Association Between Life's Essential 8 Cardiovascular Health Metrics With Cardiovascular Events in the Cardiovascular Disease Lifetime Risk Pooling Project

BACKGROUND

The American Heart Association recently launched updated cardiovascular health metrics, termed Life's Essential 8 (LE8). Compared with Life's Simple 7 (LS7), the new approach added sleep health as an eighth metric and updated the remaining 7 health factors and behaviors. The association of the updated LE8 score with long-term cardiovascular disease (CVD) outcomes and death is unknown.

METHODS

We pooled individual-level data from 6 contemporary US-based cohorts from the Cardiovascular Lifetime Risk Pooling Project. Total LE8 score (0-100 points), LE8 score without sleep (0-100 points), and prior LS7 scores (0-14 points) were calculated separately. We used multivariable-adjusted Cox models to evaluate the association of LE8 with CVD, CVD subtypes, and all-cause mortality among younger, middle, and older adult participants. Net reclassification improvement analysis was used to measure the improvement in CVD risk classification with the addition of LS7 and LE8 recategorization based on score quartile rankings.

RESULTS

Our sample consisted of 32 896 US adults (7836 [23.8%] Black; 14 941 [45.4%] men) followed for 642 000 person-years, of whom 9391 developed CVD events. Each 10-point higher overall LE8 score was associated with lower risk by 22% to 40% for CVD, 24% to 43% for congenital heart disease, 17% to 34% for stroke, 23% to 38% for heart failure, and 17% to 21% for all causes of mortality events across age strata. LE8 score provided more granular differentiation of the related CVD risk than LS7. Overall, 19.5% and 15.5% of the study participants were recategorized upward and downward based on LE8 versus LS7 categories, respectively, and the recategorization was significantly associated with CVD risk in addition to LS7 score. The addition of recategorization between LE8 and LS7 categories improved CVD risk reclassification across age groups (clinical net reclassification improvement, 0.06-0.12; P<0.01).

CONCLUSIONS

These findings support the improved utility of the LE8 algorithm for assessing overall cardiovascular health and future CVD risk.

Covariate-constrained randomization in cluster randomized 2x2 factorial trials: Application to a diabetes prevention study

Background

Cluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs.

Methods

Motivated by the BEGIN study-a CRT for weight loss among patients with pre-diabetes-we develop methods for performing CR in 2x2 factorial cluster randomized trials. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, and analysis strategies.

Results

Compared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small.

Conclusions

CR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and Type 1 error rates at their nominal levels.

Joint modeling the frequency and duration of accelerometer-measured physical activity from a lifestyle intervention trial

Physical activity (PA) guidelines recommend that PA be accumulated in bouts of 10 minutes or more in duration. Recently, researchers have sought to better understand how participants in PA interventions increase their activity. Participants can increase their daily PA by increasing the number of PA bouts per day while keeping the duration of the bouts constant; they can keep the number of bouts constant but increase the duration of each bout; or participants can increase both the number of bouts and their duration. We propose a novel joint modeling framework for modeling PA bouts and their duration over time. Our joint model is comprised of two sub-models: a mixed-effects Poisson hurdle sub-model for the number of bouts per day and a mixed-effects location scale gamma regression sub-model to characterize the duration of the bouts and their variance. The model allows us to estimate how daily PA bouts and their duration vary together over the course of an intervention and by treatment condition and is specifically designed to capture the unique distributional features of bouted PA as measured by accelerometer: frequent measurements, zero-inflated bouts, and skewed bout durations. We apply our methods to the Make Better Choices study, a longitudinal lifestyle intervention trial to increase PA. We perform a simulation study to evaluate how well our model is able to estimate relationships between outcomes.

Prior Ground: Selection of Prior Distributions When Analyzing Clinical Trial Data Using Bayesian Methods

Increasingly, investigators are choosing to use Bayesian methods for the analysis of clinical trial data. Unlike classical statistical methods that treat model parameter values (such as treatment effects) as fixed, Bayesian methods view parameters as following a probability distribution. As we have written previously,1 by analyzing clinical trial data using Bayesian methods one can obtain quantities that may be of interest to clinicians, providers, and patients, such as the probability that a treatment effect is more or less than 0, that is, the probability that a treatment is effective.

Effect of the Fit2Thrive Intervention on Patient-reported Outcomes in Breast Cancer Survivors: A Randomized Full Factorial Trial

BACKGROUND

Moderate to vigorous physical activity (MVPA) interventions improve patient-reported outcomes (PROs) of physical and psychological health among breast cancer survivors (BCS); however, the effects of specific intervention components on PROs are unknown.

PURPOSE

To use the Multiphase Optimization Strategy (MOST) to examine overall effects of the Fit2Thrive MVPA promotion intervention on PROs in BCS and explore whether there are intervention component-specific effects on PROs.

METHODS

Physically inactive BCS [n = 269; Mage = 52.5 (SD = 9.9)] received a core intervention (Fitbit + Fit2Thrive smartphone app) and were randomly assigned to one of 32 conditions in a full factorial experiment of five components ("on" vs. "off"): (i) support calls, (ii) deluxe app, (iii) text messages, (iv) online gym, and (v) buddy. Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires assessed anxiety, depression, fatigue, physical functioning, sleep disturbance and sleep-related impairment at baseline, post-intervention (12-week), and 24-week follow-up. Main effects for all components at each time point were examined using an intention to treat mixed-effects model.

RESULTS

All PROMIS measures except sleep disturbance significantly improved (p's < .008 for all) from baseline to 12-weeks. Effects were maintained at 24-weeks. The "on" level of each component did not result in significantly greater improvements on any PROMIS measure compared to the "off" level.

CONCLUSIONS

Participation in Fit2Thrive was associated with improved PROs in BCS, but improvements did not differ for "on" vs. "off" levels for any component tested. The low-resource Fit2Thrive core intervention is a potential strategy to improve PROs among BCS. Future studies should test the core in an RCT and examine various intervention component effects in BCS with clinically elevated PROs.

Design and rationale of behavioral nudges for diabetes prevention (BEGIN): A pragmatic, cluster randomized trial of text messaging and a decision aid intervention for primary care patients with prediabetes

BACKGROUND

Among 96 million U.S. adults with prediabetes, adoption of evidence-based treatment to prevent diabetes remains low. Primary care represents an essential venue for preventing diabetes, yet providers in this setting have limited time to address prevention. This highlights the need for low-touch interventions that promote diabetes prevention and are not delivered by primary care providers. Text messaging and decision aids displaying disease risk and treatment information have improved outcomes in prior research. However, these approaches have not been definitively studied for managing prediabetes.

METHODS

The Behavioral Nudges for Diabetes Prevention (BEGIN) trial is a pragmatic, cluster randomized trial testing the effectiveness of text messaging about diabetes prevention and a prediabetes decision aid. These interventions are being studied in 8 primary care clinics using a 2 × 2 factorial design, in which pairs of clinics are randomized in a 1:1:1:1 ratio to receive usual care, text messaging alone, prediabetes decision aid alone, or both interventions. A total of 656 patients are recruited to participate, receive the study interventions, and contribute data at baseline and 12 months. The primary outcome is 12-month weight change, and the secondary outcome is adoption of evidence-based treatment to prevent diabetes. Change in hemoglobin A1c is an exploratory outcome that will be assessed among participants with available values.

CONCLUSION

Findings from the BEGIN trial will provide evidence about the effectiveness of two novel, low-touch interventions focused on diabetes prevention in primary care, where patients are diagnosed with prediabetes and there is little prior research.

TRIAL REGISTRY

NCT04869917.

Longitudinal Sedentary Time and Symptoms in Breast Cancer Patients during Chemotherapy Using Ecological Momentary Assessment

PURPOSE

To examine the relationship between daily fluctuations in symptoms and sedentary behavior (SB) during chemotherapy (CT) for breast cancer.

METHODS

Breast cancer patients ( N = 68, M age = 48.5 ± 10.4 yr) undergoing CT wore an activity monitor on their hip to assess daily SB and completed prompts assessing symptoms (affect, anxiety, depression, fatigue, pain, and physical and cognitive functioning) for 10 consecutive days (3 d pre-CT, day of, and 6 d post-CT) at the beginning, middle and end cycles of CT. Mixed models assessed the bidirectional between-person (BP) and within-person (WP) associations of current day symptoms with minutes of SB measured on 1) the same day and 2) the next day, controlling for relevant covariates.

RESULTS

Within person same-day results revealed a significant association between affect, anxiety, fatigue, physical functioning, pain, and cognitive functioning and same-day SB. Worse than average symptom ratings on a given day were associated with more SB that day. There was a significant WP relationship between previous-day anxiety, depression, and physical function and next-day SB (i.e., worse than average symptom ratings the previous day were associated with more SB the next day). Within person same-day results revealed a significant association between same-day SB and affect, anxiety, fatigue, pain, physical functioning, and cognitive functioning. The WP relationships were significant for previous-day SB and next-day affect and pain (i.e., higher than average SB associated with lower ratings). Relationships persisted when controlling for moderate-to-vigorous physical activity. There were no significant BP results.

CONCLUSIONS

Higher symptom ratings were associated with increased SB and higher SB was associated with worse symptoms. Future work should identify SB reduction intervention approaches tailoring to daily symptom burden during CT for breast cancer.

Association of American Identity with Cardiovascular Health in South Asian Americans: The MASALA Study

Background

Ethnic and national identity may influence cardiovascular health (CVH)-related behaviors, such as dietary preference. To better understand how acculturation is related to CVH among South Asian American adults, we evaluated the association of self-rated American identity with CVH factors among participants of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study.

Methods

Multivariable linear regression quantified the associations of self-rated American identity [1 (low American identity) to 10 (high American identity)] with CVH factors, including measures of cholesterol, blood pressure, and blood glucose. The role of diet quality, physical activity, and social support in mediating these associations was evaluated.

Results

Participants (n = 771) lived in the United States for an average of 27 (SD 11) years. The mean self-rated American identity score was 5.5 (2.4). After adjustment, a 5-point higher American identity score was associated with 6.5 mg/dL higher low-density lipoprotein cholesterol, 6.6 mg/dL higher total cholesterol, 2.9 mmHg higher systolic blood pressure, and 1.4 mmHg higher diastolic blood pressure. Accounting for diet quality, physical activity, or social support does not alter these associations.

Conclusions

Higher self-rated American identity is associated with worse CVH factors among South Asian American adults.

Bayesian (re)-Analyses of Clinical Trial Data

The Therapeutic Hypothermia after Pediatric Cardiac Arrest Out-of-Hospital (THAPCA-OH) trial (ClinicalTrials.gov number, NCT00878644) compared therapeutic hypothermia (33°C) with therapeutic normothermia (36.8°C) in 295 children suffering from out-of-hospital cardiac arrest. In the original results, published in the New England Journal of Medicine in 2015, good neurobehavioral outcome and survival at 1 year were higher in the hypothermia group (20% vs. 12% and 38% vs. 29%, respectively); however, these differences did not meet the planned statistical threshold of P<0.05.1.

Association of Cumulative Viral Load With the Incidence of Hypertension and Diabetes in People With HIV

BACKGROUND

HIV induces several metabolic derangements that contribute to cardiovascular disease, but it is unclear if HIV increases diabetes or hypertension risk. Refining longitudinal relationships between HIV-specific factors and cardiovascular disease risk factors across different care settings may help inform cardiovascular disease prevention among people with HIV (PWH).

METHODS

We tested the hypothesis that long-term higher cumulative viral load (viremia-copy-year) is associated with higher risk of diabetes and hypertension by analyzing electronic records of PWH from 2 distinct health systems in Chicago (Northwestern Medicine and Howard Brown Health Care) receiving care in 2004 to 2019. We used joint longitudinal-survival models to assess multivariable-adjusted associations. Subgroup analyses per site were also conducted.

RESULTS

We observed 230 (3.0%) incident diabetes cases in 7628 PWH without baseline diabetes and 496 (6.7%) hypertension cases in 7450 PWH without baseline hypertension. Pooled analysis showed a direct association of viremia-copy-year with incident hypertension (hazards ratio, 1.20 [95% CI, 1.14-1.26]) but not with diabetes (hazards ratio, 1.03 [95% CI, 0.96-1.10]). However, site-specific differences existed whereby the Northwestern-only analysis demonstrated a significant association of viremia-copy-year with hypertension (hazards ratio, 1.29 [95% CI, 1.08-1.32]). Additionally, higher social deprivation index (both sites) and diagnosis of mental health disorder (Howard Brown Health only) was associated with higher diabetes and hypertension risk.

CONCLUSIONS

Cumulative viral load may be associated with incident hypertension among PWH. Associations of HIV control with cardiovascular disease risk factors among PWH may differ by health care system context.

The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention

BACKGROUND

Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss.

OBJECTIVE

The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect.

STUDY DESIGN

The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities.

SIGNIFICANCE

The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.

Using ecological momentary assessment to understand associations between daily physical activity and symptoms in breast cancer patients undergoing chemotherapy

PURPOSE

Understanding real-time relationships between physical activity (PA) and symptoms during chemotherapy (CT) could have important implications for intervention. This study used ecological momentary assessment to examine the relationship between objective PA and symptoms during CT.

METHODS

Breast cancers patients (n = 67; M_age = 48.6 (SD = 10.3)) participated in data collection at three time points during CT: beginning, middle, and end. At each time point, participants answered four prompts assessing symptoms and wore an accelerometer for 10 days (3 days pre-CT, day of CT, and 6 days post-CT). Multilevel linear regression models examined the between- and within-person associations between moderate to vigorous (MVPA) and light-intensity physical activity (LPA) and same and next-day symptom ratings controlling for covariates.

RESULTS

On days when individuals engaged in more LPA or MVPA, separately, they reported improved affect, anxiety, fatigue, physical functioning (walking and activities of daily living), pain, and cognition that day (p < 0.001 for all). Findings were consistent for next-day symptom ratings with the exception that only previous day LPA was related to next-day fatigue and neither LPA nor MVPA were related to next-day cognition (p < 0.001 for all). No between-person effects were found.

CONCLUSIONS

Within person higher than usual PA on a given day, regardless of intensity, is associated with improved symptoms ratings on the current and next day.

IMPLICATIONS FOR CANCER SURVIVORS

Encouraging breast cancer patients undergoing CT to engage in daily PA could help manage CT-associated symptoms.

Optimizing a Behavioral Sleep Intervention for Gynecologic Cancer Survivors: Study Design and Protocol

Sleep difficulties, particularly symptoms of insomnia and circadian disruption, are among the primary complaints of gynecologic cancer survivors before, during, and after treatment. Moreover, difficulty sleeping has been linked to poorer health-related quality of life and elevated symptom burden in this population. Although leading behavioral sleep interventions have demonstrated efficacy among cancer survivors, up to 50% of survivors are non-adherent to these treatments, likely because these interventions require labor-intensive behavior and lifestyle changes. Therefore, there is a need for more effective and acceptable approaches to diminish sleep disturbance among cancer survivors. This manuscript describes the methodology of a two-part study guided by the Multiphase Optimization Strategy (MOST) framework to identify a streamlined behavioral sleep intervention for gynecologic cancer survivors. Three candidate intervention components previously shown to decrease sleep disturbance will be evaluated, including sleep restriction, stimulus control, and systematic bright light exposure. Participants will be adult women with a history of non-metastatic gynecologic cancer who have completed primary treatment and who report current poor sleep quality. Fifteen participants will be recruited for Part 1 of the study, which will utilize qualitative methods to identify barriers to and facilitators of intervention adherence. Results will inform changes to the delivery of the candidate intervention components to promote adherence in Part 2, where 80 participants will be recruited and randomized to one of eight conditions reflecting every possible combination of the three candidate intervention components in a full factorial design. Participants will complete assessments at baseline, post-intervention, and 3-months post-intervention. Part 2 results will identify the combination of candidate intervention components that yields the most efficacious yet efficient 6-week intervention for diminishing sleep disturbance. This is the first known study to apply the MOST framework to optimize a behavioral sleep intervention and will yield a resource-efficient treatment to diminish sleep disturbance, improve health-related quality of life, and decrease symptom burden among gynecologic cancer survivors. ClinicalTrials.gov Identifier: NCT05044975.

Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors: Results from Fit2Thrive

BACKGROUND

The benefits of moderate to vigorous physical activity (MVPA) for breast cancer survivors are well established. However, most are insufficiently active. Fit2Thrive used the Multiphase Optimization Strategy methodology to determine the effect of 5 intervention components on MVPA in this population.

METHODS

Two hundred sixty-nine participants (mean age, 52.5 years; SD, 9.9 years) received a core intervention (the Fit2Thrive self-monitoring app and Fitbit) and were randomly assigned to 5 intervention components set to on/off in a full factorial experiment: support calls, deluxe app, buddy, online gym, and text messages. The intervention was delivered over 12 weeks with a 12-week follow-up. MVPA was measured via accelerometry at the baseline (T1), at 12 weeks (T2), and at 24 weeks (T3). The main effects and interaction effects at each time point were examined for all components.

RESULTS

Trial retention was high: 91.8% had valid accelerometer data at T2 or T3. Across all conditions, there were significant increases in MVPA (+53.6 min/wk; P < .001) and in the proportion of survivors meeting MVPA guidelines (+22.3%; P < .001) at T2 that were maintained but attenuated at T3 (MVPA, +24.6 min/wk; P < .001; meeting guidelines, +12.6%; P < .001). No individual components significantly improved MVPA, although increases were greater for the on level versus the off level for support calls, buddy, and text messages at T2 and T3.

CONCLUSIONS

The Fit2Thrive core intervention (the self-monitoring app and Fitbit) is promising for increasing MVPA in breast cancer survivors, but the components provided no additional increases in MVPA. Future research should evaluate the core intervention in a randomized trial and determine what components optimize MVPA behaviors in breast cancer survivors.

Cardiovascular health and subclinical atherosclerosis in second generation South Asian Americans: The MASALA study

We describe cardiovascular health (CVH) in second-generation (U.S.-born) South Asian Americans in the MASALA study, a population for whom CVH is not previously described. CVH factors in second-generation (N = 21) compared with first-generation (N = 495) South Asian Americans included: total cholesterol (199 ± 31 versus 191 ± 35 mg/dL, p = 0.25), low-density lipoprotein cholesterol (121 ± 27 versus 115 ± 30 mg/dL, p = 0.41), triglycerides (163 ± 197 versus 138 ± 72 mg/dL, p = 0.10), diet score (66 ± 8 versus 70 ± 6 points, p = 0.06), BMI (27.6 ± 4.9 versus 26.2 ± 4.1 kg/m², p = 0.12), and CAC prevalence (26.3% versus 23.9%, p = 0.34). Age- and sex-adjusted differences were not statistically significant. Further investigation of CVH in this risk-enhanced population may help identify differences between second-generation and first-generation immigrant South Asians in the U.S.

Development and Validation of a Large Synthetic Cohort for the Study of Cardiovascular Health Across the Life Span

We developed and validated a synthetic cohort approach to examine numbers of cardiovascular risk factors (CRFs) and adverse clinical events, including incident cardiovascular disease and all-cause mortality, across the life span from ages 20 years to 90 years. The current analysis included 40,875 participants from 7 large, population-based longitudinal epidemiologic studies (1948-2016). On the basis of a joint multilevel imputation model, we multiply imputed each participant's life-span numbers of CRFs and events using available records. To validate the imputed values, we partially removed the observed data and then compared the imputed and observed values. The complete life-span synthetic data set reflected the original observed data trends well. In our validation sample, the distributions of imputed CRFs and events were close to the observed distributions but with less variability. Bland-Altman plots indicated that there was a slightly negative trend in general, and the agreement bias was relatively small for the continuous CRFs. The hypothetical linear regression model suggested that the relationships between the CRFs and events were preserved in the imputed data set. This approach generated valid estimates of CRFs and events across the life span for African-American and White participants. The synthetic cohort may be sufficiently accurate to be useful in assessing the origins and timing of accumulating cardiovascular risk that can inform efforts to avoid cardiovascular disease development.

Social cognitive variables and physical activity during chemotherapy for breast cancer: An intensive longitudinal examination

OBJECTIVE

Although physical activity is associated with better health outcomes in breast cancer survivors (BCS), activity often declines during cancer treatment. Social cognitive theory (SCT) constructs have been associated with physical activity in post-treatment BCS, but little is known about the relation between these constructs and physical activity during chemotherapy.

METHODS

BCS (n = 67; M_age  = 48.6 [SD = 10.3]) undergoing chemotherapy wore accelerometers and completed prompts in the morning and at night assessing same-day and next-day exercise self-efficacy, physical and psychological outcome expectations, and goal-setting for 10 consecutive days (3 days pre-, day of, and 6 days post-chemotherapy dose) at three time points (beginning, middle, and end of chemotherapy). Separate mixed models assessed between- and within-person associations of each of the SCT constructs associations with same- and next-day moderate to vigorous physical activity (MVPA) and light physical activity (LPA), independently.

RESULTS

Within-person differences in all SCT variables were statistically significantly related to same-day MVPA (p's < 0.001) and LPA (p's < 0.001). Every one-point increase in SCT construct related to an increase in MVPA ranging from (a) 3.70 (self-efficacy) to 8.02 (physical outcome expectations) minute increase in MVPA and (b) 12.72 (self-efficacy) to 20.38 (physical outcome expectations) increase in LPA that day. No same-day between-person effects nor any next-day effects were significant.

CONCLUSION

MVPA and LPA were related to same-day within-person differences in SCT variables. Interventions targeted at increasing or mitigating chemotherapy-related declines in daily within-person changes in SCT constructs could help to increase physical activity among BCS during chemotherapy.

Effects of differential measurement error in self-reported diet in longitudinal lifestyle intervention studies

Background

Lifestyle intervention studies often use self-reported measures of diet as an outcome variable to measure changes in dietary intake. The presence of measurement error in self-reported diet due to participant failure to accurately report their diet is well known. Less familiar to researchers is differential measurement error, where the nature of measurement error differs by treatment group and/or time. Differential measurement error is often present in intervention studies and can result in biased estimates of the treatment effect and reduced power to detect treatment effects. Investigators need to be aware of the impact of differential measurement error when designing intervention studies that use self-reported measures.

Methods

We use simulation to assess the consequences of differential measurement error on the ability to estimate treatment effects in a two-arm randomized trial with two time points. We simulate data under a variety of scenarios, focusing on how different factors affect power to detect a treatment effect, bias of the treatment effect, and coverage of the 95% confidence interval of the treatment effect. Simulations use realistic scenarios based on data from the Trials of Hypertension Prevention Study. Simulated sample sizes ranged from 110-380 per group.

Results

Realistic differential measurement error seen in lifestyle intervention studies can require an increased sample size to achieve 80% power to detect a treatment effect and may result in a biased estimate of the treatment effect.

Conclusions

Investigators designing intervention studies that use self-reported measures should take differential measurement error into account by increasing their sample size, incorporating an internal validation study, and/or identifying statistical methods to correct for differential measurement error.

Steps per Day and All-Cause Mortality in Middle-aged Adults in the Coronary Artery Risk Development in Young Adults Study

IMPORTANCE

Steps per day is a meaningful metric for physical activity promotion in clinical and population settings. To guide promotion strategies of step goals, it is important to understand the association of steps with clinical end points, including mortality.

OBJECTIVE

To estimate the association of steps per day with premature (age 41-65 years) all-cause mortality among Black and White men and women.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study was part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants were aged 38 to 50 years and wore an accelerometer from 2005 to 2006. Participants were followed for a mean (SD) of 10.8 (0.9) years. Data were analyzed in 2020 and 2021.

EXPOSURE

Daily steps volume, classified as low (<7000 steps/d), moderate (7000-9999 steps/d), and high (≥10 000 steps/d) and stepping intensity, classified as peak 30-minute stepping rate and time spent at 100 steps/min or more.

MAIN OUTCOMES AND MEASURES

All-cause mortality.

RESULTS

A total of 2110 participants from the CARDIA study were included, with a mean (SD) age of 45.2 (3.6) years, 1205 (57.1%) women, 888 (42.1%) Black participants, and a median (interquartile range [IQR]) of 9146 (7307-11 162) steps/d. During 22 845 person years of follow-up, 72 participants (3.4%) died. Using multivariable adjusted Cox proportional hazards models, compared with participants in the low step group, there was significantly lower risk of mortality in the moderate (hazard ratio [HR], 0.28 [95% CI, 0.15-0.54]; risk difference [RD], 53 [95% CI, 27-78] events per 1000 people) and high (HR, 0.45 [95% CI, 0.25-0.81]; RD, 41 [95% CI, 15-68] events per 1000 people) step groups. Compared with the low step group, moderate/high step rate was associated with reduced risk of mortality in Black participants (HR, 0.30 [95% CI, 0.14-0.63]) and in White participants (HR, 0.37 [95% CI, 0.17-0.81]). Similarly, compared with the low step group, moderate/high step rate was associated with reduce risk of mortality in women (HR, 0.28 [95% CI, 0.12-0.63]) and men (HR, 0.42 [95% CI, 0.20-0.88]). There was no significant association between peak 30-minute intensity (lowest vs highest tertile: HR, 0.98 [95% CI, 0.54-1.77]) or time at 100 steps/min or more (lowest vs highest tertile: HR, 1.38 [95% CI, 0.73-2.61]) with risk of mortality.

CONCLUSIONS AND RELEVANCE

This cohort study found that among Black and White men and women in middle adulthood, participants who took approximately 7000 steps/d or more experienced lower mortality rates compared with participants taking fewer than 7000 steps/d. There was no association of step intensity with mortality.

Association of Social Network Characteristics With Cardiovascular Health and Coronary Artery Calcium in South Asian Adults in the United States: The MASALA Cohort Study

Background

South Asian adults have worse cardiovascular health (CVH) and more coronary artery calcium compared with other race/ethnicities. The impact of the social environment has not been examined as a potential driver of CVH or coronary artery calcium in this population. We evaluated associations of social network characteristics with CVH and coronary artery calcium in South Asian American adults to inform strategies for CVH promotion in this at‐risk population.

Methods and Results

Using data from the MASALA (Mediators of Atherosclerosis in South Asians Living in America) cohort study, multinomial and multivariable logistic regression were used to evaluate associations of participant social network size and density, proportion of network who are kin or South Asian ethnicity and reported health of participant's identified social network members (“alters”), with participant CVH and presence of coronary artery calcium. The 699 MASALA participants included were mean age 59.2 (SD, 9.2) years and 42.9% women. After adjustment, a 1‐person larger social network size was associated with 13% higher odds of ideal CVH (odds ratio [OR], 1.13; 95% CI, 1.01–1.27). Reporting an alter with high blood pressure was associated with lower odds of ideal CVH (OR, 0.51; 95% CI, 0.29–0.88), and reporting an alter with high cholesterol was associated with lower odds of ideal CVH (OR, 0.54; 95% CI, 0.30–0.94).

Conclusions

Social network characteristics are associated with CVH in South Asian American adults. Engaging social networks may help promote CVH in this population.

Calibrating validation samples when accounting for measurement error in intervention studies

Many lifestyle intervention trials depend on collecting self-reported outcomes, such as dietary intake, to assess the intervention's effectiveness. Self-reported outcomes are subject to measurement error, which impacts treatment effect estimation. External validation studies measure both self-reported outcomes and accompanying biomarkers, and can be used to account for measurement error. However, in order to account for measurement error using an external validation sample, an assumption must be made that the inferences are transportable from the validation sample to the intervention trial of interest. This assumption does not always hold. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample, and we also formally investigate when bias due to poor transportability may arise. Lastly, we examine the performance of the methods using simulation, and illustrate them using PREMIER, a lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study measuring both self-reported diet and urinary biomarkers.

Characterizing Measurement Error in Dietary Sodium in Longitudinal Intervention Studies

Background: Previous measurement error work that investigates the relationship between a nutritional biomarker and self-reported intake levels has typically been at a single time point, in a single treatment group, or with respect to basic patient demographics. Few studies have examined the measurement error structure in longitudinal randomized trials, and whether the error varies across time or group. This structure is crucial to understand, however, in order to correct for measurement error in self-reported outcomes and properly interpret the longitudinal effects of dietary interventions. Methods: Using two longitudinal randomized controlled trials with internal longitudinal validation data (urinary biomarkers and self-reported values), we examine the relationship between urinary sodium and self-reported sodium and whether this relationship changes as a function of time and/or treatment condition. We do this by building a mixed effects regression model, allowing for a flexible error variance-covariance structure, and testing all possible interactions between time, treatment condition, and self-reported intake. Results: Using a backward selection approach, we arrived at the same final model for both validation data sets. We found no evidence that measurement error changes as a function of self-reported sodium. However, we did find evidence that urinary sodium can differ by time or treatment condition even when conditioning on self-reported values. Conclusion: In longitudinal nutritional intervention trials it is possible that measurement error differs across time and treatment groups. It is important for researchers to consider this possibility and not just assume non-differential measurement error. Future studies should consider data collection strategies to account for the potential dynamic nature of measurement error, such as collecting internal validation data across time and treatment groups when possible.

Generalizing randomized trial findings to a target population using complex survey population data

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large-scale surveys in fields such as health and education with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of our proposed estimator through simulations in comparison to estimators that ignore the complex survey design. We then apply the methods to generalize findings from two trials-a lifestyle intervention for blood pressure reduction and a web-based intervention to treat substance use disorders-to their respective target populations using population data from complex surveys. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample.

Comparing Accelerometer and Self-Reported Treatment Effects in a Technology-Supported Physical Activity Intervention

BACKGROUND AND AIMS

To estimate and compare the change in moderate-to-vigorous physical activity (MVPA) between an accelerometer and technology-supported physical activity (PA) log across a 3-week PA intervention.

METHOD

Participants (N = 204, 77% female, age = 33 ± 11 years, body mass index = 28.2 ± 7.1 kg/m²) were randomized to one of two activity-related intervention arms: (1) increase MVPA intervention or (2) decrease sedentary behavior active control. Participants wore an accelerometer while simultaneously completing a technology-based PA log every day for 5 weeks: a 2-week baseline assessment phase and a 3-week intervention phase. Bivariate linear mixed-effects models and correlations were used to characterize the relationship of MVPA between measurement methods throughout the intervention. Effect sizes were calculated to determine the intervention effect by measurement method.

RESULTS

At baseline, PA log MVPA was 28 minutes greater than accelerometer-based minutes of MVPA in the active control group. This difference was 35 minutes (95% CI [23.7, 46.1]) greater at follow-up than at baseline measurement in the MVPA intervention group. In the active control group, there was a significant 16-minute (95% CI [6.0, 26.5]) increase between the two measures from baseline to follow-up. The intervention effect size based on the PA log was 0.27 (95% CI [0.14, 0.39]) and 0.42 (95% CI [0.28, 0.56]) when using the accelerometer.

DISCUSSION AND CONCLUSIONS

Our results indicate that PA log MVPA and accelerometer MVPA estimate significantly different minutes per day of MVPA. It is important researchers use caution when comparing MVPA intervention outcomes from different measurement methods.

A mHealth intervention to preserve and promote ideal cardiovascular health in college students: Design and protocol of a cluster randomized controlled trial

BACKGROUND

Cardiovascular disease (CVD) remains the leading cause of death globally. Seven health factors are associated with ideal cardiovascular health: being a non-smoker; not overweight; physically active; having a healthy diet; and normal blood pressure; fasting plasma glucose and cholesterol. Whereas approximately half of U.S. youth have ideal levels in at least 5 of the 7 components of cardiovascular health, this proportion falls to 16% by adulthood.

OBJECTIVE

We will evaluate whether the NUYou cardiovascular mHealth intervention is more effective than an active comparator to promote cardiovascular health during the transition to young adulthood.

METHODS

302 incoming freshmen at a midwest university will be cluster randomized by dormitory into one of two mHealth intervention groups: 1) Cardiovascular Health (CVH), addressing behaviors related to CVD risk; or 2) Whole Health (WH), addressing behaviors unrelated to CVD. Both groups will receive smartphone applications, co-designed with students to help them manage time, interact with other participants via social media, and report health behaviors weekly. The CVH group will also have self-monitoring features to track their risk behaviors. Cardiovascular health will be assessed at the beginning of freshman year and the end of freshman and sophomore years. Linear mixed models will be used to compare groups on a composite of the seven cardiovascular-related health factors.

SIGNIFICANCE

This is the first entirely technology-mediated multiple health behavior change intervention delivered to college students to promote cardiovascular health. Findings will inform the potential for primordial prevention in young adulthood.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov #NCT02496728.

Daily Physical Activity and Symptom Reporting in Breast Cancer Patients Undergoing Chemotherapy: An Intensive Longitudinal Examination

BACKGROUND

Increased activity is beneficial during chemotherapy, but treatment-related symptoms may be a barrier. This study examines the relationship between daily fluctuations in symptoms and activity during chemotherapy.

METHODS

Women undergoing chemotherapy for breast cancer [n = 67; M _age = 48.6 (SD = 10.3)] wore an accelerometer 24 hours/day and received four text prompts/day to rate symptoms for 10 consecutive days at the beginning, middle, and end of chemotherapy. Mixed-effects models were used to examine the between and within-person relationships between symptom ratings on a given day and moderate to vigorous physical activity (MVPA) and light physical activity (LPA) on that day and the following day controlling for relevant covariates and using the Bonferroni correction for multiple comparisons.

RESULTS

For MVPA and LPA, within-person associations were statistically significant for same day affect, fatigue, pain, walking, activities of daily living (ADL) physical function, and cognitive function. Previous day anxiety was associated with next day LPA. Every one point worse symptom rating than an individual's overall average was associated with: (i) between 1.49 (pain) and 4.94 (fatigue) minutes less MVPA and between 4.48 (pain) and 24.72 (ADL physical function) minutes less LPA that day, and (ii) 11.28 minutes less LPA the next day. No between-person effects were significant for MVPA or LPA.

CONCLUSIONS

Daily within-person variations in symptoms were associated with MVPA and LPA during chemotherapy for breast cancer.

IMPACT

Future work should explore relationships between symptoms and activity further and identify whether tailoring to symptoms enhances efficacy of physical activity promotion interventions during chemotherapy.

Social network characteristics are correlated with dietary patterns among middle aged and older South Asians living in the United States (U.S.)

Background

Social and cultural norms, operating through social networks, may influence an individual’s dietary choices. We examined correlations between social network characteristics and dietary patterns among South Asians in the United States (U.S.)

Methods

Data from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Social Network study were analyzed among 756 participants (mean age = 59 y standard deviation [SD] = 9 y; 44% women). A culturally adapted, validated food frequency questionnaire was used for dietary assessment. A posteriori dietary patterns using principal component analysis were named 1) animal protein, 2) fried snacks, sweets and high-fat dairy, and 3) fruits, vegetables, nuts and legumes. Social network characteristics were assessed using a standard egocentric approach, where participants (egos) self-reported data on perceived dietary habits of their network members. Partial correlations between social network characteristics and egos’ dietary patterns were examined.

Results

The mean social network size of egos was 4.2 (SD = 1.1), with high proportion of network members being family (72%), South Asian ethnicity (89%), and half having daily contact. Animal protein pattern scores were negatively correlated with fruits and cooked vegetables consumption of network. Fried snacks, sweets and high-fat dairy pattern scores were positively correlated with sugar-sweetened beverages, South Asian sweets, fried/fast foods and ghee (clarified butter) consumption of network. Fruits, vegetables, nuts and legumes pattern scores were positively correlated with vegetables, fruits, and brown rice/quinoa consumption of network.

Conclusions

Network member characteristics and their perceived dietary behaviors were correlated with dietary patterns of egos. Dietary intervention studies among South Asians should consider social network characteristics as candidate components for dietary intervention.

A Factorial Experiment to Optimize Remotely Delivered Behavioral Treatment for Obesity: Results of the Opt-IN Study

OBJECTIVE

Intensive behavioral obesity treatments face scalability challenges, but evidence is lacking about which treatment components could be cut back without reducing weight loss. The Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (Opt-IN) study applied the Multiphase Optimization Strategy to develop an entirely remotely delivered, technology-supported weight-loss package to maximize the amount of weight loss attainable for ≤$500.

METHODS

Six-month weight loss was examined among adults (N = 562) with BMI ≥ 25 who were randomly assigned to conditions in a factorial experiment crossing five dichotomous treatment components set to either low/high (12 vs. 24 coaching calls) or off/on (primary care provider reports, text messaging, meal replacements, and buddy training).

RESULTS

About 84.3% of participants completed the final assessment. The treatment package yielding maximum weight loss for ≤$500 included 12 coaching calls, buddy training, and primary care provider progress reports; produced average weight loss of 6.1 kg, with 57.1% losing ≥5% and 51.8% losing ≥7%; and cost $427 per person. The most expensive candidate-treatment component (24 vs. 12 coaching calls) was screened out of the optimized treatment package because it did not increase weight loss.

CONCLUSIONS

Systematically testing each treatment component's effect on weight loss made it possible to eliminate more expensive but less impactful components, yielding an optimized, resource-efficient obesity treatment for evaluation in a randomized controlled trial.

The South Asian Healthy Lifestyle Intervention (SAHELI) trial: Protocol for a mixed-methods, hybrid effectiveness implementation trial for reducing cardiovascular risk in South Asians in the United States

Intensive lifestyle interventions targeting diet and physical activity are recommended for reducing atherosclerotic cardiovascular disease (ASCVD) risk in adults. However, existing interventions often do not reach immigrant populations because of a mismatch between the social, cultural, and environmental context of immigrants and Western bio behavioral models which underpin evidence-based lifestyle interventions. The South Asian Healthy Lifestyle Intervention (SAHELI) study is a type 1 hybrid design randomized controlled trial aimed at reducing ASCVD risk in South Asian Americans, a group at higher ASCVD risk than whites and other Asian Americans. The objective is to evaluate the clinical effectiveness and implementation potential of a community-based, culturally-adapted lifestyle intervention for South Asian adults. Participants (n = 550) will be randomized to printed healthy lifestyle education materials or SAHELI, a group-based lifestyle change program that includes weekly classes for 16 weeks and 4 booster classes though month 11. SAHELI integrates evidence-based behavior change strategies with culturally-adapted strategies and group motivational interviewing to improve diet, physical activity, and stress management. Follow-up assessments will occur at 6 and 12 months. We hypothesize that the SAHELI intervention group will have greater improvements in clinical ASCVD risk factors (weight, blood pressure, glycated hemoglobin, and lipids), physical activity, and psychosocial outcomes than the print material group at 6- and 12- months. We will use mixed-methods to examine SAHELI's potential for reach, adoption, implementation, and maintenance from the perspective of multiple stakeholders. This study offers the potential to increase the reach and effectiveness of evidence-based lifestyle interventions for South Asian adults at increased risk for ASCVD.

Abstract 07: Association of Social Network Characteristics With Cardiovascular Health and Coronary Artery Calcium in South Asians: The Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study

Introduction: South Asian Americans (SAs) have disproportionately high burden of poor cardiovascular health (CVH) and CVD, which may be influenced by people within their social network (SN). We examined the association of SN characteristics and SN member (“alter”) health with CVH and coronary artery calcium (CAC) in SAs to identify targets for CVD prevention in this high-risk community.

Hypothesis: Smaller SN size and worse alter health is associated with poor CVH and CAC in SAs.

Methods: In 699 SAs in the MASALA Study, SN characteristics (size, density, proportion of kin or SA ethnicity), alter health status (self-report of an alter with high blood pressure [HTN], hyperlipidemia [HL], heart disease, diabetes, or stroke), CVH score (0-14, based on poor, intermediate, or ideal blood pressure, cholesterol, glucose, physical activity, diet, weight, and smoking), and CAC data were collected between 2016-2018. Multiple logistic regression evaluated the association of SN characteristics or alter health with prevalent CVH and CAC.

Results: Participants were mean age 59±9 years and 43% women. Mean CVH score was 8.9±1.9, median CAC score 8 (range 0 - 4217). SNs were mean 6±3 people, density 79±26%, 72±28% kin, 88±23% SA ethnicity; 48% had an alter with HTN, 42% with HL, 18% with heart disease, 40% with diabetes, and 2% with stroke. A 1-person larger SN size was associated with a 19% higher odds of ideal vs. poor CVH in men (p=0.02), and an 11% lower odds of CAC in women (p=0.05, Table). In men, having at least 1 alter with HTN or HL was associated with a 58% or 54% lower odds of ideal vs. poor CVH (p=0.03, p=0.04), and having at least 1 alter with HL was associated with a 78% higher odds of CAC (p=0.05). No associations were seen between other SN characteristics, nor alters with other CVD risk factors, and CVH or CAC.

Conclusions: In SAs, larger SN size was associated with better CVH. Having a SN member with HTN or HL may be associated with poorer CVH and CAC. Interventions to increase SN size or target SN member CVH may promote CVH in this high-risk population.

Bidirectional 10-year associations of accelerometer-measured sedentary behavior and activity categories with weight among middle-aged adults

BACKGROUND

Although higher sedentary behavior (SB) with low light intensity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) are thought to increase risk for obesity, other data suggest excess weight may precede these behaviors in the causal pathway. We aimed to investigate 10-year bidirectional associations between SB and activity with weight.

METHODS

Analysis included 886 CARDIA participants (aged 38-50 years, 62% female, 38% black) with weight and accelerometry ( ≥ 4 days with ≥ 10 h/day) collected in 2005-6 (ActiGraph 7164) and 2015-6 (ActiGraph wGT3X-BT). Accelerometer data were calibrated, harmonized, and expressed as counts per minute (cpm) and time-dependent intensity categories (min/day of SB, LPA, and MVPA; SB and MVPA were also separated into long-bout and short-bout categories). Linear regression models were constructed to estimate adjusted associations of baseline activity with 10-year change in weight and vice versa. When activity categories were the independent variables, standardized regression coefficients (βstd.) estimated associations of replacing SB with a one SD increase in other categories, adjusted for accelerometer wear time.

RESULTS

Over 10-years, weight increased by a mean 2.55 ± 8.05 kg and mean total activity decreased by 50 ± 153 cpm. In adjusted models, one SD higher baseline mean total activity (βstd. = -1.4 kg, p < 0.001), LPA (βstd. = -0.80 kg, p = 0.013), total MVPA (βstd. = -1.07 kg, p = 0.001), and long-bout MVPA (βstd. = -1.20 kg, p < 0.001) were associated with attenuated 10-year weight gain. Conversely, a one SD higher baseline weight was associated with unfavorable 10-year changes in daily activity profile including increases in SB (βstd. = 12.0 min, p < 0.001) and decreases in mean total activity (βstd. = 14.9 cpm, p = 0.004), LPA (βstd. = 8.9, p = 0.002), and MVPA (βstd. = 3.5 min, p = 0.001). Associations varied by race and gender.

CONCLUSIONS

Higher SB with lower activity and body weight were bidirectionally related. Interventions that work simultaneously to replace SB with LPA and long-bout MVPA while also using other methods to address excess weight may be optimal.

Association of Social Networks and Physical Activity in South Asians: The Mediators of Atherosclerosis in South Asians Living in America Cohort Study

BACKGROUND

Eliminating racial/ethnic disparities in physical activity remains a challenge in the United States. South Asian immigrants in the United States have particularly low physical activity levels, and evidence suggests that social context may be important. This study examined associations between personal social networks and moderate to vigorous leisure-time physical activity (MVPA) among South Asians in the United States.

METHODS

We used cross-sectional data (2014-2017) from 689 South Asians (aged 43-85 y) who participated in the Mediators of Atherosclerosis in South Asians Living in America study. Self-reported physical activity and egocentric network data were collected from participants about their network members. Regression models were used to determine associations between social network characteristics and participants' MVPA.

RESULTS

Participants were on average 59 years old (SD = 9) and reported 1335 metabolic equivalent minutes per week of MVPA (interquartile range = 735, 2212). Having network members who exercised or who were exercise partners associated with increased MVPA in men (β coefficient = 241 MET min/wk [95% confidence interval, 63 to 419] and β = 520 MET min/wk [95% confidence interval, 322 to 718], respectively). For women, the association was only significant if the exercise partner was a spouse.

CONCLUSION

Physical activity interventions utilizing network members as exercise partners may have potential in South Asians but must consider gender differences.

Individualized Relative-Intensity Physical Activity Accelerometer Cut Points

PURPOSE

Physical activity (PA) intensity is expressed as either absolute or relative intensity. Absolute intensity refers to the energy required to perform an activity. Relative intensity refers to a level of effort that takes into account how hard an individual is working relative to their maximum capacity. We sought to develop methods for obtaining individualized relative-intensity accelerometer cut points using data from a maximal graded exercise treadmill test (GXT) so that each individual has their own cut point.

METHODS

A total of 2363 men and women 38 to 50 yr old from the CARDIA fitness study wore ActiGraph 7164 accelerometers during a maximal GXT and for seven consecutive days in 2005-2006. Using mixed-effects regression models, we regressed accelerometer counts on heart rate as a percentage of maximum (%HRmax) and on RPE. Based on these two models, we obtained a moderate-intensity (%HRmax = 64% or RPE = 12) count cut point that is specific to each participant. We applied these subject-specific cut points to the available CARDIA accelerometer data.

RESULTS

Using RPE, the mean moderate-intensity accelerometer cut point was 4004 (SD = 1120) counts per minute. On average, cut points were higher for men (4189 counts per minute) versus women (3865 counts per minute) and were higher for Whites (4088 counts per minute) versus African Americans (3896 counts per minute). Cut points were correlated with body mass index (rho = -0.11) and GXT duration (rho = 0.33). Mean daily minutes of absolute- and relative-intensity moderate to vigorous PA were 34.1 (SD = 31.1) min·d and 9.1 (SD = 18.2) min·d, respectively. RPE cut points were higher than those based on %HRmax. This is likely due to some participants ending the GXT before achieving their HRmax.

CONCLUSIONS

Accelerometer-based relative-intensity PA may be a useful measure of intensity relative to maximal capacity.

Sedentary Time and Physical Activity Across Occupational Classifications

PURPOSE

To examine differences in activity patterns across employment and occupational classifications.

DESIGN

Cross-sectional.

SETTING

A 2005-2006 Coronary Artery Risk Development in Young Adults (CARDIA) study.

SAMPLE

Participants with valid accelerometry data (n = 2068).

MEASURES

Uniaxial accelerometry data (ActiGraph 7164), accumulated during waking hours, were summarized as mean activity counts (counts/min) and time spent (min/d) in long-bout sedentary (≥30 minutes, SED_≥30), short-bout sedentary (<30 minutes, SED_<30), light physical activity (LPA), short-bout moderate-to-vigorous physical activity (<10 minutes, MVPA_<10), and long-bout MVPA (≥10 minutes, MVPA_≥10) using Freedson cut-points. Employment status was self-reported as full time, part time, unemployed, keeping house, or raising children. Self-reported job duties were categorized into 23 major groups using the 2010 Standard Occupational Classification.

ANALYSIS

Omnibus differences were analyzed using adjusted analysis of covariance and repeated after stratification by race (black/white) and sex (female/male).

RESULTS

SED_≥30, SED_<30, LPA, and MVPA_<10 differed significantly by employment and occupational categories (P ≤ .05), while MVPA_≥10 did not (P ≥ .50). SED_≥30, SED_<30, and LPA differed by occupational classification in men, women, blacks, and whites (P < .05). Mean activity counts, MVPA_<10, and MVPA_≥10 were significantly different across occupational classifications in whites (P ≤ .05), but not in blacks (P > .05). Significant differences in mean activity counts and MVPA_<10 across occupational classifications were found in males (P ≤ .001), but not in females (P > .05).

CONCLUSION

Time within activity intensity categories differs across employment and occupational classifications and by race and sex.

SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management

BACKGROUND

Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components.

OBJECTIVE

The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss.

STUDY DESIGN

Four hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m2 will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving <0.5 lb. weight loss on average per week, assessed by wireless scale at 2, 4, and 8 weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12 months.

SIGNIFICANCE

Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.

Online, cross-disciplinary team science training for health and medical professionals: Evaluation of COALESCE (teamscience.net)

INTRODUCTION

The National Academies of Sciences (NAS) emphasize the need for interdisciplinary team science (TS) training, but few training resources are available. COALESCE, an open-access tool developed with National Institutes of Health support and located at teamscience.net, is considered a gold standard resource but has not previously been evaluated. COALESCE launched four learning modules in 2011. The Science of TS (SciTS) module, an interactive encyclopedia, introduces foundational concepts. Three scenario-based modules simulate TS challenges in behavioral, clinical, and basic biomedical sciences. This study examined user characteristics, usage patterns, and effects of completing the four modules on TS knowledge, attitudes, and skills.

METHODS

Repeated measures ANOVA tested for pre-post changes in performance and compared learning by users with biomedical versus other disciplinary backgrounds.

RESULTS

From 2011 through 2017, the site attracted 16,280 new users who engaged in 6461 sessions that lasted more than 1 min. The modal registrant identified as working in a biomedical field (47%), in an academic institution (72%), and expressed greater interest in the practice than the SciTS (67%). Those completing pre- and post-tests (n = 989) showed significant improvement in knowledge, attitudes, and skills after taking all scenario-based modules (p < 0.005); knowledge and attitudes were unchanged after the SciTS encyclopedia. Biomedical and other health professionals improved comparably.

CONCLUSION

Evaluation of the TS training tool at teamscience.net indicates broad dissemination and positive TS-related outcomes. Site upgrades implemented between 2018 and 2020, including adding five new modules, are expected to increase the robustness and accessibility of the COALESCE training resource.

Social Network Characteristics Are Correlated with Dietary Patterns Among Middle Aged and Older South Asians (SA) Living in the United States (US) (P04-124-19)

Objectives

Social and cultural norms, operating through social networks, may influence an individual's dietary choices. We examined the correlations among social network characteristics and dietary patterns among SA in the US.

Methods

Data from the Mediators of Atherosclerosis in South Asians Living in America Social Network study were analyzed among 756 participants (mean age 59 y standard deviation [SD] = 9 y; 44% women). A culturally adapted, validated food frequency questionnaire was used for dietary assessment. Principal component analysis yielded, three dietary patterns named: “Animal protein”, “Fried snacks, sweets and high-fat dairy”, and “Fruits, vegetables, nuts and legumes”, based on contribution of food groups. Social network characteristics were assessed using a standard egocentric approach, where participants (egos) self-reported data on perceived dietary habits of their network members. Partial correlations between social network characteristics and egos’ dietary patterns were examined.

Results

The mean social network size of egos was 4.2 (SD = 1.1), with high proportion of network members being family (72%), SA ethnicity (89%), and half having daily contact. Higher scores for the “Animal protein” pattern among egos, were negatively correlated with daily fruits and cooked vegetables consumption of their network. This pattern was also positively correlated with the proportion of network who weekly consumed non-South Asian foods, diet drinks, non-vegetarian foods, processed meat and fried foods, and dined out. Scores for the “Fried snacks, sweets and high-fat dairy” pattern were positively correlated with proportion of network who weekly consumed sugar-sweetened beverages, South Asian sweets, fried foods, fast foods and ghee (clarified butter). Higher scores for the “Fruits, vegetables, nuts and legumes” pattern were positively associated with proportion of network who daily consumed both raw and cooked vegetables and fruits, and brown rice/quinoa weekly.

Conclusions

Network member characteristics and perceived dietary behaviors of network members were correlated with dietary patterns of SA in the US. Dietary intervention studies among SA should consider social network characteristics as candidate components for dietary intervention.

Funding Sources

National Institutes of Health.

Supporting Tables, Images and/or Graphs

Evaluating Community-Based Translational Interventions Using Historical Controls: Propensity Score vs. Disease Risk Score Approach

Many community-based translations of evidence-based interventions are designed as one-arm studies due to ethical and other considerations. Evaluating the impacts of such programs is challenging. Here, we examine the effectiveness of the lifestyle intervention implemented by the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project, a translational lifestyle intervention among American Indian and Alaska Native communities. Data from the landmark Diabetes Prevention Program placebo group was used as a historical control. We compared the use of propensity score (PS) and disease risk score (DRS) matching to adjust for potential confounder imbalance between groups. The unadjusted hazard ratio (HR) for diabetes risk was 0.35 for SDPI-DP lifestyle intervention vs. control. However, when relevant diabetes risk factors were considered, the adjusted HR estimates were attenuated toward 1, ranging from 0.56 (95% CI 0.44-0.71) to 0.69 (95% CI 0.56-0.96). The differences in estimated HRs using the PS and DRS approaches were relatively small but DRS matching resulted in more participants being matched and smaller standard errors of effect estimates. Carefully employed, publicly available randomized clinical trial data can be used as a historical control to evaluate the intervention effectiveness of one-arm community translational initiatives. It is critical to use a proper statistical method to balance the distributions of potential confounders between comparison groups in this kind of evaluations.

Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials

Integrative Data Analysis (IDA) encompasses a collection of methods for data synthesis that pools participant-level data across multiple studies. Compared with single-study analyses, IDA provides larger sample sizes, better representation of participant characteristics, and often increased statistical power. Many of the methods currently available for IDA have focused on examining developmental changes using longitudinal observational studies employing different measures across time and study. However, IDA can also be useful in synthesizing across multiple randomized clinical trials to improve our understanding of the comprehensive effectiveness of interventions, as well as mediators and moderators of those effects. The pooling of data from randomized clinical trials presents a number of methodological challenges, and we discuss ways to examine potential threats to internal and external validity. Using as an illustration a synthesis of 19 randomized clinical trials on the prevention of adolescent depression, we articulate IDA methods that can be used to minimize threats to internal validity, including (1) heterogeneity in the outcome measures across trials, (2) heterogeneity in the follow-up assessments across trials, (3) heterogeneity in the sample characteristics across trials, (4) heterogeneity in the comparison conditions across trials, and (5) heterogeneity in the impact trajectories. We also demonstrate a technique for minimizing threats to external validity in synthesis analysis that may result from non-availability of some trial datasets. The proposed methods rely heavily on latent variable modeling extensions of the latent growth curve model, as well as missing data procedures. The goal is to provide strategies for researchers considering IDA.

Assessment of the influence of food attributes on meal choice selection by socioeconomic status and race/ethnicity among women living in Chicago, USA: A discrete choice experiment

Large and persistent obesity disparities exist in the US by socioeconomic status (SES) and race/ethnicity, and weight loss interventions have traditionally been less effective in these populations. Thus, a better understanding is needed of the behavioral, economic, and geographic factors that influence obesity risk factors such as eating behaviors. We used a discrete choice experiment to evaluate the impact of different meal attributes on meal choice and to test whether the relative importance of these attributes varied by SES and race/ethnicity. Study participants (n = 228) were given a series of 10 choice tasks and asked to choose among 4 meals, each rated based on the following attributes: taste; healthfulness; preparation time; travel time to food outlet for meal/ingredients; and price. SES was measured using education and self-reported difficulty paying for basics. Race/ethnicity was categorized as Hispanic/Latina, non-Hispanic black, non-Hispanic white, and non-Hispanic other. Data were analyzed using mixed logit regression models with interaction terms to determine whether meal attributes influenced meal choices differentially by SES and race/ethnicity. Healthfulness and taste were the most important attributes for all participants. Price was a more important attribute among those in the lowest SES group compared with those in the higher SES groups. Travel was the least important attribute for low SES participants, and it was not significantly related to meal choice in these groups. Discrete choice experiments as illustrated here may help pinpoint the most salient targets for interventions to improve eating behaviors and reduce obesity disparities. Specifically, our findings suggest interventions should incorporate strategies to target the pricing of healthy and unhealthy food options.

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

Sedentary behavior after breast cancer: motivational, demographic, disease, and health status correlates of sitting time in breast cancer survivors

PURPOSE

Sedentary behavior is associated with poor health outcomes including obesity, lower quality of life, and mortality in breast cancer survivors. This study sought to identify motivational, demographic, and disease characteristics of breast cancer survivors who engage in greater amounts of sedentary behavior.

METHODS

Multivariate linear regression models estimated associations between demographic, disease, and health characteristics with reported sitting in breast cancer survivors (n = 279; M_age = 60.7 (± 9.7) years). Regression models estimated associations between motivational factors and reported sitting adjusted for demographic and disease and health covariates.

RESULTS

Working at least part-time and marital status were associated various sitting domains including weekday and non-leisure sitting. Higher BMI was associated with more average daily, weekend, and weekday sitting. High income was additionally associated with less non-leisure sitting. The belief that sedentary behavior is bad for health, physical function, and self-evaluative OE, and lifestyle self-efficacy were associated with multiple sitting domains in both univariate and covariate-adjusted models.

CONCLUSIONS

Future work should examine the relationships between motivational, demographic, and disease predictors and objectively measured sedentary behavior over time and across different sedentary behavior domains. Understanding activity changes during and after treatment is needed to identify intervention targets and develop effective interventions.

Propensity Score-Based Estimators With Multiple Error-Prone Covariates

Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.

Twenty-five-year trajectories of insulin resistance and pancreatic β-cell response and diabetes risk in nonalcoholic fatty liver disease

BACKGROUND & AIMS

Insulin resistance is a risk marker for non-alcoholic fatty liver disease, and a risk factor for liver disease progression. We assessed temporal trajectories of insulin resistance and β-cell response to serum glucose concentration throughout adulthood and their association with diabetes risk in non-alcoholic fatty liver disease.

METHODS

Three thousand and sixty participants from Coronary Artery Risk Development in Young Adults, a prospective bi-racial cohort of adults age 18-30 years at baseline (1985-1986; Y0) who completed up to 5 exams over 25 years and had fasting insulin and glucose measurement were included. At Y25 (2010-2011), non-alcoholic fatty liver disease was assessed by noncontrast computed tomography after exclusion of other liver fat causes. Latent mixture modelling identified 25-year trajectories in homeostatic model assessment insulin resistance and β-cell response homeostatic model assessment-β.

RESULTS

Three distinct trajectories were identified, separately, for homeostatic model assessment insulin resistance (low-stable [47%]; moderate-increasing [42%]; and high-increasing [12%]) and homeostatic model assessment-β (low-decreasing [16%]; moderate-decreasing [63%]; and high-decreasing [21%]). Y25 non-alcoholic fatty liver disease prevalence was 24.5%. Among non-alcoholic fatty liver disease, high-increasing homeostatic model assessment insulin resistance (referent: low-stable) was associated with greater prevalent (OR 95% CI = 8.0, 2.0-31.9) and incident (OR = 10.5, 2.6-32.8) diabetes after multivariable adjustment including Y0 or Y25 homeostatic model assessment insulin resistance. In contrast, non-alcoholic fatty liver disease participants with low-decreasing homeostatic model assessment-β (referent: high-decreasing) had the highest odds of prevalent (OR = 14.1, 3.9-50.9) and incident (OR = 10.3, 2.7-39.3) diabetes.

CONCLUSION

Trajectories of insulin resistance and β-cell response during young and middle adulthood are robustly associated with diabetes risk in non-alcoholic fatty liver disease. Thus, how persons with non-alcoholic fatty liver disease develop resistance to insulin provides important information about risk of diabetes in midlife above and beyond degree of insulin resistance at the time of non-alcoholic fatty liver disease assessment.