Identification of weight loss interventions for translation among endometrial cancer survivors: A RE-AIM analysis

Abstract

Interventions for obesity-related cancers that combine nutrition and physical activity for weight loss exist; however, their application to survivors of endometrial cancer is unknown. Furthermore, little is known about pre-implementation perceptions of existing programs from a variety of interested persons (physicians, researchers) who may be part of the implementation team. Adapting an existing intervention rather than developing a new intervention may speed the translational lag time as long as intervention characteristics and fit within the delivery system are considered during the planning phase. To describe the process of determining the core elements of obesity-related interventions for cancer survivors and determine which one might be best delivered by an urban healthcare system that predominantly serves individuals who live in rural areas of Virginia and West Virginia. A pragmatic review of the literature was conducted via PubMed and Google Scholar with broad search terms of cancer survivor AND weight loss AND health intervention. Identified interventions were scored related to the Practical, Robust Implementation and Sustainability Model—which is an extension of RE-AIM framework to guide the understanding of who, what, where, when, and how the intervention was conducted. Intervention characteristics are reported. In addition, ratings from three independent reviewers on the validated 5-point Likert scale of an intervention’s acceptability, appropriateness, and feasibility in the intended delivery system were collected and summarized. Twelve interventions were identified with an average sample size of 241(±195) and a range of 48–683 participants. Target populations included survivors of colorectal, breast, and endometrial cancers as well as general cancer survivors and included both men and women or only women. Most participants (74%) identified as white/Caucasian and average age ranged from 47.1 to 65.9 years. Program duration ranged from 4 weeks to 18 months, with an average duration of 32 weeks. Intervention dosage ranged from three times a week to once a month. Intervention acceptability, appropriateness, and feasibility had average and standard deviation ratings of 3.52(±0.46), 3.41(±0.45), and 3.21(±0.46), respectively, out of 5. The four interventions with the highest combined acceptable, appropriate, and feasible scores are being considered for potential use as an obesity-related intervention for survivors of endometrial cancer. Future work is needed to determine relevant adaptations and efficacy among survivors of endometrial cancer with obesity. Our approach may be beneficial for other interventionists aiming to speed intervention development and implementation.

Review of existing weight loss interventions for cancer survivors and predicting their fit within the delivery system may shorten the lag time for adapting programs for different cancer types.

Implications.

Practice: Those who are selecting or creating new interventions can benefit from determining the characteristics of existing interventions and potential feasibility, acceptability, and appropriateness of an intervention before its initial launch may improve the translational lag time.

Policy: Policymakers who want to decrease impacts of obesity-related cancers should support the exploration of the extant literature, embrace (and study) adaptations needed for program fit.

Research: Future research should be aimed at core elements of interventions and adaptations to ensure that it meets patient and setting needs.

Introduction

Evidence-based interventions for almost all chronic conditions have been developed, tested, and adapted for well over the last three decades. However, there continues to be a science-to-practice or science-to-service gap, where those in need of intervention are not receiving the available evidence-based care [1]. This can include the fact that robust, explanatory, and efficacious interventions do not fit in real-world settings [2–7] or the need to de-implement low-value care [8].

Moreover, a seminal paper from 2000 established the translational lag time (from research to practice) as 17 years [9]. This paper is often cited as the reason for the emergence of participatory research, pragmatic designs, and the increased attention to Dissemination and Implementation (D&I) science [10–12]. D&I science represents the methodologies to investigate the distribution and packaging of information (dissemination) and the integration of evidence-based interventions into practice (implementation) [11, 13]. While D&I is a relatively new and transdisciplinary field [14], its overall purpose is to “begin with the end in mind” [15] and understand the characteristics of the intervention, the people responsible for delivering it, and the infrastructure that may speed or impede its sustainability in the desired setting [16–18]. In a more recent investigation published in 2021, the translational lag time of evidence to cancer care continuum was noted as 15 years and is, as the authors state, only a “marginal improvement” over the past two decades [19]. The authors’ call to action is to continue use of relevant implementation science theories, models, and frameworks to test strategies to overcome intervention integration [19].

There are over 100 D&I theories, models, and frameworks from which to choose [20–22]. While methods, process, and models are imperative, to advance translational research, implementation outcomes are equally important. Proctor et al. [23] initially established implementation outcomes as Acceptability, Adoption, Appropriateness, Costs, Feasibility, Fidelity, Penetration, and Sustainability. In the first psychometrically validated implementation outcomes scale, Weiner et al. [24] found that items related to the feasibility, acceptability, and appropriateness of interventions are primary factors that lead to intervention integration. These implementation outcomes align with domains of the Practical, Robust Implementation and Sustainability Model (PRISM) which accounts for the perceptions of individuals who would contribute to the sustainment of an intervention and the infrastructure needed to do so. The model also includes the outcomes of the interventions’ reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) dimensions for considering “if it works, for whom, and under what conditions.” Essentially, the intervention characteristics, what targeted users think about them, and the resources to fund and deliver the program matter (unsurprising) [16, 17, 25, 26]. The study presented here explored existing interventions, applied PRISM to analyze intervention characteristics, and used a validated tool to score the degree to which interventions would fit within the intended delivery system; in this case, a healthcare system that predominantly serves patients from rural areas. The combination of recipient and delivery personnel perceptions of an intervention’s characteristics are paramount for intervention uptake, impact, and sustainability. In this study, we focus on the perceptions of the former.

One way to systematically and iteratively garner data for each stage in the intervention development process is the Obesity-Related Behavioral Intervention Trials (ORBIT) model [27]. This model acknowledges an iterative process (i.e. cyclical) of defining and refining intervention components for the intended target audience [27]. The model includes several phases. Briefly, it posits to start with a significant clinical question then move to Phase I design (a. define and b. refine); Phase II preliminary testing (a. proof-of-concept and b. pilots); Phase III (efficacy trial); and Phase IV (effectiveness) where “optimization” of intervention components are iterative, ongoing, and bidirectional.

One specific target population of particular interest is survivors of endometrial cancer (EC). Obesity has a strong impact on EC risk. Specifically, morbid adiposity increases the risk of EC by up to 7-fold, compared to the risk of less than 2-fold for most other cancers [28, 29]. Over 70% of survivors of EC typically remain overweight and obese following treatment and comprise the second-largest female cancer survivorship population [30, 31]. This growing group of survivors of EC have lower reported quality of life and increasing morbidity [32, 33]. Also relative to survivors with a normal weight category of the body mass index (BMI), survivors with obesity have poorer outcomes [34–36]. For example, when comparing survivors of EC with at least a 10% difference in BMI, those with a higher BMI have a 9.2% increased chance of death [35]. Furthermore, when mortality rates of survivors of EC with a BMI of >40 kg/m² were compared to those with a BMI of >25 kg/m², those survivors of EC with higher BMIs had a 6.25-fold increased chance of death [32]. When examining mortality etiology, cardiovascular disease, related to excess adiposity, is the leading cause of death for survivors of EC [37]. Therefore, reducing adiposity has the potential to improve the survival of this population. However, despite these obesity-related disparities and potential benefits, research on healthy lifestyle interventions for this population are limited [38]. Considering the relationship between obesity and EC as well as the increased risks associated with obesity after EC treatment, the first step is to determine the preferences and barriers for survivors of EC should be addressed [39–41].

Overall, regardless of target audience, context, or intervention characteristics the old paradigm of developing a new intervention and taking it through efficacy to effectiveness to dissemination has long been challenged and strategies to evaluate existing programs characteristics and potential fit have been promoted. However, the literature is nascent on the specific processes and determinations made by research teams. Therefore, the purpose of this paper was to (i) pragmatically [3, 42] identify and evaluate existing weight loss interventions for obesity-related cancers that may be acceptable, feasibly, and appropriate for survivors of EC and (ii) share utility of this process for others to replicate and speed the adaptation of interventions [43].

Methods

Search Terms and Eligibility

Individuals who are tasked with selecting, adapting, and delivering evidence-based interventions often start with two primary dissemination strategies: a review of the literature or learnings from conferences [20, 43, 44]. A pragmatic review of the literature is similar to steps taken in the field: What interventions exists and do they apply in my setting? While a pragmatic review does not apply systematic methodology, it provides more than a cursory glance of the literature. Given that research on survivors of EC is nascent, the search terms in PubMed and Google Scholar were kept broad: cancer survivor AND weight loss AND health intervention. We initially aimed to search for only randomized controlled trials, but there were too few with this specific audience or with intervention characteristics. Therefore, we broadened to any studies that (i) reported intervention characteristics; (ii) had weight loss as a key focus of the intervention; (iii) included of female participants; (iv) focused on participants with a BMI in the overweight (25–30 kg/m²) or obese category (>30 kg/m²); (v) described strategies for behavior change; and (vi) described intervention logistics (intervention strategies, delivery personnel, and location). Items 5 and 6 were included to aid in replication and understanding of the mechanism of behavior change and ultimate weight loss.

Coding

The RE-AIM framework (reach, effectiveness, adoption, implementation, maintenance) [18, 45] was used to code for target audience and the characteristics of the sample (reach); the behavior theories and behavioral change strategies utilized based on Michie et al.’s Behavior Change Wheel [46] and primary outcome reported (efficacy/effectiveness); the intervention characteristics of frequency, duration, and mode of delivery (implementation); and setting and staff characteristics (adoption). The maintenance dimension was a priori outside the scope of this study due to the limited number of interventions that report maintenance of participants’ outcomes and even fewer that reported the degree to which the intervention was sustained in the intended system, in the literature in general.

Coders

To determine program fit of potential weight loss interventions for survivors of EC, three members of the research team independently rated each intervention identified via the search. One coder was a gynecologic oncologist with over a decade of work centered on the development and implementation of behavioral interventions for survivors of EC. The second coder has experience in D&I science, is a national leader for the RE-AIM Framework, and has led community-based interventions for rural populations, older adults, and those with obesity or cancer. The third coder was a medical student with prior experience with community-based interventions and the RE-AIM Framework. The gynecologic oncologist and medical student were affiliated with a tertiary medical center serving rural and urban patients, serving 20 counties in central and southwestern Virginia as well as southern West Virginia, while the third coder is based at a land-grant institution and has extensive experience with community-based interventions. Taken together, this group has over 25 years of intervention-related experience and with different vantage points related to the success of an intervention.

Acceptability, Appropriateness, and Feasibility

Proctor et al. provided a list of key implementation outcomes that determine the potential integration of evidence-based interventions in practice; each from the perception of the facilitators or those otherwise involved in implementation, not the client or patient perspective. Further work conducted by Weiner et al. [24] found that acceptability, appropriateness, and feasibility were most predictive of staff and setting adoption. Acceptability refers to the degree to which an evidence-based intervention is perceived as agreeable, palatable, or satisfactory. Appropriateness relates to the perceived fit, relevance, or compatibility of the evidence-based intervention. Feasibility is the extent to which the evidence-based intervention can be successfully used or carried out within the intended practice setting [23]. The Weiner scale included 12 items (4 each) including statements such as: “I like (INSERT INTERVENTION)”; “(INSERT INTERVENTION) seems like a good match”; and “(INSERT INTERVENTION) seems easy to use” [24]. Each item was scored from 1 completely disagree to 5 completely agree. Means and standard deviations for each category were calculated. Post-scoring discussion occurred to identify key intervention components that drove scores for each category.

Results

Led by a medical student intern, a pragmatic literature search was conducted to identify articles that had cancer survivors as primary patient population, weight loss, and that had an intervention description included in the manuscript. Using PubMed and Google Scholar, 315 were identified in the original search. After removing duplicates and ineligible articles (n = 152), 163 articles underwent abstract screening, of which 120 articles were ineligible (i.e. cancer survivors not the primary patient population, no intervention description). Forty-three manuscripts were sought for retrieval; and 5 were unable to be located with 38 assessed for full review and 26 being excluded due to one or more eligibility reasons. Twelve studies were identified, and data were extracted based on the intervention characteristics such as frequency, mode, and outcomes.

Table 1 includes the summary of intervention characteristics. Sample sizes of selected interventions ranged from 48 to 693 participants, with an average sample size of 241(±195). Target populations included survivors of colorectal, breast, and ECs as well as general cancer survivors and included both men and women (n = 4; 33.3%) or only women (n = 8; 66.7%) for an average of 88.5% women. Most participants identified as white/Caucasian (74%) with 23% Black and 3% Other, and had an average age range from 47.1 to 65.9 years. Fifty percent of the studies were conducted outside of the USA [47–52] and 50% were within the USA [53–58].

Table 1.

Summary of characteristics of weight loss interventions for obesity-related cancers

Name of intervention	Reach: target audience, sample characteristics					Effectiveness: What theories and strategies guided the behavioral intervention?			Adoption: Who delivered the intervention and where?		Implementation: What was the frequency, duration, and mode of delivery?
	Target audience	Demographic characteristics and study location	Sample size	BMI ±SD	Age ±SD	Behavioral theory or construct used	Behavioral change strategiesa	Summary of primary outcome	Who led the sessions?	Where were the sessions held?	Intervention duration (weeks)	Intervention frequency	Mode of delivery
BeWEL+ (Anderson et al. [47])	Colorectal cancer survivors	Men (74%) and Women (26%) White/Caucasian (99%) Scotland, UK	329 (163 treatment group, 166 control group)	31 (4.5)	63.5 (7)	Not applicable	Incentivization	Weight loss Treatment group: 3.50 kg (SD 4.91) Control group: 0.78 kg (SD 3.77)	Lifestyle counsellors	Home and research center	52 weeks	Once a month	First 3 months: in person (1-hour visits), Last 9 months: over phone (15-minute calls)
LivingWELL+ (Anderson et al. [48])	Colorectal and breast cancer survivors	Men (12%) and Women (88%) White/Caucasian (99%) Scotland, UK	78 (39 treatment group, 39 control group)	33.1 (6.3)	47.1 (12.8)	Leventhal's Self-Regulatory Theory, SCT, Health Action Process Approach	Persuasion, Environmental Restructuring, Training	Primary outcome: Feasibility in terms of recruitment, retention, and measurements Secondary outcome of weight loss: Treatment group: 37% Control group: 0%	Lifestyle counsellors	Home and research center	12 weeks	Once every 3 weeks	First visit face to face (65-minute consultation following baseline assessment), 15-minute calls at weeks 2, 5, 9, and 12
N/S (Finocchiaro et al. [49])	Breast cancer survivors	All women Turin, Italy	100 (all treatment group)	28.7 (5.9)	55.5 (9.3)	SCT	Training, Enablement	Weight loss 4.2%; 43% of patients had lost >5% of initial weight	Oncologists, nutritionists/dieticians, sports physician, nurses	Clinic	4 weeks	Once a week	In person
ENERGY (Rock et al. [58])	Breast cancer survivors	All Women White/Caucasian (84%) University of California, USA	693 (345 treatment group, 348 control group)	31.6 (4.7)	56.1 (9.4)	SCT	Modeling, Education	Protocol paper, no outcomes reported	Facilitators with backgrounds in dietetics, psychology, and/or exercise physiology	Community centers	52 weeks	Once a week for first 4 months, every other week for next 2 months, once a month for last 6 months	In person group sessions, accompanied by additional guidance by telephone and/or email
WISER (Winkels et al. [57])	Breast cancer survivors	All Women White/Caucasian (62%) Penn State Cancer Institute, USA	351 (90 in control group, 261 in one of three treatment groups)	34 (6)	59 (8.5)	Self-Efficacy	Enablement, Education	Protocol paper, no outcomes reported	Fitness professionals, registered dieticians	Recreational and community centers	52 weeks	Varies by intervention group	In person sessions and phone calls
I.CAN+ (Ristevsk et al. [50])	Breast and colorectal cancer, lymphoma, and other cancer survivors	Men (29%) and Women (71%) Eastern Victoria, Australia	48 (all treatment group)	N/S	65.9 (2)	Not applicable	Education	Percent meeting exercise recommendations increase from 51% at baseline to 86% at 3 months	Clinicians	Clinics and community centers	6 weeks	Once a week	In person
Moving Forward+ (Stolley et al. [56])	Breast cancer survivors	All women All Black/African American Milwaukee, Wisconsin	246 (121 control group, 125 treatment group)	36.1 (6.2)	57.5 (10.1)	SCT, Socioecological Model, Self-Efficacy	Enablement, Environmental Restructuring, Education	Weight loss Treatment group: 3.5 kg Control group: 1.3 kg	Study-trained community nutritionist and exercise trainer	Neighborhood facilities	26 weeks	Twice a week	In person and text messages
iMove More for Life+ (Short et al. [51])	Breast cancer survivors	All women Adelaide, Australia	370 (program goal feedback) 156 (program website feedback)	N/S	N/S	SCT	Education	Qualitative data on 1 year goal setting and program website	N/S	Home	12 weeks	Dependent on intervention: monthly, weekly, or one time session	Online modules
N/S (Fazzino et al. [55])	Breast cancer survivors	All women White/Caucasian (98.9%) Kansas City, Kansas	186 (all treatment group)	33.9 (4.4)	58.8 (8.2)	SCT	Education, Training	Qualitative feedback on a weight loss intervention that group support and the convenience were key positive program perceptions	Trained study staff	Home	78 weeks	Weekly for first 6 months, weekly for last 12 months OR newsletters only	Phone sessions, newsletter
ENRICH (James et al. [52])	Cancer survivors or their caretakers	Men (23%) and Women (77%) New Castle, Australia	174 (89 treatment, 85 wait-list control, received treatment later)	27.5 (6.1)	58.1 (11.2)	Self-Efficacy	Education, Training	Weight loss Treatment group: 1.4 kg Control group: −0.04 kg (gained)	Exercise specialist and accredited dietician	Community centers	8 weeks	6 sessions over 8 weeks	In person
REWARD (Nock et al. [54])	Endometrial cancer survivors	All women White/Caucasian (80%) Black/African American (20%) Cleveland, Ohio, USA	120 (two treatment groups)	41.6 (6.6)	61 (7)	Behavior Modification	Enablement, Training	Protocol paper, no outcomes reported	Exercise specialists and registered dieticians	University/hospital facilities	28 weeks	3 days a week for 16 weeks for exercise program, once a week for diet program	In person
SUCCEED (von Gruenigen et al. [53])	Endometrial cancer survivors	All women Cleveland, Ohio, USA	75 (41 in treatment group, 34 in control group)	36.4 (5.5)	57 (8.6)	Behavior Modification, Social Cognitive Theory	Enablement, Training, Education	Weight loss Treatment group: 2.5 kg Control group: 0.8 kg	Physicians, registered dieticians, physical therapists	Hospital facilities, home	52 weeks	16 group sessions, 3 individual sessions over 6 months	In person first 6 months, followed by phone call/emails

N/S = not specified; Kg = kilograms; SD = standard deviation; SCT= social cognitive theory; ⁺ = not an abbreviation, program name; ENERGY = Enhance Recovery and Good Health for You; WISER = women in steady exercise research; REWARD: revving-up exercise for sustained weight loss by altering neurological reward and drive; SUCCEED = survivors of uterine cancer empowered by exercise and healthy diet; ENRICH = Exercise and Nutrition Routine Improving Cancer Health.

^aBehavioral change strategies are based on classification from Michie et al. [46].

Program duration ranged from 4 weeks to 18 months, with an average duration of 32 weeks (standard deviation of 24 weeks). Intervention dosage ranged from three times a week to once a month, with several programs decreasing the dosage as the program progressed. Most programs (n = 10; 85%) were fully in-person or had an in-person component [47–50, 52–54, 56–58], two were conducted virtually through online modules or phone calls and newsletters [51, 55]. The social cognitive theory [59] was the most frequently utilized behavioral theory, used in 7 of 12 interventions [48, 49, 51, 53, 55, 56, 58]. In contrast, the choice of behavior change strategies was more diverse and included enablement, training, education, environmental restructuring, modeling, persuasion, and incentivization. There were a variety of facilitators involved in implementation with four reporting one delivery person type (i.e. lifestyle counselor [47, 48], clinicians [50], or trained study staff [55]) and eight reporting multiple levels of delivery personnel in like dieticians/nutritionists, exercise specialists, nurses, physical therapists, and lifestyle counselors; see Table 1 for more details. Interventions were delivered in clinic/hospital or the research center, recreational or community centers, or the homes of the participants. All coded program details can be found in Table 1.

Scores from three independent coders, across all 12 interventions were, on average (M(±SD)) of acceptable (3.52(±0.46)), appropriate (3.41(±0.45)), and feasible (3.21(±0.46)). The range was from 2.92 to 4.33; 2.58 to 4; and 2.25 to 3.83 for acceptability, appropriateness, and feasibility, respectively. See Table 2 for more details on the scores for each intervention.

Table 2.

Acceptability, appropriateness, and feasibility (AAF) scores for selected interventions

Name of intervention	Acceptability M	Acceptability SD	Appropriateness M	Appropriateness SD	Feasibility M	Feasibility SD	Total AAF M (SD)
Four top scoring interventions
LivingWELL	4.08	0.67	3.83	0.58	3.83	0.72	3.91 (±0.07)
BeWEL	4.33	0.65	4.00	0.43	3.08	0.67	3.80 (±0.13)
Moving Forward	3.92	0.51	3.83	0.39	3.67	0.65	3.80 (±0.13)
ENERGY	3.83	0.58	4.00	0.43	3.33	0.78	3.72 (±0.18)
Remaining intervention scores
iMove More for Life	3.33	0.78	3.42	0.51	3.42	0.67	3.39 (±0.14)
SUCEED	3.92	1.00	3.67	0.65	2.58	0.67	3.39 (±0.19)
Name not specified (Finocchiaro et al.49)	2.92	0.67	3.17	0.72	3.75	0.45	3.23 (±0.14)
WISER	3.42	0.51	3.17	0.94	3.08	0.67	3.22 (±0.21)
I. CAN	3.00	1.04	3.17	0.39	3.17	0.39	3.11 (±0.38)
ENRICH	3.17	0.72	2.58	0.79	3.42	0.51	3.06 (±0.15)
Name not specified (Fazzino et al.55)	3.17	0.72	2.92	0.67	3.00	0.60	3.03 (±0.06)
REWARD	3.25	1.06	3.17	0.83	2.25	0.45	2.89 (±0.31)

After the independent coding, the scores were collated, and the research team met to discuss their perceptions. Factors driving decreasing acceptability were location of the intervention that favored programs requiring participation at one location especially if that is a clinic (as opposed to local rec center or remote delivery), and delivery staff with limited bandwidth to administer the intervention (i.e. clinicians). Appropriateness scores were higher for interventions with limited resource burden related to delivery (i.e. text messages and phone calls instead of in-person visits) and utilization of community-trained staff. Studies were rated as less feasible if the staff and resource burden were high, including higher frequency of visits, need for coordination between several clinical providers (i.e. nutritionist, exercise physiologists, and clinicians), and longer duration of the intervention.

Discussion

The purpose of this study was to pragmatically identify, compare, and determine the potential feasibility, acceptability, and appropriateness of existing weight loss interventions for cancer survivors for potential replication of key components in a clinic that serves survivors of EC with obesity who live in both rural and urban areas of Virginia and West Virginia.

The interventions identified varied in their dose, delivery personnel, use of behavior change strategies, and locations. This is not entirely surprising as the core elements of interventions, and their mechanism of effect, are constantly debated and explored [60–62]. However, the added layer of data coding in our paper (i.e. acceptability, appropriateness, and feasibility) allowed a unique application of components of intervention characteristics as well as researcher and provider perceptions on the potential fit of the intervention in the specific community setting [23, 24]. While traditional reviews focused on efficacy and effectiveness are important, our approach provides a critical and novel intervention evaluation strategy. First, adapting an existing intervention with high acceptability, appropriateness, and feasibility rating has the potential to speed the translational research pipeline for survivors of EC obesity-related interventions. Second, adapting interventions with high ratings may also bolster the adoption, implementation, and sustainability of the interventions for survivors of EC as well as other cancer survivors. Within PRISM, representatives from the delivery system may have different perspectives or values related to intervention characteristics that are predictive of whether or not they deliver or support it; hence, the importance of their feedback.

For our future work, the critiques of the four programs that scored the best [47, 48, 56, 58] (see Table 2) will be layered with patient perceptions of interventions that may meet their needs. Principles in each of these programs will be presented to patients in a brainwriting premortem [63] so patients can identify why replicating a similar intervention in their specific context may fail. Thus, we can address any pre-implementation issues and make adaptations intended to increase patient adherence and satisfaction; and, ultimately, improve their health behaviors.

This investigation was the first step in ORBIT model in which the ORBIT consortium proposes a flexible and progressive process for refinement of interventions [27]. This work provided an investigative foundation via selecting a significant clinical and pragmatic research question. Facilitator and expert opinion defined program components suitable on the system level that require further exploration with end users. This process will eventually lead to determining proof-of-concept in the adaptations needed for survivors of EC.

Of note, the work described in this manuscript was conducted prior to the release of a Cochrane review of interventions for survivors of EC, of which those insights may have determined a best-fit intervention in a different way than this current study reports. It is also important to note that the Cochrane review included pharmacologic and surgical trials that were not captured using our search criteria, while other included trials were published after our search was conducted. To expedite potential fit and feasibility, researchers may benefit from identifying a systematic review that has already looked at effectiveness data and then layer it with feasibility, acceptability, and appropriateness to more readily capture what might work in the real world.

Related still, to use the Feasibility, Acceptability, and Appropriateness scale to determine potential fit within the system, there is no identified field threshold in order to predict uptake and sustainability. This is indeed a future area of research for D&I scientists to explore.

Several limitations should be considered. First, as this was not a systematic review, our search was limited to PubMed and Google Scholar and no additional reverse citation methodologies were employed. All results are presented with this caveat and the limitation that this is not an exhaustive list of weight loss interventions for cancer survivors, but rather a pragmatic review of the literature like many adoption decision makers would conduct (such as searching a cancer intervention repository) [42]. Second, we purposefully had inclusion of female participants because of our interest in survivors of EC. Thus the 12 identified interventions do not represent the breath of interventions that may be available, including those that target only men. Additionally, it is possible that our coders’ comments could vary from those of others in different environments, but the literature does suggest that facilitators’ perceptions are predictive of intervention staff adoption [24]. Third, we did not limit our studies to those that presented efficacy data in terms of percent change in weight loss; the outcomes, comparisons, and reporting of efficacy varied across the included studies. See Table 1. Fourth, we identified interventions for populations other than survivors of EC and it is possible these intervention components may not be successful; however, our coders had experience with this population so their opinions regardless of disease site are valuable. Finally, a full RE-AIM/PRISM comparison (with more focus on organizational context and domains) was not employed and may assist with further translational efforts.

Overall, this work identified weight loss interventions that facilitators believed were feasible, acceptable, and appropriate for survivors of EC. The next step is to obtain feedback from survivors about the acceptability of the components of these interventions. These opinions will help to guide the next steps of interventions aimed at improving survivors’ of EC health and quality of life. This work also provides a novel research strategy can be replicated for other populations targeted by behavioral interventions to speed the translational lag time of compared to de novo program development. Future important areas of research include understanding system and potential delivery personnel thresholds for the feasibility, acceptability, and appropriateness of an intervention to inform uptake—and potential interventions to increase positive perceptions of the intervention.

Funding Sources

Shannon Armbruster is an iTHRIV Scholar. The iTHRIV Scholars Program is supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR003015 and KL2TR003016.

Conflict of interest statement. None declared.

Human Rights

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

This study does not involve human participants and informed consent was therefore not required.

Welfare of Animals

This article does not contain any studies with animals performed by any of the authors.

Transparency Statement

Study Registration: This study was not registered. Analytic Plan Pre-Registration: this study is descriptive in nature with no analytical plan registered. Materials Availability: no additional materials are available but the corresponding author is available to discuss any questions.

Data Availability

Data are available and included as tables in the manuscript.