A Conceptual Framework for Supporting Adaptive Personalized Help-on-Demand Services

Burns, William C. G.; Chen, Liming; Nugent, Chris; Donnelly, Mark; Skillen, Kerry-Louise; Solheim, Ivar

doi:10.1007/978-3-642-34898-3_38

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…In future ED recovery apps, EMI could be utilized to suggest coping strategies drawn from EBTs that the patient reports are most helpful, feedback based on concurrent EMA, individual counseling, or messages based on pre‐intervention behavior patterns. Furthermore, future apps should incorporate the capability of smartphones to execute programmed machine learning, such that an app can become acquainted with a particular user's behavior patterns, predicting times of need and deliver a customized coping strategy (Burns et al, ). The high‐risk situations could be identified by data drawn from the numerous smartphone sensors (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Though app development is certainly progressing, there is room for improvement. For instance, more advanced technology is capable of generating automatic, but personalized feedback on treatment progress on both assigned goals/homework and self‐monitoring records (Burns et al, ). This feature may be able to reduce clinician burden, increase user adherence, and potentially counteract the potential downside of apps offering contradicting recommendations to clinicians; however, it is not utilized by any available apps for consumers.…”

Section: Discussionmentioning

confidence: 99%

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Review of Smartphone Applications for the Treatment of Eating Disorders

Juarascio

Manasse

Goldstein

et al. 2014

Euro Eating Disorders Rev

128

101

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mHealth tools may be a feasible modality for delivering evidence-based treatments and principles (EBPs), and may enhance treatment for eating disorders (EDs). However, research on the efficacy of mHealth tools for EDs and the extent to which they include EBPs is lacking. The current study sought to (i) review existing apps for EDs, (ii) determine the extent to which available treatment apps utilize EBPs, and (iii) assess the degree to which existing smartphone apps utilize recent advances in smartphone technology. Overall, existing ED intervention apps contained minimal EBPs and failed to incorporate smartphone capabilities. For smartphone apps to be a feasible and effective ED treatment modality, it may be useful for creators to begin taking utilizing the abilities that set smartphones apart from in-person treatment while incorporating EBPs. Before mHealth tools are incorporated into treatments for EDs, it is necessary that the feasibility, acceptability, and efficacy be evaluated.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Review of Smartphone Applications for the Treatment of Eating Disorders

Juarascio

Manasse

Goldstein

et al. 2014

Euro Eating Disorders Rev

128

101

View full text Add to dashboard Cite

show abstract

“…The reasoning engine will take as inputs user profiles, context and user requests of assistance to reason against the rules to decide how ondemand services provide assistance for the user. The personalised service requests will then be sent to HoD services for processing [42].…”

Section: System Architecturementioning

confidence: 99%

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Skillen

Chen

Nugent

et al. 2014

Future Generation Computer Systems

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“…The Adaptation Framework [16] contains four core services, Personalisation, Profile and Context in addition to a Dialog Manager, that serves as a part of the HoD application and allows the services, content and UI of the system to be adapted and persoanlised based on the user's usage patterns and profile. The Adaptation Framework, as presented in Figure 1, is a component of the HoD application and is used to collect usage data, personalise content, ascertain the user's context and manage requests and responses to and from the CMS.…”

Section: Adaptation Frameworkmentioning

confidence: 99%

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

Burns

Chen

Nugent

et al. 2013

Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments

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Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively.

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A Conceptual Framework for Supporting Adaptive Personalized Help-on-Demand Services

Cited by 6 publications

References 6 publications

Review of Smartphone Applications for the Treatment of Eating Disorders

Review of Smartphone Applications for the Treatment of Eating Disorders

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

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