Investigating the Provision and Context of Use of Hearing Aid Listening Programs From Real-world Data: Observational Study.

BACKGROUND: Listening programs enable hearing aid (HA) users to change device settings for specific listening situations and thereby personalize their listening experience. However, investigations into real-world use of such listening programs to support clinical decisions and evaluate the success of HA treatment are lacking. OBJECTIVE: We aimed to investigate the provision of listening programs among a large group of in-market HA users and the context in which the programs are typically used. METHODS: First, we analyzed how many and which programs were provided to 32,336 in-market HA users. Second, we explored 332,271 program selections from 1312 selected users to investigate the sound environments in which specific programs were used and whether such environments reflect the listening intent conveyed by the name of the used program. Our analysis was based on real-world longitudinal data logged by smartphone-connected HAs. RESULTS: In our sample, 57.71% (18,663/32,336) of the HA users had programs for specific listening situations, which is a higher proportion than previously reported, most likely because of the inclusion criteria. On the basis of association rule mining, we identified a primary additional listening program, Speech in Noise, which is frequent among users and often provided when other additional programs are also provided. We also identified 2 secondary additional programs (Comfort and Music), which are frequent among users who get ≥3 programs and usually provided in combination with Speech in Noise. In addition, 2 programs (TV and Remote Mic) were related to the use of external accessories and not found to be associated with other programs. On average, users selected Speech in Noise, Comfort, and Music in louder, noisier, and less-modulated (all P<.01) environments compared with the environment in which they selected the default program, General. The difference from the sound environment in which they selected General was significantly larger in the minutes following program selection than in the minutes preceding it. CONCLUSIONS: This study provides a deeper insight into the provision of listening programs on a large scale and demonstrates that additional listening programs are used as intended and according to the sound environment conveyed by the program name.

Effects of Structured Supervised Exercise Training or Motivational Counseling on Pregnant Women's Physical Activity Level: FitMum - Randomized Controlled Trial.

BACKGROUND: Physical activity (PA) during pregnancy is an effective and safe way to improve maternal health in uncomplicated pregnancies. However, compliance with PA recommendations remains low among pregnant women. OBJECTIVE: The purpose of this study was to evaluate the effects of offering structured supervised exercise training (EXE) or motivational counseling on PA (MOT) during pregnancy on moderate-to-vigorous intensity physical activity (MVPA) level. Additionally, complementary measures of PA using the Pregnancy Physical Activity Questionnaire (PPAQ) and gold standard doubly labeled water (DLW) technique were investigated. The hypotheses were that both EXE and MOT would increase MVPA in pregnancy compared with standard care (CON) and that EXE would be more effective than MOT. In addition, the association between MVPA and the number of sessions attended was explored. METHODS: A randomized controlled trial included 220 healthy, inactive pregnant women with a median gestational age of 12.9 (IQR 9.4-13.9) weeks. A total of 219 women were randomized to CON (45/219), EXE (87/219), or MOT (87/219). The primary outcome was MVPA (minutes per week) from randomization to the 29th gestational week obtained by a wrist-worn commercial activity tracker (Vivosport, Garmin International). PA was measured by the activity tracker throughout pregnancy, PPAQ, and DLW. The primary outcome analysis was performed as an analysis of covariance model adjusting for baseline PA. RESULTS: The average MVPA (minutes per week) from randomization to the 29th gestational week was 33 (95% CI 18 to 47) in CON, 50 (95% CI 39 to 60) in EXE, and 40 (95% CI 30 to 51) in MOT. When adjusted for baseline MVPA, participants in EXE performed 20 (95% CI 4 to 36) minutes per week more MVPA than participants in CON (P=.02). MOT was not more effective than CON; EXE and MOT also did not differ. MVPA was positively associated with the number of exercise sessions attended in EXE from randomization to delivery (P=.04). Attendance was higher for online (due to COVID-19 restrictions) compared with physical exercise training (P=.03). Adverse events and serious adverse events did not differ between groups. CONCLUSIONS: Offering EXE was more effective than CON to increase MVPA among pregnant women, whereas offering MOT was not. MVPA in the intervention groups did not reach the recommended level in pregnancy. Changing the intervention to online due to COVID-19 restrictions did not affect MVPA level but increased exercise participation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03679130; https://clinicaltrials.gov/ct2/show/NCT03679130. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-043671.

Effects of Two Physical Activity Interventions on Sleep and Sedentary Time in Pregnant Women.

Pregnancy is often associated with poor sleep and high sedentary time (SED). We investigated the effect of physical activity (PA) interventions on sleep and SED in pregnant women. A secondary analysis of a randomized controlled trial (n = 219) explored the effect of structured supervised exercise training (EXE) or motivational counseling on PA (MOT) compared to standard prenatal care (CON) on sleep and SED during pregnancy. Three times during pregnancy, sleep was determined by the Pittsburgh Sleep Quality Index (PSQI) and SED by the Pregnancy Physical Activity Questionnaire (PPAQ). Also, a wrist-worn consumer activity tracker measured sleep and SED continuously. Data from the activity tracker confirmed that sleep time decreases, and SED increases by approx. 30 and 24 min/day, respectively, from baseline (maximum gestational age (GA) week 15) to delivery. Compared to CON, the global PSQI score was better for EXE in GA week 28 (-0.8 [-1.5; -0.1], p = 0.031) and for both EXE and MOT in GA week 34 (-1 [-2; -0.5], p = 0.002; -1 [-2; -0.1], p = 0.026). In GA week 28, SED (h/day) from PPAQ was lower in EXE compared to both CON and MOT (-0.69 [-1; -0.0], p = 0.049; -0.6 [-1.0; -0.02], p = 0.042). In conclusion, PA interventions during pregnancy improved sleep quality and reduced SED.

Clustering Users Based on Hearing Aid Use: An Exploratory Analysis of Real-World Data.

While the assessment of hearing aid use has traditionally relied on subjective self-reported measures, smartphone-connected hearing aids enable objective data logging from a large number of users. Objective data logging allows to overcome the inaccuracy of self-reported measures. Moreover, data logging enables assessing hearing aid use with a greater temporal resolution and longitudinally, making it possible to investigate hourly patterns of use and to account for the day-to-day variability. This study aims to explore patterns of hearing aid use throughout the day and assess whether clusters of users with similar use patterns can be identified. We did so by analyzing objective hearing aid use data logged from 15,905 real-world users over a 4-month period. Firstly, we investigated the daily amount of hearing aid use and its within-user and between-user variability. We found that users, on average, used the hearing aids for 10.01 h/day, exhibiting a substantial between-user (SD = 2.76 h) and within-user (SD = 3.88 h) variability. Secondly, we examined hearing aid use hourly patterns by clustering 453,612 logged days into typical days of hearing aid use. We identified three typical days of hearing aid use: full day (44% of days), afternoon (27%), and sporadic evening (26%) day of hearing aid use. Thirdly, we explored the usage patterns of the hearing aid users by clustering the users based on the proportion of time spent in each of the typical days of hearing aid use. We found three distinct user groups, each characterized by a predominant (i.e., experienced ~60% of the time) typical day of hearing aid use. Notably, the largest user group (49%) of users predominantly had full days of hearing aid use. Finally, we validated the user clustering by training a supervised classification ensemble to predict the cluster to which each user belonged. The high accuracy achieved by the supervised classifier ensemble (~86%) indicated valid user clustering and showed that such a classifier can be successfully used to group new hearing aid users in the future. This study provides a deeper insight into the adoption of hearing care treatments and paves the way for more personalized solutions.

Structured supervised exercise training or motivational counselling during pregnancy on physical activity level and health of mother and offspring: FitMum study protocol.

INTRODUCTION: A physically active lifestyle during pregnancy improves maternal and offspring health but can be difficult to follow. In Denmark, less than 40% of pregnant women meet physical activity (PA) recommendations. The FitMum study aims to explore strategies to increase PA during pregnancy among women with low PA and assess the health effects of PA. This paper presents the FitMum protocol, which evaluates the effects of structured supervised exercise training or motivational counselling supported by health technology during pregnancy on PA level and health of mother and offspring. METHODS AND ANALYSIS: A single-site three-arm randomised controlled trial that aims to recruit 220 healthy, pregnant women with gestational age (GA) no later than week 15 and whose PA level does not exceed one hour/week. Participants are randomised to one of three groups: structured supervised exercise training consisting of three weekly exercise sessions, motivational counselling supported by health technology or a control group receiving standard care. The interventions take place from randomisation until delivery. The primary outcome is min/week of moderate-to-vigorous intensity PA (MVPA) as determined by a commercial activity tracker, collected from randomisation until GA of 28 weeks and 0-6 days, and the secondary outcome is gestational weight gain (GWG). Additional outcomes are complementary measures of PA; clinical and psychological health parameters in participant, partner and offspring; analyses of blood, placenta and breastmilk samples; process evaluation of interventions; and personal understandings of PA. ETHICS AND DISSEMINATION: The study is approved by the Danish National Committee on Health Research Ethics (# H-18011067) and the Danish Data Protection Agency (# P-2019-512). Findings will be disseminated via peer-reviewed publications, at conferences, and to health professionals via science theatre performances. TRIAL REGISTRATION NUMBER: NCT03679130. PROTOCOL VERSION: This paper was written per the study protocol version 8 dated 28 August 2019.

SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events.

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.

The GO-ACTIWE randomized controlled trial - An interdisciplinary study designed to investigate the health effects of active commuting and leisure time physical activity.

Regular physical activity is efficacious for improving metabolic health in overweight and obese individuals, yet, many adults lead sedentary lives. Most exercise interventions have targeted leisure time, but physical activity also takes place in other domains of everyday life. Active commuting represents a promising alternative to increase physical activity, but it has yet to be established whether active commuting conveys health benefits on par with leisure time physical activity (LTPA). A 6-month randomized controlled trial was designed to investigate the effects of increased physical activity in transport (bicycling) or leisure time domains (moderate or vigorous intensity endurance exercise). We included 188 overweight and class 1 obese sedentary women and men (20-45years) of which 130 were randomized to either sedentary controls (n=18), active commuting (n=35) or moderate (n=39) or vigorous (n=38) intensity LTPA. At baseline and after 3 and 6months, participants underwent a rigorous 3-day biomedical test regimen followed by free-living measurements. In a sub-sample, physical activity level and energy expenditure were monitored by means of personal assistive technology and the doubly labeled water technique. Additionally, the delivery, reception and routinization of the exercise regimens were investigated by ethnological fieldwork. One year after termination of the intervention, participants will be invited for a follow-up visit to investigate sustained health effects and continuous physical activity adherence. By combining biomedical, technological and humanistic approaches, we aim to understand the health benefits of physical activity in different domains of everyday life, as well as how to improve adherence to physical activity.

The smartphone brain scanner: a portable real-time neuroimaging system.

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

Measuring large-scale social networks with high resolution.

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

Smartphones as pocketable labs: visions for mobile brain imaging and neurofeedback.

Mobile brain imaging solutions, such as the Smartphone Brain Scanner, which combines low cost wireless EEG sensors with open source software for real-time neuroimaging, may transform neuroscience experimental paradigms. Normally subject to the physical constraints in labs, neuroscience experimental paradigms can be transformed into dynamic environments allowing for the capturing of brain signals in everyday contexts. Using smartphones or tablets to access text or images may enable experimental design capable of tracing emotional responses when shopping or consuming media, incorporating sensorimotor responses reflecting our actions into brain machine interfaces, and facilitating neurofeedback training over extended periods. Even though the quality of consumer neuroheadsets is still lower than laboratory equipment and susceptible to environmental noise, we show that mobile neuroimaging solutions, like the Smartphone Brain Scanner, complemented by 3D reconstruction or source separation techniques may support a range of neuroimaging applications and thus become a valuable addition to high-end neuroimaging solutions.

An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality.

EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup's dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.