Identifying the Heterogeneity in the Association between Workforce Diversity and Retention in Opioid Treatment among Black clients.

Background: This study investigates the impact of workforce diversity, specifically staff identified as Black/African American, on retention in opioid use disorder (OUD) treatment, aiming to enhance patient outcomes. Employing a novel machine learning technique known as 'causal forest,' we explore heterogeneous treatment effects on retention. Methods: We relied on four waves of the National Drug Abuse Treatment System Survey (NDATSS), a nationally representative longitudinal dataset of treatment programs. We analyzed OUD program data from the years 2000, 2005, 2014 and 2017 (n = 627). Employing the 'causal forest' method, we analyzed the heterogeneity in the relationship between workforce diversity and retention in OUD treatment. Interviews with program directors and clinical supervisors provided the data for this study. Results: The results reveal diversity-related variations in the association with retention across 61 out of 627 OUD treatment programs (less than 10%). These programs, associated with positive impacts of workforce diversity, were more likely private-for-profit, newer, had lower percentages of Black and Latino clients, lower staff-to-client ratios, higher proportions of staff with graduate degrees, and lower percentages of unemployed clients. Conclusions: While workforce diversity is crucial, our findings underscore that it alone is insufficient for improving retention in addiction health services research. Programs with characteristics typically linked to positive outcomes are better positioned to maximize the benefits of a diverse workforce in client retention. This research has implications for policy and program design, guiding decisions on resource allocation and workforce diversity to enhance retention rates among Black clients with OUDs.

Examining the impacts of public transit on healthy aging through a natural experiment: study protocols and lessons learned from the Active El Paso project.

This paper describes protocols and experiences from a seven-year natural-experiment study in El Paso, Texas, a border city of predominantly Latino/Hispanic population. The study focuses on how Bus Rapid Transit (BRT) impacts physical activity and thus plays a role in alleviating obesity and related chronic diseases that impact healthy aging. Our protocols describe a longitudinal and case-comparison study, which compared residents exposed to new BRT stations with those who were not. This paper also introduces lessons and experiences to overcome the following challenges: delays in the BRT opening (the main intervention), the COVID-19 pandemic, methodological challenges, participant recruitment and retention, and predatory survey takers. Our transdisciplinary approach was pivotal in addressing these challenges. We also proposed and tested multi-level intervention strategies to reduce modifiable barriers to transit use. Our most important takeaway for researchers, practitioners, and policy makers is the importance of being flexible and ready to adapt to new circumstances. Future natural-experiment researchers need to become more versatile in an increasingly volatile and uncertain world.

Predicting and responding to change: Perceived environmental uncertainty among substance use disorder treatment programs.

INTRODUCTION: Substance use disorder (SUD) treatment programs offering addiction health services (AHS) must be prepared to adapt to change in their operating environment. These environmental uncertainties may have implications for service delivery, and ultimately patient outcomes. To adapt to a multitude of environmental uncertainties, treatment programs must be prepared to predict and respond to change. Yet, research on treatment programs preparedness for change is sparse. We examined reported difficulties in predicting and responding to changes in the AHS system, and factors associated with these outcomes. METHODS: Cross-sectional surveys of SUD treatment programs in the United States in 2014 and 2017. We used linear and ordered logistic regression to examine associations between key independent variables (e.g., program, staff, and client characteristics) and four outcomes, (1) reported difficulties in predicting change, (2) predicting effect of change on organization, (3) responding to change, and (4) predicting changes to make to respond to environmental uncertainties. Data were collected through telephone surveys. RESULTS: The proportion of SUD treatment programs reporting difficulty predicting and responding to changes in the AHS system decreased from 2014 to 2017. However, a considerable proportion still reported difficulty in 2017. We identified that different organizational characteristics are associated with their reported ability to predict or respond to environmental uncertainty. Findings show that predicting change is significantly associated with program characteristics only, while predicting effect of change on organizations is associated with program and staff characteristics. Deciding how to respond to change is associated with program, staff, and client characteristics, while predicting changes to make to respond is associated with staff characteristics only. CONCLUSIONS: Although treatment programs reported decreased difficulty predicting and responding to changes, our findings identify program characteristics and attributes that could better position programs with the foresight to more effectively predict and respond to uncertainties. Given resource constraints at multiple levels in treatment programs, this knowledge might help identify and optimize aspects of programs to intervene upon to enhance their adaptability to change. These efforts may positively influences processes or care delivery, and ultimately translate into improvements in patient outcomes.

Workforce Diversity and disparities in wait time and retention among opioid treatment programs.

BACKGROUND: Workforce diversity is a key strategy to improve treatment engagement among members of racial and ethnic minority groups. In this study, we seek to determine whether workforce diversity plays a role in reducing racial and ethnic differences in wait time to treatment entry and retention in different types of opioid use disorder treatment programs. METHODS: We conducted comparative and predictive analysis in a subsample of outpatient opioid treatment programs (OTPs), who completed access and retention survey questions in four waves of the National Drug Abuse Treatment System Survey (162 OTPs in 2000, 173 OTPs in 2005, 282 OTPs in 2014, and 300 OTPs in 2017). We sought to assess the associations between workforce diversity on wait time and retention, accounting for the role of Medicaid expansion and the moderating role of program ownership type (i.e., public, non-profit, for-profit) among OTPs located across the United States. RESULTS: We found significant differences in wait time to treatment entry and retention in treatment across waves. Average number of waiting days decreased in 2014 and 2017; post Medicaid expansion per the Affordable Care Act, while retention rates varied across years. Key findings show that programs with high diversity, measured by higher percent of African American staff and a higher percent of African American clients, were associated with longer wait times to enter treatment, compared to low diversity programs. Programs with higher percent of Latino staff and a higher percent of Latino clients were associated with lower retention in treatment compared with low diversity programs. However, program ownership type (public, non-profit and for-profit) played a moderating role. Public programs with higher percent of African American staff were associated with lower wait time, while non-profit programs with higher percent of Latino staff were related to higher retention. CONCLUSIONS: Findings show decreases in wait time over the years with significant variation in retention during the same period. Concordance in high workforce and client diversity was associated with higher wait time and lower retention. But these relations inverted (low wait time and high retention) in public and non-profit programs with high staff diversity. Findings have implications for building resources and service capacity among OTPs that serve a higher proportion of minority clients.

Matched case-control data with a misclassified exposure: what can be done with instrumental variables?

Matched case-control studies are used for finding the association between a disease and an exposure after controlling the effect of important confounding variables. It is a known fact that the disease-exposure association parameter estimators are biased when the exposure is misclassified, and a matched case-control study is of no exception. Any bias correction method relies on validation data that contain the true exposure and the misclassified exposure value, and in turn the validation data help to estimate the misclassification probabilities. The question is what we can do when there are no validation data and no prior knowledge on the misclassification probabilities, but some instrumental variables are observed. To answer this unexplored and unanswered question, we propose two methods of reducing the exposure misclassification bias in the analysis of a matched case-control data when instrumental variables are measured for each subject of the study. The significance of these approaches is that the proposed methods are designed to work without any validation data that often are not available when the true exposure is impossible or too costly to measure. A simulation study explores different types of instrumental variable scenarios and investigates when the proposed methods work, and how much bias can be reduced. For the purpose of illustration, we apply the methods to a nested case-control data sampled from the 1989 US birth registry.

Analysis of linear transformation models with covariate measurement error and interval censoring.

Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data, flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time-to-event data with different models, in this paper, a general approach is proposed for fitting the semiparametric linear transformation model to interval-censored data when a covariate is measured with error. The semiparametric linear transformation model is a broad class of models that includes the proportional hazard model and the proportional odds model as special cases. The proposed method relies on a set of estimating equations to estimate the regression parameters and the infinite-dimensional parameter. For handling interval censoring and covariate measurement error, a flexible imputation technique is used. Finite sample performance of the proposed method is judged via simulation studies. Finally, the suggested method is applied to analyze a real data set from an AIDS clinical trial.

Confidence intervals for the difference in the success rates of two treatments in the analysis of correlated binary responses.

In clinical studies, we often compare the success rates of two treatment groups where post-treatment responses of subjects within clusters are usually correlated. To estimate the difference between the success rates, interval estimation procedures that do not account for this intraclass correlation are likely inappropriate. To address this issue, we propose three interval procedures by direct extensions of recently proposed methods for independent binary data based on the concepts of design effect and effective sample size used in sample surveys. Each of them is then evaluated with four competing variance estimates. We also extend three existing methods recommended for complex survey data using different weighting schemes required for those three existing methods. An extensive simulation study is conducted for the purposes of evaluating and comparing the performance of the proposed methods in terms of coverage and expected width. The interval estimation procedures are illustrated using three examples in clinical and social science studies. Our analytic arguments and numerical studies suggest that the methods proposed in this work may be useful in clustered data analyses.

Fear of Outdoor Falling Among Community-Dwelling Middle-Aged and Older Adults: The Role of Neighborhood Environments.

Background and Objectives: Fear of falling is a substantial barrier to walking and has been associated with increased fall risks. This study examines neighborhood environmental risk factors related to fear of outdoor falling in middle-aged and older adults. Research Design and Methods: A total of 394 participants aged 50 years or older living independently in the community were recruited between 2013 and 2014 from an integrated health care network serving Central Texas. Fear of outdoor falling and perceived neighborhood environmental variables were assessed using self-reported questionnaires. Logistic regression identified perceived neighborhood environmental variables associated with fear of outdoor falling. Results: Sixty-nine (17.9%) of 385 participants reported having a fear of outdoor falling. Compared to those who did not report a fear of outdoor falling, those who reported having a fear of outdoor falling were more likely to be adults aged 65 years or older (odds ratio [OR] = 2.974, 95% confidence interval [CI] = 1.247-7.094), be female (OR = 4.423, 95% CI = 1.830-10.689), have difficulty with walking for a quarter of a mile (OR = 2.761, 95% CI = 1.124-6.782), and have had a fall in the past year (OR = 4.720, 95% CI = 1.472-15.137). Among the neighborhood environmental characteristics examined, low traffic speed on streets (OR = 0.420, 95% CI = 0.188-0.935), drainage ditches (OR = 2.383, 95% CI = 1.136-5.000), and broken sidewalks (OR = 3.800, 95% CI = 1.742-8.288) were associated with the odds of having a fear of outdoor falling. Discussion and Implications: In addition to the individual factors, findings from this study suggest the importance of addressing the environmental risk factors in identifying and reducing fear of outdoor falling among middle-aged and older adults.

Confidence intervals for the common intraclass correlation in the analysis of clustered binary responses.

In cluster randomized trials, it is often of interest to estimate the common intraclass correlation at the design stage for sample size and power calculations, which are greatly affected by the value of a common intraclass correlation. In this article, we construct confidence intervals (CIs) for the common intraclass correlation coefficient of several treatment groups. We consider the profile likelihood (PL)-based approach using the beta-binomial models and the approach based on the concept of generalized pivots using the ANOVA estimator and its asymptotic variance. We compare both approaches with a number of large sample procedures as well as both parametric and nonparametric bootstrap procedures in terms of coverage and expected CI length through a simulation study, and illustrate the methodology with two examples from biomedical fields. The results support the use of the PL-based CI as it holds the preassigned confidence level very well and overall gives a very competitive length.

Determinants of Walking among Middle-Aged and Older Overweight and Obese Adults: Sociodemographic, Health, and Built Environmental Factors.

BACKGROUND: This study examined the association between selected sociodemographic, health, and built environmental factors and walking behaviors of middle-aged and older overweight/obese adults. METHODS: Subjective data were obtained from surveys administered to community-dwelling overweight/obese adults aged ≥50 years residing in four Texas cities from October 2013 to June 2014, along with objective data on neighborhood walkability (Walk Score™). Multivariate logistic regression identified factors predicting the odds of walking the recommended ≥150 minutes per week for any purpose. RESULTS: Of 253 participants, the majority were non-Hispanic white (81.8%), married (74.5%), and male (53.4%) and reported an annual income of ≥$50,000 (65.5%). Approximately, half were employed (49.6%) or had at least a college degree (51.6%). Walking the recommended ≥150 minutes per week for any purpose (n = 57, 22.5%) was significantly associated with having at least a college degree (OR = 5.55, 95% CI = 1.79-17.25), having no difficulty walking a quarter of a mile (OR = 5.18, 95% CI = 1.30-20.83), and being unemployed (OR = 3.25, 95% CI = 1.18-8.93) as well as perceived presence of sidewalks/protected walkways (OR = 3.56, 95% CI = 1.10-11.50) and perceived absence of distracted drivers in the neighborhood (OR = 4.08, 95% CI = 1.47-11.36). CONCLUSION: Addressing neighborhood conditions related to distracted drivers and pedestrian infrastructure may promote walking among middle-aged and older overweight/obese individuals.

Semiparametric Bayesian analysis of censored linear regression with errors-in-covariates.

The accelerated failure time (AFT) model is a well-known alternative to the Cox proportional hazard model for analyzing time-to-event data. In this paper we consider fitting an AFT model to right censored data when a predictor variable is subject to measurement errors. First, without measurement errors, estimation of the model parameters in the AFT model is a challenging task due to the presence of censoring, especially when no specific assumption is made regarding the distribution of the logarithm of the time-to-event. The model complexity increases when a predictor is measured with error. We propose a non-parametric Bayesian method for analyzing such data. The novel component of our approach is to model (1) the distribution of the time-to-event, (2) the distribution of the unobserved true predictor, and (3) the distribution of the measurement errors all non-parametrically using mixtures of the Dirichlet process priors. Along with the parameter estimation we also prescribe how to estimate survival probabilities of the time-to-event. Some operating characteristics of the proposed approach are judged via finite sample simulation studies. We illustrate the proposed method by analyzing a data set from an AIDS clinical trial study.

Correlates of Receiving a Recommendation for More Physical Activity From a Primary Care Provider.

INTRODUCTION: Primary care providers (PCPs) are strategically positioned to communicate with their overweight/obese patients about positive behavioral changes to improve health and functioning. Demographic and behavioral correlates of receiving a recommendation for more physical activity (PA) from a PCP by overweight/obese patients were assessed. METHODS: Community-dwelling adults aged ≥50 years from four Texas cities who were seen by a family physician in a primary care clinic were surveyed from October 2013 to June 2014. Multivariate logistic regression predicted the likelihood of receiving a PA recommendation from a PCP, controlling for sociodemographic factors, health conditions, and walking behaviors. The analysis was conducted in 2016. RESULTS: Of the total 388 participants (survey response rate, 6.8%), 30.1% were obese, 55.4% were female, and most were non-Hispanic white (82.9%), married (75.6%), or reported an annual household income of ≥$50,000 (66.8%). Receipt of a PA recommendation from a PCP (n=151, 38.9%) was significantly correlated with reporting poor to fair health (OR=7.33, 95% CI=2.6, 20.32), obesity (OR=2.95, 95% CI=1.69, 5.14), having only a little or some difficulty walking for a quarter of a mile (OR=2.94, 95% CI=1.41, 5.88), not walking the recommended ≥150 minutes for any purpose (OR=2.60, 95% CI=1.25, 5.38), and being employed (OR=2.11, 95% CI=1.13. 3.94). CONCLUSIONS: PCPs seem to be targeting obese, inactive individuals with poor to fair health, populations traditionally not encouraged to be more physically active. These findings are consistent with the current trend in medical care to recommend positive lifestyle changes to a broader range of the population.

Simulating the Lunar Environment: Partial Weightbearing and High-LET Radiation-Induce Bone Loss and Increase Sclerostin-Positive Osteocytes.

Exploration missions to the Moon or Mars will expose astronauts to galactic cosmic radiation and low gravitational fields. Exposure to reduced weightbearing and radiation independently result in bone loss. However, no data exist regarding the skeletal consequences of combining low-dose, high-linear energy transfer (LET) radiation and partial weightbearing. We hypothesized that simulated galactic cosmic radiation would exacerbate bone loss in animals held at one-sixth body weight (G/6) without radiation exposure. Female BALB/cByJ four-month-old mice were randomly assigned to one of the following treatment groups: 1 gravity (1G) control; 1G with radiation; G/6 control; and G/6 with radiation. Mice were exposed to either silicon-28 or X-ray radiation. (28)Si radiation (300 MeV/nucleon) was administered at acute doses of 0 (sham), 0.17 and 0.5 Gy, or in three fractionated doses of 0.17 Gy each over seven days. X radiation (250 kV) was administered at acute doses of 0 (sham), 0.17, 0.5 and 1 Gy, or in three fractionated doses of 0.33 Gy each over 14 days. Bones were harvested 21 days after the first exposure. Acute 1 Gy X-ray irradiation during G/6, and acute or fractionated 0.5 Gy (28)Si irradiation during 1G resulted in significantly lower cancellous mass [percentage bone volume/total volume (%BV/TV), by microcomputed tomography]. In addition, G/6 significantly reduced %BV/TV compared to 1G controls. When acute X-ray irradiation was combined with G/6, distal femur %BV/TV was significantly lower compared to G/6 control. Fractionated X-ray irradiation during G/6 protected against radiation-induced losses in %BV/TV and trabecular number, while fractionated (28)Si irradiation during 1G exacerbated the effects compared to single-dose exposure. Impaired bone formation capacity, measured by percentage mineralizing surface, can partially explain the lower cortical bone thickness. Moreover, both partial weightbearing and (28)Si-ion exposure contribute to a higher proportion of sclerostin-positive osteocytes in cortical bone. Taken together, these data suggest that partial weightbearing and low-dose, high-LET radiation negatively impact maintenance of bone mass by lowering bone formation and increasing bone resorption. The impaired bone formation response is associated with sclerostin-induced suppression of Wnt signaling. Therefore, exposure to low-dose, high-LET radiation during long-duration spaceflight missions may reduce bone formation capacity, decrease cancellous bone mass and increase bone resorption. Future countermeasure strategies should aim to restore mechanical loads on bone to those experienced in one gravity. Moreover, low-doses of high-LET radiation during long-duration spaceflight should be limited or countermeasure strategies employed to mitigate bone loss.

Using Walk Score™ and Neighborhood Perceptions to Assess Walking Among Middle-Aged and Older Adults.

We aimed to determine the relationship between neighborhood characteristics (walkability, cohesion/safety) and recommended activity levels among community-dwelling middle-aged and older adults. Subjective and objective data on 394 individuals aged ≥50 years were used to assess the likelihood of walking ≥150 min/week. Environmental factors associated with a greater likelihood of any walking ≥150 min/week included living in a neighborhood with high perception of cohesion/safety versus low, living in walkable areas versus car-dependent, and living in an area with a low-moderate median income versus the lowest. Middle-aged and older adults were more likely to walk ≥150 min/week in a walkable, perceived safe/cohesive neighborhood. Identifying neighborhood factors associated with promoting walking among this population can enable stakeholders (e.g., researchers, planners, and policy makers) to direct interventions focusing on the built environment.

A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data.

Interval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

Evaluating the Influence of the Revised Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Food Allocation Package on Healthy Food Availability, Accessibility, and Affordability in Texas.

BACKGROUND: The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) was implemented to improve the health of pregnant women and children of low socioeconomic status. In 2009, the program was revised to provide a wider variety of healthy food choices (eg, fresh fruits, vegetables, and whole-grain items). OBJECTIVES: The purpose of this study was to evaluate (1) the impact of the revised WIC Nutrition Program's food allocation package on the availability, accessibility, and affordability of healthy foods in WIC-authorized grocery stores in Texas; and (2) how the impact of the policy change differed by store types and between rural and urban regions. DESIGN: WIC-approved stores (n=105) across Texas were assessed using a validated instrument (88 items). Pre- (June-September 2009) and post-new WIC package implementation (June-September 2012) audits were conducted. Paired-sample t tests were conducted to compare the differences between pre- and post-implementation audits on shelf width and number of varieties (ie, availability), visibility (ie, accessibility), and inflation-adjusted price (ie, affordability). RESULTS: Across the 105 stores, post-implementation audits showed increased availability in terms of shelf space for most key healthy food options, including fruit (P<0.001), vegetables (P<0.01), cereal (P<0.001), and varieties of vegetables (P<0.001). Food visibility increased for fresh juices (P<0.001). Visibility of WIC labeling improved for foods such as fruits (P<0.05), WIC cereal (P<0.05), and whole-grain or whole-wheat bread (P<0.01). Inflation-adjusted prices decreased only for bread (P<0.001) and dry grain beans (P<0.001). The positive effects of the policy change on food availability and visibility were observed in stores of different types and in different locations, although smaller or fewer effects were noted in small stores and stores in rural regions. CONCLUSIONS: Implementation of the revised WIC food package has generally improved availability and accessibility, but not affordability, of healthy foods in WIC-authorized stores in Texas. Future studies are needed to explore the impact of the revised program on healthy food option purchases and consumption patterns among Texas WIC participants.

The direct integral method for confidence intervals for the ratio of two location parameters.

In a relative risk analysis of colorectal caner on nutrition intake scores across genders, we show that, surprisingly, when comparing the relative risks for men and women based on the index of a weighted sum of various nutrition scores, the problem reduces to forming a confidence interval for the ratio of two (asymptotically) normal random variables. The latter is an old problem, with a substantial literature. However, our simulation results suggest that existing methods often either give inaccurate coverage probabilities or have a positive probability to produce confidence intervals with infinite length. Motivated by such a problem, we develop a new methodology which we call the Direct Integral Method for Ratios (DIMER), which, unlike the other methods, is based directly on the distribution of the ratio. In simulations, we compare this method to many others. These simulations show that, generally, DIMER more closely achieves the nominal confidence level, and in those cases that the other methods achieve the nominal levels, DIMER has comparable confidence interval lengths. The methodology is then applied to a real data set, and with follow up simulations.

Children's active commuting to school: an interplay of self-efficacy, social economic disadvantage, and environmental characteristics.

BACKGROUND: Active commuting to school (ACS) can promote children's physical activity and may help prevent childhood obesity. Previous researchers in various disciplines, e.g., health, urban planning, and transportation, have identified various predictors of ACS. However, little research has been carried out into investigating the effect of self-efficacy on ACS. The purpose of this study is to investigate the roles of children's and parents' self-efficacy in children's ACS, controlling for sociodemographic and objective environmental characteristics. METHODS: This study is part of the Texas Childhood Obesity Prevention Policy Evaluation (T-COPPE) project, which includes data from 857 parent/child pairs from 74 schools who lived within two miles of school in Texas. Measures included children's usual modes of commuting to school, participants' sociodemographics, perceived self-efficacy toward ACS, sources of children's self-efficacy, school settings, and objective environmental constraints. Multilevel structural equation modeling (SEM) was employed to test the hypothesized pathways using Mplus 7.0. RESULTS: Around 18% of the children were active commuters. Two sources of children's self-efficacy were identified, i.e., emotional states (ß = 0.36, p < 0.001) and social modeling (ß = 0.28, p < 0.01). Compared with children's self-efficacy (ß = 0.16, p < 0.001), parents' self-efficacy (ß = 0.63, p < 0.001) had a stronger influence on children's ACS. Participants' social economic disadvantage (ß = 0.40, p < 0.001), environmental constraints (ß = -0.49, p < 0.001), and school setting (ß = -0.17, p = 0.029) all had statistically significant direct effects on children's ACS. CONCLUSIONS: Future initiatives should consider both parents' and children's self-efficacy in developing strategies for promoting children's ACS. Social disadvantage and environmental constraints also need to be addressed for effective interventions. The work reported here provides support for the continuing exploration of the role of self-efficacy in children's ACS.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large.

Efficacy of a Mobile-Enabled Web App (iCanFit) in Promoting Physical Activity Among Older Cancer Survivors: A Pilot Study.

BACKGROUND: The benefits of physical activity for cancer survivors are well documented. However, few older cancer survivors are engaged in regular physical activity. Mobile technologies may be an effective method to deliver physical activity promotion programs for older cancer survivors. iCanFit, a mobile-enabled Web-based app, was developed based on formative research and usability testing. This app includes interactive features of physical activity, goal setting and tracking, and receiving personalized visual feedback. OBJECTIVE: The aim of this study is to pilot test the initial efficacy of iCanFit. METHODS: Older cancer survivors (N=30) were recruited online through our collaborative partnership with a cancer survivor's organization. After the participants completed an online baseline survey, they were asked to use the iCanFit website. Instructional videos on how to use the web app were available on the website. Participants were asked to complete a follow-up survey 2-3 months later. Participants' physical activity, quality of life, and their experience with iCanFit were measured. RESULTS: A total of 30 participants completed the baseline survey, and 26 of them (87%, 26/30) also completed a follow-up survey 2-3 months later. The median age of participants was 69 years (range 60-78). Participants' quality of life and engagement in regular physical activity improved significantly after the use of iCanFit. Participants indicated a general affinity towards the key function "Goals" in iCanFit, which motivated continued activity. They also provided suggestions to further improve the app (eg, adding a reminder functionality, easier or alternative ways of entering activities). CONCLUSION: The interactive Web-based app iCanFit has demonstrated initial efficacy. Even though our study was limited by a small sample size, convenience sampling, and a short follow-up period, results suggest that using mobile tools to promote physical activity and healthy living among older cancer survivors holds promise. Next steps include refining iCanFit based on users' feedback and developing versatile functionality to allow easier physical activity goal setting and tracking. We also call for more studies on developing and evaluating mobile and web apps for older cancer survivors.