Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd

Understanding Fitness App Users’ Loyalty and Word of Mouth through Gameful Experience and Flow Theory

In this study, we examine the effect that a gameful experience and personalization have on the flow experience of fitness app users. We also test the association between flow experience and satisfaction in using fitness apps and whether satisfied users remain loyal and spread word of mouth regarding fitness apps. We use the belief-attitude- behavior framework as a theoretical lens and flow theory to explore the proposed relationships. Four hundred thirty- one fitness app users from India participated in the study. The results indicate that gameful experience and personalization lead to flow experience. We found a positive association between flow and satisfaction wherein satisfied fitness app users spread word of mouth and remained loyal to using fitness apps. Our findings will help fitness app developers identify factors to retain fitness app users and attract new ones

Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Experience-driven procedural content generation (extended abstract)

Procedural content generation is an increasingly important area of technology within modern human-computer interaction with direct applications in digital games, the semantic web, and interface, media and software design. The personalization of experience via the modeling of the user, coupled with the appropriate adjustment of the content according to user needs and preferences are important steps towards effective and meaningful content generation. This paper introduces a framework for procedural content generation driven by computational models of user experience we name Experience-Driven Procedural Content Generation. While the framework is generic and applicable to various subareas of human computer interaction, we employ games as an indicative example of content-intensive software that enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects C2Learn (318480) and iLearnRW (318803).peer-reviewe

Dementia, music and biometric gaming: Rising to the Dementia Challenge

In 2012, the U.K. government launched its Dementia Challenge, authorizing additional funding for dementia research and health care. The search for curative medicines is ongoing, but scientific research reveals evidence that music can play a positive role in general health, and in dementia and Alzheimer’s disease in particular. This article considers whether some of the challenges that dementia presents could be addressed through music therapy and proposes that biometric gaming might offer one means of channeling such associated health benefits to sufferers of dementia, even in the final stages of the disease

A Model for patient engagement integration in perinatal eHealth development and quality assurance

The aim of this study was to construct a model for patient engagement integration in perinatal eHealth development and quality assurance. The model was developed in four phases. The first three phases produced evidence for the development of a model. In the final phase, a qualitative interpretive synthesis was conducted using grounded theory to articulate a patient engagement model composed of three steps. The first phase was a scoping review aimed at describing the nature and range of patient engagement from the perspective of access, personalization, commitment, and therapeutic alliance within perinatal eHealth. A narrative synthesis was used to describe findings. Phase two consisted of two studies exploring engagement practices of pregnant users during their use of a self-monitoring health promotion eHealth system. A descriptive comparative analysis was completed to understand user engagement patterns based on physical use of the wearable device. A mixed-methods convergence evaluation was conducted to understand the process of accessing the health promotion eHealth system. In phase three a process evaluation tool for parent participation and collaboration (in the neonatal intensive care unit) was developed and psychometrically tested. For the interpretive synthesis, articles from the first three phases of this study were purposively sampled. A deductive codebook was developed using Donabedian’s model, and an adapted version of Lewin’s Action Research Cycle. Donabedian’s model consists of quality assurance through the examination of structure, process, and outcomes. Lewin’s Action Research Cycle informs iterative steps in development and implementation of health systems. Phase four resulted in a model for patient engagement integration in perinatal eHealth development and quality assurance. Three steps of the model were identified as being: Person-centered Perinatal eHealth program mapping; Process evaluation through monitoring of patient engagement processes; and Co-creation of perinatal eHealth programs through real-life testing of perinatal eHealth systems.Malli potilaan osallistumisesta perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen Tutkimuksen tavoitteena oli kehittää malli ohjaamaan potilaan osallistumista perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen. Malli kehitettiin neljässä vaiheessa. Kolmessa ensimmäisessä vaiheessa tuotettiin tutkimusnäyttöä kehittämisen tueksi. Viimeisessä vaiheessa laadullisen tulkitsevan synteesin avulla muodostettiin potilaan sitoutumisen malli. Ensimmäisessä vaiheessa tehtiin kartoittava kirjallisuuskatsaus, joka kuvasi potilaiden sähköiseen terveydenhuoltoon osallistumisen tavat ja laajuuden saatavuuden, yksilöllisyyden, sitoutumisen ja terapeuttisen hoitosuhteen näkökulmasta. Aineisto analysoitiin teorialähtöisellä sisällönanalyysillä ja tulokset kuvattiin narratiivisen synteesin avulla. Toinen vaihe muodostui kahdesta tutkimuksesta, jotka tarkastelivat itsemonitorointisysteemin avulla raskaana olevien henkilöiden osallistumistapoja terveydenedistämiseen. Tutkimuksissa odottajat käyttivät itsemonitorointisysteemiä. Osallistumistapoja analysoitiin puettavan laitteen käyttöajan pohjalta tehtyjen vertailevien analyysien avulla. Monimenetelmällisessä tutkimuksessa muodostettiin analyysin pohjalta ymmärrys itsemonitorointisysteemin saatavuuteen liittyvästä prosessista. Kolmannessa vaiheessa kehitettiin ja psykometrisesti testattiin prosessievaluaatiomittari arvioimaan vanhempien osallistumista ja yhteistyötä henkilökunnan kanssa vastasyntyneiden teho-osastolla. Viimeisen vaiheen tulkitsevaa synteesiä varten valittiin tarkoituksenmukaisia artikkeleita. Donabedianin terveydenhuollon laadunvarmistuksen malli ja Lewinin muokatun toimintatutkimuksen syklin pohjalta muodostettiin teorialähtöinen analyysirunko. Neljännen vaiheen tuloksena muodostettiin malli potilaan osallistumisesta perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen. Malli kostuu kolmesta askeleesta: Yksilökeskeisen sähköisen terveydenhuollon kartoitus, potilaan osallistumisprosessin monitorointiin perustuva prosessievaluaatio ja perinataaliajan sähköisen terveydenhuollon yhteiskehittäminen kliinisessä todellisuudessa