Detecting Usability and User Experience Issues in Stroke Caregiving Apps: An Analysis of User Reviews (Preprint)

2021 ◽  
Author(s):  
Elton Lobo ◽  
Mohamed Abdelrazek ◽  
Anne Frølich ◽  
Lene Juel Rasmussen ◽  
Patricia M. Livingston ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
User Experience ◽  
Participatory Design ◽  
User Participation ◽  
User Reviews ◽  
App Store ◽  
Healthcare Needs ◽  
Recovery Trajectory ◽  
Mhealth Apps

BACKGROUND Stroke caregivers often experience negative impacts when caring for a person living with a stroke. Technologically based interventions such as mHealth apps have demonstrated potential in supporting the caregivers during the recovery trajectory. Hence, there is an increase in apps in popular app stores, with a few apps addressing the healthcare needs of stroke caregivers. Since most of these apps were published without explanation of their design and evaluation processes, it is necessary to identify the usability and user experience issues to help app developers and researchers to understand the factors that affect long-term adherence and usage in stroke caregiving technology. OBJECTIVE The purpose of this study was to determine the usability and user experience issues in commercially available mHealth apps from the user reviews published within the app store to help researchers and developers understand the factors that may affect long-term adherence and usage. METHODS User reviews were extracted from the previously identified 47 apps that support stroke caregiving needs using a python-scraper for both app stores (i.e. Google Play Store and Apple App Store). The reviews were pre-processed to (i) clean the dataset and ensure unicode normalization, (ii) remove stop words and (iii) group words together with similar meanings. The pre-processed reviews were filtered using sentiment analysis to exclude positive and non-English reviews. The final corpus was classified based on usability and user experience dimensions to highlight issues within the app. RESULTS Of 1,385,337 user reviews, only 162,095 were extracted due to the limitations in the app store. After filtration based on the sentiment analysis, 15,818 reviews were included in the study and were filtered based on the usability and user experience dimensions. Findings from the usability and user experience dimensions highlight critical errors/effectiveness, efficiency and support that contribute to decreased satisfaction, affect and emotion and frustration in using the app. CONCLUSIONS Commercially available mHealth apps consist of several usability and user experience issues due to their inability to understand the methods to address the healthcare needs of the caregivers. App developers need to consider participatory design approaches to promote user participation in design. This might ensure better understanding of the user needs and methods to support these needs; therefore, limiting any issues and ensuring continued use.

A Sentiment Analysis of User Reviews of Depression Apps Features (Preprint)

2019 ◽  
Author(s):  
Julien Meyer
Keyword(s):  
Natural Language ◽  
User Experience ◽  
Negative Emotions ◽  
Therapeutic Treatment ◽  
Medical Assessment ◽  
User Reviews ◽  
Barriers To Access ◽  
Single Feature ◽  
Starting Point ◽  
Mhealth Apps

BACKGROUND Mhealth apps are promising to overcome barriers to access mental health care. Adoption and continuous use, however, depends on users’ decisions. App reviews both reflect and influence users’ attitude and experience towards apps and influence their propensity to use mhealth apps. OBJECTIVE We investigate user app reviews on specific features in depression apps (psychoeducation, medical assessment, therapeutic treatment, supportive resources and entertainment). METHODS We extracted 3,261 user reviews of depression apps, isolated reviews associated with single feature apps. We then analyzed reviews using LIWC, a natural language analytical tool and contrasted language patterns associated with different features. RESULTS Medical Assessment features stand out for the strong negative emotions and negative ratings they generate, as users receive potentially disturbing feedback on their condition. Symptom Management and Entertainment features generate less negative emotions and anxiety. Therapeutic Treatment features also generate more positive and fewer negative emotions, even though user experience is less authentic (i.e., reflecting a personal experience). CONCLUSIONS Developers should be cautious in their choice of features when they are targeting potentially vulnerable users. Medical assessment feedback being riskier while offering information, contacts or even games may be a safer starting point to engage people with depression. App features emerged as a key dimension to consider when investigating user experience with mhealth apps. Methodologically, app reviews can be leveraged to investigate specific app features at the level of a family of apps. Specifically, Natural Language Analysis proved to be a responsive tool to investigate behaviors related to a quickly changing app environment.

EFFECTIVNESS OF SPECIFIC MOBILE HEALTH APPLICATIONS (mHEALTH-APPS)in GESTATIONAL DIABTETES MELLITUS: A SYSTEMATIC REVIEW (Preprint)

2020 ◽  
Author(s):  
Claudia Eberle ◽  
Maxine Löhnert
Keyword(s):  
Mobile Health ◽  
Cochrane Library ◽  
Clinical Parameters ◽  
Control Groups ◽  
Manual Search ◽  
Mobile Health Applications ◽  
Short And Long Term ◽  
Mhealth Apps ◽  
Health Applications

BACKGROUND Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), Metabolic Syndrome (MetS) as well as cardiovascular disease (CD). Against this background mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM clearly. OBJECTIVE Since there is – to our knowledge – no systematic literature review published, which focusses on the effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes of mother and child, we conducted these much-needed analyses. METHODS Data sources: A systematic literature search in Medline (Pubmed), Cochrane Library, Embase, CINAHL and Web of Science was performed including full text publications since 2008 up to date. An additional manual search in references and Google Scholar was conducted subsequently. Study Eligibility Criteria: Women diagnosed with GDM using specific mHealth-Apps during pregnancy compared to control groups, which met main clinical parameters and outcomes in GDM management as well as maternity and offspring care. Study appraisal and synthesis methods: Study quality was assessed and rated “strong”, “moderate” or “weak” by using the Effective Public Health Practice Project (EPHPP) tool. Study results were strongly categorized by outcomes; an additional qualitative summary was assessed. Study selection: Overall, n= 114 studies were analyzed, n= 46 duplicates were removed, n=5 studies met the eligible criteria and n=1 study was assessed by manual search subsequently. In total, n=6 publications, analyzing n=408 GDM patients in the interventional and n=405 women diagnosed with GDM in the control groups, were included. These studies were divided into n=5 two-arm randomized controlled trials (RCT) and n=1 controlled clinical trial (CCT). RESULTS Distinct improvements in clinical parameters and outcomes, such as fasting blood glucoses (FBG), 2-hour postprandial blood glucoses (PBG), off target blood glucose measurements (OTBG), delivery modes and patient compliance were analyzed in GDM patients using specific mHealth-Apps compared to matched control groups. CONCLUSIONS mHealth-Apps clearly improve clinical outcomes in management of GDM effectively. More studies need to be done more in detail.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Discrepancy Detection between Actual User Reviews and Numeric Ratings of Google App Store using Deep Learning

2021 ◽  
pp. 115111
Author(s):  
Saima Sadiq ◽  
Muhammad Umer ◽  
Saleem Ullah ◽  
Seyedali Mirjalili ◽  
Vaibhav Rupapara ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Reviews ◽  
App Store ◽  
Discrepancy Detection

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Building Capabilities Through Democratic Dialogues

Design Issues ◽  
2018 ◽  
Vol 34 (4) ◽  
pp. 80-95 ◽  
Author(s):  
Liesbeth Huybrechts ◽  
Katrien Dreessen ◽  
Ben Hagenaars
Keyword(s):  
Design Process ◽  
Participatory Design ◽  
Design Approach ◽  
Railway Track ◽  
Alternative Futures ◽  
Design Processes ◽  
Self Organized ◽  
Complex Design ◽  
Developing Strategies

Designers are increasingly involved in designing alternative futures for their cities, together with or self-organized by citizens. This article discusses the fact that (groups of) citizens often lack the support or negotiation power to engage in or sustain parts of these complex design processes. Therefore the “capabilities” of these citizens to collectively visualize, reflect, and act in these processes need to be strengthened. We discuss our design process of “democratic dialogues” in Traces of Coal—a project that researches and designs together with the citizens an alternative spatial future for a partially obsolete railway track in the Belgian city of Genk. This process is framed in a Participatory Design approach and, more specifically, in what is called “infrastructuring,” or the process of developing strategies for the long-term involvement of participants in the design of spaces, objects, or systems. Based on this process, we developed a typology of how the three clusters of capabilities (i.e., visualize, reflect, and act) are supported through democratic dialogues in PD processes, linking them to the roles of the designer, activities, and used tools.

Evaluating Long Term User Experience with Children

2019 ◽  
Author(s):  
Matthew Horton ◽  
Gavin Sim ◽  
Bieke Zaman ◽  
Karin Slegers
Keyword(s):  
User Experience

scholarly journals Striving for Inclusion—A Systematic Review of Long-Term Participation in Strategic Management of Urban Green Spaces

2021 ◽  
Vol 3 ◽  
Author(s):  
Hanna Fors ◽  
Frederik Aagaard Hagemann ◽  
Åsa Ode Sang ◽  
Thomas B. Randrup
Keyword(s):  
Systematic Review ◽  
Strategic Management ◽  
Process Model ◽  
User Participation ◽  
Green Spaces ◽  
Cyclic Process ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Marginalized Groups

This systematic review contributes to the research field of user participation by suggesting a new holistic approach comprising a cyclic process model for long-term participation in the strategic management of urban green spaces, including analysis, design, and implementation phases, each followed by an evaluation. User participation in urban green spaces is encouraged in international conventions. Such initiatives aim to involve citizens more closely in decisions regarding local spaces, based on the premise that this will create better, more inclusive, and sustainable local environments. However, a social inclusion perspective is largely absent in the growing body of European scientific literature on urban green spaces. Further, user participation processes are often carried out within projects, with uncertainties about which strategic management phase (planning, design, construction, and/or maintenance) to emphasize and about the long-term sustainability of project-based participation. Therefore, the literature was examined for tools for participation with the focus on participation of local users in the strategic management of urban green spaces, and in particular, marginalized groups. A systematic review based on peer-reviewed scientific papers revealed the necessity for adapting participation processes to the known needs of different participant groups, including those of marginalized groups often excluded in the past. Local authorities have several pathways to socially inclusive and long-term participation. These include choosing and employing a suitable participation approach, anchoring repeated project-based participation in existing municipal long-term strategies, continuously supporting participating users and evaluating ongoing participation processes, and employing a mix of participation types and approaches. The “cyclic process model for long-term participation in strategic management of UGS” presented in this paper could guide such efforts.

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