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Background: Ambulatory assessments offer opportunities to study physical activity level (PAL) and affect at the group and person-level. We examined bidirectional associations between PAL and affect in a 3-h timeframe and evaluated whether associations differ between people with and without current or remitted depression/anxiety.Methods: Two-week ecological momentary assessment (EMA) and actigraphy data of 359 participants with current (n = 93), remitted (n = 176), or no (n = 90) Composite International Diagnostic Interview depression/anxiety diagnoses were obtained from the Netherlands Study of Depression and Anxiety. Positive affect (PA) and negative affect (NA) were assessed by EMA 5 times per day. Average PAL between EMA assessments were calculated from actigraphy data.Results: At the group-level, higher PAL was associated with subsequent higher PA (b = 0.109, p Conclusions: Higher PAL may improve affect, especially among depression/anxiety patients. As the relationships vary at the person-level, ambulatory assessments may help identify who would benefit from behavioral interventions.

BackgroundConsidering the heterogeneity of depression, distinct depressive symptom dimensions may be differentially associated with more objective actigraphy-based estimates of physical activity (PA), sleep and circadian rhythm (CR). We examined the association between PA, sleep, and CR assessed with actigraphy and symptom dimensions (i.e. mood/cognition, somatic/vegetative, sleep).MethodsFourteen-day actigraphy data of 359 participants were obtained from the Netherlands Study of Depression and Anxiety. PA, sleep, and CR estimates included gross motor activity (GMA), sleep duration (SD), sleep efficiency (SE), relative amplitude between daytime and night-time activity (RA) and sleep midpoint. The 30-item Inventory of Depressive Symptomatology was used to assess depressive symptoms, which were categorised in three depression dimensions: mood/cognition, somatic/vegetative, and sleep.ResultsGMA and RA were negatively associated with higher score on all three symptom dimensions: mood/cognition (GMA: β = −0.155, p < 0.001; RA: β = −0.116, p = 0.002), somatic/vegetative (GMA: β = −0.165, p < 0.001; RA: β = −0.133, p < 0.001), sleep (GMA: β = −0.169, p < 0.001; RA: β = −0.190, p < 0.001). The association with sleep was more pronounced for two depression dimensions: longer SD was linked to somatic/vegetative (β = 0.115, p = 0.015) dimension and lower SE was linked to sleep (β = −0.101, p = 0.011) dimension.ConclusionAs three symptom dimensions were associated with actigraphy-based low PA and dampened CR, these seem to be general indicators of depression. Sleep disturbances appeared more linked to the somatic/vegetative and sleep dimensions; the effectiveness of sleep interventions in patients reporting somatic/vegetative symptoms may be explored, as well as the potential of actigraphy to monitor treatment response to such interventions.

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

BACKGROUND: Ambulatory assessments offer opportunities to evaluate daily dynamics of sleep and momentary affect using mobile technologies. This study examines day-to-day bidirectional associations between sleep and affect using mobile monitoring, and evaluates whether these associations differ between people without and with current or remitted depression/anxiety.METHODS: Two-week ecological momentary assessment (EMA) and actigraphy data of 359 participants with current (n = 93), remitted (n = 176) or no (n = 90) CIDI depression/anxiety diagnoses were obtained from the Netherlands Study of Depression and Anxiety. Objective sleep duration (SD) and efficiency were obtained from actigraphy data. Self-reported SD, sleep quality (SQ), positive affect (PA) and negative affect (NA) were assessed by electronic diaries through EMA.RESULTS: A bidirectional longitudinal association was found between self-reported SQ and affect, while no association was found for self-reported SD and objective SD and efficiency. Better SQ predicted affect the same day (higher PA: b = 0.035, p < 0.001; lower NA: b = -0.022, p < 0.001), while lower NA on the preceding day predicted better SQ (b = -0.102, p = 0.001). The presence of current depression/anxiety disorders moderated the association between better SQ and subsequent lower NA; it was stronger for patients compared to controls (p = 0.003).LIMITATIONS: Observational study design can only point to areas of interest for interventions.CONCLUSIONS: This 2-week ambulatory monitoring study shows that, especially among depression/anxiety patients, better self-reported SQ predicts higher PA and lower NA the same day, while lower NA predicts better self-reported SQ. The value of mobile technologies to monitor and potentially intervene in patients to improve their affect should be explored.

BACKGROUND Analyzing actigraphy data using standard circadian parametric models and aggregated nonparametric indices may obscure temporal information that may be a hallmark of the circadian impairment in psychiatric disorders. Functional data analysis (FDA) may overcome such limitations by fully exploiting the richness of actigraphy data and revealing important relationships with mental health outcomes. To our knowledge, no studies have extensively used FDA to study the relationship between sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics and daily motor activity patterns assessed with actigraphy in a sample of individuals with and without depression/anxiety. OBJECTIVE We aimed to study the association between daily motor activity patterns assessed via actigraphy and (1) sociodemographic, health and lifestyle, and sampling factors, and (2) psychiatric clinical characteristics (ie, presence and severity of depression/anxiety disorders). METHODS We obtained 14-day continuous actigraphy data from 359 participants from the Netherlands Study of Depression and Anxiety with current (n=93), remitted (n=176), or no (n=90) depression/anxiety diagnosis, based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition. Associations between patterns of daily motor activity, quantified via functional principal component analysis (fPCA), and sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics were assessed using generalized estimating equation regressions. For exploratory purposes, function-on-scalar regression (FoSR) was applied to quantify the time-varying association of sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics on daily motor activity. RESULTS Four components of daily activity patterns captured 77.4% of the variability in the data: overall daily activity level (fPCA1, 34.3% variability), early versus late morning activity (fPCA2, 16.5% variability), biphasic versus monophasic activity (fPCA3, 14.8% variability), and early versus late biphasic activity (fPCA4, 11.8% variability). A low overall daily activity level was associated with a number of sociodemographic, health and lifestyle, and psychopathology variables: older age (PP=.005), higher BMI (P=.009), greater number of chronic diseases (P=.02), greater number of cigarettes smoked per day (P=.02), current depressive and/or anxiety disorders (P=.05), and greater severity of depressive symptoms (PP=.02) and summer (reference: winter; P=.03). Earlier morning activity was associated with older age (P=.02), having a partner (P=.009), work/school days (PP=.02 and PP=.005). Biphasic activity was associated with work/school days (PPP=.005), work/school days (PPP=.005, respectively). In FoSR analyses, age, work/school days, and season were the main determinants having a time-varying association with daily motor activity (all P CONCLUSIONS Features of daily motor activity extracted with fPCA reflect commonly studied factors such as the intensity of daily activity and preference for morningness/eveningness. The presence and severity of depression/anxiety disorders were found to be associated mainly with a lower overall activity pattern but not with the time of the activity. Age, work/school days, and season were the variables most strongly associated with patterns and time of activity, and thus future epidemiological studies on motor activity in depression/anxiety should take these variables into account.

BACKGROUND: Actigraphy may provide a more valid assessment of sleep, circadian rhythm (CR), and physical activity (PA) than self-reported questionnaires, but has not been used widely to study the association with depression/anxiety and their clinical characteristics.METHODS: Fourteen-day actigraphy data of 359 participants with current (n = 93), remitted (n = 176), or no (n = 90) composite international diagnostic interview depression/anxiety diagnoses were obtained from the Netherlands Study of Depression and Anxiety. Objective estimates included sleep duration (SD), sleep efficiency, relative amplitude (RA) between day-time and night-time activity, mid sleep on free days (MSF), gross motor activity (GMA), and moderate-to-vigorous PA (MVPA). Self-reported measures included insomnia rating scale, SD, MSF, metabolic equivalent total, and MVPA.RESULTS: Compared to controls, individuals with current depression/anxiety had a significantly different objective, but not self-reported, PA and CR: lower GMA (23.83 vs. 27.4 milli-gravity/day, p = .022), lower MVPA (35.32 vs. 47.64 min/day, p = .023), lower RA (0.82 vs. 0.83, p = .033). In contrast, self-reported, but not objective, sleep differed between people with current depression/anxiety compared to those without current disorders; people with current depression/anxiety reported both shorter and longer SD and more insomnia. More depressive/anxiety symptoms and number of depressive/anxiety diagnoses were associated with larger disturbances of the actigraphy measures.CONCLUSION: Actigraphy provides ecologically valid information on sleep, CR, and PA that enhances data from self-reported questionnaires. As those with more severe or comorbid forms showed the lowest PA and most CR disruptions, the potential for adjunctive behavioral and chronotherapy interventions should be explored, as well as the potential of actigraphy to monitor treatment response to such interventions.

Background There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. Methods RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). Discussion This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed. Electronic supplementary material The online version of this article (10.1186/s12888-019-2049-z) contains supplementary material, which is available to authorized users.

Lower physical activity and sleep problems are linked to depressive and anxiety disorders and can be assessed objectively with wrist-worn actigraphy devices that measure gross motor activity. Standard methods to model 24-h actigraphy are often based on summary measures such as activity counts per day or parametric functional curves (e.g., cosinor functions), losing important information that may be a hallmark of the circadian impairment in psychiatric disorders. Functional Principal Component Analysis (fPCA), a powerful data-driven technique, may better describe daily activity patterns and provide a greater insight into the relationship with depression and anxiety. We assessed patterns of daily activity extracted with fPCA and will evaluate how they are associated with DSM-IV depression and anxiety disorders. Two-week actigraphy data of 367 participants with current (n=94), remitted (n=176) or no (n=90) depression and/or anxiety were obtained from the Netherlands Study of Depression and Anxiety. Daily minute-to-minute activity data were represented with a set of nine Fourier-based functions and summarized with a smaller set of functional principal components with the R package fda. Preliminary analyses resulted in four functional principal components explaining 77.4% of variability (Fig. 1): 1) fPCA1 represented high (+) versus dampened and shifted delayed (-) activity; 2) fPCA2 described morning (+) versus evening (-) activity; 3) fPCA3 showed biphasic (+) versus monophasic (-) activity; 4) fPCA4 represented biphasic morning (+) versus biphasic evening (-) activity. First analyses on the association of the extracted features with depression and anxiety using GEE analysis seem to indicate that current depression and anxiety are significantly associated with lower fPCA1 (?= -0.298, standard error (se) = 0.042, p = 0.006) but not with the other components. Further analyses will be conducted to evaluate if specific depression and anxiety characteristics (e.g. age of onset, duration, comorbidity) are associated with the components.

Risk of psychotic relapse in schizophrenic patients is commonly measured by social functioning (SF), which focuses on patients' daily activities. Monitoring of SF usually relies on infrequent clinic visits, limiting the capacity to detect sudden changes. GPS data that is passively collected with smartphones introduce new opportunities to monitor SF. We conducted a five-day pilot study with five schizophrenic patients to assess the feasibility of this approach. Participants used a smartphone to continuously record their GPS location, and completed a paper-based SF diary to register out-of-home activities. We implemented a time-based method and a density-based method to identify the geolocations visited and then we clustered geolocations visited in places visited. Finally, we used semantic enrichment to classify places types and associated activities. We evaluated the performance of the two approaches by comparing the activities detected from the GPS data with those recorded in the SF diary. Recall was better for the density-based method, ranging from 0.686 (Standard Deviation [SD] 0.168) to 0.771 (SD 0.264) while precision was better for the time-based method (0.722 (SD 0.197) to 0.954 (SD 0.093)). To conclude, using routinely collected GPS data and relatively simple analytical methods we detected patients' out-of-home activities with moderate recall, more sophisticated analytical methods may obtain better performance.