IT-Based Self-Monitoring Interventions to Promote Physical Activity and Weight Loss: A Meta-Analysis of Change-from-Baseline Effects

2019 | HICSS | Citations: 0

Authors: Jiang, Jinglu; Cameron, Ann-Frances

Abstract: IT-based self-monitoring (ITSM) has attracted increasing interest as a strategy ... Expand

Abstract: IT-based self-monitoring (ITSM) has attracted increasing interest as a strategy for chronic disease self-management and day-to-day fitness promotion. Despite the increasing popularity of various selfmonitoring technologies such as fitness trackers and biosensors, their effectiveness is less certain. The objective of the current review is to determine the effectiveness of ITSM interventions on two types of key chronic care outcomes: weight management and physical activity. A systematic review employing a meta-analysis identified 42 ITSM studies that report change-from-baseline effects on weight and physical activity-related outcomes. Overall, a small effect size is found for body weight, BMI, waist circumferences, and step-based physical activity. The effect estimates on time-based physical activity are moderate. However, the effects on physical activity show variability and potential publication bias. A post-hoc analysis of the effects of ITSM on self-efficacy exhibit a small yet significant effect size, which shows the potential mediating role of patients’ psychological outcomes on the ultimate behavioral outcomes. In summary, ITSM is a potentially useful approach to manage weight and physical activity. Further study is needed to determine the source of heterogeneity as well as the types of ITSM interventions that are effective for weight, physical activity, but also other chronic care outcomes. Collapse

Topics: self efficacy database system missing data smart wearable device mobile system

Methods: meta analysis post-hoc analysis descriptive statistic structured literature research experiment

Effects of user experience on user resistance to change to the voice user interface of an in‑vehicle infotainment system: Implications for platform and standards competition

2016 | International Journal of Information Management | Citations: 0

Authors: Kim, Dong-hyu; Lee, Heejin

Abstract: a b s t r a c tThis study examines the effects of user experience on user resis ... Expand

Abstract: a b s t r a c tThis study examines the effects of user experience on user resistance to change-particularly, on the relationship between user resistance to change and its antecedents (i.e. switching costs and perceived value) in the context of the voice user interface of an in-vehicle infotainment (IVI) system. This research offers several salient findings. First, it shows that user experience positively moderates the relationship between uncertainty costs (one type of switching cost) and user resistance. It also negatively moderates the association between perceived value and user resistance. Second, the research test results demonstrate that users with a high degree of prior experience with the voice user interface of other smart devices exhibit low user resistance to change to the voice user interface in an IVI system. Third, we show that three types of switching costs (transition costs, in particular) may directly influence users to resist a change to the voice user interface. Fourth, our test results empirically demonstrate that both switching costs and perceived value affect user resistance to change in the context of an IVI system, which differs from the traditional IS research setting (i.e. enterprise systems). These findings may guide not only platform leaders in designing user interfaces, user experiences, and marketing strategies, but also firms that want to defend themselves from platform envelopment while devising defensive strategies in platform and standards competition. Collapse

Topics: resistance to change user experience switching cost usability user interface

Methods: parametric test data transformation Student's t-test partial least squares regression survey

Theories: theory of planned behavior status quo bias theory loss aversion

An experimental comparison of real and artificial deception using a deception generation model

2012 | Decision Support Systems | Citations: 0

Authors: Yang, Yanjuan; Mannino, Michael V.

Abstract: To develop a data mining approach for a deception application, data collection c ... Expand

Abstract: To develop a data mining approach for a deception application, data collection costs can be prohibitive because both deceptive data and truthful data are necessary to be collected. To reduce data collection costs, artificially generated deception data can be used, but the impact of using artificially generated deception data is not well understood. To study the relationship between artificial and real deception, this paper presents an experimental comparison using a novel deception generation model. The deception and truth data were collected from financial aid applications, a document centric area with limited resources for verification. The data collection provided a unique data set containing truth, natural deception, and boosted deception. To simulate deception, the Application Deception Model was developed to generate artificial deception in different deception scenarios. To study differences between artificial and real deception, an experiment was performed using deception level and data generation method as factors and directed distance and outlier score as outcome variables. Our results provided evidence of a reasonable similarity between artificial and real deception, suggesting the possibility of using artificially generated deception to reduce the costs associated with obtaining training data. Collapse

Topics: misinformation decision support system systems design

Methods: experiment computational algorithm machine learning statistical hypothesis test anomaly detection

Classification algorithm sensitivity to training data with non representative attribute noise

2009 | Decision Support Systems | Citations: 8

Authors: Mannino, Michael; Yang, Yanjuan; Ryu, Young

Abstract: We present an empirical comparison of classification algorithms when training da ... Expand

Abstract: We present an empirical comparison of classification algorithms when training data contains attribute noise levels not representative of field data. To study algorithm sensitivity, we develop an innovative experimental design using noise situation, algorithm, noise level, and training set size as factors. Our results contradict conventional wisdom indicating that investments to achieve representative noise levels may not be worthwhile. In general, over representative training noise should be avoided while under representative training noise is less of a concern. However, interactions among algorithm, noise level, and training set size indicate that these general results may not apply to particular practice situations. Collapse

Topics: logistics management Oracle database

Methods: computational algorithm experiment classification method machine learning AdaBoost