Consent to Data Linkage for Different Data Domains – The Role of Question Order, Question Wording, and Incentives

As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimentally explore three factors of interest to survey designers seeking to maximize consent rates: consent question order, consent question wording, and incentives. The results of the study using a German online sample (n = 3,374) show that survey respondents have a relatively high probability of consent to share smartphone usage data, Facebook data, and biomarkers, while they are least likely to share their bank data in a survey. Of the three experimental factors, only the consent question order affected consent rates significantly. Additionally, the study investigated the interactions between the three experimental manipulations and the seven data domains, of which only the interaction between the data domains and the consent question order showed a consistent significant effect.

Predicting Uptake of the COVID Coach App Among U.S. Military Veterans: A Funnel Analysis Using a Probability-Based Panel (Preprint)

BACKGROUND Although the pandemic has not led to a uniform increase of mental health concerns among older adults, there is evidence to suggest that some older veterans did experience an exacerbation of pre-existing mental health conditions, and that mental health difficulties were associated with a lack of social support and increasing numbers of pandemic-related stressors. Mobile mental health apps are scalable, may be a helpful resource for managing stress during the pandemic and beyond, and could potentially provide services that are not accessible due to the pandemic. However, overall comfort with mobile devices and factors influencing the uptake and usage of mobile apps during the pandemic among older veterans are not well known. COVID Coach is a free, evidence-informed mobile app designed for pandemic-related stress. Public usage data have been evaluated, but its uptake and usage among older veterans has not been explored. OBJECTIVE The purpose of the current study was to characterize smartphone ownership rates among U.S. veterans, identify veteran characteristics associated with downloading and use of COVID Coach, and characterize key content usage within the app. METHODS Data were analyzed from the 2019-2020 National Health and Resilience in Veterans Study (NHRVS), which surveyed a nationally representative, prospective cohort of 3,078 U.S. military veterans before and one year into the pandemic. The NHRVS sample was drawn from KnowledgePanel®, a research panel of more than 50,000 households maintained by Ipsos, Inc. Median time to complete the survey was nearly 32 minutes. The research version of COVID Coach was offered to all veterans who completed the peri-pandemic follow-up assessment on a mobile device (n = 814; weighted 34.2% of total sample). App usage data from all respondents who downloaded the app (n = 34; weighted 3.3% of the mobile completers sample) were collected between November 14, 2020 and November 7, 2021. RESULTS We found that most U.S. veterans own smartphones and veterans with higher education, greater number of adverse childhood experiences, higher extraversion, and greater severity of pandemic-related PTSD symptoms were more likely to download COVID Coach. Although uptake and usage of COVID Coach was relatively low (3.3% of eligible participants, n = 34), 50% of the participants returned to the app for more than one day of use. The interactive tools for managing stress were used most frequently. CONCLUSIONS Although the coronavirus pandemic has increased the need for and creation of digital mental health tools, these resources may require tailoring for older veteran populations. Future research is needed to better understand how to optimize digital mental health tools, such as apps, to ensure uptake and usage among older adults, particularly those who have experienced traumas across the lifespan.

New Lexis in the Interaction of Languages and Cultures: The Case of "Selfie" in the Lithuanian Language

New Lexis in the Interaction of Languages and Cultures: The Case of Selfie in the Lithuanian LanguageThis article deals with the adaptation of the English neologism selfie in the Lithuanian language. It sheds light on how selfie first appeared in Australian English back in 2002 and on the socialisation and lexicalisation of this word in the English and Lithuanian languages. The aim here is to analyse the characteristics of the usage of the neologism selfie and its adapted form selfis in the Lithuanian language as well as its rivalry with other Lithuanian equivalents of the word.Based on the usage data obtained from the Database of Lithuanian Neologisms, the online corpus WebCorp, and the Google search engine, the loanword selfie was found to have first appeared in Lithuanian blogs back in 2013 at the latest. After a brief period of time, in early 2014 or sooner, it began vying with its Lithuanian equivalent, asmenukė. Eventually, with the formational families of selfis and asmenukė expanding, two rival lexical semantic systems have emerged in the Lithuanian language, both consisting of what usually are variations of the name of the object (selfie, selfis and asmenukė, asmenutė, asmeninukė), actor (selfininkas, -ė, selfukininkas, -ė and asmenukininkas, -ė), action (selfintis and asmenukintis), and additional tool (selfi stikas, selfio lazda and asmenuklazdė), as well as the different new versions thereof.After it had made its way into the Lithuanian language, the English neologism selfie (selfis), as the name for a new sociocultural phenomenon with its own semantic and formational family, became anchored there and was adapted to the inflectional system of the host language just as it provided an impetus for producing local equivalents. This case can be considered to be a typical mini-model, one that demonstrates what happens when a loanword for a new and trendy element of reality, which therefore has a considerable potential to spread, enters the Lithuanian language. Without a shadow of doubt, similar processes are also taking place in other languages that have borrowed this word. New comparative neological studies of other languages would help us form a better understanding of the origin, functioning, and prevalence of neologisms, as well as the mechanisms of how local equivalents of borrowings are made and how they compete with them. Nowa leksyka w interakcji języków i kultur: przypadek selfie w języku litewskimNiniejszy artykuł jest poświęcony adaptacji angielskiego neologizmu selfie w języku litewskim. Rzuca światło na to, jak słowo selfie pojawiło się po raz pierwszy w australijskiej odmianie języka angielskiego w 2002 roku oraz na jego socjalizację i leksykalizację w języku angielskim i języku litewskim. Opracowanie ma na celu analizę cech użycia neologizmu selfie i zaadaptowanej formy selfis w języku litewskim oraz jego rywalizacji z innymi litewskimi odpowiednikami.Na podstawie informacji z Bazy Danych Litewskich Neologizmów, internetowego korpusu WebCorp i wyszukiwarki Google, stwierdzono, że zapożyczenie selfie pojawiło się po raz pierwszy na litewskich blogach najpóźniej w 2013 roku. Wkrótce, na początku 2014 roku lub wcześniej, zaczęło rywalizować ze swoim litewskim odpowiednikiem: asmenukė. Ostatecznie, wraz z rozwojem rodzin wyrazów selfis i asmenukė, w języku litewskim pojawiły się dwa rywalizujące ze sobą leksykalne systemy semantyczne, składające się z odmian nazwy obiektu (selfie, selfis i asmenukė, asmenutė, asmeninukė), aktora (selfininkas, -ė, selfukininkas, -ė i asmenukininkas, -ė), czynności (selfintis i asmenukintis) i dodatkowego narzędzia (selfi stikas, selfio lazda i asmenuklazdė) oraz rozmaitych nowych wersji tych nazw.Po przejściu do języka litewskiego, angielski neologizm selfie (selfis), jako nazwa nowego zjawiska społeczno-kulturowego z własną rodziną semantyczną i słowotwórczą, został w nim zakotwiczony i dostosowany do rodzimego systemu fleksyjnego i dał impuls do tworzenia lokalnych odpowiedników. Ten przypadek można w pewnym sensie uznać za modelowy, pokazuje bowiem, co się dzieje, gdy zapożyczenie nazwy na określenie nowego i bardzo modnego elementu rzeczywistości, który ma zatem znaczny potencjał rozprzestrzeniania się, wchodzi do języka litewskiego. Podobne procesy bez wątpienia zachodzą również w innych językach, które zapożyczyły to słowo. Podjęcie nowych badań porównawczych pomogłoby lepiej zrozumieć powstawanie, funkcjonowanie i rozpowszechnianie się neologizmów, a także mechanizmy tworzenia rodzimych odpowiedników i ich konkurowania z zapożyczeniami.

Measures of Engagement with mHealth Interventions in Patients with Heart Failure: A Scoping Review (Preprint)

BACKGROUND The computing and communication features of mobile devices are increasingly leveraged in mHealth interventions to provide comprehensive and tailored support that may have positive outcomes in patients with heart failure (HF). However, examination of mHealth intervention effectiveness has provided mixed findings. Considering that patient engagement is a prerequisite for the effectiveness of interventions, understanding how patients engage with mHealth interventions, and the effects of patient engagement on HF outcomes may explain the mixed findings. OBJECTIVE Our aim was to synthesize current evidence on measures of patient engagement with mHealth interventions, and the effects of engagement on HF outcomes METHODS A comprehensive search of the literature was conducted in 7 databases for relevant studies published in the English Language from 2009 to September 2021. Descriptive characteristics of studies were reported. Content analysis was conducted to identify themes that described patient engagement with mHealth in the qualitative studies included in the review. RESULTS We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (67.9% male), ranging from a sample of 7 to 1571, with a median of 53.3 patients. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data (71.8%, 23/32), only qualitatively based on data from semi-structured interviews and focus groups (6.3%, 2/32), and by a combination of both quantitative and qualitative data (21.9%, 7/32). System usage data were evaluated using 6 metrics of engagement: (1) number of physiological parameters transmitted (63.3%, 19/30); (2) number of HF questionnaires completed (6.7%, 2/30); (3) numbers of logins (13.3%, 4/30); (4) number of short message service (SMS) responses (3.3%, 1/30); (5) time spent (16.7%, 5/30); (6) number of features accessed/screen viewed (9.5%, 4/30). There was a lack of consistency in how system usage metrics were reported across the studies. Eighty percent of the studies reported only the descriptive characteristics of the system usage data. Emotional, cognitive, and behavioral domains of patient engagement were identified in qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalizations were inconclusive. CONCLUSIONS The operational definitions of patient engagement with mHealth interventions are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. More research focused on developing optimal and standardized measures of patient engagement that may be applied across different study designs is warranted.

Exploring Feedback and Gamification in a Data Modeling Learning Tool

Data modeling is an essential part of IT studies. Learning how to design and structure a database is important when storing data in a relational database and is common practice in the IT industry. Most students need much practice and tutoring to master the skill of data modeling and database design. When a student is in a learning process, feedback is important. As class sizes grow and teaching is no longer campus based only, providing feedback to each individual student may be difficult. Our study proposes a tool to use when introducing database modeling to students. We have developed a web-based tool named LearnER to teach basic data modeling skills, in a collaborative project between the University of South-Eastern Norway (USN) and Kristiania University College (KUC). The tool has been used in six different courses over a period of four academic years. In LearnER, the student solves modeling assignments with different levels of difficulty. When they are done, or they need help, they receive automated feedback including visual cues. To increase the motivation for solving many assignments, LearnER also includes gamifying elements. Each assignment has a maximum score. When students ask for help, points are deducted from the score. When students manage to solve many assignments with little help, they may end up at a leaderboard. This paper tries to summarize how the students use and experience LearnER. We look to see if the students find the exercises interesting, useful and of reasonable difficulty. Further, we investigate if the automated feedback is valuable, and if the gamifying elements contribute to their learning. As we have made additions and refinements to LearnER over several years, we also compare student responses on surveys and interviews during these years. In addition, we analyze usage data extracted from the application to learn more about student activity. The results are promising. We find that student activity increases in newer versions of LearnER. Most students report that the received feedback helps them to correct mistakes when solving modeling assignments. The gamifying elements are also well received. Based on LearnER usage data, we find and describe typical errors the students do and what types of assignments they prefer to solve.