Browsing by Author "Lu, Danni"

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    Factors predicting older Adults’ attitudes toward and intentions to use stair mobility assistive designs at home
    Tural, Elif; Lu, Danni; Cole, D. Austin (Elsevier, 2020-03-16)
    Home modifications that increase stair accessibility of existing housing stock are significant for older adults who want to age in place. This sequential mixed-methods study investigated older adults’ attitudes toward and intentions to use currently available stair mobility assistive design features, and explored which factors influence these attitudes and intentions to use. The data were collected through a cross-sectional survey of community dwelling 50 + adults from Southwest Virginia (n = 89) and a focus group (n = 15) in 2018. The survey questionnaire was based on a modified version of the Technology Acceptance Model, and focused on three stair mobility assistive design products representative of varying costs, and a range of mobility challenges: half-steps, StairSteady handrail, and stairlift. Ordinal regression analyses indicated that perceived usefulness consistently predicts older adults’ attitudes and intentions to use the three examined stair mobility products. The other factors associated with attitudes and willingness to use the products are dependent on some degree to the examined mobility device. Older age and presence of others in the household negatively influenced attitudes toward stair mobility products. Product aesthetics/unobtrusiveness, fear of falling, and person-environment fit are the three themes emerged from the focus group data analysis as the factors that most influence community dwelling older adults’ attitudes and intention to use stair-mobility assistive features. The findings have implications for design professionals, as they underscore the need for avoiding an institutional look in residential designs, specifying products with high customizability for user needs and preferences, and involvement of users in the decision-making processes.
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    Representation Learning Based Causal Inference in Observational Studies
    Lu, Danni (Virginia Tech, 2021-02-22)
    This dissertation investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in training data to derive valid causal conclusions that generalize. By incorporating ideas from representation learning, adversarial matching, generative causal estimation, and invariant risk modeling, this dissertation establishes a causal framework that balances the covariate distribution in latent representation space to yield individualized estimations, and further contributes novel perspectives on causal effect estimation based on invariance principles. The dissertation begins with a systematic review and examination of classical propensity score based balancing schemes for population-level causal effect estimation, presented in Chapter 2. Three causal estimands that target different foci in the population are considered: average treatment effect on the whole population (ATE), average treatment effect on the treated population (ATT), and average treatment effect on the overlap population (ATO). The procedure is demonstrated in a naturalistic driving study (NDS) to evaluate the causal effect of cellphone distraction on crash risk. While highlighting the importance of adopting causal perspectives in analyzing risk factors, discussions on the limitations in balance efficiency, robustness against high-dimensional data and complex interactions, and the need for individualization are provided to motivate subsequent developments. Chapter 3 presents a novel generative Bayesian causal estimation framework named Balancing Variational Neural Inference of Causal Effects (BV-NICE). Via appealing to the Robinson factorization and a latent Bayesian model, a novel variational bound on likelihood is derived, explicitly characterized by the causal effect and propensity score. Notably, by treating observed variables as noisy proxies of unmeasurable latent confounders, the variational posterior approximation is re-purposed as a stochastic feature encoder that fully acknowledges representation uncertainties. To resolve the imbalance in representations, BV-NICE enforces KL-regularization on the respective representation marginals using Fenchel mini-max learning, justified by a new generalization bound on the counterfactual prediction accuracy. The robustness and effectiveness of this framework are demonstrated through an extensive set of tests against competing solutions on semi-synthetic and real-world datasets. In recognition of the reliability issue when extending causal conclusions beyond training distributions, Chapter 4 argues ascertaining causal stability is the key and introduces a novel procedure called Risk Invariant Causal Estimation (RICE). By carefully re-examining the relationship between statistical invariance and causality, RICE cleverly leverages the observed data disparities to enable the identification of stable causal effects. Concretely, the causal inference objective is reformulated under the framework of invariant risk modeling (IRM), where a population-optimality penalty is enforced to filter out un-generalizable effects across heterogeneous populations. Importantly, RICE allows settings where counterfactual reasoning with unobserved confounding or biased sampling designs become feasible. The effectiveness of this new proposal is verified with respect to a variety of study designs on real and synthetic data. In summary, this dissertation presents a flexible causal inference framework that acknowledges the representation uncertainties and data heterogeneities. It enjoys three merits: improved balance to complex covariate interactions, enhanced robustness to unobservable latent confounders, and better generalizability to novel populations.
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    Safely and Actively Aging in Place: Older Adults’ Attitudes and Intentions Toward Smart Home Technologies
    Tural, Elif; Lu, Danni; Cole, D. Austin (SAGE, 2021-01-01)
    As smart technology use is growing in residential environments, research on how such technologies can provide opportunities for safely and actively aging in place by integrating physical activity into daily routines and reducing sedentariness is scarce. This study investigated older adults’ intentions to use and attitudes toward currently available smart home technologies that could contribute to safe and active lives in and around home. The focus was on four representative technologies: smart lighting, smart door locks, smart fire prevention devices, and smart home systems/home automation. This paper presents the results of a sequential mixed-methods study comprised of online and in-person surveys (n = 129), and a focus group of community-dwelling older adults, aged 50+ (n = 15). Ordinal regression analyses indicated that perceived usefulness consistently predicts older adults’ attitudes and willingness to use smart home products. While smart fire prevention devices were viewed most favorably due to their potential safety benefits, perceived affordability significantly influenced older adults’ intentions to use them in their homes. The focus group findings underscore technology skepticism, privacy concerns and return on investment as significant determinants of attitudes toward the smart design products. The study has implications of designers and manufacturers by providing insights on how to prioritize smart home technology integrations to homes.