Browsing by Author "Zhou, Dali"

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    Comparison of an alternative schedule of extended care contacts to a self-directed control: a randomized trial of weight loss maintenance
    (BMC, 2017-08-15) Dutton, Gareth R.; Gowey, Marissa A.; Tan, Fei; Zhou, Dali; Ard, Jamy; Perri, Michael G.; Lewis, Cora E.; Mathematical Sciences, School of Science
    Background Behavioral interventions for obesity produce clinically meaningful weight loss, but weight regain following treatment is common. Extended care programs attenuate weight regain and improve weight loss maintenance. However, less is known about the most effective ways to deliver extended care, including contact schedules. Methods We compared the 12-month weight regain of an extended care program utilizing a non-conventional, clustered campaign treatment schedule and a self-directed program among individuals who previously achieved ≥5% weight reductions. Participants (N = 108; mean age = 51.6 years; mean weight = 92.6 kg; 52% African American; 95% female) who achieved ≥5% weight loss during an initial 16-week behavioral obesity treatment were randomized into a 2-arm, 12-month extended care trial. A clustered campaign condition included 12 group-based visits delivered in three, 4-week clusters. A self-directed condition included provision of the same printed intervention materials but no additional treatment visits. The study was conducted in a U.S. academic medical center from 2011 to 2015. Results Prior to randomization, participants lost an average of −7.55 ± 3.04 kg. Participants randomized to the 12-month clustered campaign program regained significantly less weight (0.35 ± 4.62 kg) than self-directed participants (2.40 ± 3.99 kg), which represented a significant between-group difference of 2.28 kg (p = 0.0154) after covariate adjustments. This corresponded to maintaining 87% and 64% of lost weight in the clustered campaign and self-directed conditions, respectively, which was a significant between-group difference of 29% maintenance of lost weight after covariate adjustments, p = 0.0396. Conclusions In this initial test of a clustered campaign treatment schedule, this novel approach effectively promoted 12-month maintenance of lost weight. Future trials should directly compare the clustered campaigns with conventional (e.g., monthly) extended care schedules. Trial registration Clinicaltrials.gov NCT02487121. Registered 06/26/2015 (retrospectively registered)
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    Massive data K-means clustering and bootstrapping via A-optimal Subsampling
    (2019-08) Zhou, Dali; Tan, Fei; Peng, Hanxiang; Boukai, Benzion; Sarkar, Jyotirmoy; Li, Peijun
    For massive data analysis, the computational bottlenecks exist in two ways. Firstly, the data could be too large that it is not easy to store and read. Secondly, the computation time could be too long. To tackle these problems, parallel computing algorithms like Divide-and-Conquer were proposed, while one of its drawbacks is that some correlations may be lost when the data is divided into chunks. Subsampling is another way to simultaneously solve the problems of the massive data analysis while taking correlation into consideration. The uniform sampling is simple and fast, but it is inefficient, see detailed discussions in Mahoney (2011) and Peng and Tan (2018). The bootstrap approach uses uniform sampling and is computing time in- tensive, which will be enormously challenged when data size is massive. k-means clustering is standard method in data analysis. This method does iterations to find centroids, which would encounter difficulty when data size is massive. In this thesis, we propose the approach of optimal subsampling for massive data bootstrapping and massive data k-means clustering. We seek the sampling distribution which minimize the trace of the variance co-variance matrix of the resulting subsampling estimators. This is referred to as A-optimal in the literature. We define the optimal sampling distribution by minimizing the sum of the component variances of the subsampling estimators. We show the subsampling k-means centroids consistently approximates the full data centroids, and prove the asymptotic normality using the empirical pro- cess theory. We perform extensive simulation to evaluate the numerical performance of the proposed optimal subsampling approach through the empirical MSE and the running times. We also applied the subsampling approach to real data. For massive data bootstrap, we conducted a large simulation study in the framework of the linear regression based on the A-optimal theory proposed by Peng and Tan (2018). We focus on the performance of confidence intervals computed from A-optimal sub- sampling, including coverage probabilities, interval lengths and running times. In both bootstrap and clustering we compared the A-optimal subsampling with uniform subsampling.
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    Similar weight loss and maintenance in African American and White women in the Improving Weight Loss (ImWeL) trial
    (Taylor & Francis, 2021) Kinsey, Amber W.; Gowey, Marissa A.; Tan, Fei; Zhou, Dali; Ard, Jamy; Affuso, Olivia; Dutton, Gareth R.; Mathematical Sciences, School of Science
    Objective: African Americans (AA) are often underrepresented and tend to lose less weight than White participants during the intensive phase of behavioral obesity treatment. Some evidence suggests that AA women experience better maintenance of lost weight than White women, however, additional research on the efficacy of extended care programs (i.e. continued contacts to support the maintenance of lost weight) is necessary to better understand these differences. Methods: The influence of race on initial weight loss, the likelihood of achieving ≥5% weight reduction (i.e. extended care eligibility), the maintenance of lost weight and extended care program efficacy was examined in 269 AA and White women (62.1% AA) participating in a 16-month group-based weight management program. Participants achieving ≥5% weight reduction during the intensive phase (16 weekly sessions) were randomized to a clustered campaign extended care program (12 sessions delivered in three, 4-week clusters) or self-directed control. Results: In adjusted models, race was not associated with initial weight loss (p = 0.22) or the likelihood of achieving extended care eligibility (odds ratio 0.64, 95% CI [0.29, 1.38]). AA and White women lost −7.13 ± 0.39 kg and −7.62 ± 0.43 kg, respectively, during initial treatment. There were no significant differences in weight regain between AA and White women (p = 0.64) after adjusting for covariates. Clustered campaign program participants (AA: −6.74 ± 0.99 kg, White: −6.89 ± 1.10 kg) regained less weight than control (AA: −5.15 ± 0.99 kg, White: −4.37 ± 1.04 kg), equating to a 2.12 kg (p = 0.03) between-group difference after covariate adjustments. Conclusions: Weight changes and extended care eligibility were comparable among all participants. The clustered campaign program was efficacious for AA and White women. The high representation and retention of AA participants may have contributed to these findings.