Decision, Operations & Information Technologies Research Works

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    Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans
    (PLoS (Public Library of Science), 2018-03-07) Dugas, Michelle; Crowley, Kenyon; Gao, Guodong Gordon; Xu, Timothy; Agarwal, Ritu; Kruglanski, Arie W.; Steinle, Nanette
    mHealth tools to help people manage chronic illnesses have surged in popularity, but evidence of their effectiveness remains mixed. The aim of this study was to address a gap in the mHealth and health psychology literatures by investigating how individual differences in psychological traits are associated with mHealth effectiveness. Drawing from regulatory mode theory, we tested the role of locomotion and assessment in explaining why mHealth tools are effective for some but not everyone. A 13-week pilot study investigated the effectiveness of an mHealth app in improving health behaviors among older veterans (n = 27) with poorly controlled Type 2 diabetes. We developed a gamified mHealth tool (DiaSocial) aimed at encouraging tracking of glucose control, exercise, nutrition, and medication adherence. Important individual differences in longitudinal trends of adherence, operationalized as points earned for healthy behavior, over the course of the 13-week study period were found. Specifically, low locomotion was associated with unchanging levels of adherence during the course of the study. In contrast, high locomotion was associated with generally stronger adherence although it exhibited a quadratic longitudinal trend. In addition, high assessment was associated with a marginal, positive trend in adherence over time while low assessment was associated with a marginal, negative trend. Next, we examined the relationship between greater adherence and improved clinical outcomes, finding that greater adherence was associated with greater reductions in glycated hemoglobin (HbA1c) levels. Findings from the pilot study suggest that mHealth technologies can help older adults improve their diabetes management, but a “one size fits all” approach may yield suboptimal outcomes.
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    Online Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”
    (2017) Peng, Yijie; Chen, Chun-Hung; Fu, Michael; Hu, Jian-Qiang
    This is the online appendix, which includes theoretical and numerical supplements containing some technical details and three additional numerical examples, which could not fit in the main body due to page limits by the journal for a technical note. The abstract for the main body is as follows: In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy (G-MAP) that takes the induced correlations into account, reflecting a trade-off between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.
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    Online Appendix for “Ranking and Selection as Stochastic Control”
    (2017-04) Peng, Yijie; Chong, Edwin K. P.; Chen, Chun-Hung; Fu, Michael C.
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    Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’
    (2016) Peng, Yijie; Fu, Michael
    In this online appendix, we test the performance of the AOMAP (asymptotically optimal myopic allocation policy) algorithm under the unknown variances scenario and compare it with EI (expected improvement) and OCBA (optimal computing budget allocation).
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    Instances for the Generalized Regenerator Location Problem
    (2015) Chen, Si; Ljubic, Ivana; Raghavan, S.