The Feasibility of Incentivizing Participation in an Online Social Network Weight Loss Program

Engagement in online social network-delivered weight loss interventions is a predictor of weight loss. Incentivizing engagement in a subset of participants may increase group engagement and subsequent weight loss. In a pilot feasibility trial, 56 adults with obesity were randomized to two Facebook-delivered weight loss interventions, one had 10% users incentivized to engage daily and the other did not. We compared conditions on engagement and weight loss, and then compared incentivized users and natural high engagers on weight loss. Participants were 46.3 (SD: 10.3) years and 89% female. The incentivized user condition had greater total engagement (p=0.0361), but weight loss did not differ (p=0.2096). Three natural superusers emerged in each condition. Natural superusers lost more weight than incentivized users (p=0.0358). Natural superusers’ posts elicited more comments than incentivized superusers (p=0.0107). Incentivized superusers may engage differently than natural superusers. Future studies should explore ways to promote engagement in online interventions

What Type of Engagement Predicts Success in a Facebook Weight Loss Group?

Studies of social media-delivered behavioral interventions generally reveal that engagement, as defined as total number of posts, is associated with better outcomes. Little is known about whether the type of engagement, volume of content posted, or timing of engagement matters. In the present study, we analyzed the content, volume, and timing of participant posts in a Facebook weight loss intervention. Content was analyzed via thematic analyses. Volume was defined as total characters posted. We explored types of posts and for each, how frequency and volume overall, and in first half and second half of intervention were related to weight loss at end of treatment. Findings revealed that reporting a healthy choice was the most common type of post. The frequency and volume of most types of posts except negative posts predicted weight loss, but those occurring in second half of intervention were more strongly related to weight loss

Realizing the potential of distributed energy resources and peer-to-peer trading through consensus-based coordination and cooperative game theory

Driven by environmental and energy security concerns, a large number of small-scale distributed energy resources (DERs) are increasingly being connected to the distribution network. This helps to support a cost-effective transition to a lower-carbon energy system, however, brings coordination challenges caused by variability and uncertainty of renewable energy resources (RES). In this setting, local flexible demand (FD) and energy storage (ES) technologies have attracted great interests due to their potential flexibility in mitigating the generation and demand variability and improving the cost efficiency of low-carbon electricity systems. The combined effect of deregulation and digitalization inspired new ways of exchanging electricity and providing management/services on the paradigm of peer-to-peer (P2P) and transparent transactions. P2P energy trading enables direct energy trading between prosumers, which incentivizes active participation of prosumer in the trading of electricity in the distribution network, in the meantime, the efficient usage of FD and ES owned by the prosumers also facilitates better local power and energy balance. Though the promising P2P energy trading brings numerous advancements, the existing P2P mechanisms either fail to coordinate energy in a fully distributed way or are unable to adequately incentivize prosumers to participate, preventing prosumers from accessing the highest achievable monetary benefits and/or suffering severely from the curse of dimensionality. Therefore, this thesis aims at proposing three P2P energy trading enabling mechanisms in the aspect of fully distributed efficient balanced energy coordination through consensus-based algorithm and two incentivizing pricing and benefit distribution mechanisms through cooperative game theory. Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of DER due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of FD and ES resources. This thesis demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. Moreover, this thesis proposes two computationally efficient pricing and benefit distribution mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tatonnement process while the second involves a novel pricing mechanism based on the solution of single linear programming. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.Open Acces

Incentivizing nutrition: how to apply incentive mechanisms to accelerate improved nutrition outcomes: a practitioner’s compendium

Malnutrition is a driver of poverty. Reducing malnutrition is essential to achieving the World Bank’s goals of eliminating extreme poverty and enhancing shared prosperity. This compendium offers practical information on how to plan, implement, and monitor incentivized operations for improving nutrition results for World Bank client countries. For more detailed background information, see the World Bank report Incentivizing Nutrition: Incentive Mechanisms to Accelerate Improved Nutrition Outcomes

App-based feedback on safety to novice drivers: learning and monetary incentives

An over-proportionally large number of car crashes is caused by novice drivers. In a field experiment, we investigated whether and how car drivers who had recently obtained their driving license reacted to app-based feedback on their safety-relevant driving behavior (speeding, phone usage, cornering, acceleration and braking). Participants went through a pre-measurement phase during which they did not receive app-based feedback but driving behavior was recorded, a treatment phase during which they received app-based feedback, and a post-measurement phase during which they did not receive app-based feedback but driving behavior was recorded. Before the start of the treatment phase, we randomly assigned participants to two possible treatment groups. In addition to receiving app-based feedback, the participants of one group received monetary incentives to improve their safety-relevant driving behavior, while the participants of the other group did not. At the beginning and at the end of experiment, each participant had to fill out a questionnaire to elicit socio-economic and attitudinal information. We conducted regression analyses to identify socio-economic, attitudinal, and driving-behavior-related variables that explain safety-relevant driving behavior during the pre-measurement phase and the self-chosen intensity of app usage during the treatment phase. For the main objective of our study, we applied regression analyses to identify those variables that explain the potential effect of providing app-based feedback during the treatment phase on safety-relevant driving behavior. Last, we applied statistical tests of differences to identify self-selection and attrition biases in our field experiment. For a sample of 130 novice Austrian drivers, we found moderate improvements in safety-relevant driving skills due to app-based feedback. The improvements were more pronounced under the treatment with monetary incentives, and for participants choosing higher feedback intensities. Moreover, drivers who drove relatively safer before receiving app-based feedback used the app more intensely and, ceteris paribus, higher app use intensity led to improvements in safety-related driving skills. Last, we provide empirical evidence for both self-selection and attrition biases

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

Benefits to Opening a Comprehensive Outpatient Medical Nutrition Therapy Center

Medical Nutrition Therapy (MNT) can reduce costs related to medication, physician utilization, and hospital admissions in addition to improving patient clinical outcomes (Morris et al., 2017). MNT can be a cost-effective way to manage co-morbidities and prevent or delay the progression of chronic disease(s) (Morris et al., 2017). While registered dietitians have always played a vital role in the health of their patients, reimbursement for medical nutrition therapy services is limited. During a patient’s hospital admission, dietitians prescribe nutrition interventions and implement a plan of care. After a patient is discharged, they often are not followed by a dietitian. Current healthcare systems lack the ability for patients to continue the reinforcement education needed to sustain nutritional improvements. The development of a comprehensive outpatient MNT center can provide patients with access to a dietitian post-discharge. Literature review consensus is favorable towards post-discharge nutrition interventions being effective at reducing unplanned readmissions, decreasing mortality rates and improving patient quality of life. Increasing patient access to MNT can not only yield profits from services rendered but, downstream the healthcare system can see positive impacts through the reduction of unplanned readmissions, costs of hospital services rendered, length of stay and Physician utilization (Briggs Early & Stanley, 2018; Toulson Davisson Correia et al., 2021)