The Science of Diabetes Self-Management and Care2024, Vol. 50(5) 373–382© The Author(s) 2024Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106241268372journals.sagepub.com/home/tde
AbstractPurpose: The purpose of this study was to identify the independent factors associated with intertemporal decision-making and to examine its relationship with diabetes self-management behaviors, glucose variability, and diabetes complications in patients with diabetes.
Methods: A cross-sectional study using convenience sampling (n = 368) was conducted in patients with type 2 diabetes (T2DM) between November 2021 and April 2023. Data were collected using self-reported questionnaires and retrieval of clinical information from medical records. Intertemporal decision-making was operationalized using delay discounting. The outcome variables included diabetes self-management behaviors, A1C, diabetic retinopathy, and carotid artery disease. Hierarchical regression and binary logistic regression models were used to explore the relationships among intertemporal decision-making, self-management, A1C, and carotid artery disease.
Results: The analyses showed that intertemporal decision-making was negatively associated with physical activity and carotid artery disease, in which individuals with lower delay discounting tended to have healthier physical activity; when the delay discounting rate increased 1 unit, the risk of the carotid artery disease increased by 39.8%.
Conclusions: The study reveals that a lower delay discounting can promote healthier physical activity and decrease the incidence of carotid artery disease. These results offer new knowledge for researchers and clinicians to consider intertemporal decision-making in developing interventional programs to improve physical activity and reduce carotid artery complication in patients with T2DM when providing care.
Diabetes is a public health issue worldwide; it affects 12.8% of Chinese adults who are age 18 years or older, and it remains a continuing growth trend in mainland China.1 As estimated by the International Diabetes Federation,2 by 2045, the prevalence of diabetes will reach 174.4 million in China. Empirical evidence has shown that individuals with diabetes have at least a twofold increase in cardiovascular disease mortality compared to those without diabetes.3 Carotid artery disease is a major macrovascular complication of diabetes, and it increases the risk for developing cardiovascular disease and stroke greatly.4,5 A1C is identified as the gold standard for assessing glucose variability. Unfortunately, more than 50% of patients with diabetes did not achieve the recommended goal for A1C (<7%), which significantly increases the risks for developing macrovascular diseases.6,7
National recommendations suggest that self-management is essential to achieve optimal glycemic control, and it requires individuals to make necessary lifestyle modifications.8 However, previous studies have shown that the related self-management activities (eg, physical activity, diet, foot care, medication taking, and self-monitoring of blood glucose) among patients with diabetes are suboptimal.7 Sustaining healthy behaviors over time remains a challenge for patients with diabetes. Therefore, exploring the influencing factors that are associated with health behavior change and health outcomes becomes crucial.
The National Standards for Diabetes Self-Management Education and Support (DSMES) points out that patients with diabetes should have ongoing decision-making support to achieve adequate self-management behaviors.9 However, in the decision-making process, it is easy for individuals to deliberately perform unhealthy and harmful behaviors to get immediate gratification rather than choose healthy choices that exert relatively larger benefits at a later time point. This refers to “intertemporal decision-making” or “intertemporal choice,” which is value-based decision-making. It describes the extent to which the reward loses its value with temporal delay.10 For example, following a healthy diet may bring a greater outcome to the individual, but when delay occurs in actualizing the health benefits, value may decrease as the delay persists.11 In general, most people always discount the value of future rewards.10 In recent years, the concept of intertemporal decision-making has been applied to behavioral research in chronic disease management. Health intertemporal decision-making has been defined by researchers as individuals making behavioral choices between short-term costs and long-term health benefits.12 Delay discounting is often used as an operational definition of intertemporal decision-making. It describes the process of the depreciation of the subjective value of the reward over time, and it facilitates the quantitative comparison of the subjective value over different time periods.13
Previous evidence has confirmed that intertemporal decision-making is associated with maladaptive behaviors, such as addictive behaviors (excessive alcohol intake, substance abuse, and cocaine and nicotine dependence) and health-related behaviors (unhealthy dietary behaviors and obesity).14-18 Empirical evidence also has identified that consideration of intertemporal decision-making is important in reducing risks of diabetes, and researchers suggested that individuals with higher levels of delay discounting were associated with lower engagement in self-care behaviors in type 2 diabetes (T2DM),19 lower treatment adherence,20 and higher A1C value in type 1 diabetes.21 As a potential common cause of risky behaviors, most studies have explored the relationship between delay discounting and short-term outcomes, with limited evidence examining its effect on long-term health outcomes, such as diabetes complications in patients with T2DM. Therefore, the main purpose of the current study was to identify the independent factors associated with intertemporal decision-making (delay discounting) and to examine its relationship with diabetes self-management behaviors, glucose variability, and selected diabetes complications in patients with T2DM. It is hypothesized that a higher rate of delay discounting is related to lower engagement in diabetes self-management behaviors, poorer glucose variability, and higher risk of diabetes complications.
This cross-sectional study was conducted following convenient sampling in an outpatient clinic at a tertiary hospital in Beijing, China. This study was approved by the institutional ethical review board at Capital Medical University, Beijing, China (No. Z2021SY082), and permission was obtained from the study hospital before approaching patients at the clinic.
Participants were recruited if they were (1) with a clear diagnosis of T2DM by a clinician, (2) more than 18 years of age, and (3) able to read and write Chinese. Reasons for exclusion included participants who (1) were pregnant and (2) had psychiatric conditions, such as severe psychiatric illness. After researchers explained the study details and eligible patients provided the informed consent, participants were asked to complete a set of paper-based questionnaires in a face-to-face format, which took approximately 15 minutes to 20 minutes to complete. At the conclusion of this study, a total of 368 patients completed the questionnaire.
Data collection was completed from November 2021 through April 2023 by the principal investigator (PJ). At the outpatient clinic of the endocrine metabolism and immune diseases center of the study hospital, potential participants were referred to the investigator by clinical nurses who worked at the center for further screening, based on the inclusion and exclusion criteria. Eligible patients were asked by the researcher if they were interested in participating in the study, and informed consent was provided by study participants before data collection. All data were recovered on the spot to ensure the completeness of the questionnaire.
Participants’ sociodemographic information, including sex, age, educational level, monthly income, family history of diabetes, employment status, and marital status, was collected using an investigator-developed form. Meanwhile, patients’ height and weight were obtained using unified equipment by nurses at the clinic (one-time measure). In addition, clinical information, including patients’ A1C (within the last 3 months), carotid disease (within the prior 1 year), and diabetic retinopathy (DR; within the prior 1 year), were retrieved and obtained from the medical records. The occurrence of carotid intima-media thickening and carotid plaque were considered as carotid artery abnormality in this study.
Delay discounting rate (k values) was assessed using the 27-item Monetary Choice Questionnaires (MCQ).22 The MCQ contains 27 dichotomous monetary choice tasks for which participants make choices between larger delayed rewards (LDRs; eg, “595 Chinese Yuan [CNY] 7 days from now”) and smaller immediate rewards (SIRs; eg, 217 CNY today), in which 0 value was given if one chose the SIR and 1 was given if one chose the LDR. Responses from each participant were entered by the principal researcher (PJ) to an Excel-based spreadsheet with built-in algorism that allows the k value being calculated automatically following the formula K = A/(1 + kD); K is SIR, A is LDR, and D is the delay associated with A.22 In final analysis, due to the skewed distribution of the overall k values, log transformation of the overall k was used to approximate normal distribution, as suggested in a previous study.23
The outcome variables in the current study included self-management behaviors, A1C, and selected diabetes complications.
Self-management behaviors. Diabetes self-management behaviors, including medication taking, dietary behavior, physical activity, self-monitoring of blood glucose (SMBG), and foot care, were evaluated using the Summary of Diabetes Self-Care Activities (SDSCA) scale.24 We employed the modified Chinese version of the SDSCA,25 which demonstrated acceptable reliability among Chinese patients with T2DM, with the internal consistency coefficient at 0.68.25 It contains a dietary subscale (4 items), physical activity subscale (2 items), SMBG subscale (2 items), foot care subscale (2 items), and a 1-item medication taking subscale; they were used to evaluate the related behaviors within the past 7 days. The internal consistency coefficient of this scale was found to be 0.71 in the current study.
Glucose variability. A1C is the gold standard in evaluating participants’ glycemic variability, which was measured by blood test. In this study, the A1C level was retrieved from patients’ medical record, and only those with testing values within the past 3 months were included.
Diabetes complications. Carotid artery disease was screened via carotid ultrasound examination that measured the intima-media thickness (IMT). Researchers have demonstrated that individuals with intima-media thickening at 1 mm ≤ IMT ≤ 1.2 mm and identified stenosis at IMT > 1.2 mm are considered to be at higher risk of developing cardiovascular disease.4 In the present study, IMT ≥ 1 mm or with visible plaque formation was identified as carotid artery abnormality. DR was evaluated by an ophthalmologist using the equipment (Topcon TRC. NW8) available at the study hospital. In this study, the grading of the DR is termed as mild, moderate, severe nonproliferative DR, and proliferative DR; any presence of these indicators suggested the presence of DR.
Epidata 3.1 software (Epidata Association, Odense, Denmark) was used for data entry and verification. All analyses were run using the SPSS 26.0.26 Among the 368 participants who completed the study, 14 were missing data on body mass index (BMI), disease duration, or A1C (Little’s missing completely at random test: χ2 = 1.357, df = 3, P = 0.716), and they were missing at random: Discrete missing values were each assigned a value of 999. Categorical variables were described using frequencies and percentages. We tested the normality of the sample variables using the skewness statistic test. Normally distributed continuous variables were described using means and standard deviations, and nonnormally distributed data were identified using median and interquartile ranges. Delay discounting rate by demographic characteristics was analyzed using one-way analysis of variance. Pearson correlation analysis and multiple linear regression models were computed to assess the relationships between delay discounting, self-management behaviors, and A1C. Carotid artery abnormality and DR were termed as binary variables (coded as 1 if present and 0 otherwise) to reflect the presence or absence of the corresponding condition. Binary logistic regression models were applied to determine if delay discounting was significantly associated with the occurrence of carotid artery abnormality or DR. In all analyses, statistical significance was set at a priori of P values <0.05, two-tailed.
Based on a similar study, delay discounting was associated with the A1C value (r = .18) among patients with type 1 diabetes.20 In the current study, using effect size at .18, an estimated sample size of 237 to 391 patients was deemed necessary following correlation analysis using G* Power Version 3.1 (the effect size set at .24, α = .05, power set at .80 to .95, and two-tailed). A total of 368 patients were included in the final analysis, which was sufficient to test the significance among study variables.
A total of 368 individuals completed the survey, and their basic characteristics are shown in Table 1. In this sample, the mean age of the participants was 45.41 years old (SD = 9.98, ranged = 18-72 years old, 58.2% male). Majority of the participants (71.5%) were currently employed, and 56.5% of the participants were with college degree or above. In this sample, participants had an average of 6 years (72.51 months) of T2DM since their first diagnosis, and more than half of the participants (65.2%) acknowledged that they had a family history of diabetes. In addition, participants were generally overweight, with the average BMI at 27.05 kg/m2, and had a mean A1C value at 7.68% (SD = 1.87%), indicating that most participants had slightly less than optimal glucose variability.
Table 2 describes the influencing factors associated with delay discounting. The results indicated that educational level, age, and so on were not significantly associated with delay discounting rate in this sample.
Multiple linear regression models were established to exam the relationship between delay discounting, selfmanagement behaviors (diet, physical activity, SMBG, and foot care), and A1C. The results are shown in Table 3. In the unadjusted model (data not shown), the results showed that delay discounting was negatively and significantly associated with dietary behavior (B = −0.133; 95% CI, −0.263 to −0.003) and physical activity (B = −0.338; 95% CI, −0.579 to −0.096). However, after adjusting for sample characteristics, delay discounting was only negatively associated with physical activity (B = −0.256; 95% CI, −0.483 to −0.028).
Of the participants who completed this study, 315 individuals completed the carotid ultrasound examination, and 330 performed fundoscopy screening. As shown in Table 4, binary logistic regression analysis was used to test whether delay discounting was correlated with carotid artery abnormality. After adjusting for sample characteristics, the analysis showed that delay discounting was significantly associated with diabetes carotid atherosclerosis (OR = 1.398, 95% CI, 1.063-1.838), and the Nagelkerke’s R2 showed that the overall model explained 38.8% of the variance for the outcome. However, after controlling the covariates, binary logistic regression analysis showed that delay discounting was not associated with DR (OR = 0.905, 95% CI, 0.704-1.164) in this sample, as shown in Table 5.
The purpose of this study was to identify the independent factors associated with intertemporal decision-making and to examine its relationship with self-management behaviors and health outcomes in patients with diabetes. The results showed that delay discounting was significantly associated with self-management behavior related to physical activity and with the occurrence of carotid artery abnormality in a sample of Chinese adults with T2DM. The results partially supported the hypotheses. In accordance with the primary hypothesis, these results demonstrated that individuals who focus on the future had more engagement in physical activity and had less risk in developing carotid artery abnormality, after controlling potential confounding variables.
Overall, these findings provide new knowledge for understanding the relationship among delay discounting, healthy behaviors, and clinical outcomes among patients with T2DM. The current study further supports the evidence demonstrated in earlier research that delay discounting is associated with self-care behaviors among individuals with diabetes and prediabetes.19,27,28 By demonstrating that lower delay discounting was significantly related to better physical activity, these results suggest that delay discounting may be an important factor for encouraging long-term engagement of self-management behaviors among these patients.
Previous research suggested that delay discounting was significantly related to A1C21,27,29 and that a greater rate of delay discounting was associated with higher A1C level in individuals with prediabetes.30 However, the results of the current study were similar to the study findings from Campbell et al,19 which also did not demonstrate significant relationship between delay discounting and glucose variability among US adults with T2DM. This inconsistency is possibly due to a different culture background and use of different tools in evaluating delay discounting. Another possibility was that the cross-sectional design used in this study and Campbell et al’s19 study may have limited the ability in detecting the significant changes of behavior in relation to the change in A1C, suggesting longitudinal studies may be needed to better clarify the relationship between delay discounting and glucose variability. Interestingly, although these results did not demonstrate a positive relationship between delay discounting and glucose variability in the Chinese population, this is one of the first studies exploring associations between delay discounting and diabetes complications. The results of this study have demonstrated that lower delay discounting with preference for future gratification is a protective factor for reducing risk of carotid artery abnormality. This finding suggests that intertemporal decision-making may be a novel target for accomplishing the long-term goal of preventing diabetes complications, such as carotid artery disease. Clearly, to prevent atherosclerotic cardiovascular disease from occurring, one must consider future benefits of adapting certain healthy behaviors, such as following a healthy diet, acquiring adequate physical activity, and so on.
It is worth mentioning that the current study did not find age as an influencing factor of delay discounting. However, available evidence also suggested that delay discounting was not associated with age in T2DM.19 One reason may be due to sample selection bias, in which the mean age was 46 years old in this sample and more than half of the patients were 18 to 44 years old in the current study. In addition, the results demonstrated that delay discounting was not associated with patients’ education level, not like the results reported from previous studies, in which significant negative relationships were identified between the 2 variables in patients with T2DM and prediabetes.19,27,30,31 This may be because the level of education is largely at college level among participants in this sample, which did not provide enough variation in detecting the significance.
Taken together, given the growing burden of diabetes complication among patients with T2DM, the concept of intertemporal decision-making has implications for researchers and clinicians in promoting effective diabetes management. Moreover, because delay discounting is considered a modifiable factor,32 developing interventions to target intertemporal decision-making may improve engagement in self-management behaviors in this population.
From the research standpoint, related evidence on intertemporal decision-making is still limited; therefore, additional research is needed to explore the underlying mechanisms of how delay discounting influences selfmanagement behaviors and health outcomes. Given the potential for reducing individuals’ delay discounting to maintain healthy behaviors and reduce diabetes complications, researchers have identified a variety of methodologies to reduce discounting of the future.33,34 A systematic review indicated that episodic future thinking (EFT) combined with existing therapeutic interventions to reduce impulsive decision-making and nudge behaviors in advantageous directions34 are effective strategies in reducing the rate of delay discounting. Empirical evidence has shown that EFT can decrease delay discounting in patients with prediabetes.32,35 In addition, EFT has been shown to reduce demand for high energy food in overweight or obese individuals.35 However, with most evidence coming from findings of cross-sectional studies,19,29 very few interventions targeting intertemporal decision-making have been developed for patients with T2DM. Thus, there is a need to provide more evidence to examine the correlations of delay discounting, self-management behaviors, and diabetes health outcomes via future longitudinal studies. Meanwhile, integrating intertemporal decision-making into interventions to reframe value for future health is essential to promote optimal self-management behaviors and effective decision-making. Ongoing high-quality surveillance of diabetes outcomes is imperative to delay the occurrence of complications associated with T2DM.
This study does have several limitations. First, this study was a single center study conducted at the outpatient clinic of a tertiary hospital, which limited the generalizability of study findings to patients with T2DM from other settings. Second, this study uses a cross-sectional design, and thus it cannot testify a causal relationship among study variables. Future research should consider prospective assessment of delay discounting, health behaviors, and clinical outcomes with a longitudinal approach. Third, although this study considers carotid artery disease and DR as the representative clinical outcomes, other diabetic complications, such as diabetic kidney disease, peripheral neuropathy, and so on, are also important health outcomes to be considered in the future to evaluate their relationships with intertemporal decision-making. In addition, the current study only used subjective data and did not collect objective data related to dietary consumption and physical activity, which may introduce information bias and alter the study results. Future studies should consider use of objective data to strengthen the accuracy of study findings.
The current study reveals that a lower delay discounting can promote healthier physical activity and decrease the incidence of carotid artery abnormality. These results offer new knowledge for researchers and clinicians to take into consideration of intertemporal decision-making in developing interventional programs to improve physical activity and reduce carotid artery complication when providing care in patients with T2DM.
We would like to acknowledge the following people who have made contributions to the current study. During our study, we received support from the Beijing Luhe Hospital (affiliated hospital with Capital Medical University) by giving us permission to conduct the current study, especially help received from the doctors (Dong Zhao, PhD, professor, director physician, and her team) and clinical nurses. Also, thanks to all patients who enrolled in this study.
Pina Jin and Xiaojing Wang have contributed equally to the study. Meihua Ji obtained funding for the study, designed the study, and analyzed the data. Pina Jin, Xiaojing Wang, Aihua Li, Huan Dong, Kailu Wu, and Aichun Wen enrolled data collection. Pina Jin drafted the manuscript, designed the study, and analyzed the data. Meihua Ji and Xiaojing Wang critically reviewed and revised the manuscript. All authors critically revised the manuscript for intellectual content and approved the final manuscript.
All authors have no conflicts of interest to report.
This study was supported by the R&D Program of Beijing Municipal Education Commission (No. SZ202310025009)
Kailu Wu https://orcid.org/0009-0001-9458-5876
Meihua Ji https://orcid.org/0000-0002-9421-6077
Li Y, Teng D, Shi X, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020;369:m997. doi:10.1136/bmj.m997
International Diabetes Federation. IDF Diabetes Atlas. 10th ed. 2021. https://www.diabetesatlas.org
Taylor KS, Heneghan CJ, Farmer AJ, et al. All-cause and cardiovascular mortality in middle-aged people with type 2 diabetes compared with people without diabetes in a large U.K. primary care database. Diabetes Care. 2013;36(8):2366-2371. doi:10.2337/dc12-1513
Cobble M, Bale B. Carotid intima-media thickness: knowledge and application to everyday practice. Postgrad Med. 2010;122(1):10-18. doi:10.3810/pgm.2010.01.2091
Ratchford EV, Evans NS. Carotid artery disease. Vasc Med. 2014;19(6):512-515. doi:10.1177/1358863X14557722
Aschner P, Gagliardino JJ, Ilkova H, et al. Persistent poor glycaemic control in individuals with type 2 diabetes in developing countries: 12 years of real-world evidence of the International Diabetes Management Practices Study (IDMPS). Diabetologia. 2020;63(4):711-721. doi:10.1007/s00125-019-05078-3
Ji M, Ren D, Gary-Webb TL, Dunbar-Jacob J, Erlen JA. Characterizing a sample of Chinese patients with type 2 diabetes and selected health outcomes. Diabetes Educ. 2019;45(1):105-115. doi:10.1177/0145721718811561
Chan JCN, Lim LL, Wareham NJ, et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet. 2021;396(10267):2019-2082. doi:10.1016/S0140-6736(20)32374-6
Beck J, Greenwood DA, Blanton L, et al. 2017 national standards for diabetes self-management education and support. Sci Diabetes Self Manag Care. 2021;47(1):14-29. doi:10.1177/0145721720987926
Lempert KM, Steinglass JE, Pinto A, Kable JW, Simpson HB. Can delay discounting deliver on the promise of RDoC? Psychol Med. 2019;49(2):190-199. doi:10.1017/S0033291718001770
Clare S, Helps S, Sonuga-Barke EJS. The quick delay questionnaire: a measure of delay aversion and discounting in adults. Atten Defic Hyperact Disord. 2010;2(1):43-48. doi:10.1007/s12402-010-0020-4
Chapman GB. Temporal discounting and utility for health and money. J Exp Psychol Learn Mem Cogn. 1996;22(3):771-791. doi:10.1037//0278-7393.22.3.771
Green L, Myerson J. A discounting framework for choice with delayed and probabilistic rewards. Psychol Bull. 2004;130(5):769-792. doi:10.1037/0033-2909.130.5.769
Businelle MS, McVay MA, Kendzor D, Copeland A. A comparison of delay discounting among smokers, substance abusers, and non-dependent controls. Drug Alcohol Depend. 2010;112(3):247-250. doi:10.1016/j.drugalcdep.2010.06.010
Fernie G, Peeters M, Gullo MJ, et al. Multiple behavioural impulsivity tasks predict prospective alcohol involvement in adolescents. Addiction. 2013;108(11):1916-1923. doi:10.1111/add.12283
García-Rodríguez O, Secades-Villa R, Weidberg S, Yoon JH. A systematic assessment of delay discounting in relation to cocaine and nicotine dependence. Behav Processes. 2013;99:100-105. doi:10.1016/j.beproc.2013.07.007
Dassen FCM, Houben K, Jansen A. Time orientation and eating behavior: unhealthy eaters consider immediate consequences, while healthy eaters focus on future health. Appetite. 2015;91:13-19. doi:10.1016/j.appet.2015.03.020
Barlow P, Reeves A, McKee M, Galea G, Stuckler D. Unhealthy diets, obesity and time discounting: a systematic literature review and network analysis. Obes Rev. 2016;17(9):810-819. doi:10.1111/obr.12431
Campbell JA, Williams JS, Egede LE. Examining the relationship between delay discounting, delay aversion, diabetes selfcare behaviors, and diabetes outcomes in U.S. adults with type 2 diabetes. Diabetes Care. 2021;44(4):893-900. doi:10.2337/dc20-2620
Stoianova M, Tampke EC, Lansing AH, Stanger C. Delay discounting associated with challenges to treatment adherence and glycemic control in young adults with type 1 diabetes. Behav Processes. 2018;157:474-477. doi:10.1016/j.beproc.2018.06.013
Lansing AH, Stanger C, Crochiere R, Carracher A, Budney A. Delay discounting and parental monitoring in adolescents with poorly controlled type 1 diabetes. J Behav Med. 2017;40(6):864-874. doi:10.1007/s10865-017-9856-9
Kaplan BA, Amlung M, Reed DD, Jarmolowicz DP, McKerchar TL, Lemley SM. Automating scoring of delay discounting for the 21- and 27-item monetary choice questionnaires. Behav Anal. 2016;39(2):293-304. doi:10.1007/s40614-016-0070-9
Sze YY, Stein JS, Bickel WK, Paluch RA, Epstein LH. Bleak present, bright future: online episodic future thinking, scarcity, delay discounting, and food demand. Clin Psychol Sci. 2017;5(4):683-697. doi:10.1177/2167702617696511
Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23(7):943-950. doi:10.2337/diacare.23.7.943
Xu Y, Savage C, Toobert D, Pan W, Whitmer K. Adaptation and testing of instruments to measure diabetes self-management in people with type 2 diabetes in mainland China. J Transcult Nurs. 2008;19(3):234-242. doi:10.1177/1043659608319239
IBM SPSS Statistics for Windows. Version 26.0. IBM Corp; 2019.
Epstein LH, Paluch RA, Stein JS, et al. Delay discounting, glycemic regulation and health behaviors in adults with prediabetes. Behav Med. 2021;47(3):194-204. doi:10.1080/08964289.2020.1712581
Reach G, Boubaya M, Brami Y, Lévy V. Disruption in time projection and non-adherence to long-term therapies. Patient Prefer Adherence. 2018;12:2363-2375. doi:10.2147/PPA.S180280
Lebeau G, Consoli SM, Le Bouc R, et al. Delay discounting of gains and losses, glycemic control and therapeutic adherence in type 2 diabetes. Behav Processes. 2016;132:42-48. doi:10.1016/j.beproc.2016.09.006
Epstein LH, Paluch RA, Stein JS, et al. Role of delay discounting in predicting change in HBA1c for individuals with prediabetes. J Behav Med. 2019;42(5):851-859. doi:10.1007/s10865-019-00026-3
Mørkbak MR, Gyrd-Hansen D, Kjær T. Can present biasedness explain early onset of diabetes and subsequent disease progression? Exploring causal inference by linking survey and register data. Soc Sci Med. 2017;186:34-42. doi:10.1016/j.socscimed.2017.05.050
Bickel WK, Stein JS, Paluch RA, et al. Does episodic future thinking repair immediacy bias at home and in the laboratory in patients with prediabetes? Psychosom Med. 2020;82(7):699-707. doi:10.1097/PSY.0000000000000841
Koffarnus MN, Jarmolowicz DP, Mueller ET, Bickel WK. Changing delay discounting in the light of the competing neurobehavioral decision systems theory: a review. J Exp Anal Behav. 2013;99(1):32-57. doi:10.1002/jeab.2
Rung JM, Madden GJ. Experimental reductions of delay discounting and impulsive choice: a systematic review and metaanalysis. J Exp Psychol Gen. 2018;147(9):1349-1381. doi:10.1037/xge0000462
Stein JS, Craft WH, Paluch RA, et al. Bleak present, bright future: II. Combined effects of episodic future thinking and scarcity on delay discounting in adults at risk for type 2 diabetes. J Behav Med. 2021;44(2):222-230. doi:10.1007/s10865-020-00178-7
From Beijing Luhe Hospital, Capital Medical University, Beijing, China (Ms Jin); Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China (Ms Wang, Ms Li, Ms Dong); and School of Nursing, Capital Medical University, Beijing, China (Miss Wu, Miss Wen, Dr Ji).
Corresponding Author:Meihua Ji, School of Nursing, Capital Medical University, 10 YouAnmenWai Xitoutiao, Fengtai District, Beijing, 100069, China.Email: mjshouyi@ccmu.edu.cn