGFR was >60 mL=min=1.73 m2, the hazard ratios of decreasing renal function were 0.95 (95% CI, 0.83–1.09) in the stage 2 group, 0.76 (95% CI, 0.63–0.92) in the stage 3 group, 0.83 (95% CI, 0.67–1.04) in the stage 4 group, and 0.84 (95% CI, 0.69–1.03) in the stage 5 group, compared with the stage 1 group. When we included 12,049 employees whose eGFR was 45–60 mL=min= 1.73 m2, the hazard ratios of decreasing renal function were 0.78 (95% CI, 0.49–1.26) in the stage 2 group, 0.81 (95% CI, 0.45–1.48) in the stage 3 group, 0.19 (95% CI, 0.06–0.61) in the stage 4 group, and 0.65 (95% CI, 0.34–1.22) in the stage 5 group, compared with the stage 1 group. When we included 1,039 employees whose eGFR was ≤45 mL=min=1.73 m2, the hazard ratios of decreasing renal function were 0.98 (95% CI, 0.60–1.58) in the stage 2 group, 0.87 (95% CI, 0.50–1.52) in the stage 3 group, 1.19 (95% CI, 0.63–2.23) in the stage 4 group, and 0.70 (95% CI, 0.40–1.23) in the stage 5 group, compared with the stage 1 group. The trend of point estimates did not change fundamentally in other subgroups.

The sensitivity analysis also showed similar hazard ratios. When we excluded employees aged 60 or more years, the results were 0.95 (95% CI, 0.82–1.09) in the stage 2 group, 0.77 (95% CI, 0.63–0.94) in the stage 3 group, 0.83 (95% CI, 0.65–1.05) in the stage 4 group, 0.75 (95% CI, 0.60–0.95) in the stage 5 group, and 0.99 (95% CI, 0.84–1.18) for the unknown stage group, compared with the stage 1 group. When we excluded employees who had taken any medication for hypertension, diabetes, or dyslipidemia, the results were 1.07 (95% CI, 0.91–1.25) in the stage 2 group, 0.79 (95% CI, 0.63–1.00) in the stage 3 group, 0.68 (95% CI, 0.50–0.93) in the stage 4 group, 0.75 (95% CI, 0.56–0.99) in the stage 5 group, and 1.14 (95% CI, 0.94–1.37) in the unknown stage group, compared with the stage 1 group.

DISCUSSION

We found that persons in stages 3–5 had a habit of healthier behaviors with a lower risk of eGFR decline after adjusting for confounding factors, compared with those who were in stage 1. In particular, those who were in stage 3 (preparation stage) showed less eGFR decline than those in stages 4 or 5 (action, maintenance stage), whereas those in stage 4 or 5 had a slightly higher risk of eGFR decline than those in stage 3.

TTM is a therapeutic theory that led people to realize the importance of healthier behaviors according to their consciousness of the behaviors.4 In several studies, TTM theory has been applied to subjects with lifestyle diseases and improved their behaviors on weight management, adherence to antihypertensive medication, and adherence to lipid-lowering drugs.5–7

The present study, with 1-year follow-up questionnaires, is shown in Table 1. It demonstrated that those in stages 3–5, but not in stages 1–2, improved their various kinds of behaviors. Although our study targeted the general population, similar findings were observed in CKD patients. A systematic review revealed that undertaking physical activity is correlated with mortality rates and the reduction of adverse clinical events in CKD patients,12 suggesting that such healthy behaviors contribute to slowing eGFR decline in stages 3–5. Currently, the KDIGO guideline recommends that CKD patients undertake more physical activities.

Table 2. Cox proportional hazards regression models showing the effects on the risk of estimated glomerular filtration rate decline

Whereas those in stages 3–5 were associated with a lower decline of renal function, those in stage 3 had a better prognosis than stages 4–5. The difference could be explained by the status of physical activity and diet. According to Prochaska,4 stages 3 is defined as "the stage in which people intend to take action in the immediate future, usually measured as the next month". This means that employees in stage 3 do not conduct physical actions and diets but intend to improve their behavior. Therefore, improvement of their behavior would be achieved and lead to a beneficial outcome. In contrast, those in stages 4–5 are subjects who have already made significant modifications to their lifestyle such that they have little room to further improve their behaviors.

The benefit of TTM-based intervention for improving health outcomes remains controversial. Some researchers often failed to show a positive effect.13,14 As a result, a Cochrane systematic review could not conclude that TTM-based intervention might be effective in weight loss.15 This discrepancy could be accounted for in part by the distinct target stages. A TTM-based method may lead us to classify target stages of TTM in which patients' health conditions could improve effectively in terms of the results of laboratory tests, such that we can promote patients' awareness of healthy behaviors to elevate their TTMstages. In our case, an analysis was performed for subjects in each stage. As a result, we found positive results only in stages 3–5, suggesting that the effect might vary between each stage. Therefore, when all stages were combined in other reports, a positive effect in any specific stage may have been canceled out by no effect in other stages. Consistently, a previous randomized trial showed that HbA1c was significantly reduced in diabetic patients in pre-action stages, whereas such an effect was canceled when subjects in all stages were analyzed together.16 Our results would imply that targeting specific stages would improve the results of laboratory tests effectively.

The next issue is how to move patients from stages 1–2 to stages 3–5 in clinical settings. In this regard, Prochaska et al developed a TTM-based intervention aiming to lead patients to move onto different stages.4 They showed that four processes are important to change stages: "Consciousness raising", getting the facts; "Dramatic relief", paying attention to feelings; "Environmental reevaluation", noticing your effect on others; and "Self-liberation", committing. Perhaps, it may be of importance for us to educate patients to understand what healthy behaviors are, how to change and consolidate their behaviors, and what they can do for the health of those around them. These processes could proceed to a change in healthy behaviors to reduce kidney injury.

In addition to improving healthy behaviors, maintaining the habit is another critical issue in traditional cognitive behavior therapies. Cooper et al examined the effect of cognitive-behavioral treatment on body weight in obese people. It was found that the effect of behavioral therapy was transient, and the great majority regained almost all the weight that they had lost with behavioral treatment during the 3 years in the randomized controlled trial,17 indicating that maintaining healthy behaviors would be difficult. Alternatively, several researchers have indicated that new cognitive therapies, including acceptance-based behavioral treatment or mindful intervention, might be an option to improve the maintenance of healthy behaviors.18,19 Future studies might discover more effective methods of intervention.

There were several limitations to our study. First, we dealt with all the competing events, including acute kidney injury (AKI), as censored events, and it might cause healthy worker bias. However, AKI might occur regardless of the stages of behavioral change, AKI incidence is reported at a low rate of 500 persons per 1,000,000,20, and all-cause mortality in patients with AKI is estimated as one in four or less.21 Second, our study did not consider several unmeasured confounding factors, including dietary or exercise habits. Further studies, including a causal mediation analysis, are needed to confirm the impact of such lifestyle habits, even though it is difficult to quantify them accurately.

Conclusion

Compared with the pre-contemplation stage (stage 1), the preparation, action, and maintenance stages (stages 3, 4, and 5), were associated with healthier behavior and a lower risk of eGFR decline after adjusting for confounding factors. The effect of TTM-based therapy may be clarified further in a specific population that performs healthy behaviors.

ACKNOWLEDGEMENTS

We acknowledge all the participants. Ethical considerations: The study protocol was approved by the ethics committee of Kyoto University Graduate School and the Faculty of Medicine (approval number: R1631) and approved by the ethics committee of the Japan Health Insurance Association. The participants were informed to join statistical surveys and research, which handle personal information in a formthat makes it hard to identify, in records. The data underlying this article cannot be shared publicly due to the Japanese law: "Act on the Protection of Personal Information". The data will be shared on reasonable request to the corresponding author.

Conflicts of interest: None declared. Authors' contributions: AK, TI, and DT performed statistical analysis and had full access to all of the data in this study. SK and YI contributed to the design and conduct of the study. YI is the principal investigator of the study. All authors approved the final manuscript version to be published and agreed to be accountable for all aspects of the work. Each author contributed important intellectual content during manuscript drafting or revision, accepts personal accountability for the author's contributions, and agrees to ensure that questions about the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Funding source: The publication charges for this article were funded by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (16H02634, 19H01075). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

REFERENCES

1. Levin A, Tonelli M, Bonventre J, et al. Global kidney health 2017 and beyond a roadmap for closing gaps in care, research, and policy. Lancet. 2017;390:1888–1917.

2. Inker LA, Astor BC, Fox CH, et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD. Am J Kidney Dis. 2014;63:713–735.

3. Oyeyemi SO, Braaten T, Skeie G, Borch KB. Competing mortality risks analysis of prediagnostic lifestyle and dietary factors in colorectal cancer survival: the Norwegian Women and Cancer Study. BMJ Open Gastroenterol. 2019;6:e000338.

4. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12:38–48.

5. Johnson SS, Driskell MM, Johnson JL, et al. Transtheoretical model intervention for adherence to lipid-lowering drugs. Dis Manag. 2006;9:102–114.

6. Johnson SS, Driskell MM, Johnson JL, Prochaska JM, Zwick W, Prochaska JO. Efficacy of a transtheoretical model-based expert system for antihypertensive adherence. Dis Manag. 2006;9:291– 301.

7. Johnson SS, Paiva AL, Cummins CO, et al. Transtheoretical model-based multiple behavior intervention for weight management: effectiveness on a population basis. Prev Med. 2008;46:238–246.