Daily fluctuations of negative affect are only weakly associated with tremor symptoms in functional and organic tremor patients

BACKGROUND: There is a long-standing research history on the presumed psychological origin of functional movement disorders. Most studies do not address the heterogeneity in functional movement disorders and do not distinguish between risk factors, causes and consequences. We studied the associations between negative affect and objective as well as subjective symptom levels in patients with functional and organic tremor. METHODS: Thirty-three patients with a functional (14) or organic tremor (19) completed a web-based diary on subjective symptom burden and negative affect, five times a day for 30 days (total number of observations = 4759). During the same period, the participants wore an accelerometer to objectively record tremor. Vector autoregressive modelling was used to determine the time-lagged and contemporaneous associations between negative affect and objective/subjective tremor symptoms, both on an individual and a group level. RESULTS: In contrast to previous literature, patients with a functional or organic tremor showed a weak contemporaneous association between negative affect and objective/subjective tremor symptoms (on average r = 0.038 and 0.174 respectively). Time-lagged associations between negative affect and objective/subjective tremor symptoms were mixed in effect and direction and only present in a subset of patients, with no differences between patients with functional or organic tremor. CONCLUSIONS: Negative affect is only weakly associated with objective/subjective tremor symptoms, both on the contemporaneous and time-lagged associations, and these associations were mainly similar between patients with functional or organic tremor. These results argue against a strong influence of daily stress on tremor symptoms in patients with a functional or organic tremor

Personalized lifestyle advice alters affective reactivity to negative events in anhedonic young adults

BACKGROUND: Anhedonia is a common symptom of several disorders, but cost-effective treatments that focus on anhedonia specifically have been lacking. Therefore, personalized lifestyle advice has recently been investigated as a suitable means of enhancing pleasure and positive affect (PA) in young adults with anhedonia. This intervention provided individuals with a personalized lifestyle advice which was based on observed individual patterns of lifestyle behaviors and experienced pleasure in daily life. The present study extends this previous work by examining a potential mechanism of treatment success, affective reactivity. METHODS: We explored changes in affective reactivity to events in daily life from pre- to post-intervention in a subclinical sample of young adults with anhedonia (N = 69). Using the Experience Sampling Method (ESM), participants answered questions on their activities, their pleasure levels, PA and negative affect (NA) before and after the intervention. RESULTS: Multilevel analysis revealed that participants did not experience an altered affective reactivity to positive events after the intervention. The affective reactivity to negative events depended on the level of improvement in mean-PA after the lifestyle advice intervention. LIMITATIONS: The present study used a subclinical sample with the majority of participants being female which limited the generalizability of the findings. CONCLUSION: This study suggests that an altered affective reactivity to negative events is an underlying mechanism of the effectiveness of a personalized lifestyle advice

Exploring the emotional dynamics of subclinically depressed individuals with and without anhedonia:An experience sampling study

Background: Anhedonia has been linked to worse prognosis of depression. The present study aimed to construct personalized models to elucidate the emotional dynamics of subclinically depressed individuals with versus without symptoms of anhedonia. Methods: Matched subclinically depressed individuals with and without symptoms of anhedonia (N = 40) of the HowNutsAreTheDutch sample completed three experience sampling methodology assessments per day for 30 days. For each individual, the impact of physical activity, stress experience, and high/low arousal PA/NA on each other was estimated through automated impulse response function analysis (IRF). These individual IRF associations were combined to compare anhedonic versus non-anhedonic individuals. Results: Physical activity had low impact on affect in both groups. In non-anhedonic individuals, stress experience increased NA and decreased PA and physical activity more strongly. In anhedonic individuals, PA high arousal showed a diminished favorable impact on affect (increasing NA/stress experience, decreasing PA/physical activity). Finally, large heterogeneity in the personalized models of emotional dynamics were found. Limitations: Stress experience was measured indirectly by assessing level of distress; the timeframe in between measurements was relatively long with 6 h; and only information on one of the two hallmarks of anhedonia, loss of interest, was gathered. Conclusions: Our results suggest different pathways of emotional dynamics underlie depressive symptomatology. Subclinically depressed individuals with anhedonic complaints are more strongly characterized by diminished favorable impact of PA high arousal and heightened NA reactivity, whereas subclinically depressed individuals without these anhedonic complaints seem more characterized by heightened stress reactivity. The automatically generated personalized models may offer patient-specific insights in emotional dynamics, which may show clinical relevance

Personalizing cognitive behavioral therapy for cancer-related fatigue using ecological momentary assessments followed by automated individual time series analyses:A case report series

Introduction: A common approach to personalizing psychological interventions is the allocation of treatment modules to individual patients based on cut-off scores on questionnaires, which are mostly based on group studies. However, this way, intraindividual variation and temporal dynamics are not taken into account. Automated individual time series analyses are a possible solution, since these can identify the factors influencing the targeted symptom in a specific individual, and associated modules can be allocated accordingly. The aim of this study was to illustrate how automated individual time series analyses can be applied to personalize cognitive behavioral therapy for cancer-related fatigue in cancer survivors and how this procedure differs from allocating modules based on questionnaires.Methods: This study was a case report series (n = 3). Patients completed ecological momentary assessments at the start of therapy, and after three treatment modules (approximately 14 weeks). Assessments were analyzed with AutoVAR, an R package that automates the process of finding optimal vector autoregressive models. The results informed the treatment plan.Results: Three cases were described. From the ecological momentary assessments and automated time series analyses three individual treatment plans were constructed, in which the most important predictor for cancer-related fatigue was treated first. For two patients, this led to the treatment ending after the follow-up ecological momentary assessments. One patient continued treatment until six months, the standard treatment time in regular treatment. All three treatment plans differed from the treatment plans informed by questionnaire scores.Discussion: This study is one of the first to apply time series analyses in systematically personalizing psychological treatment. An important strength of this approach is that it can be used for every modular cognitive behavioral intervention where each treatment module addresses specific maintaining factors. Whether or not personalized CBT is more efficacious than standard, non-personalized CBT remains to be determined in controlled studies comparing it to usual care.</p

Let's get Physiqual - An intuitive and generic method to combine sensor technology with ecological momentary assessments

The emergence of wearables and smartwatches is making sensors a ubiquitous technology to measure daily rhythms in physiological measures, such as movement and heart rate. An integration of sensor data from wearables and self-report questionnaire data about cognition, behaviors, and emotions can provide new insights into the interaction of mental and physiological processes in daily life. Hitherto no method existed that enables an easy-to-use integration of sensor and self-report data. To fill this gap, we present 'Physiqual', a platform for researchers that gathers and integrates data from commercially available sensors and service providers into one unified format for use in Ecological Momentary Assessments (EMA) or Experience Sampling Methods (ESM), and Quantified Self (QS). Physiqual currently supports sensor data provided by two well-known service providers and therewith a wide range of smartwatches and wearables. To demonstrate the features of Physiqual, we conducted a case study in which we assessed two subjects by means of data from an EMA study combined with sensor data as aggregated and exported by Physiqual. To the best of our knowledge, the Physiqual platform is the first platform that allows researchers to conveniently aggregate and integrate physiological sensor data with EMA studies. (C) 2016 Elsevier Inc. All rights reserved

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care:A Proof-of-Principle Study

BACKGROUND: Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. OBJECTIVE: This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. METHODS: We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). RESULTS: An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. CONCLUSIONS: Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use

Study protocol for a randomized controlled trial to explore the effects of personalized lifestyle advices and tandem skydives on pleasure in anhedonic young adults

Background:  Anhedonia is generally defined as the inability to feel pleasure in response to experiences that are usually enjoyable. Anhedonia is one of the two core symptoms of depression and is a major public health concern. Anhedonia has proven particularly difficult to counteract and predicts poor treatment response generally. It has often been hypothesized that anhedonia can be deterred by a healthy lifestyle. However, it is quite unlikely that a one-size-fits-all approach will be effective for everyone. In this study the effects of personalized lifestyle advice based on observed individual patterns of lifestyle behaviors and experienced pleasure will be examined. Further, we will explore whether a tandem skydive following the personalized lifestyle advice positively influences anhedonic young adults' abilities to carry out the recommended lifestyle changes, and whether this ultimately improves their self-reported pleasure. Methods:  Our study design is an exploratory intervention study, preceded by a cross-sectional survey as a screening instrument. For the survey, 2000 young adults (18-24 years old) will be selected from the general population. Based on survey outcomes, 72 individuals (36 males and 36 females) with persistent anhedonia (i.e., more than two months) and 60 individuals (30 males and 30 females) without anhedonia (non-anhedonic control group) will be selected for the intervention study. The non-anhedonic control group will fill out momentary assessments of pleasure and lifestyle behaviors three times a day, for one month. The anhedonic individuals will fill out momentary assessments for three consecutive months. After the first month, the anhedonic individuals will be randomly assigned to (1) no intervention, (2) lifestyle advice only, (3) lifestyle advice plus tandem skydive. The personalized lifestyle advice is based on patterns observed in the first month. Discussion:  The present study is the first to examine the effects of a personalized lifestyle advice and tandem skydive on pleasure in anhedonic young adults. Results of the present study may improve treatment for anhedonia, if the interventions are found to be effective

Stress Detection Using Experience Sampling: A Systematic Mapping Study

Stress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Molde University College, Specialized Univ. Norway, through support of the Open Access fund.Scopus2-s2.0-8512936724

Automating Vector Autoregression on Electronic Patient Diary Data

Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still require statistical expertize to operate. We propose a new application called Autovar, for the automation of finding vector autoregression models for time series data. The approach closely resembles the way in which experts work manually. Our proposal offers improvements over the manual approach by leveraging computing power, e.g., by considering multiple alternatives instead of choosing just one. In this paper, we describe the design and implementation of Autovar, we compare its performance against experts working manually, and we compare its features to those of the most used commercial solution available today. The main contribution of Autovar is to show that vector autoregression on a large scale is feasible. We show that an exhaustive approach for model selection can be relatively safe to use. This study forms an important step toward making adaptive, personalized treatment available and affordable for all branches of healthcare