scholarly journals Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

2018 ◽  
Vol 5 (4) ◽  
pp. e58 ◽  
Author(s):  
Paul Matthews ◽  
Phil Topham ◽  
Praminda Caleb-Solly
Keyword(s):  
Mobile Phone ◽  
Social Participation ◽  
Large Scale ◽  
Cloud Services ◽  
Anxiety Reduction ◽  
Behavioral Engagement ◽  
Anxiety Management ◽  
User Behaviors ◽  
Self Help ◽  
Usage Patterns

BackgroundSAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum (“the Social Cloud”). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.ObjectiveThe objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation.MethodsWe use data mining techniques on aggregate data obtained from 105,380 registered users of the app’s cloud services.ResultsEngagement generally conformed to common mobile participation patterns with an inverted pyramid or “funnel” of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation.ConclusionsThe data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement.

scholarly journals User engagement and utilisation of an app for anxiety management: analysis and evaluation of usage patterns (Preprint)

2017 ◽  
Author(s):  
Paul Matthews ◽  
Phil Topham ◽  
Praminda Caleb-Solly
Keyword(s):  
Social Participation ◽  
Mobile App ◽  
Cloud Services ◽  
Anxiety Reduction ◽  
User Engagement ◽  
Anxiety Management ◽  
Self Help ◽  
The Social ◽  
Behavioural Engagement ◽  
Usage Patterns

BACKGROUND SAM is a mobile app providing self-help for anxiety management. Launched in 2013, the app has achieved over a 1 million downloads and favourable ratings on the platform app stores. Key features of the app are anxiety monitoring, self-help techniques and social support via an online forum (“the social cloud”). This paper presents unique insights into e-mental health app usage patterns and explores user behaviour and usage of self-help techniques. OBJECTIVE To investigate behavioural engagement and to establish discernible usage patterns of the app linked to the features of: anxiety monitoring, rating of self-help techniques and social participation. METHODS We use data mining techniques on aggregate data from registered users of the app’s cloud services RESULTS Engagement in general conforms to common online participation patterns, with an inverted pyramid or “funnel” of engagement of increasing intensity. We further identify four distinct groups of behavioural engagement, differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users show a marked reduction in the first few days of usage, with some “bounce back” effect thereafter. A small group of users with demonstrable long-term anxiety reduction (using a robust measure) typically monitored for 12-110 days with 10-30 discrete updates and showed low levels of social participation. CONCLUSIONS The data supports our expectation of different usage patterns given flexible user journeys and varying commitment in an unstructured mobile usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally engaged users. We find several commonalities between these patterns and traditional therapy engagement.

Establishing Smartphone User Behavior Model Based on Energy Consumption Data

2021 ◽  
Vol 16 (2) ◽  
pp. 1-40
Author(s):  
Ming Ding ◽  
Tianyu Wang ◽  
Xudong Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Feature Vector ◽  
User Behavior ◽  
Lower Layer ◽  
Training Data ◽  
Usage Pattern ◽  
User Behaviors ◽  
Consumption Data ◽  
Usage Patterns

In smartphone data analysis, both energy consumption modeling and user behavior mining have been explored extensively, but the relationship between energy consumption and user behavior has been rarely studied. Such a relationship is explored over large-scale users in this article. Based on energy consumption data, where each users’ feature vector is represented by energy breakdown on hardware components of different apps, User Behavior Models (UBM) are established to capture user behavior patterns (i.e., app preference, usage time). The challenge lies in the high diversity of user behaviors (i.e., massive apps and usage ways), which leads to high dimension and dispersion of data. To overcome the challenge, three mechanisms are designed. First, to reduce the dimension, apps are ranked with the top ones identified as typical apps to represent all. Second, the dispersion is reduced by scaling each users’ feature vector with typical apps to unit ℓ 1 norm. The scaled vector becomes Usage Pattern, while the ℓ 1 norm of vector before scaling is treated as Usage Intensity. Third, the usage pattern is analyzed with a two-layer clustering approach to further reduce data dispersion. In the upper layer, each typical app is studied across its users with respect to hardware components to identify Typical Hardware Usage Patterns (THUP). In the lower layer, users are studied with respect to these THUPs to identify Typical App Usage Patterns (TAUP). The analytical results of these two layers are consolidated into Usage Pattern Models (UPM), and UBMs are finally established by a union of UPMs and Usage Intensity Distributions (UID). By carrying out experiments on energy consumption data from 18,308 distinct users over 10 days, 33 UBMs are extracted from training data. With the test data, it is proven that these UBMs cover 94% user behaviors and achieve up to 20% improvement in accuracy of energy representation, as compared with the baseline method, PCA. Besides, potential applications and implications of these UBMs are illustrated for smartphone manufacturers, app developers, network providers, and so on.

Analyzing the workload dynamics of a mobile phone network in large scale events

2012 ◽  
Author(s):  
Faber Henrique Zacarias Xavier ◽  
Lucas Maia Silveira ◽  
Jussara Marques de Almeida ◽  
Artur Ziviani ◽  
Carlos Henrique Silva Malab ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Mobile Phone Network

Empirical User Modeling: Command Usage Analyses for Deriving Models of Users

1987 ◽  
Vol 31 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Matthew P. Anderson ◽  
James E. McDonald ◽  
Roger W. Schvaneveldt
Keyword(s):  
Operating System ◽  
User Modeling ◽  
Procedural Knowledge ◽  
Structural Models ◽  
Network Representation ◽  
User Behaviors ◽  
Unix Operating System ◽  
Usage Patterns ◽  
Unix Command ◽  
Pathfinder Network

Models of users' procedural knowledge were derived from the records of command usage obtained from nine experienced users of the Unix operating system. Pairwise transitions between user command entries were analyzed for the purpose of identifying salient command patterns associated with task-based user behaviors. Structural models of command usage patterns were obtained from Pathfinder network scaling of Unix command events. The network representation of command patterns was evaluated as a method for abstracting users' procedural knowledge. These network scaling solutions revealed patterns that were common both within and across users' command usage.

GOVERNMENT REGULATIONS OF THE USING CLOUD SERVICES

2022 ◽  
Author(s):  
Olexander Melnikov ◽  
◽  
Konstantin Petrov ◽  
Igor Kobzev ◽  
Viktor Kosenko ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Government Agencies ◽  
Remote Work ◽  
Cloud Infrastructure ◽  
Wide Range ◽  
Technological Base

The article considers the development and implementation of cloud services in the work of government agencies. The classification of the choice of cloud service providers is offered, which can serve as a basis for decision making. The basics of cloud computing technology are analyzed. The COVID-19 pandemic has identified the benefits of cloud services in remote work Government agencies at all levels need to move to cloud infrastructure. Analyze the prospects of cloud computing in Ukraine as the basis of e-governance in development. This is necessary for the rapid provision of quality services, flexible, large-scale and economical technological base. The transfer of electronic information interaction in the cloud makes it possible to attract a wide range of users with relatively low material costs. Automation of processes and their transfer to the cloud environment make it possible to speed up the process of providing services, as well as provide citizens with minimal time to obtain certain information. The article also lists the risks that exist in the transition to cloud services and the shortcomings that may arise in the process of using them.

Resource Configuration for the Collaborative Development of Product

2012 ◽  
Vol 215-216 ◽  
pp. 540-543
Author(s):  
Fu Hong Zeng ◽  
Lan Hua Zhou
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Collaborative Planning ◽  
Matching Model ◽  
Collaborative Development ◽  
Resource Configuration ◽  
Manufacturing Enterprises

In order to meet the reasonable matching of resource for collaborative development of products in manufacturing enterprises including involvement of suppliers on a large scale, a Generalized Design Resource Pool (GDRP) and It’s Resource Particles (RP) are defined, a multi-project collaborative planning and resource particles constraint-matching model with realization algorithm is presented. Finally, a case of developing mobile phone to an enterprise is presented to verify the effectiveness and feasibility of the presented approach.

scholarly journals Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

2016 ◽  
Vol 5 (3) ◽  
pp. e160 ◽  
Author(s):  
Galen Chin-Lun Hung ◽  
Pei-Ching Yang ◽  
Chia-Chi Chang ◽  
Jung-Hsien Chiang ◽  
Ying-Yeh Chen
Keyword(s):  
Mobile Phone ◽  
Exploratory Study ◽  
Negative Emotions ◽  
Usage Patterns ◽  
Mobile Phone Usage

scholarly journals Digitalization of Business Processes of Enterprises of the Ecosystem of Industry 4.0: Virtual-Real Aspect of Economic Growth Reserves

2021 ◽  
Vol 18 ◽  
pp. 569-580
Author(s):  
Kateryna Kraus ◽  
Nataliia Kraus ◽  
Oleksandr Manzhura
Keyword(s):  
Economic Growth ◽  
Virtual Reality ◽  
Data Storage ◽  
Industry 4.0 ◽  
Large Scale ◽  
Business Processes ◽  
Digital Transformation ◽  
Cloud Services ◽  
Scale Production ◽  
Cloud Platform

The purpose of the research is to present the features of digitization of business processes in enterprises as a foundation on which the gradual formation of Industry 4.0 and the search for economic growth in new virtual reality, which has every chance to be a decisive step in implementing digital strategy for Ukraine and development of the innovation ecosystem. Key problems that arise during the digitalization of business processes in enterprises are presented, among which are: the historical orientation of production to mass, “running” sizes and large batches; large-scale production load; the complexity of cooperation and logic between production sites. It is determined that high-quality and effective tools of innovation-digital transformation in the conditions of virtual reality should include: a single system of on-line order management for all enterprises (application registration – technical expertise – planning – performance control – shipment); Smart Factory, Predictive Maintenance, IIoT, CRM, SCM. Features of digital transformation in the part of formation of enterprises of the ecosystem of Industry 4.0 are revealed. The capabilities and benefits of using Azure cloud platform in enterprises, which includes more than 200 products and cloud services, are analyzed. Azure is said to support open source technologies, so businesses have the ability to use tools and technologies they prefer and are more useful. After conducting a thorough analysis of the acceleration of deep digitalization of business processes by enterprises, authors proposed to put into practice Aruba solution for tracking contacts in the fight against COVID-19. Aruba technology helps locate, allowing you to implement flexible solutions based on Aruba Partner Ecosystem using a USB interface. It is proposed to use SYNTEGRA – a data integration service that provides interactive analytics and provides data models and dashboards in order to accelerate the modernization of data storage and management, optimize reporting in the company and obtain real-time analytics. The possibilities of using Azure cloud platform during the digitization of business processes of enterprises of the ecosystem of Industry 4.0 in the conditions of virtual reality are determined.

Death Anxiety Reduction as the Result of Exposure to a Death and Dying Symposium

1984 ◽  
Vol 14 (4) ◽  
pp. 323-328 ◽  
Author(s):  
David A. Waldman ◽  
Charles Davidshofer
Keyword(s):  
Undergraduate Students ◽  
Media Coverage ◽  
Death Anxiety ◽  
Large Scale ◽  
Death And Dying ◽  
Anxiety Reduction ◽  
Future Research ◽  
Control Group ◽  
Control Group Design ◽  
Colorado State University

The purpose of this study was to measure the effect of a three-week death and dying symposium at Colorado State University on attitudes and anxiety related to death, dying, and grief. A prepost control group design was employed using psychology undergraduate students as participants. Because of an overall finding of low participant attendance at the symposium events, participants who attended at least one event were assigned to the treatment group for data analyses. Results indicated lower death anxiety for students in both the treatment and control groups. The findings are discussed in terms of the widespread media coverage and informal discussions which accompany large-scale symposiums. Future research is suggested regarding the dynamic effects of such death and dying symposiums in relation to both attendants and nonattendants.

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