Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.
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ABSTRACT:
SUBMITTER: Xia CH
PROVIDER: S-EPMC9163291 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesCharacterizing daily patterns of sleep, stress, presleep worry, and mood in adolescents with and without insomnia symptomatology.DesignTwo months of continuous wearable tracking and daily diary ratings.SettingFree-living conditions.ParticipantsNinety-three adolescents (59 girls; 16-19 years old) with (N = 47; 26 with clinical and 21 with sub-clinical) and without (N = 46; control) DSM-5 insomnia symptomatology.MeasurementsFitbit Charge 3 tracked sleep, heart rate, and steps. Evening electronic diaries collected ratings of daily stress, presleep worry, and mood.ResultsWhile sleep duration (control: 6.88 ± 1.41 hours; insomnia: 6.92 ± 1.28 hours), architecture, timing, and night-to-night variability were similar between groups, the insomnia group reported higher levels of stress and worry, being mainly related to "school". At the intraindividual level, stress and worry predicted shorter sleep duration and earlier wake up times, which, in turn, predicted higher stress the following day. Moreover, higher-than-usual stress predicted higher sleep-time heart rate, with a more consistent effect in adolescents with insomnia. Results were overall consistent after controlling for covariates and several robustness checks.ConclusionsThere is a bidirectional relationship between daily stress and sleep, with daily stress negatively impacting sleep, which in turn leads to more stress in adolescents with and without insomnia symptoms. Findings also highlight the complexity of insomnia in adolescence, in which the core clinical features (perceived sleep difficulties) and the critical factors (stress/worry) implicated in the pathophysiology of the disorder are not necessarily reflected in objective sleep indicators.
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Project description:The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.
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Project description:BackgroundSleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications.ObjectiveOur goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model.MethodsWe used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting.ResultsMost models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2≤0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set.ConclusionsThe approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic.
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Project description:Individual traits and population parameters can be used as proxies of processes taking place within a range of scales, thus improving the way we can evaluate species response to environmental variability. In intertidal rocky shores, patterns at the within-site scale, i.e., between centimeters to hundreds of meters, are important for understanding the population response into these highly variable environments. Here, we studied a rocky-shore mussel population at the within-site spatial scale (1) to test how intertidal height and orientation of the shore affect individual traits and population parameters, (2) to infer the link between individual and population level features, and (3) to explore the upscaling mechanisms driving population structure and processes. We analyzed the patterns of six population parameters: density, biomass, crowding, median individual size, recruitment and mortality rate, and four individual traits: growth rate, spawning phenology, size and condition index. Crowding was defined as the degree of overlapping of individuals within a given area, for which we created a "crowding index". Mussels were studied along the intertidal height gradient in two rocky shores with contrasted orientation at one site over a full year. Our results showed a significant effect of intertidal height and shore orientation on most of individual traits and population parameters studied. In contrast, biomass contained in a full covered surface did not vary in space nor in time. This pattern likely results from relatively constant crowding and a trade-off between median individuals' size and density. We hypothesize that growth, mortality and recruitment rates may all play roles in the stability of the crowding structure of mussel aggregations. Variation in spawning phenology between the two shores in the study site was also observed, suggesting different temporal dynamics of microclimate conditions. Interestingly, despite the different population size distribution between the two shores, our estimates indicate similar potential reproductive output. We hypothesize that the structure of the patches would tend to maintain or carry a maximum of biomass due to trade-offs between density and size while maintaining and maximizing the reproductive output. The patterns of spatial variability of individual traits and population parameters in our study site suggest that heterogeneous within-site conditions influence variation in individual performance and population processes. These results provide insights about the relationship between individual traits and how these relationships make patterns at the population level emerge. They provide baseline information necessary to improve models of metapopulation with spatially explicit processes.
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Project description:Understanding how people move within a geographical area, e.g. a city, a country or the whole world, is fundamental in several applications, from predicting the spatio-temporal evolution of an epidemic to inferring migration patterns. Mobile phone records provide an excellent proxy of human mobility, showing that movements exhibit a high level of memory. However, the precise role of memory in widely adopted proxies of mobility, as mobile phone records, is unknown. Here we use 560 million call detail records from Senegal to show that standard Markovian approaches, including higher order ones, fail in capturing real mobility patterns and introduce spurious movements never observed in reality. We introduce an adaptive memory-driven approach to overcome such issues. At variance with Markovian models, it is able to realistically model conditional waiting times, i.e. the probability to stay in a specific area depending on individuals' historical movements. Our results demonstrate that in standard mobility models the individuals tend to diffuse faster than observed in reality, whereas the predictions of the adaptive memory approach significantly agree with observations. We show that, as a consequence, the incidence and the geographical spread of a disease could be inadequately estimated when standard approaches are used, with crucial implications on resources deployment and policy-making during an epidemic outbreak.
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Project description:Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level,while the aggregated displacement distribution follows a power law with an exponential cutoff. Given the constraint on total travelling cost, this aggregated scaling law can be analytically predicted by the mixture nature of human travel under the principle of maximum entropy. A direct corollary of such theory is that the displacement distribution of a single mode of transportation should follow an exponential law, which also gets supportive evidences in known data. We thus conclude that the travelling cost shapes the displacement distribution at the aggregated level.
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Project description:Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity-cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases.
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Project description:Growing evidence suggests greater vulnerability of women than men to the adverse effects of alcohol on mood and sleep. However, the underlying neurobiological mechanisms are still poorly understood. Here, we examined sex difference in resting state functional connectivity in alcohol use disorder using a whole-brain data driven approach and tested for relationships with mood and self-reported sleep. To examine whether sex effects vary by severity of alcohol use disorder, we studied two cohorts: non-treatment seeking n = 141 participants with alcohol use disorder (low severity; 58 females) from the Human Connectome project and recently detoxified n = 102 treatment seeking participants with alcohol use disorder (high severity; 34 females) at the National Institute on Alcohol Abuse and Alcoholism. For both cohorts, participants with alcohol use disorder had greater sleep and mood problems than healthy control, whereas sex by alcohol use effect varied by severity. Non-treatment seeking females with alcohol use disorder showed significant greater impairments in sleep but not mood compared to non-treatment seeking males with alcohol use disorder, whereas treatment-seeking females with alcohol use disorder reported greater negative mood but not sleep than treatment-seeking males with alcohol use disorder. Greater sleep problems in non-treatment seeking females with alcohol use disorder were associated with lower cerebello-parahippocampal functional connectivity, while greater mood problems in treatment-seeking females with alcohol use disorder were associated with lower fronto-occipital functional connectivity during rest. The current study suggests that changes in resting state functional connectivity may account for sleep and mood impairments in females with alcohol use disorder. The effect of severity on sex differences might reflect neuroadaptive processes with progression of alcohol use disorder and needs to be tested with longitudinal data in the future.
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