Feasibility of Measuring Context Specific Sedentary Behavior and Pulse Wave Velocity in Endometrial Cancer Survivors

Abstract

Purpose:

Sedentary behavior (SB) contributes to the heightened risk of cardiovascular disease (CVD) in endometrial cancer survivors (ECS). This feasibility study aimed to evaluate key outcomes to assess the practicality of SB reduction interventions for ECS. Secondary aims included SB domain assessment and preliminary efficacy testing of the relationship between SB and arterial stiffness.

Methods:

Forty stage-1 ECS (BMI ≥ 25.0 kg/m², aged 50-80, <12 months post-treatment) participated in the study, which measured total and domain-specific SB using accelerometry and ecological momentary assessment (EMA). Aortic pulse wave velocity (PWV) was estimated via Mobil-O-Graph, and linear regression models examined the association between SB and PWV.

Results:

The study achieved 4 of 5 assessed outcomes, with a 63% consent rate in 10 months. Retention was high, with 90% of participants completing all aspects. Fidelity was strong, though EMA compliance was 69%, slightly below the 70% target. Self-reported SB was 71.4% lower than accelerometer-measured SB [MD: −5.00 hours/day (95% CI: −6.57 to −3.43), P < .001]. ECS exhibited a PWV of 9.04 ± 1.80 m/s, 13.4% higher than normative values, with occupational SB significantly associated with PWV.

Conclusions:

This study highlights high SB and PWV levels in ECS, indicating the need for interventions, particularly for occupational SB. The high retention and consent rates suggest ECS are willing to engage in behavior change, pointing to future research focusing on strategies to reduce SB and improve cardiovascular health.

Introduction

Endometrial cancer survivors (ECS) exhibit a notably heightened prevalence of cardiovascular disease (CVD).^1,2 Comparative analysis with survivors of 28 other cancer types reveals a 38% elevated risk of CVD across all post-treatment periods among ECS. ³ The heightened CVD risk in ECS is largely attributable to detrimental lifestyle habits,^4,5 including high levels of sedentary behavior (SB). ⁶ Although there is a scarcity of epidemiological evidence estimating the association between SB and CVD risk in ECS, a moderate-to-strong association exists between SB and CVD risk in the general population^7-9 that allows us to infer that SB may be an important target for ECS. Additionally, a body of evidence suggests that most cancer survivors are not engaging in the recommended levels of physical activity. ¹⁰ Specifically, in endometrial cancer, only 12% of survivors meet physical activity guidelines. ¹¹ After diagnosis, many cancer survivors face barriers to physical activity, ¹² such as side effects (eg, fatigue, pain), social stigma (eg, weight/appearance change), and lack of time or access to physical activity facilities. We hypothesize that reducing SB (standing or walking to break up prolonged sitting) could be a more feasible approach to improving lifestyle behaviors and reducing CVD risk in ECS. Over time, reducing SB could progress in a stepwise ⁶ manner to light-intensity physical activity, followed by moderate-to-vigorous intensity physical activity, fostering sustainable behavior change.

Any waking behavior in a seated or reclined posture with an energy expenditure of less than 1.5 metabolic equivalents is classified as SB. ¹³ However, different forms of SB may vary in terms of associated disease risk and other co-occurring behaviors. For instance, the health impacts of watching television while eating might differ from those of sitting while working or driving.^14,15 Beale et al, ¹⁶ defined domain-specific SB into categories such as occupation, television, transport, and leisure. Despite this, there is a paucity of studies objectively measuring domain-specific SB, ⁶ particularly in the context of ECS, where objective measurement of SB is lacking. Furthermore, pulse wave velocity (PWV)—the gold standard for non-invasive measurement of arterial stiffness and a key indicator of CVD risk—has yet to be explored in ECS research. Notably, a 1 m/s increase in PWV correlates with a 15% higher risk of CVD. ¹⁷ It is hypothesized that SB induces an acute increase in arterial stiffness driven by local hemodynamic changes (ie, blood pooling) compounded by additional metabolic, hormonal, and autonomic factors. ⁹ Therefore, it is essential to investigate modifiable CVD risk factors in ECS to develop effective interventions that can reduce their CVD risk. ¹⁸

Ecological momentary assessment (EMA) paired with accelerometry ¹⁹ is a novel and feasible approach to capture domain-specific SB, with this method being previously used in a young, healthy population. ¹⁹ These methods used simultaneously offer insights into participants’ SB patterns, including contextual information which is crucial for designing targeted interventions. ²⁰ Although EMA has been previously employed in cancer populations to explore different health parameters (eg, symptom burden, fatigue, quality of life),^21,22 its application in investigating different domains of SB remains largely unexplored.

Our long-term goal is to develop an intervention aimed at reducing SB in ECS to lower CVD risk. To advance this objective, the first step was to conduct a feasibility trial. This trial evaluated key outcomes to ascertain whether SB reduction interventions are practical for ECS: (i) recruitment rate, (ii) consent rate, (iii) compliance, (iv) study retention, and (v) fidelity. Additionally, the secondary aims were to descriptively characterize ECS, which included SB domain assessment, SB recall accuracy, and preliminary efficacy testing of the relationship between SB domains and arterial stiffness. An exploratory purpose of the study was to describe SB context including who participants were with and where to further inform future intervention design.

Methodology

All procedures of this feasibility study were approved by the Protocol Review Committee and the University of North Carolina at Chapel Hill Institutional Review Board (IRB: 22-3145) in February 2023. The study was performed in accordance with the Declaration of Helsinki and STROBE guidelines ²³ were followed. The study opened to accrual in March 2023, and closed in January 2024 when the participant accrual target was met.

Participants

Stage I endometrioid type ECS were recruited from a gynecology oncology clinic using a standardized purposive sampling approach.^24,25 Participants were eligible if they were: (i) <12 months post-treatment—during which the risk of a CVD event is at its highest ³ ; (ii) aged 50 to 80 years old; (iii) overweight (BMI ≥ 25 kg/m²); and (iv) English-speaking. Participants were excluded if they had received ≥7 weeks of chemotherapy/beam radiation therapy because more intensive treatment would suggest a more aggressive tumor histology. This criterion was selected to capture the average characteristics of early-stage ECS.

Study Design and Overview

This feasibility study included in-clinic outcome assessments, followed by 7 days of SB monitoring. Additionally, an online survey collected sociodemographic characteristics, zip code, medical history, menopausal history, and self-reported SB (hour/day), physical activity (minutes/week), and sleep durations (hours/day). ²⁶ Zip code was used to determine the social-vulnerability index (SVI) of the primary county of residence according to the CDC Social Vulnerability Index ²⁷ where low-to-medium was defined as 0.0 to 0.4, medium-to-high as 0.5 to 0.6, and high as 0.7 to 1.0. Medical history was verified via medical records including treatment information (type, time since diagnosis, and time since treatment).

Feasibility Outcomes

Five feasibility outcomes were assessed: (i) recruitment rate, (ii) consent rate, (iii) compliance, (iv) study retention, and (v) fidelity. ²⁸ Recruitment rate was the number of participants enrolled per month, with a goal to reach study accrual within 12 months. Consent rate was the proportion of eligible participants who agreed to participate in the study, with a 50% consent rate target. EMA compliance was the number of prompts answered relative to the number sent, with a 70% EMA compliance target. Study retention was the number of participants who completed all study activities and returned the accelerometers, with a 75% retention target. Finally, fidelity assessed the degree to which the study was implemented according to the planned protocol including adherence to protocol, consistency, quality of delivery, and participant engagement.

In-Clinic Assessment

The assessment included arterial stiffness, blood pressure, body composition, and physical function. Arterial stiffness was measured supine in duplicate via the Mobil-O-Graph (I.E.M., Stolberg, Germany). The Mobil-O-Graph is an Oscillometric automated cuff-based ambulatory blood pressure monitoring device, ²⁹ which uses a proprietary algorithm based on age, systolic blood pressure, and waveform characteristics to estimate aortic PWV. ³⁰ Estimated PWV were averaged and compared versus a healthy age-matched referent values [7.30 m/s (median), 7.90 m/s (mean)]. ³¹ Body composition was measured using bio-impedance analysis (QuadScan 4000, Bodystat, UK) ³² and physical function using the short performance physical battery (SPPB). ³³ Due to the focus on recruitment feasibility, participants did not follow standard pre-assessment guidelines, as consent and enrollment were conducted immediately after their scheduled oncologist appointments.

Monitoring Period: Accelerometry and Ecological Momentary Assessment

SB was objectively recorded using accelerometry (MOX-1, Maastricht Instruments, Maastricht, The Netherlands) and then partitioned into domains using EMA. Participants wore the accelerometer on the mid-thigh according to manufacturer directions for the entire monitoring period (24-hours/day for 7-days). To ensure accelerometer validity, >13 hours/day of wear time ³⁴ was required. Additionally, participants were given a sleep diary to record sleep/wake times to verify sleep was not contributing to SB.

The EMA application ³⁵ was installed on the participant’s personal smart phone. They were given a written copy of instructions to guide accelerometer care and EMA application use as well as given time to practice. EMA captures data in real-time from participants in their natural environments. Participants responded to a series of EMA questionnaires to capture data on activity, location, and social interactions (Supplemental Figure 1).

The response choices were adapted from Liao et al, ³⁶ and Diana et al, ³⁷ and were pilot tested. Previous literature indicates that 2 to 12 prompts are sufficient for capturing physical activity and SB ³⁸ as well as recommended for smart phone-based EMA daily prompts. ³⁹ Therefore, beginning the day after their in-clinic assessment, the SEMA ³ application notified participants to answer the EMA questionnaires 9-times throughout the day across 7 days (63 total prompts). Participants reported sleep/wake times and prompts were sent randomly during 30-minute time-blocks every 2 hours between 0700 and 2300 to capture adequate spacing throughout a 24-hour day while avoiding overnight sleep. ⁴⁰ Participants had 15-minutes to respond to each prompt and were instructed to ignore prompts if they came during an inaccessible time (eg, driving). To ensure standardized collection and reporting of EMA data we adhered to the Checklist for Reporting EMA Studies (CREMAS) ³⁶ Each participant received a $25 electronic gift card following device return.

Data Management and Analysis

Descriptive data are reported as mean ± standard deviation, and mean differences (MD, 95% confidence intervals), unless noted otherwise. All data were reviewed for missingness, outliers, and skewness, and EMA completion rate was calculated. Only participants with complete data were included in the analysis, however all EMA responses were included. The alpha level was set a priori to P < .05. T-tests were used to investigate self-report versus objectively measured movement behaviors and weekday versus weekend results. A single sample t-test was used to compare average PWV to normative healthy age-matched PWV data with a referent value was 7.9 m/s. ³¹ A 1 m/s second difference in PWV is a clinically significant difference accounting for 15% increased risk of CVD. ¹⁷

MOX software was used to generate accelerometry data. EMA and accelerometer data were then uploaded into RStudio for aligning and analysis. Accelerometer data were time-stamped and linked with EMA data. First, domain-specific SB from the EMA data (what behavior type) was calculated to contextualize the accelerometer data. Then, average domain-specific SB was determined for weekdays, weekends, and daily (adjusted for the number of weekdays and weekend-days each week: [(weekday SB × 5) + (weekend day × 2)/7]). An exploratory aim of this study is to describe with whom and where participants are when engaging in SB. Therefore, percentages of time are reported derived from EMA data for who participants are with and where when responding to prompts.

To determine associations between total SB and domain-specific SB with PWV, separate linear regression models were used. Then, hierarchical multiple regression analysis was performed on significant predictors of the separate models to investigate relationships with PWV as the dependent (continuous) variable. Covariates were centered and independent variables (all continuous) that typically explain variation in PWV were entered sequentially into model 2 (+ age) and model 3 (+ age + mean arterial pressure). Additional covariates including occupation status, MVPA, sleep, and body fat percentage were included in all models. Since the sample size is N < 50, the adjusted R² was used for interpretation. The effect size of R² is defined as .0 to .2: weak/small, .3 to .6: moderate/medium, and >.6: strong and standardized coefficients (β) are reported. All variance inflation factors (VIF) were <2, indicating low multicollinearity. ⁴¹

Results

Participants

Participant characteristics are reported in Table 1. On average, participants were white, post-menopausal older women within ~6 months of treatment residing in counties with high social vulnerability. Just over half of participants were married and employed, with an average household income of ~$80 000. Of those employed, 62% reported having a sedentary job (>8 hours/day sitting).

Table 1.

Participant Characteristics.

Participant characteristics	Mean ± SD/N (%)
Age (y)	63.0 ± 7.0
Mass (kg)	92.4 ± 21.0
Height (cm)	164.5 ± 6.8
Body fat (%)	43.0 ± 5.4
BMI (m/kg2)	33.9 ± 7.2
Self-identified race
White	32 (80.0)
Black	4 (10.0)
Asian	1 (2.5)
American Indian	3 (7.5)
Social vulnerability index
Low-moderate (0.0-0.4)	11 (27.5)
Moderate-high (0.5-0.6)	7 (17.5)
High (0.7-1.0)	20 (50.0)
Marital status
Married	22 (55.0)
Widowed	7 (17.5)
Never married	7 (17.5)
Divorced	4 (10.0)
Employment status
Full time/part time	23 (57.5)
Retired	13 (32.5)
Unemployed	4 (10.0)
Highest level of education
High school/some college	12 (30.0)
Associate’s degree	3 (7.5)
Bachelor’s degree	13 (32.5)
Master’s/professional degree	12 (30.0)
Annual household income	$80, 368 ± $58, 208
Treatment
Time since diagnosis (M)	7.1 ± 4.5
Time since treatment (M)	5.6 ± 3.9
Treatment type
Surgery	22 (55.0)
Surgery + radiation	16 (40.0)
Surgery + chemotherapy	1 (2.5)
Intrauterine device	1 (2.5)

Data were analyzable for N = 40.

Abbreviations: y, years; kg, kilogram; cm, centimeter; m, meter; %, percentage; M, months.

Recruitment Rate

The gynecology-oncology clinic provides specialized care for women diagnosed with both pre-invasive and malignant cancers of the reproductive system. Of the 2779 patients who were screened, 64 met the eligibility criteria, and 40 consented to participate (Figure 1). The study began in March 2023 and reached full accrual within 10 months (target 12 months). The recruitment rate was 63%, surpassing the target of 50%, but it varied significantly between months, ranging from 40% to 100%. A generally positive trend in recruitment was observed over time, with particularly high rates (78%, 100%, 70%, and 100%) across the last 4 months, respectively. Participants were recruited from 25 counties across the Southeastern United States. Half of participants were recruited from a county with high social vulnerability (50%). Some were also recruited from counties with medium-to-high social vulnerability (17.5%) or low-to-medium (27.5%).

Figure 1.

Participant recruitment flow diagram.

Participant recruitment flow chart depicting total number of participants screened, eligible, and consented as well as non-eligible and refusals. Potential participants meeting inclusion criteria were identified weekly through medical record screening of upcoming appointments. Once identified, providers were contacted in the electronic medical record system. Potential participants were introduced to study staff at the end of their clinic visit by a provider or nurse whenever possible. Hand-Off was when a clinical provider introduced the potential participant to the research staff. Staff approach was when the research staff directly approached the potential participant without an introduction from clinical staff.

Abbreviations: Gyn-Onc, Gynecology-Oncology; EC, endometrial cancer; tx, treatment; BMI, body mass index.

Consent Rate

A total of 64 eligible ECS were identified, of whom 40 consented to participate, resulting in a 63% consent rate (Figure 1) that exceeded the target (50%). Among the 24 ECS who declined, the most common reason cited was disinterest or reluctance to discuss lifestyle behaviors or blood pressure, accounting for 25% (N = 6) of the refusals. Among those who consented, the majority were introduced through providers (60%, N = 24) or nurses (30%, N = 12), with a smaller portion being directly approached by study staff (10%, N = 4).

Compliance

Compliance responding to EMA prompts was 69% (range: 50%-89%) of the 63 total prompts, which was just below the study target (70%). There was no significant variation in compliance between weekdays or weekends.

Study Retention

All consented participants (N = 40) completed in-clinic assessments (PWV, body composition, SPPB) and wore the accelerometer for the entire 7-day monitoring period. However, 4 participants did not complete the EMA portion of the study due to not owning a smartphone (N = 2), no internet/data access (N = 1), or no desire to use smartphone application (N = 1). With 90% of participants completing all aspects of the study, the 75% study retention rate was achieved.

Fidelity

The study was executed as planned without deviation from study protocol, thus meeting the study goal. Consistency was ensured by following standard operating procedures for all study assessments and quality was maintained through device calibration, pilot testing, and research staff training. Finally, participant engagement was kept consistent by the same member of the study staff completing all recruitment and assessments.

Secondary Aims

Descriptive data from the assessment outcomes are presented in Table 2. On average, PWV was 9.04 ± 1.80 m/s, which is 13.4% higher [MD: 0.25 m/s, (95% CI: 0.65-1.63), P < .001] when compared to normative age-matched data (7.9 ± 1.3 m/s). Participants had a total SPPB score of 9 ± 3, which is pre-frail. Self-reported sedentary behavior (hours/day) was 71.4% lower than accelerometry measured SB [MD: −5.00 hours/day, (95% CI: −6.57 to −3.43), P < .001]. Self-reported MVPA was 79.0% higher than accelerometry measured MVPA [MD: 132 minutes/week, (95% CI: 4.98-259.02), P = .042]. Self-reported sleep (hour/day) was 14.3% lower than sleep diary derived sleep (logged sleep and wake times) [MD: −1.00 hour/day, (95% CI: −1.45 to 0.55), P < .001].

Table 2.

Assessment Visit Outcomes.

Outcome	Mean ± SD/N (%)
Cardiovascular		P-value	Normative data
Pulse wave velocity (m/s)	9.04 ± 1.80	<.001*	7.9 31
AIX@75	31 ± 13		37 42
Systolic BP (mmHg)	134 ± 25		139 43
MAP (mmHg)	105 ± 19		85 43
Diastolic BP (mmHg)	81 ± 14		68 43
Heart rate (bpm)	70 ± 15		73 43
Short performance physical battery
Balance score	4 ± 1		3.7 44
Gait speed (m/s)	4.9 ± 1.1		-
Gait speed score	3 ± 1		2.9 44
Chair stand time (s)	13.0 ± 3.5		-
Chair stand score	3 ± 1		2.3 44
Total composite score	9 ± 3		8.9 44
Accelerometer data
Daily sedentary behavior (h)	12.2 ± 2.3	<.001#	8.4 45
Sedentary Bout duration (min)	17 ± 52		9.6 46
# Of sedentary Bouts ≥ 120 min	5 ± 5		-
Daily standing (min)	112 ± 108		-
Daily light physical activity (min)	72 ± 30		69 47
Daily MVPA (min)	27 ± 18		32 47
MVPA (min/wk)	167 ± 127	<.001#	226 48
Daily sleep (h/d)	8 ± 1	<.001#	7 49
Percentage of day
Sedentary (%)	51 ± 10		-
Standing (%)	8 ± 8		-
Physical activity (%)	7 ± 3		-
Sleep (%)	35 ± 6		-
Self-report 24-h activity behaviors
Sedentary behavior (h/d)	7 ± 4		-
MVPA (min/wk)	299 ± 383		-
Sleep (h/d)	7 ± 1		-

Data were analyzable for N = 40. The cardiovascular and short performance physical battery assessments were conducted in clinic. The accelerometer data was obtained from 7-days of wear in the free-living environments. Self-reported 24-hour activity behaviors were assessed via online questionnaire.

Abbreviations: %, percentage; m/s, meters per second; s, seconds; mmHg, millimeter of mercury; bpm, beats per minute: min, minute; h: hour; d, day; MVPA, moderate to vigorous physical activity.

^*P < .05 versus normative data.

^#P < 0.05 versus self-report activity behavior.

Accelerometry Paired with Ecological Momentary Assessment

Participants spent ~50% of their day engaged in SB. There was only a 0.1 hours/day [(95% CI: −1.3 to 1.1), P = .86] difference in total SB between weekdays and weekends days. As such, daily average SB is reported. Adjusted for occupation status (ie, employed vs unemployed), daily domain-specific sedentary was calculated at 1.1 ± 0.8 hours in leisure screen time (9%), 4.0 ± 3.7 hours in occupational (33%), 3.9 ± 3.2 hours in other (32%), 2.1 ± 0.8 hours in television (17%), and 1.1 ± 0.7 hours in transportation (9%) per day.

Domain-Specific Sedentary Behavior and Cardiovascular Disease Risk

Across the different domains of SB, only occupational SB was associated with PWV (β = .2, P = .007) with a weak/small effect size (Figure 2). Occupation SB was the only outcome that demonstrated a significant association with PWV and was examined using our hierarchal models. Model 1 demonstrated 17.5% of the variance in PWV is explained by daily occupation SB (β = .16, 95% CI: 0.05-0.27, P = .007). In Model 2, there was a 28.7% increase in R² where PWV increased by .10 m/s for each year of life (β = .59, 95% CI: .31-.87, P < .001, Model 2). In Model 3, an additional 10.7% increase in R² was observed where PWV increased by 0.04 m/s for every 1 mmHg increase in MAP (β = .43, 95% CI: .19-.68, P < .001), and a moderate effect (R² = 55.5%) was observed where a 1-hour increase in occupational SB per day was associated with a 0.09 m/s increase in PWV.

Figure 2.

Associations between sedentary behavior domains and pulse wave velocity.

Associations between pulse wave velocity and daily sedentary behavior types. The regression models depicted in the figure show the relationship between pulse wave velocity and each type of SB. Daily occupation sitting was the only statistically significant association (adjusted R² = .18, P = .007).

Abbreviations: SB, sedentary behavior; h, hour; PWV, pulse wave velocity; m, meters; s, seconds.

Exploratory Aim: Describing Sedentary-Behavior Context

An exploratory purpose of this study was to describe the context of domain-specific sedentary behavior, including how participants spend their time with and where. While underpowered to detect associations between these factors and CVD risk, this provides important insights for future interventions. In occupational SB, 52% of participants worked outside the home and reported spending 54% of their time with co-workers. In leisure screen time SB, participants spent 59% of the time alone and exclusively at home (100%). Television SB was spent mostly (76%) with family while also spending some time alone (22%) but rarely with friends (2%) nearly always at home (98%). Participants reported that 47% of other SB was spent alone, followed by with family (27%) or with friends (25%). Other SB occurred mostly at home (75%) but also varied across locations including restaurant (31%), other (9%), or car (2%). Finally, transportation SB was reported with family (43%), friends (29%), and alone (29%) and exclusively by car.

Discussion

The feasibility study successfully achieved 4 of the 5 assessed outcomes: recruitment rate, consent rate, study retention, and fidelity. Over a 10-month period, the study reached full accrual with a consent rate of 63%, surpassing our goal of a 50% consent rate within 12 months. Of the 40 participants, 36 completed all aspects of the study, with only 4 not participating in the EMA component but completing all other aspects (our target was 30/40). The study demonstrated high fidelity in implementation with no protocol deviations. EMA compliance was 69%, slightly below the 70% target.

Regarding the secondary aims of the study, which were to descriptively characterize ECS, 3 key findings emerged that will be important for designing future interventions. First, self-reported measurements of SB and PA/sleep by ECS were found to be inaccurate. Second, ECS spent the most time in occupational or other types of SB, followed by television, leisure screen time, and transportation. Lastly, while total SB did not show a significant association with PWV, occupational SB was significantly associated with it. Specifically, each additional hour of occupational SB was associated with a 0.09 m/s increase in PWV, indicating that interventions targeting occupational SB could be crucial.

Limitations and Strengths

To aid interpretation, the study limitations are presented first. Participants were recruited immediately following their regular clinic appointments and did not adhere to standardized pre-assessment guidelines, including fasting and caffeine intake. While this may have influenced PWV (ie, potentially increasing PWV)^9,50,51 and body composition results, ⁵² recruitment efficiency improved in this hard-to-reach population, minimizing participant burden by avoiding a subsequent visit. Additionally, after accounting for waiting room time, the duration of the appointment, and the consent process, participants had at least 2 hours without eating or drinking prior to the assessment, minimizing this concern. Our sample was predominantly white despite comparable recruitment rates among non-white participants (67% non-white vs 63% overall). Only 12 non-white participants were eligible. Enrolling endometroid type ECS may have inadvertently restricted diversity, given known disparities in histologic subtypes among non-Hispanic Black women. ⁵³ Finally, our participants were predominantly sedentary (all but 1 spent >8 hours in SB daily). Thus, the limited variability in total SB may have contributed to the non-significant findings regarding its association with PWV.

This study has many strengths, notably the objective measurement of domain-specific SB using a novel technique of EMA paired with accelerometry. It is also the first to investigate SB patterns and PWV assessment in ECS, alongside successful recruitment of the targeted population. A recent systematic review of exercise studies in cancer survivors reports an average recruitment rate of 38%, ⁵⁴ yet a 63% recruitment rate was achieved, which underscores the importance of integrating such research into the clinical care pathway.

Feasibility

Overall, these data show that ECS are a highly engaged and receptive group, willing to participate in technology-based research and EMA studies on SB. The ability to capture the context of SB provides valuable insights for targeting future interventions and addresses a current gap in the literature. Additionally, our recruitment rate of 63% over a 10-month period surpasses typical rates reported in the literature. ⁵⁴ This success can be attributed to several factors: (i) the integration of research into the clinical setting, (ii) strong support from the clinical team, and (iii) proactive in-person recruitment efforts. The high study retention rate further underscores participants’ enthusiasm and commitment to this type of research.

There was a generally positive trend in recruitment rate throughout the study’s duration which may be attributed to several factors, including the season (fall), increased familiarity of clinical staff with the study, which likely encouraged participation, and the study recruiter’s growing experience, which contributed to more effective engagement with potential participants. The successful consent rate was likely due to the purposive sampling approach. Potentially eligible participants were identified through 2 methods: tumor boards and electronic medical record (EMR) screening. After identification, providers were contacted via the EMR system to request permission to approach patients. Providers usually introduced the study and research team to patients. However, due to constraints in clinic schedules, this was not always possible. In such cases, nurses or study staff introduced the study to the patients.

Finally, study retention and fidelity were high potentially because the study was strategically designed to seamlessly integrate into the clinical care flow, ensuring that participation was convenient for all involved participants. Given that many individuals commute, some up to 4 hours for care, making additional trips to the hospital for research purposes was impractical. A dedicated research space was established within the clinic conveniently located across from patient examination rooms which was critical for the implementation of this research.

Self-Reported Sedentary Behavior and Physical Activity Duration are Inaccurate

Self-reporting of SB and other behaviors such as MVPA or sleep are common because of cost-effectiveness, expedience, and reduced burden compared to objective assessment methods. However, a notable finding this study is the participants’ lack of accuracy in self-reporting SB and MVPA with SB underestimated by 71.4% (equivalent to 5 hours/day) and MVPA overestimated by 79% (equivalent to 132 minutes/week). Therefore, it is imperative that future research uses accelerometry or wearable devices to objectively measure activity behaviors or participants may be misclassified as sedentary or active which could impact screening or study results.

ECS Have Heightened Pulse Wave Velocity

To our knowledge, this is the first study to assess PWV in ECS. Traditionally, CVD risk has been assessed through CVD-related deaths, which offer crucial epidemiological insights into disease burden. However, when considering potential interventions for individuals, PWV can be utilized non-invasively to gage early CVD risk, thus rendering it an important metric to consider. Higher SB is associated with higher arterial stiffness in the general population. ⁵⁵ Compared to a healthy age-matched reference group (average PWV of 7.90 m/s), ³¹ had an average PWV of 9.04 m/s, which represents a 13.4% difference (MD: 1.14 m/s). A 1 m/s rise in PWV is clinically significant, associated with 15% higher risk of adverse cardiovascular events. ¹⁷ Our participants exceed this threshold; our data supports the elevated CVD risk documented in the literature. The cross-sectional nature of this study leaves uncertainty regarding the rate of change but would be an important consideration for future work.

Occupational Sitting is Associated with Increased Cardiovascular Disease Risk

Limited research exists on SB among ECS. A novel aspect of this study was pairing EMA with accelerometry to capture social-ecological data regarding SB. This revealed that ECS predominantly engage in occupational SB, with 48% reporting remote work. Additionally, they spend the most time at home and alone across all SB domains. These findings highlight 2 potential implications. First, occupational SB may involve longer uninterrupted bouts as sitting. On average, occupational SB were 11-minute bouts whereas leisure screen time and other SB were 5-minute bouts. Further investigation is needed, but it appears that occupational SB may involve lengthier bouts of SB, which is consistent with the higher percentage of ECS who reported sedentary jobs. Second, we hypothesize that occupational SB is associated with higher levels of psychological stress while leisure screen time or other SB may involve relaxing activities that reduce stress, potentially lowering PWV. Although beyond the current project’s scope, future research using wearable devices to capture heart rate variability could shed light on this hypothesis. Currently, no studies have directly measured SB patterns or interventions targeting SB reduction in this population. However, de Neufville Lucas, ⁵⁶ found that ECS average around 5000 steps per day, consistent with our accelerometry findings. A recent study by Alansare et al, ⁵⁷ examining occupational versus non-occupational SB in desk workers (N = 273, 59% female, age 45 years, BMI 30.8), reported differing findings. The study found detrimental associations between both total SB and non-occupational SB with PWV, but not with occupational SB. ⁵⁷ However, the association between non-occupational SB and PWV was attenuated after adjusting for BMI. ⁵⁷ In contrast, we did not observe an association between total SB and PWV. It is important to note that our participants were older, had higher blood pressure and PWV, and were less physically active. Further research in older adult and clinical populations is needed to better contextualize the findings of our study.

Implications and Future Directions

Unlike many other cancers, the incidence of endometrial cancer is rising. Early-stage disease often requires minimal treatment, making this growing population of survivors suitable for lifestyle interventions to reduce CVD risk. Currently, there are no interventions targeting SB reduction specifically for ECS, who present with increased CVD risk and high SB volume. Based on our initial data, tailored strategies should target occupational SB while creating a home environment conducive to greater movement and social support. A recent meta-analysis of SB reduction interventions in cancer survivors reported a trend in SB reduction in intervention groups. However, only 9 studies were included, most being small pilot trials, highlighting the need for larger, high-quality trials to determine efficacy. Many studies in the meta-analysis utilized wearable devices. Thus, measuring baseline SB and MVPA will be critical in identifying where ECS fall on the activity behavior spectrum, particularly given self-reported discrepancies. Different strategies will likely be needed for those meeting MVPA guidelines versus those who do not. We previously proposed a Stepwise Model⁶ to increase MVPA by starting with SB reduction as a physical activity initiation strategy. In this model, SB is replaced with light-intensity PA (eg, housework, walking) and progressively increases in intensity over time, ultimately aiming for sustainable MVPA (eg, jogging). Given PA and SB are distinct CVD risk factors, this approach could customize future interventions for ECS to enhance success.

Conclusions

Recruitment of ECS and assessment of domain-specific SB/PWV was feasible in ECS. ECS exhibit high levels of SB and elevated PWV compared to norms. Occupational SB shows a significant positive association with PWV, and most SB occurs at home and alone. These findings reveal prevalent sedentary habits and potential intervention targets. Future research should focus on effective intervention strategies tailored to this population. Specifically, addressing occupational SB and home-based SB is crucial. Additional gaps include determining optimal intervention doses, exploring social support’s role in behavior change, and developing implementation strategies targeting practical and sustainable approaches to reducing SB in ECS.