Introduction. The COVID-19 pandemic brought about new and far-ranging uses of technology that have engendered numerous privacy concerns among the public. This study examines discussions in r/privacy, a privacy-focused online community, to gain insight into how the pandemic influenced privacy concerns and behaviours among privacy-interested people. Method. Our research design included a mixed-method comparative analysis of 540 coded r/privacy posts from three time periods: immediately before the pandemic, during the emergence of COVID-19, and two years after the onset of the pandemic. Analysis. We performed an inductive, qualitative analysis to understand themes in the data, as well as a statistical analysis to identify key trends and differences in the frequency of themes in the three time periods. Results. We observed that while some changes were temporary (e.g., viewing the government as a key privacy threat during the pandemic onset), others were long-lasting and increased over time (e.g., seeking privacy-related support). Conclusions. Our work contributes to the growing literature on how people use social media to collectively make sense of major events by exploring online discourse about privacy issues and offers insights to designers and regulators of new technologies on specific privacy concerns associated with a period of massive sociotechnical upheaval.

What makes a technology privacy-enhancing? In this study, we construct an explanation grounded in the technologies and practices that people report using to enhance their privacy. We conducted an online survey of privacy experts (i.e., privacy researchers and professionals who attend to privacy conferences and communication channels) and laypersons that catalogs the technologies they identify as privacy enhancing and the various privacy strategies they employ. The analysis of 123 survey responses compares not only self-reported tool use but also differences in how privacy experts and laypersons explain their privacy practices and tools use. Differences between the two samples show that privacy experts and laypersons have different styles of reasoning when considering PETs: Experts think of PETs as technologies whose primary function is enhancing privacy, whereas laypersons conceptualize privacy enhancement as a supplemental function incorporated into other technologies. The paper concludes with a discussion about potential explanations for these differences, as well as questions they raise about how technologies can best facilitate communication and collaboration while enhancing privacy.

The emergent, dynamic nature of privacy concerns in a shifting sociotechnical landscape creates a constant need for privacy-related resources and education. One response to this need is community-based privacy groups. We studied privacy groups that host meetings in diverse urban communities and interviewed the meeting organizers to see how they grapple with potentially varied and changeable privacy concerns. Our analysis identified three features of how privacy groups are organized to serve diverse constituencies: situating (finding the right venue for meetings), structuring (finding the right format/content for the meeting), and providing support (offering varied dimensions of assistance). We use these findings to inform a discussion of “privacy pluralism” as a perennial challenge for the HCI privacy research community, and we use the practices of privacy groups as an anchor for reflection on research practices.

This paper examines volitionality of Facebook usage, that is, which individuals feel they have a choice about whether or not to use the site. It analyzes data from two large surveys, conducted three years apart. Across the two surveys, a variety of factors impacted whether or not respondents saw their Facebook usage as a matter of their own choice, such as engaging in non-use behaviors, measures of Facebook addiction, a sense of their own agency, and, across both studies, level of education. These results expand on prior literature around technology use and non-use, especially in terms of which populations may feel obligated to use, or be unwillingly prevented from using, social media such as Facebook. Furthermore, they provide potential implications both for future work and for technology policy.

This paper addresses challenges in conceptualizing privacy posed by algorithmic systems that can infer sensitive information from seemingly innocuous data. This type of privacy is of imminent concern due to the rapid adoption of machine learning and artificial intelligence systems in virtually every industry. In this paper, we suggest informational friction, a concept from Floridi's ethics of information, as a valuable conceptual lens for studying algorithmic aspects of privacy. Informational friction describes the amount of work required for one agent to access or alter the information of another. By focusing on amount of work, rather than the type of information or manner in which it is collected, informational friction can help to explain why automated analyses should raise privacy concerns independently of, and in addition to, those associated with data collection. As a demonstration, this paper analyze law enforcement use of facial recognition, andFacebook's targeted advertising model using informational friction and demonstrate risks inherent to these systems which are not completely identified in another popular framework, Nissenbaum's Contextual Integrity.The paper concludes with a discussion of broader implications, both for privacy research and for privacy regulation.

Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. These methods are demonstrated by applying them to survey data about Facebook non/use. The results demonstrate that the different methods proposed here identify fairly comparable types of non/use. They also illustrate how the two methods make different trade offs between the granularity of the resulting typology and the total sample size. The paper also demonstrates how the different typologies resulting from these methods can be used in predictive modeling, allowing for the two methods to corroborate or disconfirm results from one another. The discussion considers implications and applications of these methods, both for research on technology non/use and for studying social computing more broadly.

Purpose This study evaluates the differences in periodic leg movement (PLM) rates for Restless Legs Syndrome (RLS) and healthy controls when using the updated PLM scoring criteria developed by IRLSSG in 2016 versus the prior PLM scoring criteria developed by IRLSSG in 2006. Four major problems with the prior standards had been objectively identified, i.e. minimum inter-movement interval should be 10 not 5 s, non-PLM leg movements should end any preceding PLM sequence, a leg movement (LM) can be any length > 0.5 s, and a PLM should be a persisting movement not a couple or a series of closely spaced, very brief events. Each of these led to including, erroneously, various random leg movements as PLM. Correcting these problems was expected to increase specificity, reducing the number of PLM detected, particularly in situations producing relatively more random leg movements, e.g. wake vs. sleep and controls without PLMD vs. RLS patients. Methods This study evaluated the putative benefits of the updated, 2016-scoring criteria. The LMs from 42 RLS patients and 30 age- and gender-matched controls were scored for PLMS and PLMW from standard all-night PSG recordings using both 2006 and 2016 WASM criteria. Results/Conclusion The results confirmed that that the 2016 compared to the 2006 criteria generally decreased the PLM rates with particularly large decreases for the conditions with more random non-PLM events, e.g. wake times and normal healthy controls. This supported the view that the new criteria succeeded in increasing the specificity of PLM detection. Moreover, the changes in PLM rates were generally small for the conditions with relatively few random LM, e.g. RLS and sleep. Thus the bulk of existing PLMS research does not require reconsideration of results, with possible exception of special situations with relatively more random leg movements than periodic leg movements, e.g. wake, healthy normals and children.

Relatively little work has empirically examined use and non-use of social technologies as more than a dichotomous binary, despite increasing calls to do so. This paper compares three different forms of non/use that might otherwise fall under the single umbrella of Facebook "user": (1) those who have a current active account; (2) those who have deactivated their account; and (3) those who have considered deactivating but not actually done so. A subset of respondents (N=256) from a larger, demographically representative sample of internet users completed measures for usage and perceptions of Facebook, Facebook addiction, privacy experiences and behaviors, and demographics. Multinomial logistic regression modeling shows four specific variables as most predictive of a respondent's type: negative effects from "addictive" use, subjective intensity of Facebook usage, number of Facebook friends, and familiarity with or use of Facebook's privacy settings. These findings both fill gaps left by, and help resolve conflicting expectations from, prior work. Furthermore, they demonstrate how valuable insights can be gained by disaggregating "users" based on different forms of engagement with a given technology.

Sleep disturbances are common features of multiple system atrophy (MSA) and may combine sleep fragmentation, REM-sleep Behavior Disorders (RBD) and sleep-related breathing disorders. Only few studies focused on periodic leg movements (PLM) during sleep in MSA [1], [3]. Twenty patients with MSA were recruited in Bordeaux University Hospital and were submitted to one-night polysomnography. Sleep stages were identified, apnea/hypopnea index (AHI) was calculated. Detailed analysis of PLM was also performed. Clinical (age, sex, BMI, MSA type cerebellar or parkinsonian, disease duration, UMSARS and COMPASS31 scales, levodopa or dopamine agonist therapy) and biological (haemoglobin) data were collected retrospectively in patients’ database. Of these 20 patients, median PLM index during total sleep time (PLM_TST) was 2.9/h. PLM index was higher than 15 in 3 out of 20 patients. When restricted to non-respiratory PLM (nrPLM), the median index was 2.6/h of sleep. Median AHI was 29.4 per hour and 13 out of 20 patients had more than 15 apnea/hypopnea/h of sleep. No correlation was found between disease severity scores (UMSARS and COMPASS), disease duration, MSA type nor with haemoglobin levels and PLM_TST or nrPLM_TST index. We found a trend to an inverse correlation between slow waves sleep PLM index and levodopa equivalent daily dose. This preliminary study showed that only few MSA patients (15%) had more than 15 PLM per hour of sleep. Whether this is related to dopaminergic treatment effect remains to be further established. These results have to be confirmed on a larger population of patients with MSA. Further, iron metabolism might be the focus of future studies to assess whether PLM in MSA and idiopathic PLM share a common pathophysiology [2].

•Withdrawal of DA agonists is associated with an acute insomnia.•Withdrawal from non-DA therapy produced no apparent sleep loss.•After withdrawal from DA agonist, PLM were sustained at very high rates.•RLS improves over the first four to five days following DA agonists withdrawal.

Study Objectives: Periodic leg movements during sleep (PLMS) occur within a subject as a series with a remarkably stable period defined by the intermovement interval (IMI). Sometimes a non-PLMS movement occurs intervening between two PLMS. PLMS scoring rules totally ignore these intervening leg movements (iLM). This implicitly assumes an iLM results from a process sufficiently independent from the periodic process producing PLMS that it does not affect the periodicity of the surrounding PLMS. This study for the first time tests this basic assumption and explores characteristics of iLM as a potentially significant class of leg movements during sleep.Methods: Leg movements were analyzed from two nights of polysomnography recordings from 27 RLS patients and 22 controls using the validated MATPLM1.1 program. All periods (IMI) between PLMS containing an iLM were compared to the local PLMS period defined as the immediately preceding PLMS IMI using pairwise two-sided Wilcoxon sign-rank tests. Similarly, iLM were tested to see if they started a new PLMS series by having the same period as the subsequent PLMS.Results: The periods (IMIs) containing iLM were longer than the previous periods in RLS subjects, but not controls (p <.05). The periods beginning with the iLM were shorter than the subsequent periods in both RLS and controls (p < .05).Conclusions: iLM as a separate type of LM distort PLMS periodicity and do not restart PLMS series. iLM end PLMS series.

Aim: Periodic leg movements in sleep (PLMS) are generally evaluated by the number of events per hour during sleep, but this is an unstable measure with marked nightly variability and also fails to assess the basic periodicity that essentially characterizes these movements. The inter-movement interval (IMI) evaluates a putative biological process producing the period of PLMS. By contrast, the actual number of PLMS reflects the expression of this biological process that would likely be affected by multiple factors, particularly those disrupting sleep. Thus, this study tests the hypothesis that measurement of IMI duration should be more stable over nights in comparison to any measure based on counting the number of movements. IMI approximates a log-normal distribution. This study, therefore, tests the hypothesis that the means of the log IMI are more stable from night 1 (NI) to night 2 (N2) sleep recordings compared with measures of the number of PLMS.Methods: PLMS/h and IMI were measured for two consecutive nights of full sleep recordings for 29 restless legs syndrome (RLS) patients not being treated and 22 healthy controls without RLS. N1-N2 difference between nights was measured as percent of average for the nights.Results: Mean log IMI showed little nightly variability (mean +/- SD for RLS: 3.6%+/- 3.7, controls: 7.1%+/- 7.0) significantly less p < 0.001) in comparison to PLMS/h (mean +/- SD for RLS: 43.2 +/- 37.1, controls: 63.7 +/- 40.8). The IMI nightly variability was also significantly better than that for the periodicity index. IMI also varied considerably between individuals.Conclusion: Mean log IMI is a remarkably stable measure across nights within a subject and shows differences between subjects that may have clinical and biological significance. Because of this consistency, the mean log IMI should be considered as one standard measure of PLMS alongside the PLMS index. (C) 2015 Elsevier B.V. All rights reserved.

Current standard guidelines for scoring periodic leg movements (PLM) define the start and end of a movement but fail to explicitly specify the movement morphology necessary to classify an EMG event as a PLM, rather than some other muscle event. This is currently left to the expert visual scorer to determine. This study aimed to define this morphology to provide a consistent standard for visual scoring and to improve automatic periodic leg movements in sleep scoring.

Background and Purpose: A Matrix Laboratory (MATLAB) script (MATPLM1) was developed to rigorously apply World Associations of Sleep Medicine (WASM) scoring criteria for periodic limb movements in sleep (PLMS) from bilateral electromyographic (EMG) leg recordings. This study compares MATPLM1 with both standard technician and expert detailed visual PLMS scoring.Methods and Subjects: Validation was based on a 'macro' level by agreement for PLMS/h during a night recording and on a 'micro' level by agreement for the detection of each PLMS from a stratified random sample for each subject. Data available for these analyses were from 15 restless leg syndrome (RLS) (age: 61.5 +/- 8.5, 60% female) and nine control subjects (age: 61.4 +/- 7.1, 67% female) participating in another study.Results: In the 'micro' analysis, MATPLM1 and the visual detection of PLMS events agreed 87.7% for technician scoring and 94.4% for expert scoring. The technician and MATPLM1 scoring disagreements were checked for 36 randomly selected events, 97% involved clear technician-scoring error. In the 'macro' analysis, MATPLM1 rates of PMLS/h correlated highly with visual scoring by the technician (r(2) = 0.97) and the expert scorer (r(2) = 0.99), but the technician scoring was consistently less than MATPLM1: median (quartiles) difference: 10 (5, 23). There was little disagreement with expert scorer [median (quartile) difference: -0.3 (-2.4, 0.3)].Conclusions: The MATPLM1 produces reliable scoring of PLMS that matches expert scoring. The standard visual scoring without careful measuring of events tends to significantly underscore PLMS. These preliminary results support the use of MATPLM1 as a preferred method of scoring PLMS for EMG recordings that are of a good quality and without significant sleep-disordered breathing events. (C) 2015 Elsevier B.V. All rights reserved.