Credit card-related over-spending represents an important issue for consumers. Over-spending arises in parts from reduced payment transparency compared to cash and other payment methods. ...Additionally, week-by-week credit card spending exhibits high variance even on an intrapersonal level, which makes it hard to intuitively learn from prior transactions and control one's spending. As mobile-mediated information systems have been proven effective in delivering behavior change interventions, this study investigates the efficacy of using a novel smartphone application that increases the salience of credit card transactions to help consumers control their cashless payments better and ultimately spend less. We implemented a goal-setting feature and provided weekly goal attainment feedback highlighting ordinary, exceptional, or both types of purchases. This work was conducted as a field experiment, studying a large sample of credit card consumers in the wild over several months, which yielded a significant reduction in spending with unobtrusive interventions. It further highlights the importance of including exceptional purchases in households' spending budgets and discusses how people adjusted their consumption to lower their expenditures.
•Increasing the salience of credit card transactions helps people reduce spending.•Giving feedback on both ordinary and exceptional purchases helps reduce spending.•Giving feedback on ordinary purchases alone did not have this effect.•Highlighting the frequency and volume of exceptional purchases was also ineffective.•Insights gained from large-scale field experiment with credit card transaction data.
Smart charging systems can reduce the stress on the power grid from electric vehicles by coordinating the charging process. To meet user requirements, such systems need input on charging demand, ...i.e., departure time and desired state of charge. Deriving these parameters through predictions based on past mobility patterns allows the inference of realistic values that offer flexibility by charging vehicles until they are actually needed for departure. While previous studies have addressed the task of charging demand predictions, there is a lack of work investigating the heterogeneity of user behavior, which affects prediction performance. In this work we predict the duration and energy of residential charging sessions using a dataset with 59,520 real-world measurements from 267 electric vehicles. While replicating the results put forth in related work, we additionally find substantial differences in prediction performance between individual vehicles. An in-depth analysis shows that vehicles that on average start charging later in the day can be predicted better than others. Furthermore, we demonstrate how knowledge that a vehicles charges over night significantly increases prediction performance, reducing the mean absolute percentage error of plugged-in duration predictions from over 200% to 15%. Based on these insights, we propose that residential smart charging systems should focus on predictions of overnight charging to determine charging demand. These sessions are most relevant for smart charging as they offer most flexibility and need for coordinated charging and, as we show, they are also more predictable, increasing user acceptance.
•Prediction of electric vehicle charging demand using a diverse set of real-world charging measurements.•Heterogeneous charging behavior causes large differences in prediction performance for different vehicles.•Vehicles that typically start charging in the afternoon have better duration prediction performance.•Knowledge that a vehicle is plugged in overnight significantly increases prediction performance.•Predictions of overnight charging demand are particularly relevant for smart charging applications.
Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the ...world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support.
The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms.
A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject's preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery.
A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=-.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively.
Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.
Heat pumps play an essential role in decarbonizing the building sector, but their electricity consumption can vary significantly across buildings. This variability is closely related to their cycling ...behavior (i.e., the frequency of on–off transitions), which is also an indicator for improper sizing and non-optimal settings and can affect a heat pump’s lifetime. Up to now it has been unclear which cycling behaviors are typical and atypical for heat pump operation in the field and importantly, there is a lack of methods to identify heat pumps that cycle atypically. Therefore, in this study we develop a method to monitor heat pumps with energy measurements delivered by common smart electricity meters, which also cover heat pumps without network connectivity. We show how smart meter data with 15-minute resolution can be used to extract key indicators about heat pump cycling and outline how atypical behavior can be detected after controlling for outdoor temperature. Our method is robust across different building characteristics and varying times of observation, does not require contextual information, and can be implemented with existing smart meter data, making it suitable for real-world applications. Analyzing 503 heat pumps in Swiss households over a period of 21 months, we further describe behavioral differences with respect to building and heat pump characteristics and study the relationship between heat pumps’ cycling behavior, energy efficiency, and appropriate sizing. Our results show that outliers in cycling behavior are more than twice as common for air-source heat pumps than for ground-source heat pumps.
•Over a 21-month period, monitoring 503 heat pumps in Swiss single-family houses.•Using only smart meter data (15-min resolution) and local weather data.•Developing an outlier detection method that identifies heat pumps with atypical cycling.•Comparison to best and worst 10% in heat pump energy efficiency and appropriate sizing.•Description of typical and atypical cycling of heat pumps in real-world operation.
The increasing number of edge devices with enhanced sensing capabilities, such as smartphones, wearables, and IoT devices equipped with sensors, holds the potential for innovative smart-edge ...applications in healthcare. These devices generate vast amounts of multimodal data, enabling the implementation of digital biomarkers which can be leveraged by machine learning solutions to derive insights, predict health risks, and allow personalized interventions. Training these models requires collecting data from edge devices and aggregating it in the cloud. To validate and verify those models, it is essential to utilize them in real-world scenarios and subject them to testing using data from diverse cohorts. Since some models are too computationally expensive to be run on edge devices directly, a collaborative framework between the edge and cloud becomes necessary. In this paper, we present CLAID, an open-source cross-platform middleware framework based on transparent computing compatible with Android, iOS, WearOS, Linux, macOS, and Windows. CLAID enables logical integration of devices running different operating systems into an edge-cloud system, facilitating communication and offloading between them, with bindings available in different programming languages. We provide Modules for data collection from various sensors as well as for the deployment of machine-learning models. Furthermore, we propose a novel methodology, ML-Model in the Loop for verifying deployed machine learning models, which helps to analyze problems that may occur during the migration of models from cloud to edge devices. We verify our framework in three different experiments and achieve 100% sampling coverage for data collection across different sensors as well as an equal performance of a cough detection model deployed on both Android and iOS devices. Additionally, we compare the memory and battery consumption of our framework across the two mobile operating systems.
•Open-source, cross-platform framework for digital biomarkers and mobile AI•Transparent computing integrates smartphones and servers in an edge-cloud system•Modules for data collection and machine learning on edge and cloud•Supports C++, Dart, Java, and Python•Evaluated in sampling coverage, ML performance, and resource usage experiments
Purpose - The primary objective of this paper is to explore antecedents for developing different types of services. A second objective is to address the neglected role of service development in ...manufacturing firms.Design methodology approach - A qualitative research approach is used. While the study is qualitative due to its context, it is positioned between deductive and inductive qualitative studies, being neither a test of an already developed theory nor a development of a new theory. Rather, it is an extension of existing theories on service development through dialectic interaction between field studies and existing theory.Findings - The findings suggest that three types of service (customer service, product-related services, and customer support services) differ in their configuration of antecedents for service development.Research limitations implications - The study is based on case-study research, but the external validity (generalisability) of the antecedents could not be assessed. Future research would benefit from insights obtained from quantitative data.Practical implications - The combination of different service types and antecedents forms a model that can guide managers in typical product manufacturing companies who wish to extend the service business by developing services successfully.Originality value - Based on three in-depth case studies and 18 bi-polar mini cases, this paper explores the relationship between types of services in manufacturing companies and typical antecedents that are necessary for service development.
With the emergence of the Internet of People (IoP) and its user-centric applications, novel solutions to the many issues facing today’s societies are to be expected. These problems include unhealthy ...diets, with obesity and diet-related diseases reaching epidemic proportions. We argue that the proliferation of mixed reality (MR) headsets as next generation primary interfaces provides promising alternatives to contemporary digital solutions in the context of diet tracking and interventions. Concretely, we propose the use of MR headset-mounted cameras for computer vision (CV) based detection of diet-related activities and the consequential display of visual real-time interventions to support healthy food choices. We provide an integrative framework and results from a technical feasibility as well as an impact study conducted in a vending machine (VM) setting. We conclude that current neural networks already enable accurate food item detection in real-world environments. Moreover, our user study suggests that real-time interventions significantly improve beverage (reduction of sugar and energy intake) as well as food choices (reduction of saturated fat). We discuss the results, learnings, and limitations and provide an overview of further technology- and intervention-related avenues of research required by developing an MR-based user support system for healthy food choices.
•Mixed reality headsets allow for automatic sensing of diet-related activities and real-time interventions.•Computer vision enables automatic, passive detection of food items and their nutritional properties.•Spacial computing based interventions integrated in headsets can guide users to healthier food choices.•Our technical feasibility study and in-the-wild user study suggest that mixed reality can indeed support healthier food choices.
•We compute differentiated mileage exposure metrics from 1600 vehicles.•Metrics are used in multivariate logistic regression to predict accident involvement.•After various transformations, a ...Nagelkerke R2 goodness-of-fit of 0.646 is achieved.•Multivariate mileage–risk relationship modeling offers novel insights.•PAYD-insurance data are an important opportunity for transportation research.
The increasing adoption of in-vehicle data recorders (IVDR) for commercial purposes such as Pay-as-you-drive (PAYD) insurance is generating new opportunities for transportation researchers. An important yet currently underrepresented theme of IVDR-based studies is the relationship between the risk of accident involvement and exposure variables that differentiate various driving conditions. Using an extensive commercial data set, we develop a methodology for the extraction of exposure metrics from location trajectories and estimate a range of multivariate logistic regression models in a case-control study design. We achieve high model fit (Nagelkerke’s R2 0.646, Hosmer–Lemeshow significance 0.848) and gain insights into the non-linear relationship between mileage and accident risk. We validate our results with official accident statistics and outline further research opportunities. We hope this work provides a blueprint supporting a standardized conceptualization of exposure to accident risk in the transportation research community that improves the comparability of future studies on the subject.
Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers ...assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression.
This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care.
Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg's test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers.
Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD -0.58, 95% CI -0.71 to -0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified.
Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders.
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