Real-time App Research Articles

From click to fork: An empirical analysis of system and food quality influences in online meal kit consumption

This research explores the determinants and implications of brand attachment in the fast-growing online meal-kit industry. The objectives were to explore the components constituting the system and food quality, establish their impact on brand attachment, and examine the subsequent impact on consumer behavioral intentions. Utilizing big data analytics, the study analyzed real-time app content sourced from consumer reviews using a text mining technique and employed structural model analysis to validate a proposed model with five emerging factors from the consumer review data. The findings suggest that system quality parameters—perceived ease of use and perceived usefulness—significantly influenced food quality. Enhanced food quality cultivated brand love yet reduced brand hate. Notably, brand love and hate exhibited opposing correlations with negative electronic word-of-mouth and continuous usage intention. The outcomes secure crucial insights for ongoing engagement with online meal-kit services, significantly contributing to academic research and industry practice.

Raspberry Pi Enabled Biometric Attendance System

The precision and ease of use of the BIOMETRIC Attendance System draw many users to it among the many real-time apps available today. The development of an attendance system based on fingerprints was a significant difficulty. This method proposes to use a Raspberry Pi running Linux as the attendance system. The procedure starts with the generation of the database using a fingerprint reader and continues with the system-provided recognition and authentication. The Raspberry Pi platform is used for the entire procedure. The standardized fingerprint authentication approach, which can extract an individual's finger print and compare it to a database, is presented in this study. Additionally, it can give parents and teachers a summary of attendance on a daily and monthly basis. The main goal of the study that follows is to use biometric systems for verifying and authenticating the physical attendance of inmates in any kind of organization. This is made possible by the working principle of biometrics, which is based on control of prominent scalability, flexible properties, and cost reduction for reducing the requirements of biometric systems for numerous computational resources. Key Words: Fingerprint Sensor, Interfacing, Raspberry Pi.

Gaming the System: A Fun Continuous Glucose Monitor Interface Improves Glycemic Outcomes for Children.

Achieving optimal glycemic control in children with type 1 diabetes (T1D) is challenging even when wearing a continuous glucose monitor (CGM). We measured the impact of eddii, a gamified real-time app connected to a CGM, on glycemic control. An open label, eight-week randomized controlled trial (RCT) compared glycemic control utilizing the gamified CGM app connected to Dexcom G6 with only Dexcom G6 usage. Children with T1D using Dexcom G6 were enrolled (N=92, ages 5-12 years). Time-in-range (TIR) data were collected four weeks prior to and during the study period. The gamified CGM app utilization effect was measured by difference-in-difference (D-I-D) models. The TIR and time-above-range (TAR) improved among users of the gamified CGM app; 5.38% higher and 5.80% lower than controls (P = .001 and P = .019, respectively).

#4964 ASSOCIATION OF DIETARY INTAKE WITH SERUM LEVELS OF PHOSPHORUS AND POTASSIUM IN THE ELDERLY HEMODIALYSIS PATIENTS IN KOREA

Abstract Background and Aims Dietary management of elderly (>60 years old) hemodialysis(HD) patients is very important for solving health problems and improving quality of life. Hyperphosphatemia and hyperkalemia in elderly HD patients are closely related to intake of dietary phosphorus and potassium from Koreans’ unique eating patterns. It is very difficult to manage the diet of hemodialysis patients due to the unique eating patterns of elderly Koreans. In the daily meal pattern, sodium and potassium contents are high in grain-based foods, soups, stews, and seasoned vegetables. And, there are various recipes for meals using a range of condiments, which increase serum phosphorus level. For this reason, it is necessary to educate dietary management using real-time Apps for the elderly in Korea. This study was designed to provide the base data for the development of a self-dietary management App for Korean elderly HD patients. Method This study was conducted on 237 elderly HD patients (149 males and 88 females) at nationwide 18 university hospitals. We collected anthropometric data, biochemical parameters, and dietary data of the subjects. Dietary data for usual intake were obtained by use of a food-frequency questionnaire (FFQ) consisting of 23 food meal items based on the Korean usual diets. For this dietary status evaluation, a newly constructed potassium and phosphorus content DB was used. Results The average age of the patients was 69.7± 11.6 years for men and 69.2±6.9 years for women and the duration of HD was 5.4 ± 4.9 years for men and 5.5±4.2 for women. The mean body mass index (BMI) was 23.24 ± 2.99 kg/m2, mean serum phosphorus level was 5.03 ± 9.59 mg/dl, and mean serum potassium level was 5.41 ± 8.58 mEqL. Hyperphosphatemia (> 4.5 mg/dl) was found in 45.8% of the subjects, and hyperkalemia(> 5.0 mEq/L) in 35.4%. Energy and protein were significantly lower than recommended intakes of HD patients(P = .027, P = .015). Lower intake of energy and protein was more significant in women. The intake of sodium and potassium was significantly higher than recommendation(P = .014, P = .021), which was more excessive in men. Serum phosphorus levels showed positive correlation with intake frequency of mixed grains rice (P = .011), noodle without broth(P = .000), vegetable soup(P = .039), and roasted beef(P = .047) in male patients. In case of female patients, intake of sweet potatoes was correlated with hyperphosphatemia (P = .047). Serum potassium levels showed a positive correlation with intake of Kimchi(P = .000), noodle without broth(P = .000), and ham(P = .018) in men. In women, sweet potatoes(P = .027) and medium-potassium fruits(P = .047) were correlated with serum potassium level. On the other hand, intake of white rice showed negative correlation with serum potassium levels(P = .037) in both sex. Conclusion Dietary management along with medical treatment is essential for maintaining serum phosphorus and potassium levels within acceptable ranges because the composition of the daily routine diet is very unique and varies in Korea. The results of this study showed that the intake of white rice rather than mixed grains is an important factor in sustaining normal serum phosphorus and potassium levels in the elderly Korean HD patients. In addition, limiting intake of sweet potatoes, medium and high potassium vegetables & fruits to under three servings per week is recommended. Therefore, customized dietary management needs to be regulated according to each individual's dietary pattern for the Korean elderly HD patients. Major variables found in this study can be used in the development of new App for customized self-dietary management.

Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device

Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.

Enhanced Probabilistic Route Stability (EPRS) Protocol for Healthcare Applications of WBAN

Wireless Body Area Network (WBAN) is among the most promising technologies for enhancing life quality. WBANs enable constant monitoring of physiological processes by implanting or wearing small, low-power, intelligent sensor nodes in or on the human body. These sensor nodes may be placed either invasively or non-invasively. Patient data must be disseminated reliably and promptly for WBAN’s healthcare apps to function. For this reason, medical teams may use real-time apps for sharing vital information like blood pressure, an ECG, and an EEG. Critical data packets are delay-sensitive and must arrive at sink nodes within the time constraints that satisfy QoS for WBAN. However, networks’ unpredictable and dynamic nature (node mobility, link partitioning) makes reliable data transfer a challenging task. Additionally, postural mobility and ultra-short wireless range cause rapid topology changes, resulting in network partitioning. The network partitioning causes failure of data delivery to the sink or coordinator and causes a delay as well. In the case of normal data, it is not a big issue, but it is not tolerable for emergency data because it may be life-threatening. Consequently, compromising the link reliability and stability results in higher delays, increased packet re-transmissions, and decreased throughput performance. Therefore, we propose an Enhanced Probabilistic Route Stability (EPRS) scheme to address these issues. The proposed EPRS scheme introduces a cost function called Link Assessment Cost (LAC) that makes coherent decisions regarding route reliability in determining whether an active route is a good candidate for routing and satisfying QoS requirements. The LAC is based on two critical factors about link status, i.e., Route Stability Factor (RSF) and Expected Probability of link <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E(p)$ </tex-math></inline-formula> . Based on these factors, a score is assigned to a link that determines the status (likelihood) of a link, either connected or disconnected. In this way, the multi-facet EPRS selects the most stable and reliable routes despite the disconnection in the networks, thereby improving the route stability and throughput, minimizing the end-to-end delay, route discovery calls, and re-transmissions as depicted by simulation results.

Affordable Real-Time Heart Rate, ECG &amp; SpO2 Monitoring System Using Internet of Things (IoT)

Abstract: The healthcare industry now has an incomparable platform thanks to the Internet of Things technology, making it possible to quickly and effectively deal with challenges concerning healthcare. There has been a steady rise in the number of deaths due to sudden cardiac arrest (SCA) cases in developing countries and a lack of widespread use of an AED in these countries. The purpose of this study is to create an affordable Automated External Defibrillator (AED) that can be connected to IoT and can overcome certain drawbacks of the current AEDs. In the first phase of implementation, we attempt to create a Heart Monitoring System that can read Beats Per Minute (BPM), Electrocardiogram (ECG), and Saturation of Peripheral Oxygen (SpO2) using off-the-shelf sensors and display the data on a smartphone app. These sensors are connected to a Raspberry Pi 3b+, a single-board computer (SBC), which controls the entire system. The implementation of the system is successful, transmitting heart vitals to a smartphone app in real time. Further, the limitations of the system and future improvements are also discussed. The study builds a Heart Monitoring System under $67 with a few drawbacks. Further studies with better equipment are required to make the system accessible and less error-prone.

ANALISIS DAN SIMULASI MODUL HUMAN RESOURCE MENGGUNAKAN WEBSITE ODOO PADA PT. XYZ

PT. XYZ is a company that focuses on managing outsourcing services based on labor providers (procurement workforce) and employment charters (service management). Sumer human power or labor is an asset and determining factors in the success of a company if not managed with the best assets can be a factor that interferes with the process of isnis and performance of the company's objectives. Based on the observation, it is known that the data collection process of PT XYZ Group employees always use Microsoft Excel and the management is optimal because it is done manually.In addition, data search is always done through a relatively time-consuming and erisiko stack of documents in terms of data security and inefficient in terms of storage. This research is supported by existing issues to support the HR department of PT. XYZGroup in developing integrated information systems erupa Enterprise Resource Planning (ERP) to manage recruitment modules. By storing all recruitment activities in the Odoo app in real time the HR department can easily handle employee recruitment and manage and track stored data.

Multistate Pilot Study Evaluating the Short-Term Efficacy of a Smartphone-Based Older Driver Support System on Modifying Risky Driving Behaviors

Older drivers are most at risk of serious and fatal injury in crashes per vehicle mile driven compared with other age groups owing to a combination of cognitive decline and increased fragility. A smartphone coaching application targeting risky driving behaviors (e.g., speeding, hard braking), was iteratively designed to ensure older driver acceptance and usability. A 12-week field operational test examined driving performance of 28 drivers aged 65 to 78 from Kansas and Minnesota using the RoadCoach app and an ABA reversal design. Participants’ baseline driving behaviors were recorded for 3 weeks, followed by a 6-week experimental phase of real-time app coaching, and concluded with a 3-week baseline phase with no feedback provided. Compared with the pretreatment phase, there was a significant reduction in the rates of hard braking and stop sign violations associated with system use. A protection effect was observed through the posttreatment phase. Excessive speeding violations were not significantly different across the phases; however, there were modest declines in the proportion of miles traveled 15 mph above the speed limit in the posttreatment phase compared with the prior two phases. No significant effect was found for excessive acceleration and aggressive turning measures posttreatment, despite an observed increase in both rates during the treatment phase compared with pretreatment. Overall, the results indicate that the RoachCoach app may be an effective intervention to reduce several risky driving behaviors while in use and may result in prolonged behavior changes (i.e., reduced hard braking and stop sign violations) after treatment cessation.

A Review on Firebase (Backend as A Service) for Mobile Application Development

Abstract: If we see current scenario mobile users are rapidly increasing in a very large scale. Better performance and good visuals are needed while developing any mobile applications. In the current mobile market, there are various operating system which are supported by many mobile devices. . Since these platforms have different SDKs and tools for developing mobile applications, SDK tools will include a range of things, including libraries, documentation, code samples, processes, and different guides that developers can use and integrate/combined into their own apps. Cross platform development offers the flexibility for building applications using universal language like Javascript which can then be exported to numerous smartphone platforms. Firebase is a platform for mobile and web application with tools and infrastructure which allows developers to build high quality applications. Firebase consists of features that developers can use together to meet their needs. Earlier Firebase was used as a real-time database, which provides developers an API to sync and store data across various clients. Over time, it’s product has been expanded to become a full suite for an app development. Day by day new platform approaches are evolved. Firebase is also powerful API i.e Application Program Interface to sync and store data in realtime helping to build realtime apps for the web. It also takes care of most of the backend concerns and is dubbed a Backend as a Service (BaaS) and also pushes state to your application utilizing web sockets. Keywords: Android, iOS, Cross Platform Development, Flutter, Dart, Widgets, Firebase.

The best use of technology in the health emergency operation centers during COVID-19 pandemic

Since the outbreak of the coronavirus in the Kingdom of Saudi Arabia in 2020, National Health Emergency Center aligned itself to the Saudi Arabia&amp;apos;s Vision 2030 and has played a key role to link the different health sectors in the country with Ministry of Health, through the use of state-of-the-art infrastructure, innovative digital technologies, location intelligence, data analysis, and real time data. Thereby, General Directorate of Emergency, Disasters and Medical Transportation - Deputyship of Curative Services, launched the National Health Emergency Operation Center, which integrates digital technologies to deliver substantial improvements to emergency healthcare management. Through real-time maps, apps, and dashboards, the innovative integration of different technologies has revolutionized the Center&amp;apos;s operations by providing location intelligence and evidence-based analysis that shapes sound policy and saves lives. Disaster health management has become a key goal for every nation in order to reduce the impact of disasters on human health and wellbeing. It is an important aspect of any resilient healthcare system.

Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditions

Mobility is the main key for smart living, where navigation and automatic suggestions are also a strategy for a successful life in smart cities. Big Data analytics are behind urban changes in the mobility of smart cities to bring sustainable life. By the year 2025 all over Indian states can reach the expected lifestyle by providing high security and mobility which can grow the opportunities also high. As the population is rapidly increasing, the needs of people are also increasing such that necessitating real-time apps for daily needs, communication devices, and so on. We focus our idea on the benefits of traffic and safety measures which are becoming a huge challenge nowadays. Many are preferred with sophistication when traveling for short distances. In such a way the big data analytics tools R studio and weka are used on the dataset smart city from the Kaggle website for traffic patterns during the high traffic duration. Using the dataset, the data are classified using a Support vector machine (SVM) and applied with regression such as linear, logistic regression to find the accuracy of traffic peak situations. The proposed work aims to compare the efficiency of big data technologies which can be applied using various classification and regression that can be shown on various tools such as R, Weka, map-reduce which can produce accurate results to visualize the smart cities and their traffic analysis.

587-P: Adherence, Acceptability, and Feasibility of Mobile Ecological Momentary Assessments (mEMA) in Young Adults (YA) with Type 1 Diabetes (T1D) of Racial-Ethnic Minority Status

Background: YA with T1D of racial-ethnic minority disproportionately experience stress related to social determinants of health (SDOH), which may be associated with hyperglycemia, glycemic variability, and poorer emotional well-being. Mobile EMA methodology uses mobile technology to assess contextually relevant, real-time behavioral and self-report data. Methods: Coupled with real-time CGM for two weeks, we employed mEMA methodology with three times daily cell phone-delivered surveys to study the association of momentary variations in SDOH-related stress with glycemia in YA with T1D from the Bronx, NY. We report our preliminary findings on acceptability, feasibility, and adherence of mEMA methodology in this ongoing study. We evaluated adherence by real-time app tracking of mEMA survey completion, and evaluated acceptability and feasibility by post-study questionnaires and qualitative interviews. Results: Thus far, 18 participants have completed the study: (mean age 23 yrs, 11 Hispanic, 7 non-Hispanic Black, mean HbA1c 8.0%). Adherence to assigned daily surveys was high at 85% (753/887 surveys). There were no differences in survey adherence across timepoints. Acceptability was high: participants reported good understanding of mEMA procedures (mean score 4/5 on Likert scale), high satisfaction (mean score 5/5), high willingness to participate again (mean score 5/5), and were very likely to recommend participation to others (mean score 5/5). For feasibility, the most common barrier cited was mEMA app issues (40%, n=7), specifically dropped survey notifications, which interfered with adherence. Conclusions: YA with T1D of racial-ethnic minority demonstrated high adherence, feasibility, and acceptability of mEMA methods, however there were app-related issues that need closer monitoring. mEMA is a viable tool to study real-time contextual and behavioral factors in minority YA with T1D. Disclosure S. Leung: None. G. Crespo-ramos: None. M. Finnan: None. J. S. Gonzalez: None. S. Agarwal: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5P30DK111022, K23DK115896)

Design of IoT based Real-Time Bus Tracking App using HF-RFID

Public Transportation is the major means of Bus among people. A recent survey by the National Sample Survey Organization says that about 62-66% of people use the bus as their mode of transport. Public Bus tracking system aims at providing the instant status of the bus to the users via an automated system. This paper describes a design of IoT enabled real time bus tracking system. In this work a bus tracking mobile phone app is developed, using that people can exactly locate the bus status and time to bus arrival at bus-stop. This work uses high frequency RFID tags at buses and RFID receivers at bus-stops and with NodeMCU real time RIFD tagging (bus running) information is collected and uploaded on cloud. Users can access the bus running and status from cloud on mobile app in real time.

Immediate and Continued Effects of App Real-Time Targeting along Customer Shopping Journey: A Field Experiment

Timing and shopping stages are two critical factors when designing personalized mobile targeting. However, there is scant evidence demonstrating the business impact of app real-time targeting on customers’ subsequent shopping behaviors across different shopping stages. By conducting a novel field experiment that randomizes the treatment in two shopping stages with a large Japanese retailer, this study investigates the immediate and continued effects of app real-time targeting on customers' in-store shopping and app usage behaviors. Grounded in the elaboration likelihood model, we develop a conceptual framework to explain customers' heterogeneous responses to app real-time targeting in early and late shopping stages. Our empirical results suggest that the customer shopping stage has a significant influence on the effectiveness of real-time targeting. Specifically, real-time targeting of customers in the late shopping stage has a positive impact on their current and future purchase decisions. However, real-time targeting in the early shopping stage only positively drives customers’ future purchase decisions but has no effect on facilitating customers' current shopping decisions. We also find that customers’ prior experience plays a dual moderating role in these identified effects in different shopping stages: while attenuating the targeting effect in the late stage of shopping journey, it amplifies the effect of real-time app targeting on customers’ continued purchase decisions in the early shopping stage. Our paper contributes to the literature on mobile targeting and omnichannel shopping and has significant managerial implications for cross-channel mobile targeting.

Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance in Online Ride Hailing Services

Online Ride Hailing (ORH) services enable a rider to request a taxi via a smartphone app in real time. When using ORH services, users (including riders and taxis) have to submit their locations to the ORH server. With received locations, the ORH server makes online ride matching between riders and taxis. There are serious privacy concerns for users to reveal location information to ORH servers. In this article, we propose an efficient and privacy-preserving ride matching scheme for ORH services, named EPRide. EPRide can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location information of both taxis and riders against ORH servers or other curious servers. In EPRide, we propose an efficient exact shortest road distance computation approach over encrypted data, which converts road distance computation into Hamming distance computation over packed ciphertexts by using road network hypercube embedding and somewhat homomorphic encryption. Meanwhile, we design a secure comparison protocol, which efficiently compares encrypted distances in parallel by using ciphertexts blinding and packing, without leaking any distance. Theoretical analysis and experimental evaluations show that EPRide is secure, accurate and efficient.

Indoor Localization for Visually Impaired Travelers Using Computer Vision on a Smartphone.

Wayfinding is a major challenge for visually impaired travelers, who generally lack access to visual cues such as landmarks and informational signs that many travelers rely on for navigation. Indoor wayfinding is particularly challenging since the most commonly used source of location information for wayfinding, GPS, is inaccurate indoors. We describe a computer vision approach to indoor localization that runs as a real-time app on a conventional smartphone, which is intended to support a full-featured wayfinding app in the future that will include turn-by-turn directions. Our approach combines computer vision, existing informational signs such as Exit signs, inertial sensors and a 2D map to estimate and track the user's location in the environment. An important feature of our approach is that it requires no new physical infrastructure. While our approach requires the user to either hold the smartphone or wear it (e.g., on a lanyard) with the camera facing forward while walking, it has the advantage of not forcing the user to aim the camera towards specific signs, which would be challenging for people with low or no vision. We demonstrate the feasibility of our approach with five blind travelers navigating an indoor space, with localization accuracy of roughly 1 meter once the localization algorithm has converged.

The AGILE gamma-ray satellite software system: real-time analysis and Apps for multi-wavelength and multi-messenger astronomy

I describe in this paper the AGILE real-time analysis (RTA), that is, the software system developed for the AGILE $$\gamma $$-ray Mission operations and scientific observations. Important tasks of the software include scientific alerts and follow-up observations in the multi-wavelength and multi-messenger context. Key elements of the AGILE RTA are a flexible software architecture, an efficient software management workflow, and an optimised team management implemented by the AGILE Team in more than 20 years of work. All these elements contributed to the success of the AGILE Mission, and they also constitute the basis for future projects of high-energy observatories. We also describe the smartphone APP AGILEScience that displays the scientific results of the Mission for professional and outreach purposes: it is an essential element of the AGILE Ground System and a unique “gate” to the $$\gamma $$-ray sky for the public audience.

Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

How Is Mobile User Behavior Different? — A Hidden Markov Model of Mobile Application Usage Dynamics

Mobile application usage is becoming an essential activity in many people’s daily lives. Compared with PC internet, mobile internet usage is ubiquitous, temporally fragmented, and more context-dependent. Thus far research is limited on the mechanism of mobile app usage and the effects of context, even as usage continues to grow. In this paper, we aim to develop a framework to capture the underlying mechanism of mobile app usage, taking into account its unique time-fragmented feature and the possible impacts of contextual factors. To this end, we propose a hidden Markov model (HMM) and calibrate it using a consumer panel that contains real-time app usage information. We find three hidden states driving mobile app usage: utilitarian, social, and hedonic. Consumers have multiple intentions in the utilitarian state but are relatively single-minded in either the social or hedonic state. The state dynamic is volatile: Chains of continuous utilitarian states and hedonic states intercommunicate frequently with densely intermittent social states. Such a volatile state dynamic provides an antecedent for the fragmented-time mobile usage phenomena. Contextual factors—in particular, location and time of day—influence the state dynamic, e.g., its volatility varies across different locations and times of day. In sum, our analysis depicts the following picture: In the mobile internet era, consumers take advantage of relatively long free-time windows for functional and entertainment activities, while exploiting short micro-moments for social activity. Furthermore, with the help of mobile internet technology, consumers seem to: (1) utilize more micro-moments outside the office or in the morning as a complement to work, and (2) reserve more micro-moments in the evening for relaxation and entertainment, all at the expense of social activities.