Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD).
While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors.
Patients suffering from borderline personality disorder (BPD) are at elevated risk for suicidal thoughts and behaviors (STBs), but this well-described and clinically important association is not well-understood. Prior research suggests that STBs often function as an attempt to escape aversive affect, and that people with BPD experience stronger emotion reactivity and greater discomfort with emotion than those without BPD. Here, we tested whether negative affective states are more likely to predict suicidal thoughts among those with BPD than those without this disorder. Data on affective states and suicidal thoughts were collected several times per day from 35 psychiatric inpatients using their smartphones to capture real-time associations between negative affect and suicidal thoughts. Results revealed that the association between negative affective states (e.g., abandonment, desperation, guilt, hopelessness, loneliness, rage, self-hatred, and upset), and severity of suicidal thinking was stronger among those with BPD than among those without BPD. This finding has implications for risk assessment and intervention in the clinical setting: for a given degree of reported negative affect, patients with BPD experience more suicidal ideation than those without. Further research needs to be done to elucidate the mechanism of this effect.
To examine whether there are subtypes of suicidal thinking using real-time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible.
Abstract: Two studies examined 2 important but previously unanswered questions about the experience of suicidal ideation: (a) How does suicidal ideation vary over short periods of time?, and (b) To what degree do risk factors for suicidal ideation vary over short periods and are such changes associated with changes in suicidal ideation? Participants in Study 1 were 54 adults who had attempted suicide in the previous year and completed 28 days of ecological momentary assessment (EMA; average of 2.51 assessments per day; 2,891 unique assessments). Participants in Study 2 were 36 adult psychiatric inpatients admitted for suicide risk who completed EMA throughout their time in the hospital (average stay of 10.32 days; average 2.48 assessments per day; 649 unique assessments). These studies revealed 2 key findings: (a) For nearly all participants, suicidal ideation varied dramatically over the course of most days: more than 1-quarter (Study 1 ! 29%; Study 2 ! 28%) of all ratings of suicidal ideation were a standard deviation above or below the previous response from a few hours earlier and nearly all (Study 1 ! 94.1%; Study 2 ! 100%) participants had at least 1 instance of intensity of suicidal ideation changing by a standard deviation or more from 1 response to the next. (b) Across both studies, well-known risk factors for suicidal ideation such as hopelessness, burdensomeness, and loneliness also varied considerably over just a few hours and correlated with suicidal ideation, but were limited in predicting short-term change in suicidal ideation. These studies represent the most fine-grained examination of suicidal ideation ever conducted. The results advance the understanding of how suicidal ideation changes over short periods and provide a novel method of improving the short-term prediction of suicidal ideation.
We respond to the commentaries of Critchley and Nagai, Mendes, Norman, Sabatinelli, and Richter. We agree that a theory needs to make predictions and we elaborate on the predictions we made so far. We do not agree that arousal has to have a precise definition in order to present theory about it; however, we do provide concrete answers to questions raised about multiple arousal theory.
Abstract: Using “big data” from sensors worn continuously outside the lab, researchers have observed patterns of objective physiology that challenge some of the long-standing theoretical concepts of emotion and its measurement. One challenge is that emotional arousal, when measured as sympathetic nervous system activation through electrodermal activity, can sometimes differ significantly across the two halves of the upper body. We show that traditional measures on only one side may lead to misjudgment of arousal. This article presents daily life and controlled study data, as well as existing evidence from neuroscience, supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body. We describe how a theory of multiple arousals explains the asymmetric EDA findings.
Abstract: An emerging trend in many applications is to use resource-constrained wireless devices for machine-to-machine (M2M) communications. The observed proliferation of wireless embedded systems is expected to have a significant impact on future M2M applications if the services provided can be automatically discovered and accessed at runtime. In order to realize the decoupling of M2M applications and services, energy efficient service discovery mechanisms must be designed so as to minimize human intervention during configuration and management phases. However, many traditional service discovery protocols cannot be applied to wireless constrained devices because they introduce too much overhead, fail in a duty-cycled environment or require significant memory resources. To address this, either new protocols are being proposed or existing ones are adapted to meet the requirements of constrained networks. In this article, we provide a
comprehensive overview of service discovery protocols that have been recently proposed for constrained M2M communications by the Internet Engineering Task Force (IETF). Advantages, disadvantages, performance and challenges of the existing solutions for different M2M scenarios are also analyzed.
Abstract: A common time reference across nodes is required in most Wireless Sensor Networks (WSNs) applications. It is needed, for example, to time-stamp sensor samples and for long-term duty cycling of nodes. Also many routing protocols require that nodes communicate according to some predefined schedule for reasons of energy efficiency. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. We explore whether energy efficiency can be enhanced through the use of cross-layer optimisations and present two novel cross-layer routing algorithms. The first protocol, designed for hierarchical, cluster based networks and called CLEAR (Cross Layer Efficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for e±cient duty cycling of nodes. The second method – called RISS (Routing Integrated Synchronization Service) – integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies.
We implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. We compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.