Fünf Tage ohne Smartphone: Smartphonenutzung und subjektives Wohlbefinden: ein Vergleich zwischen normaler Nutzung und Verzicht

The study examined the association between smartphone use and subjective well-being by comparing regular use with a deprivation condition. Subjective well-being is defined by cognitive and affective components. Regular smartphone use is studied in various situations and for different functions. Data were collected in a prolonged qualitative quasi-experimental deprivation study (n = 11) using diaries (n = 210 diary entries) and follow-up interviews (n = 11). Participants kept diaries for 10 days: five days during normal smartphone use and five days during deprivation. Afterwards, we compared well-being during normal use and deprivation. Results show that using the smartphone for infotainment was clearly associated with pleasant emotions, while social interaction apps caused both negative and positive emotions. However, results from the deprivation part of the study indicate that in sum, satisfaction with social relations clearly worsened when not using a smartphone. Moreover, participants had difficulty managing daily life. Taken together, non-usage seems to cause isolation and low subjective well-being.Die vorliegende Studie untersucht den Zusammenhang zwischen Smartphone-Nutzung und subjektivem Wohlbefinden. In einer qualitativen, quasi-experimentellen Verzichtsstudie (n = 11) wurde das Wohlbefinden (erhoben durch kognitive und affektive Indikatoren) während regulärer Smartphone-Nutzung sowie während eines Smartphone-Verzichts gemessen und verglichen. Nutzungssituationen und Funktionen operationalisieren die Smartphone-Nutzung, in der Verzichtsphase wurde nach gewünschten Situationen und gewünschten Funktionen gefragt. Smartphone-Nutzende führten während fünf Tagen regulärer Nutzung und weiteren fünf Tagen Verzicht Tagebücher (n = 210) und wurden unmittelbar im Anschluss in qualitativen Interviews (n = 11) befragt. Die Ergebnisse aus der regulären Nutzungsphase zeigen, dass die Nutzung des Smartphones für Infotainment eindeutig mit angenehmen Emotionen verbunden war, während Apps zur sozialen Interaktion sowohl negative als auch positive Emotionen auslösten. In der Verzichtsphase hingegen verschlechterte sich die Zufriedenheit mit den sozialen Beziehungen insgesamt deutlich. Darüber hinaus hatten die Teilnehmenden Schwierigkeiten, dastägliche Leben zu bewältigen. Insgesamt führte die Nicht-Nutzung zu verstärkter Isolation und zu einem geringen subjektiven Wohlbefinden

Loneliness and social isolation detection using passive sensing techniques: Scoping review

Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation. This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues. A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems. After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation. Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns

Examining individual differences through ‘everyday’ smartphone behaviours: Exploring theories and methods.

The mass adoption of digital technologies has instigated a transition whereby people are no longer ‘independent organic actors’ in society but have amalgamated with the technology they use on a daily basis. Consequently, people leave behind a ‘digital fingerprint’ whenever they use technologies such as smartphones, and the qualities of this trace can predict a variety of characteristics about the user. In this thesis, I explore how individual differences such as personality, demographics, and health relate to directly observable smartphone behaviours, that are logged ‘in situ’ via software installed on the device itself. By adopting an interdisciplinary approach between psychology and computer science, this thesis primarily considers the theoretical (chapter two), ethical (chapter three) and methodological (chapter four) underpinnings required to explore these human-smartphone relationships. Notably, traces of use do not have to be complex, as meta-data such as the smartphone operating system a person uses can reveal information regarding a user’s personality, as long as there is trace-to-trait relevance. Findings from chapters five and six also reveal that some individual differences can be better predicted from objective smartphone use than others. For example, age and gender can be discerned from smartphone usage logs whereas, mental health variables only had small positive correlations with smartphone screen time. However, an important contribution of this thesis resides in its methodological considerations, as self-reports of technology use can impact the relationships with individual differences and cannot be used as a substitute for objective logs. All the above has applied implications for security and health, which can benefit from the ability to infer characteristics about people, when self-reports are arduous, unfeasible or lack scientific rigour

Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users

[EN] Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener¿s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 ¿ a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener¿s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. 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