Many individuals in late modern society share experiences of an accelerating pace of life, time scarcity, and a pressure to keep up with the fast pace of change as well as a necessity to stay digitally connected. As a reaction to this, some individuals engage in attempts to disconnect from the online world and their digital devices as they seek to regain control and slow down the pace in life. This study examines deliberate disconnection through the experiences of individuals who engage in digital minimalism, referring to intentional, voluntary practices of paring back on technology in a long-term perspective. The purpose of the study was to explore challenges and benefits, and more specifically, to identify underlying motives and strategies for adopting digital minimalist practices. Analysing the above process, the thesis investigates also how these practices were negotiated over time and in what way motives were related to conscious counteraction of the overwhelming paste of contemporary life. The analysis was focused on the use of smartphones in everyday life. Qualitative semi-structured interviews were conducted with seven individuals adopting digital minimalist practices. The empirical data was analysed using concepts from Hartmut Rosa’s theory of social acceleration. The results from the study pointed out that digital minimalist practice was motivated in a strive to either reclaim or preserve control and rhythm. In line with this, motives were based in a strive to counteract unintentional actions, and therefore strategies mainly concerned attempts to increase the threshold to engage with the smartphone. Experience was marked by benefits of reclaiming control over time which fostered well-being, increased attention to social relations in real life and confidence, but also marked by challenges and ambivalence as the individuals struggled maintaining their strategies. Engaging in digital minimalism fostered those strategies and beliefs to be (re)negotiated as certain circumstances and shifts in life fostered digital availability to a higher degree.

Analyzing a person's gait is important in determining their physical and neurological health. However, typical motion analysis laboratories are only in urban specialty care facilities and can be expensive due to the specialized personnel and technology needed for these examinations. Many patients, especially those who reside in underdeveloped or isolated locations, find it impractical to go to such facilities. With the help of recent developments in high-performance computing and artificial intelligence models, it is now feasible to evaluate human movement using digital video. Over the past 20 years, various visual gait analysis tools and scales have been developed. A study of the literature and discussions with physicians who are domain experts revealed that the Edinburgh Visual Gait Score (EVGS) is one of the most effective scales currently available. Clinical implementations of EVGS currently rely on human scoring of videos. In this thesis, an algorithmic implementation of EVGS scoring based on hand-held smart phone video was implemented. Walking gait was recorded using a handheld smartphone at 60Hz as participants walked along a hallway. Body keypoints representing joints and limb segments were then identified using the OpenPose - Body 25 pose estimation model. A new algorithm was developed to identify foot events and strides from the keypoints and determine EVGS parameters at relevant strides. The stride identification results were compared with ground truth foot events that were manually labeled through direct observation, and the EVGS results were compared with evaluations by human scorers. Stride detection was accurate within 2 to 5 frames. The level of agreement between the scorers and the algorithmic EVGS score was strong for 14 of 17 parameters. The algorithm EVGS results were highly correlated to scorers' scores (r>0.80) for eight of the 17 factors. Smartphone-based remote motion analysis with automated implementation of the EVGS may be employed in a patient's neighborhood, eliminating the need to travel. These results demonstrated the viability of automated EVGS for remote human motion analysis.

Introduction: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. Method: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. Results: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that – although more systematic studies are necessary – task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. Conclusion: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

As technology has developed, Americans have come to carry their most private information around with them in their pockets in digital form on their cell phones. A cell phone has immense storage capacity and can contain a wide variety of communicative information about its owner. In the past, there had been a disagreement among the lower courts as to whether police officers could search the contents of an arrestee's cell phone when making an arrest. The United States Supreme Court settled this disagreement in Riley v. California; in that case, the Court held that the warrantless search of a cell phone incident to arrest violated the Fourth Amendment to the United States Constitution. This thesis discusses case law that preceded the United States Supreme Court case Riley v. California, that decision, and possible ramifications of that decision.

Despite its benefits to both physical and mental health, physical activity levels worldwide remain low and new solutions for behaviour change must be sought. Smartphone apps are extremely popular and prevalent across the population, however their quality is still questionable. Physical activity produces an acute ‘feel good’ effect and intervention designers should consider the role that affect (mood) plays in uptake and maintenance of behaviours. It is timely to examine the use of affect as a motivator for physical activity, using new tools that allow real-time capture of both affect and physical activity (smartphones). The existence, characteristics and quality of physical activity apps that provide feedback on affect were explored in this thesis A mixed methods approach, comprising a systematic review (study 1) and a systematic evaluation (study 2) was taken. Data collection methods included both quantitative and qualitative assessments, using pre-existing and fit-for purpose tools. Twenty-two physical activity apps that provided feedback on affect were identified. Apps often failed to target groups most at risk of poor physical activity levels. Feedback on affect was performed in a variety of ways. Quality of apps, based on 13 criteria, was mixed. Recommendations are made for researchers, app developers and funders, including the need for development of high quality physical activity apps incorporating and emphasising affective benefits, consideration of archiving processes for developed apps once development ceases, and collaboration between researchers, developers and users when designing apps.

No description available.

No description available.

No description available.

No description available.

No description available.