Abstract
PURPOSE
Obesity in breast cancer survivors (BCS) increases mortality, but lifestyle interventions have mixed outcomes. Episodic future thinking (EFT), in which individuals mentally simulate positive personal future-oriented events, has shown to reduce body weight (BW) and delay discounting (DD; the tendency to value delayed rewards less). How EFT affects BW reductions in BCS is unknown. This phase II trial evaluated the feasibility and preliminary efficacy of EFT in reducing BW in BCS with overweight/obesity.
METHODS
Women with nonmetastatic BC who completed treatment with a BMI ≥25 kg/m2 were enrolled. Patients were randomly assigned 1:1 to EFT or control (episodic recent thinking [ERT], where individuals mentally simulate positive personal recent events) and stratified by baseline DD; they received text message cues thrice daily for 12 weeks. The primary end point was EFT adherence. Secondary end points were changes in BW and DD.
RESULTS
We included 46 participants; 17/23 (74%) in EFT and 19/23 (83%) in ERT completed the intervention; 83% had low baseline DD (<0.0025). EFT adherence approached the 80% target to establish feasibility (EFT, 77% [95% CI, 0.69 to 0.85]). ERT had similarly high adherence (82% [95% CI, 78 to 86]). BW decreased overall (−2.01% [95% CI, −3.06 to −0.97]; P < .001) but did not differ between EFT and control. The 12-week change in DD did not significantly differ between groups.
CONCLUSION
A telehealth delivered behavioral intervention for weight loss is feasible in BCS, demonstrated by high participant adherence. Additional research is needed to elucidate EFT as well as ERT’s comparable role in modulating DD and as an adjunct to lifestyle interventions for weight loss in BCS.
INTRODUCTION
Obesity in breast cancer survivors (BCS) is associated with an increased risk of cancer recurrence, mortality,1–3 secondary malignancies, cardiovascular disease, insulin resistance, and poorer quality of life.1–3 Given the high prevalence of obesity among BCS, survivorship guidelines emphasize maintaining a healthy body weight (BW).4 Several clinical trials on weight loss have focused on improving healthy lifestyle behaviors (diet and physical activity) in BCS.5–9 However, lifestyle interventions have shown mixed outcomes5–10 and poor long-term adherence. Behavioral interventions incorporating cognitive training may represent a strategy to address this gap.11 Remote delivery through mobile applications can provide a scalable strategy to engage broader populations in weight loss interventions.11 The goal is long-term improvement of positive health behaviors; this is the cornerstone of consummate cancer survivorship care.
Delay discounting (DD) is a behavioral economic principle that measures an individual’s valuation of future rewards.12 When choosing between a future larger reward or an immediately available smaller reward, many individuals prefer the immediate and smaller option; the longer the delay in receiving the larger reward, the more likely one may be to choose the immediate reward. For example, a person may choose to eat a delicious but unhealthy meal rather than a less delicious option that will positively impact future health. Higher DD is correlated with impulsivity and maladaptive health behaviors,12 including poor dietary patterns, sedentary behavior, hyperglycemia, and obesity.13–19
Episodic future thinking (EFT) is a form of cognitive training that targets DD using prospection. Individuals mentally simulate positive, detailed, personally relevant future events. EFT interventions are designed to reduce bias toward immediate gratification and improve self-control to improve health-related decision making. EFT interventions have improved diet quality, decreased caloric intake, and facilitated weight loss in participants with obesity in the general population.20–26 EFT can be delivered remotely through mobile health platforms, increasing accessibility and reducing cost.20,21,27
However, DD and the impact of EFT on weight loss may affect cancer survivors differently (owing to differences in, mortality perception, psychosocial factors, socioeconomic influences, quality of life). Few studies have explored EFT as a behavioral intervention in this setting. This phase II randomized controlled trial evaluated feasibility and preliminary efficacy of a 12-week remotely delivered EFT intervention for weight loss in BCS with overweight/obesity.
METHODS
Participants
Patients were recruited from Ohio State University (OSU) Comprehensive Cancer Center (Stefanie Spielman Comprehensive Breast Center) in Columbus, Ohio, from November 2021 to August 2023. Eligible participants were women ≥18 years with nonmetastatic (stage I-III) invasive breast cancer (BC) who were overweight/obese (BMI ≥25 kg/m2); had completed curative-intent therapy (surgery, radiation, chemotherapy; endocrine therapy and trastuzumab/pertuzumab permitted); had access to a mobile device; and were motivated to lose weight. Motivation was assessed using validated readiness to change questionnaires for overweight/obesity (S-Weight and P-Weight).28–31 Those with cognitive impairment or participating in another weight loss study were excluded. All participants gave informed consent before enrollment. Survey links were sent by SMS text messages from REDCap (Research Electronic Data Capture) through Twilo, a third-party secure web service application. Study procedures followed National Institute of Health ethical guidelines and was approved by OSU Institutional Review Board. The study was registered at ClinicalTrials.gov (identifier: NCT05012176).
Study Design/Procedures
Patients were randomly assigned 1:1 to the EFT or control (episodic recent thinking [ERT]) group. In each condition, participants imagined real, positive, detailed, personal events and generated cues using standardized prompts (Data Supplement). The distinction between conditions was temporal orientation: EFT involved imagining positive future events, whereas ERT involved recalling positive events from the recent past. The ERT control isolates the effects of future thinking (prospection) in EFT by ensuring that both groups engage episodic memory, features personalized details, and are matched for vividness.21,23,32,33
Random assignment occurred through a numerical tool via REDCap34,35; patients were stratified by baseline DD rate (<0.0025 v ≥0.0025). Following the 12-week intervention, participants could choose to continue the intervention for 12 more weeks; DD and BW were also collected during this interval (Fig 1, schema).
FIG 1.
Study schema. DCIS, ductal carcinoma in situ; s/p, status post.
Patients received ≥5 minutes of lifestyle counseling from their oncology provider, consistent with BC survivorship guidelines.4 Counseling emphasized maintaining a healthy BW, limiting high-calorie foods/beverages, following a plant-forward diet (high in vegetables, fruits, whole grains, and legumes and low in saturated fats),4 and engaging in aerobic (≥150 min/wk moderate intensity or ≥75 min/wk vigorous activity) and resistance training (≥2 sessions/wk)4 Participants received educational materials (Data Supplement), were offered cancer center dietitian referral, and were given an account to MyFitnessPal36,37 to track diet, physical activity, and BW (asked to log self-measured BW at least monthly).36,37 These accounts were accessible to the research team to monitor engagement in lifestyle behaviors. Baseline socioeconomic, demographic, and clinical characteristics were collected via an online questionnaire/medical record review.
Intervention Implementation
Before initiating the intervention, we sent participants a text message link to a previously established survey to guide them through the generation of cues at future time points, for the EFT group (1 month, 6 months, 1 year, 2 years, 5 years, and 10 years), and at recent time points, for the ERT group (5 days, 12 days, and 14 days).38,39 Each patient generated cues at baseline and then generated at least three new cues every 2 weeks. The text cues were delivered randomly via a guided web-based application.
Each cue was a positive and personal event created by each participant using self-guided task instructions to elicit vivid detail (Who are you with? How are you feeling? Where are you? Data Supplement). Examples of EFT and ERT events include celebrations, vacations, social gatherings, and other personally significant events. Patients were instructed to visualize future (EFT) or past (ERT) positive experiences.20,32 To minimize habituation and optimize engagement, each cue was distinct and not repeated. After participants created a cue, they rated it on a five-point scale for enjoyment, importance, excitement, and vividness. A score <3 on any items prompted participants to edit the cue or create a different cue. Cues were reviewed by the research team, and if any was lacking in detail, the coordinator asked the participant to elaborate to attain the necessary detail.
Participants received text message cues 3 times/day (before meal times, eg, 7 am, 12 am, 6 am, or other predetermined consistent times of day) to engage in EFT or ERT. Participants were presented with one of their cues and asked to read/vividly imagine the event for 30–60 seconds in a quiet location. Patients could choose to receive additional on-demand cues (maximum 3/d) during times of day in which greater self-control over food/exercise was required. The frequency and timing of cues and durations of visits to the web application were recorded.
End Points
The primary end point was feasibility (adherence to EFT), defined as the average proportion of text message EFT cues participants opened and attended to during the 12 weeks. Adherence target rate was 80%.
DD was assessed at baseline and every 4 weeks using an adjusting-amount task in which participants chose between smaller immediate monetary rewards and larger delayed rewards.13,14,33 Indifference points across delays were used to estimate the discount rate (k) using a hyperbolic model (V = A/(1 + kD)),40 with the steepness of the discounting curve (ln k) providing a measure of DD. Detailed task procedures are provided in the Data Supplement. Monetary DD provides a generalized decision-making measure relevant to valuation of real-world behaviors including those pertaining to metabolic health.13,14
BW, waist circumference, and hip circumference were measured on a calibrated scale at baseline, 12 weeks, and 24 weeks. Other health measures (exploratory endpoints) collected at baseline and 12 weeks were patient-reported outcomes (PROs) of PROMIS-29 and Global Health Short Form41–43; blood-based biomarkers (high-sensitivity C-reactive protein [hsCRP]; Homeostatic Model Assessment for Insulin Resistance [HOMA-IR]44; and diet quality by Healthy Eating Index [HEI-2020]45,46; Data Supplement).
Statistical Analyses
All analyses followed an intention-to-treat basis. Hypothesis tests and CIs were two-sided and at the 5% significance and 95% confidence levels, respectively. A one-sample t test evaluated adherence in the EFT and ERT groups separately. Additional on-demand interactions were counted toward the numerator but not the denominator. If a patient withdrew, all scheduled prompts were included in the denominator, and prompts scheduled post–drop-out were treated as unopened. Thus, the primary end point was fully evaluable for all patients regardless of study completion.
A linear mixed-effects model assessed changes from baseline in secondary end points (BW, DD, blood-based biomarkers, PROs) from baseline was assessed. The model included baseline measure, time point (12 and 24 weeks), intervention assignment, and time point-by-intervention interaction as fixed effects, with a subject-level random effect to account for the correlation of multiple measurements from the same individual. The primary analysis evaluated the effect of assignment to the intervention at 12 weeks. This model was applied to changes in BW, DD, PROs, HOMA-IR, hsCRP, and HEI-2020. Mediation analyses used the causal steps approach.47
Power calculations assumed an effect size of 0.9 (Cohen d). In a prior EFT study with prediabetes, adherence was 102% (standard deviation [SD] = 20 percentage points), with values >100% allowed when participants accessed on-demand and scheduled cues.26 Assuming a similar SD, data on 20 participants per arm provided 80% power to detect a deviation from the 80% adherence target of ≥14 points (eg, 94%, 66%). Thus, 40 patients with complete data were required; to account for drop-out, we recruited an additional 15% (six participants); the goal accrual was 23 patients per arm.
Ethics Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board of The OSU (protocol number 2021C0029, date of approval: February 2021).
Informed Consent
Informed consent was obtained from all individual participants included in this study.
RESULTS
Accrual/Follow-Up
Patient selection is summarized in Figure 2. In total, 46 patients were randomly assigned. Of them, 17 (74%) in the EFT group and 19 (83%) in the ERT group completed the 12-week intervention. Baseline patient characteristics are shown in Table 1, and metabolic health characteristics in Table 2. Groups were balanced in baseline DD, age, menopausal status, BW, and BMI. Most were postmenopausal (72%), had hormone receptor–positive disease (89%), had ductal carcinoma in situ or stage I BC (54%), and were White (83%). The mean baseline BW was 202 lbs (SD = 36.7), and the mean baseline BMI was 33.7 kg/m2 (SD = 6.1). The baseline DD rate was found to be low (<0.0025) in 83% of participants.
FIG 2.
CONSORT diagram.
TABLE 1.
Baseline Sociodemographic and Clinicopathologic Variables
| Characteristic | EFT (n = 23), No. (%) | ERT (n = 23), No. (%) | Overall (N = 46), No. (%) |
|---|---|---|---|
| Age, years, mean (SD) | 55.6 (12.5) | 55.7 (11.2) | 56.6 (11.8) |
| Menopausal status | |||
| Premenopausal | 7 (30.4) | 6 (26.1) | 13 (28.3) |
| Postmenopausal | 16 (69.6) | 17 (73.9) | 33 (71.7) |
| Race/ethnicity | |||
| White | 17 (73.9) | 21 (91.3) | 38 (82.6) |
| Black/African American | 4 (17.4) | 1 (4.3) | 5 (10.9) |
| Asian | 1 (4.3) | 0 (0) | 1 (2.2) |
| Other | 1 (4.3) | 1 (4.3) | 2 (4.3) |
| Highest education | |||
| High school graduate | 1 (4.3) | 1 (4.3) | 2 (4.3) |
| Some college | 2 (8.7) | 5 (21.7) | 7 (15.2) |
| College graduate | 13 (56.5) | 13 (56.5) | 26 (56.5) |
| Graduate/professional degree | 7 (30.4) | 4 (17.4) | 11 (23.9) |
| Employment status | |||
| Working full- or part-time | 6 (26.1) | 5 (21.7) | 11 (23.9) |
| Keeping house or raising children full-time | 15 (65.2) | 18 (78.3) | 33 (71.7) |
| Neither working nor student | 2 (8.7) | 0 (0) | 2 (4.3) |
| Annual household income, USD | |||
| <$25,000 | 3 (13.0) | 0 (0) | 3 (6.5) |
| $25,000 to $49,999 | 2 (8.7) | 1 (4.3) | 3 (6.5) |
| ≥ $50,000 | 17 (73.9) | 19 (82.6) | 36 (78.3) |
| Missing | 1 (4.3) | 3 (13) | 4 (8.7) |
| Breast cancer subtype | |||
| Hormone receptor positive | 20 (87.0) | 21 (91.3) | 41 (89.1) |
| HER2 positive | 2 (8.7) | 5 (21.7) | 7 (15.2) |
| Cancer stage | |||
| DCIS | 4 (17.4) | 1 (4.3) | 5 (10.9) |
| 1 | 11 (47.8) | 9 (39.1) | 20 (43.5) |
| 2 | 8 (34.8) | 8 (34.8) | 16 (34.8) |
| 3 | 0 (0) | 5 (21.7) | 5 (10.9) |
| Time since breast cancer diagnosis | |||
| <1 year | 4 (17.4) | 6 (26.1) | 10 (21.7) |
| 1–2 years | 9 (39.1) | 8 (34.8) | 17 (37.0) |
| 3–5 years | 5 (21.7) | 7 (30.4) | 12 (26.1) |
| >5 years | 5 (21.7) | 2 (8.7) | 7 (15.2) |
| DD rate | |||
| <0.0025 | 18 (78.3) | 20 (87.0) | 38 (82.6) |
| ≥0.0025 | 5 (21.7) | 3 (13.0) | 8 (17.4) |
Abbreviations: DCIS, ductal carcinoma in situ; DD, delay discounting; EFT, episodic future thinking; ERT, episodic recent thinking; HER2, human epidermal growth factor receptor 2; SD, standard deviation; USD, US dollars.
TABLE 2.
Baseline Metabolic Health Characteristics
| Characteristic | EFT (n = 23), No. (%) | ERT (n = 23), No. (%) | Overall (N = 46), No. (%) |
|---|---|---|---|
| Weight, pounds; mean (SD) | 206 (41) | 198 (32) | 202 (36.7) |
| BMI, kg/m2, mean (SD) | 33.5 (7.6) | 33.9 (4.5) | 33.7 (6.1) |
| Waist-to-hip ratio, mean (SD) | 0.865 (0.047) | 0.847 (0.066) | 0.856 (0.058) |
| Missing | 1 (4.3) | 0 (0) | 1 (2.2) |
| Engage in any physical activity (aerobic or resistance exercise) | |||
| No | 5 (21.7) | 5 (21.7) | 10 (21.7) |
| Missing | 1 (4.3) | 0 (0) | 1 (2.2) |
| Yes | 17 (73.9) | 18 (78.3) | 35 (76.1) |
| Min/wk, mean (SD) | 143 (129) | 149 (172) | 146 (151) |
| Diabetes | 1 (4.3) | 1 (4.3) | 2 (4.3) |
| Cardiovascular disease | 2 (8.7) | 1 (4.3) | 3 (6.5) |
| Hypertension | 8 (34.8) | 7 (30.4) | 15 (32.6) |
Abbreviations: EFT, episodic future thinking; ERT, episodic recent thinking; SD, standard deviation.
Adherence
The average adherence rate to cues in the EFT group was near the prespecified 80% target (EFT: 77%, 95% CI, 69 to 85). The average adherence in the ERT group was 82% (95% CI = 78 to 86; Table 3, Data Supplement).
TABLE 3.
Results of Adherence and 12-Week Body Weight Change
| Week | Arm | N | Mean | SD | Min | Max | 95% CI (mean) | 95% CI (difference) | P |
|---|---|---|---|---|---|---|---|---|---|
| Adherence (%) | |||||||||
| Overall | ERT | 23 | 82.0 | 9.1 | 62.7 | 95.6 | 78 to 86 | −14.5 to 3.9 | .3 |
| Overall | EFT | 23 | 76.8 | 19.2 | 33.3 | 97.9 | 69 to 85 | ||
| Change in body weight (lb) | |||||||||
| 12 | ERT | 19 | −5.4 | 5.7 | −18.8 | 3.6 | −8.0 to −2.8 | −0.7 to 7.2 | .1 |
| 12 | EFT | 17 | −2.1 | 6.8 | −14.6 | 8.2 | −5.1 to 0.8 | ||
| Change in body weight (percentage points) | |||||||||
| 12 | ERT | 19 | −2.9 | 3.2 | −10.8 | 1.9 | −4.3 to −1.5 | −0.3 to 3.8 | .09 |
| 12 | EFT | 17 | −1.1 | 3.3 | −7.2 | 4.1 | −2.7 to 0.4 | ||
NOTE. P values are tests for differences between arms.
Abbreviations: EFT, episodic future thinking; ERT, episodic recent thinking; SD, standard deviation.
Body Weight
BW data at 12 weeks were available for 17 patients (74%) in the EFT group and 19 patients (83%) in the ERT group. On average, BW decreased from baseline to 12 weeks among all patients (−3.76 lbs [95% CI, −5.76 to −1.76]; P < .001; −2.01 [95% CI, −3.06 to −0.97]; P < .001). However, BW did not significantly differ at 12 weeks within the EFT group (−2.14 lbs [95% CI, −5.11 to 0.83]; P = .2; −1.12% [95% CI, −2.67 to 0.42]; P = .2) or between the EFT and ERT group (absolute difference 3.24 lbs [95% CI, −0.67 to 7.15]; P = .1; percentage point difference 1.78% [95% CI, −0.25 to 3.81]; P = .09; Table 3, Data Supplement).
Delay Discounting
Relative changes (ratios v baseline) in DD within EFT or ERT were not significant at 4 weeks, 8 weeks, or 12 weeks. Moreover, changes in DD did not significantly differ between EFT and ERT at 4 weeks, 8 weeks, or 12 weeks (Table 4, Data Supplement).
TABLE 4.
Results of Change in Delay Discounting at 4, 8, and 12 Weeks
| Week | ERT | EFT | Comparison | |||
|---|---|---|---|---|---|---|
| Ratio v Baseline (95% CI) | P | Ratio v Baseline (95% CI) | P | Ratio (95% CI) | P | |
| 4 | 0.77 (0.21 to 2.79) | .7 | 0.94 (0.36 to 2.49) | .9 | 1.22 (0.24 to 6.15) | .8 |
| 8 | 0.49 (0.09 to 2.62) | .4 | 1.04 (0.34 to 3.18) | .9 | 2.13 (0.28 to 15.96) | .5 |
| 12 | 0.40 (0.07 to 2.29) | .3 | 0.63 (0.19 to 2.08) | .4 | 1.56 (0.19 to 12.95) | .7 |
Abbreviations: EFT, episodic future thinking; ERT, episodic recent thinking.
PROs, Blood-Based Biomarkers, Diet Quality
Twelve-week changes in PROs, biomarkers, and diet quality did not differ significantly between EFT and ERT (Data Supplement).
DISCUSSION
To our knowledge, this is the first trial investigating EFT for weight loss in BCS. We found implementing a remotely delivered EFT intervention was feasible in this population and met the primary end point. The high EFT adherence rate (77 [95% CI, 69 to 85]) approached our 80% prespecified target, supporting the acceptability of this approach. Although not the primary end point, adherence in the control condition of ERT was similarly high (82 [95% CI, 78 to 86]). Overall, our results support the practicability of telehealth using text messages for remotely administered weight-loss interventions in BCS.
Weight loss was observed overall, but we did not detect a significant difference in change in BW or DD between the EFT and ERT groups at 12 weeks. Despite a median baseline BMI >30 kg/m2, most (83%) patients had unexpectedly low baseline DD (<0.0025). This may explain the lack of effect of EFT on this mechanistic target. EFT may more consistently reduce DD in populations with elevated DD (eg, smokers and individuals with obesity),13,48 where there is greater dynamic range for change. In contrast, low baseline DD could limit further reduction. Future studies should evaluate EFT in BCS with higher baseline DD to determine whether greater effects are observed. Additional research is also needed to elucidate how a cancer diagnosis and survivorship factors influence DD.
Another possible explanation for EFT’s lack of effect may be that participants in both groups were motivated to lose weight and received a weight loss lifestyle program consisting of diet and physical activity education, dietitian referral, and tools such as MyFitnessPal to self-monitor diet, exercise, and BW. These interventions may explain the weight loss observed in the control group and could have caused a ceiling effect, limiting additive benefit of EFT on BW. Although these strategies are standard of care by cancer survivorship guidelines,4 their uptake in routine practice remains low.
EFT dosing (number of cues/d, duration of intervention) or schedule (timing of cue delivery/cue regeneration) may not have been optimized for BCS. Our approach was based on feasibility from prior studies in individuals with prediabetes, but we acknowledge that this prescription may need modification based on the unique needs of BCS.26 Further research is also needed to determine whether ERT is a suitable control condition for EFT in this population. Although many EFT trials have used ERT,49 methodologic heterogeneity exists. Emerging evidence suggests that vividly recalling personally meaningful past events—particularly distant events—may inadvertently engage prospection, reducing DD.50 Future studies may consider alternative comparators, such as less active attention controls or standardized episodic thinking,49,51 where participants imagine structured neutral scenarios from the very recent past (eg, within 24 hours), to better isolate the future-oriented component of EFT. In summary, although EFT shows promise as a behavioral strategy in the general adult population, larger studies are needed to establish efficacy in diverse clinical settings, the optimal study design and control condition, and whether there are particular subsets of BCS who may benefit.25
Few studies have evaluated DD as a potential target to improve cancer control behaviors. For example, we demonstrated DD was a potential predictor of adjuvant endocrine therapy adherence in 89 BCS.52 Higher treatment adherence was associated with lower DD (standardized β = .328, P = .005) and a brief EFT intervention reduced DD compared with control.52 In an exploratory analysis, lower DD was also significantly associated with lower BMI (standardized β = −.32; P < .01), independent of age, race, income, time since diagnosis, and menopausal status.53 These findings support that DD may contribute to maladaptive health behaviors in BCS and may be modifiable through EFT.
However, other literature suggests DD may not apply to metabolic health in cancer populations. In a study of 1,000 cancer survivors across several cancer types (including BC), lower DD was associated with many healthy behaviors (lower tobacco and alcohol use, primary care attendance), but not with BMI, physical activity, or vegetable consumption.54 Although inclusion of heterogeneous cancer types may have obscured disease-specific association, our BC-specific cohort similarly did not demonstrate changes in DD or weight with EFT.
Cancer survivors have unique experiences that could affect their valuation of future. Socioeconomic influences (eg, financial toxicity), perceived early mortality, and cancer-related psychosocial stress (eg, fear of cancer recurrence, posttraumatic stress) could affect commitment to long-term health goals. Factors such as time since diagnosis, cancer type, and disease stage could also influence DD. For example, depression can negatively affect future thinking and may compromise the effectiveness of EFT.55 Additionally, time since diagnosis has been negatively associated with DD (P = .01); DD has shown to be the highest after cancer diagnosis but decrease over time.54 Given the lack of well-powered studies directly comparing DD between cancer and matched noncancer controls, additional research is needed to determine whether these populations differ in DD and whether EFT is an effective behavioral intervention in cancer survivors.
Our study has limitations. We recruited patients from a single academic center in Midwest United States, and most were of White race with higher education and income. Future studies should assess the applicability of EFT in diverse groups, including minority racial groups and individuals from rural or community environments. The trial was also limited by its sample size; larger prospective studies specifically powered to detect changes in DD and BW, particularly among BCS with high baseline DD, are needed.
In conclusion, this phase II randomized trial supports the feasibility of implementing behavioral digital health weight loss interventions in BCS, as reflected by high participant adherence in both the EFT and ERT groups. Weight loss was observed in the overall cohort and was likely attributed to the shared lifestyle intervention among all participants. However, these preliminary data do not establish that weight changes were mediated by DD modulation with either condition. The BW and DD findings should be considered hypothesis generating. Further studies are needed to determine optimal EFT dosing/schedule, evaluate appropriate control conditions, and formally assess EFT and ERT comparable efficacy in BCS (including for weight-loss induction versus maintenance) when combined with lifestyle or pharmacologic therapies.
Supplementary Material
CONTEXT.
Key Objective
What is the feasibility and preliminary efficacy of episodic future thinking (EFT), a cognitive training strategy that targets delay discounting (DD; valuation of future rewards), for weight loss in breast cancer survivors (BCS)?
Knowledge Generated
In this phase II randomized clinical trial of BCS with overweight/obesity, a 12-week mobile health delivered behavioral intervention by text message cues (EFT) was found to be feasible, demonstrated by a high adherence rate. Overall body weight (BW) decreased, but weight and DD changes did not differ between EFT and control groups.
Relevance (Y. Chavarri-Guerra)
This phase II randomized clinical trial evaluated EFT, a cognitive training strategy targeting DD, as a mobile health intervention for weight management among BCS with overweight or obesity. Adherence to the intervention was relatively high; however, changes in BW and DD did not differ between the EFT and control groups. These findings provide preliminary evidence on the implementation and potential role of EFT as a behavioral strategy for weight management in BCS.*
Plain Language Summary (Y. Chavarri Guerra)
Maintaining a healthy weight after breast cancer (BC) treatment can be challenging. EFT is a cognitive strategy in which individuals imagine positive, personally meaningful events to help them place greater value on future benefits. In this phase II randomized clinical trial, 46 BCS with overweight or obesity who had completed treatment for nonmetastatic BC were assigned to either EFT or a control intervention. Participants received daily text-message cues for 12 weeks prompting them to engage with their assigned intervention. The study primarily evaluated adherence to the intervention and also examined changes in BW and DD. Adherence was 77% with EFT and 82% with the control intervention, and 74% and 83% of participants, respectively, completed the 12-week intervention. Participants lost a modest amount of weight during the study, but changes in weight and DD did not differ between groups. Further research is needed to determine whether EFT can contribute to weight-management strategies for BC.†
*Relevance section written by JCO Oncology Advances Associate Editor Yanin Chavarri-Guerra, MD, MSc, FASCO.
† Plain Language Summary written by JCO Oncology Advances Associate Editor Yanin Chavarri-Guerra, MD, MSc, FASCO.
ACKNOWLEDGMENT
The authors wish to acknowledge the Transdisciplinary Research in Energetics and Cancer Training Workshop R25CA203650 (principal investigator: Melinda Irwin) and the Pelotonia Fellowship Program at The Ohio State University. We thank Madison Semro, Associate Scientific Editor, and Dawn Chalaire, Associate Director of Editing Services, in the Research Medical Library at The University of Texas MD Anderson Cancer Center for editing this article.
SUPPORT
Supported in part by the National Cancer Institute of the National Institutes of Health (NIH) under the Award Number UG1CA189823 (Alliance for Clinical Trials in Oncology NCORP Grant) and Conquer Cancer Foundation (Debra Lesavoy Women Who Conquer Cancer Young Investigator Award, supported by Nina Lesavoy Brady). J.S.S. is supported by a research career development award (K12AR084228: Building Interdisciplinary Research Careers in Women’s Health Program-BIRCWH; Berenson, PI) from the NIH/Office of the Director (OD) and National Institute of Arthritis and Musculoskeletal and Skin Disease (NIAMS).
Footnotes
DISCLAIMER
The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. It is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make the Author Accepted Manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the American Society of Clinical Oncology, Conquer Cancer, or Nina Lesavoy Brady.
PRIOR PRESENTATION
Presented in part at the ASCO 2024 Annual Meeting, Chicago, IL, May 31-June 4, 2024.
CLINICAL TRIAL INFORMATION
AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted.
I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to https://ascopubs.org/authors.
Jasmine S. Sukumar
This author is a member of the JCO Oncology Advances Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.
Honoraria: Amplity
Consulting or Advisory Role: NEJM Group
Research Funding: Bristol Myers Squibb Foundation
Maryam B. Lustberg
This author is an Associate Editor for JCO Oncology Advances. Journal policy recused the author from having any role in the peer review of this manuscript.
Honoraria: Novartis
Consulting or Advisory Role: Pfizer, AstraZeneca, Lilly, Gilead Sciences, Novartis, Menarini, Bayer, Sandoz, Daiichi-Sankyo
Other Relationship: Cynosure/Hologic
Open Payments Link: https://openpaymentsdata.cms.gov/physician/865154
Jeffrey Stein
Research Funding: Indivior (Inst)
Patrick M. Schnell
Consulting or Advisory Role: Merck
Dionisia Quiroga
Honoraria: Cardinal Health
Margaret E. Gatti-Mays
Research Funding: Sumitomo Pharma Oncology, Marengo Therapeutics (Inst)
Daniel G. Stover
Consulting or Advisory Role: Guardant Health, Natera, AstraZeneca, IQvia, Precede Bio
Research Funding: NeoGenomics Laboratories (Inst), Foundation Medicine (Inst)
Patents, Royalties, Other Intellectual Property: Intellectual Property Related to Cancer Diagnostics
Robert Wesolowski
Consulting or Advisory Role: Celcuity, Pfizer, Lilly, BioNTech SE, Signatera, Medison
Other Relationship: Merck
Jennifer Vaughn
Consulting or Advisory Role: Cogent Biosciences, Ionis Pharmaceuticals, Takeda, Disc Medicine, Sanofi, Novartis
Travel, Accommodations, Expenses: Cogent Medicine
Sagar Sardesai
Honoraria: AstraZeneca
Speakers’ Bureau: Immunomedics
Travel, Accommodations, Expenses: Novartis
No other potential conflicts of interest were reported.
DATA SHARING STATEMENT
A data sharing statement provided by the authors is available with this article at DOI https://doi.org/10.1200/OA-26-00009.
De-identified data from this study are not available in a public archive but will be made available (as allowable according to institutional review board standards) by emailing the corresponding author. The analytic code used to generate the results in this study is available from the corresponding author upon reasonable request. Materials used to conduct the study are not publicly available but are available within the article and/or available from the corresponding author upon reasonable request.
REFERENCES
- 1.Chan DSM, Vieira AR, Aune D, et al. : Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol 25:1901–1914, 2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sheng JY, Sharma D, Jerome G, et al. : Obese breast cancer patients and survivors: Management considerations. Oncology 32:410–417, 2018 [PMC free article] [PubMed] [Google Scholar]
- 3.Feigelson HS, Bodelon C, Powers JD, et al. : Body mass index and risk of second cancer among women with breast cancer. J Natl Cancer Inst 113:1156–1160, 2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Runowicz CD, Leach CR, Henry NL, et al. : American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline. J Clin Oncol 34:611–635, 2016 [DOI] [PubMed] [Google Scholar]
- 5.Harrigan M, Cartmel B, Loftfield E, et al. : Randomized trial comparing telephone versus In-Person weight loss counseling on body composition and circulating biomarkers in women treated for breast cancer: The Lifestyle, Exercise, and Nutrition (LEAN) Study. J Clin Oncol 34:669–676, 2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rock CL, Flatt SW, Byers TE, et al. : Results of the exercise and nutrition to enhance recovery and good health for you (ENERGY) trial: A behavioral weight loss intervention in overweight or Obese breast cancer survivors. J Clin Oncol 33:3169–3176, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Santa-Maria CA, Coughlin JW, Sharma D, et al. : The effects of a Remote-based Weight Loss Program on adipocytokines, metabolic markers, and telomere length in breast cancer survivors: The POWER-Remote trial. Clin Cancer Res 26:3024–3034, 2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Chlebowski RT, Blackburn GL, Thomson CA, et al. : Dietary fat reduction and breast cancer outcome: Interim efficacy results from the Women’s Intervention Nutrition Study. J Natl Cancer Inst 98: 1767–1776, 2006 [DOI] [PubMed] [Google Scholar]
- 9.Pierce JP, Natarajan L, Caan BJ, et al. : Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: The Women’s Healthy Eating and Living (WHEL) randomized trial. JAMA 298:289–298, 2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Janni W, Rack BK, Friedl TW, et al. : Lifestyle intervention and effect on disease-free survival in early breast cancer pts: Interim analysis from the randomized SUCCESS C study. Cancer Res 79:GS5–03, 2018. (suppl 4) [Google Scholar]
- 11.Dennett AM, Hirko KA, Porter KJ, et al. : Embedding lifestyle interventions into cancer care: Has telehealth narrowed the equity gap? J Natl Cancer Inst Monogr 2023:133–139, 2023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bickel WK, Yi R: Temporal discounting as a measure of executive function: Insights from the competing neuro-behavioral decision system hypothesis of addiction. Adv Health Econ Health Serv Res 20:289–309, 2008 [PubMed] [Google Scholar]
- 13.Amlung M, Petker T, Jackson J, et al. : Steep discounting of delayed monetary and food rewards in obesity: A meta-analysis. Psychol Med 46:2423–2434, 2016 [DOI] [PubMed] [Google Scholar]
- 14.Barlow P, Reeves A, McKee M, et al. : Unhealthy diets, obesity and time discounting: A systematic literature review and network analysis. Obes Rev 17:810–819, 2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Tate LM, Tsai PF, Landes RD, et al. : Temporal discounting rates and their relation to exercise behavior in older adults. Physiol Behav 152:295–299, 2015 [DOI] [PubMed] [Google Scholar]
- 16.Garza KB, Ding M, Owensby JK, et al. : Impulsivity and fast-food consumption: A cross-sectional study among working adults. J Acad Nutr Diet 116:61–68, 2016 [DOI] [PubMed] [Google Scholar]
- 17.Appelhans BM, Woolf K, Pagoto SL, et al. : Inhibiting food reward: Delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women. Obesity (Silver Spring) 19: 2175–2182, 2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rollins BY, Dearing KK, Epstein LH: Delay discounting moderates the effect of food reinforcement on energy intake among non-obese women. Appetite 55:420–425, 2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Epstein LH, Paluch RA, Stein JS, et al. : Delay discounting, glycemic regulation and health behaviors in adults with prediabetes. Behav Med 47:194–204, 2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sze YY, Daniel TO, Kilanowski CK, et al. : Web-based and mobile delivery of an episodic future thinking intervention for overweight and obese families: A feasibility study. JMIR Mhealth Uhealth 3: e97, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.O’Neill J, Daniel TO, Epstein LH: Episodic future thinking reduces eating in a food court. Eat Behav 20:9–13, 2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Dassen FCM, Jansen A, Nederkoorn C, et al. : Focus on the future: Episodic future thinking reduces discount rate and snacking. Appetite 96:327–332, 2016 [DOI] [PubMed] [Google Scholar]
- 23.Daniel TO, Said M, Stanton CM, et al. : Episodic future thinking reduces delay discounting and energy intake in children. Eat Behav 18:20–24, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Daniel TO, Stanton CM, Epstein LH: The future is now: Reducing impulsivity and energy intake using episodic future thinking. Psychol Sci 24:2339–2342, 2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Liu Y, Huang S, Feng D, et al. : The use of episodic future thinking in people with overweight or obesity: A scoping review. Medicine 102:e34269, 2023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Epstein LH, Paluch RA, Biondolillo MJ, et al. : Effects of 6-month episodic future thinking training on delay discounting, weight loss and HbA1c changes in individuals with prediabetes. J Behav Med 45:227–239, 2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hollis-Hansen K, Seidman J, O’Donnell S, et al. : An ecological momentary episodic future thinking intervention on mother’s weekly food purchases. Health Psychol 39:159–167, 2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Andrés A, Saldaña C, Gómez-Benito J: Establishing the stages and processes of change for weight loss by consensus of experts. Obesity 17:1717–1723, 2009 [DOI] [PubMed] [Google Scholar]
- 29.Andrés A, Saldaña C, Gómez-Benito J: The transtheoretical model in weight management: Validation of the processes of change questionnaire. Obes Facts 4:433–442, 2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Andrés A, Saldaña C, Beeken RJ: Assessment of processes of change for weight management in a UK sample. Obes Facts 8:43–53, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ceccarini M, Borrello M, Pietrabissa G, et al. : Assessing motivation and readiness to change for weight management and control: An in-depth evaluation of three sets of instruments. Front Psychol 6:511, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Stein JS, Sze YY, Athamneh L, et al. : Think fast: Rapid assessment of the effects of episodic future thinking on delay discounting in overweight/obese participants. J Behav Med 40:832–838, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Sze YY, Stein JS, Bickel WK, et al. : Bright future: Online episodic future thinking, scarcity, delay discounting, and food demand. Clin Psychol Sci 5:683–697, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Harris PA, Taylor R, Minor BL, et al. : The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 95:103208, 2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Harris PA, Taylor R, Thielke R, et al. : Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42:377–381, 2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Teixeira V, Voci SM, Mendes-Netto RS, et al. : The relative validity of a food record using the smartphone application MyFitnessPal. Nutr Diet 75:219–225, 2018 [DOI] [PubMed] [Google Scholar]
- 37.Patel ML, Hopkins CM, Brooks TL, et al. : Comparing self-monitoring strategies for weight loss in a smartphone app: Randomized controlled trial. JMIR Mhealth Uhealth 7:e12209, 2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.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 82:699–707, 2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.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 44:222–230, 2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Mazur J, Nevin J, Commons M: An adjusting procedure for studying delayed reinforcement. Quan Anal Behav 5:55–73, 1987 [Google Scholar]
- 41.Health Measures: PROMIS®. https://www.healthmeasures.net/explore-measurement-systems/promis
- 42.Cella D, Riley W, Stone A, et al. : The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol 63:1179–1194, 2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Hays RD, Spritzer KL, Schalet BD, et al. : PROMIS. Qual Life Res 27:1885–1891, 2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Matthews DR, Hosker JP, Rudenski AS, et al. : Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419, 1985 [DOI] [PubMed] [Google Scholar]
- 45.Kristal AR, Kolar AS, Fisher JL, et al. : Evaluation of web-based, self-administered, graphical food frequency questionnaire. J Acad Nutr Diet 114:613–621, 2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Shams-White MM, Pannucci TE, Lerman JL, et al. : Healthy eating Index-2020: Review and update process to Reflect the Dietary Guidelines for Americans, 2020–2025. J Acad Nutr Diet 123: 1280–1288, 2023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Baron RM, Kenny DA: The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51:1173–1182, 1986 [DOI] [PubMed] [Google Scholar]
- 48.MacKillop J, Amlung MT, Few LR, et al. : Delayed reward discounting and addictive behavior: A meta-analysis. Psychopharmacology 216:305–321, 2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Ding S, Ru Y, Wang J, et al. : Effects of episodic future thinking in health behaviors for weight loss: A systematic review and meta-analysis. Int J Nurs Stud 152:104667, 2024 [DOI] [PubMed] [Google Scholar]
- 50.Brown JM, Stein JS: Putting prospection into practice: Methodological considerations in the use of episodic future thinking to reduce delay discounting and maladaptive health behaviors. Front Public Health 10:1020171, 2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Hollis-Hansen K, O’Donnell SE, Seidman JS, et al. : Improvements in episodic future thinking methodology: Establishing a standardized episodic thinking control. PLoS One 14:e0214397, 2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Vaughn JE, Ammermann C, Lustberg MB, et al. : Delay discounting and adjuvant endocrine therapy adherence in hormone receptor-positive breast cancer. Health Psychol 40:398–407, 2021 [DOI] [PubMed] [Google Scholar]
- 53.Sukumar JS, Vaughn JE, Tegge A, et al. : Delay discounting as a potential therapeutic target for weight loss in breast cancer survivors. Cancers (Basel) 14:1134, 2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Sheffer CE, Miller A, Bickel WK, et al. : The treasure of now and an uncertain future: Delay discounting and health behaviors among cancer survivors. Cancer 124:4711–4719, 2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Katayama N, Nakagawa A, Umeda S, et al. : Cognitive behavioral therapy effects on frontopolar cortex function during future thinking in major depressive disorder: A randomized clinical trial. J Affect Disord 298:644–655, 2022 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
A data sharing statement provided by the authors is available with this article at DOI https://doi.org/10.1200/OA-26-00009.
De-identified data from this study are not available in a public archive but will be made available (as allowable according to institutional review board standards) by emailing the corresponding author. The analytic code used to generate the results in this study is available from the corresponding author upon reasonable request. Materials used to conduct the study are not publicly available but are available within the article and/or available from the corresponding author upon reasonable request.