Markus Reichert

How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

We present MoA2, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a formative study, we evaluated the usability and unobtrusiveness of our mood assessment. A median SUS score of 90 shows a high usability. Subjects reported an easy, fast and intuitive use. The mood recognition was evaluated in terms of classification accuracy. First, we analyzed which features are best for the recognition. Spatio-temporal attributes, i.e. daytime, day of week and location, correlate most with the monitored mood. Based on the identified attributes, we trained personalized classifiers using Naïve Bayes and applied ten-fold-cross validation. The average recognition accuracy was 0.76 which is comparable to related work.

Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects

Background Abnormalities in motor activity represent a central feature in major depressive disorder. However, measurement issues are poorly understood, limiting the use of objective measurement of motor activity for diagnostics and treatment monitoring. Methods To improve measurement issues, especially sensor placement, analytic strategies and diurnal effects, we assessed motor activity in depressed patients at the beginning (MD; n=27) and after anti-depressive treatment (MD-post; n=18) as well as in healthy controls (HC; n=16) using wrist- and chest-worn accelerometers. We performed multiple analyses regarding sensor placements, extracted features, diurnal variation, motion patterns and posture to clarify which parameters are most powerful in distinguishing patients from controls and monitoring treatment effects. Results Whereas most feature-placement combinations revealed significant differences between groups, acceleration (wrist) distinguished MD from HC (d=1.39) best. Frequency (vertical axis chest) additionally differentiated groups in a logistic regression model (R2=0.54). Accordingly, both amplitude (d=1.16) and frequency (d=1.04) showed alterations, indicating reduced and decelerated motor activity. Differences between MD and HC in gestures (d=0.97) and walking (d=1.53) were found by data analysis from the wrist sensor. Comparison of motor activity at the beginning and after MD-treatment largely confirms our findings. Limitations Sample size was small, but sufficient for the given effect sizes. Comparison of depressed in-patients with non-hospitalized controls might have limited motor activity differences between groups. Conclusions Measurement of wrist-acceleration can be recommended as a basic technique to capture motor activity in depressed patients as it records whole body movement and gestures. Detailed analyses showed differences in amplitude and frequency denoting that depressed patients walked less and slower.

Leveraging smartwatches for unobtrusive mobile ambulatory mood assessment

In ambulatory assessment, subjects are monitored in everyday life. Though, it is difficult to unobtrusively assess information -- e.g. about their context and affective state -- which results in an increased burden for the subjects. This burden is caused by a complex self-report that they need to provide or by additional wearables that need to be carried. Newest technology can solve this issue by assessing a variety of information automatically. We propose to use smartwatches in combination with smartphones to assess physiological and smartphone data from which the affective state of a user can be inferred. We present the principle idea of our app and how we intend on evaluating it. A review of state of the art approaches and available Android Wear smartwatches in terms of sensors is given. We present a number of smartphone sensors and a selection of smartwatches whose combination should be evaluated regarding usefulness for mood assessment and recognition.

Exercise versus Nonexercise Activity: E-diaries Unravel Distinct Effects on Mood.

Introduction The association between physical activity and mood is of major importance to increase physical activity as a prevention strategy for noncommunicable diseases and to improve mental health. Unfortunately, existing studies examining how physical activity and mood wax and wane within persons over time in everyday life do show ambiguous findings. Taking a closer look at these studies reveals that the aggregation levels differ tremendously. Whereas mood is conceptualized as a three-dimensional construct, physical activity is treated as a global construct not taking into account its distinct components like exercise (such as jogging) and nonexercise activity (NEA; such as climbing stairs). Methods To overcome these limitations, we conducted an ambulatory assessment study on the everyday life of 106 adults over 7 d continuously measuring NEA via accelerometers and repeatedly querying for mood in real time via GPS-triggered e-diaries. We used multilevel modeling to derive differential within-subject effects of exercise versus NEA on mood and to conduct analyses on the temporal course of effects. Results Analyses revealed that exercise increased valence (beta = 0.023; P < 0.05) and calmness (beta = 0.022; P < 0.05). A tendency of decreasing energetic arousal (beta = −0.029) lacked significance. NEA, parameterized as 15-min episodes of physical activity intensity in everyday life, increased energetic arousal (beta = 0.135; P < 0.001) and decreased calmness (stand. beta = −0.080; P < 0.001). A tendency of increasing valence (beta = 0.014) lacked significance. Using longer time intervals for NEA revealed similar findings, thus confirming our findings. Conclusion Exercise and NEA differed regarding their within-subject effects on mood, whereas exercise increased valence and calmness, NEA increased energetic arousal and decreased calmness. Therefore, it appears necessary to clearly differentiate between exercise and NEA regarding their within-subject effects on mood dimensions in both research and treatment.

Within-Subject Associations between Mood Dimensions and Non-exercise Activity: An Ambulatory Assessment Approach Using Repeated Real-Time and Objective Data

A physically active lifestyle has been related to positive health outcomes and high life expectancy, but the underlying psychological mechanisms maintaining physical activity are rarely investigated. Tremendous technological progress yielding sophisticated methodological approaches, i.e., ambulatory assessment, have recently enabled the study of these mechanisms in everyday life. In practice, accelerometers allow to continuously and objectively monitor physical activity. The combination with e-diaries makes it feasible to repeatedly assess mood states in real-time and real life and to relate them to physical activity. This state-of-the-art methodology comes with several advantages, like bypassing systematic distortions of retrospective methods, avoiding distortions seen in laboratory settings, and revealing an objective physical activity assessment. Most importantly, ambulatory assessment studies enable to analyze how physical activity and mood wax and wane within persons over time in contrast to existing studies on physical activity and mood which mostly investigated between-person associations. However, there are very few studies on how mood dimensions (i.e., feeling well, energetic and calm) drive non-exercise activity (NEA; such as climbing stairs) within persons. Recent reviews argued that some of these studies have methodological limitations, e.g., scarcely representative samples, short study periods, physical activity assessment via self-reports, and low sampling frequencies. To overcome these limitations, we conducted an ambulatory assessment study in a community-based sample of 106 adults over 1 week. Participants were asked to report mood ratings on e-diaries and to wear an accelerometer in daily life. We conducted multilevel analyses to investigate whether mood predicted NEA, which was defined as the mean acceleration within the 10-min interval directly following an e-diary assessment. Additionally, we analyzed the effects of NEA on different time frames following the e-diary prompts in an exploratory manner. Our results revealed that valence significantly and positively predicted NEA within persons (p = 0.001). Feeling more energetic was associated with significantly increased NEA (p < 0.001), whereas feeling calmer was associated with significantly decreased NEA (p < 0.001) on the within-person level. The analyses on different time frames of NEA largely confirmed our findings. In conclusion, we showed that mood predicted NEA within adults but with distinct magnitudes and directions of effects for each mood dimension.

Identification of relevant sensor sources for context-aware ESM apps in ambulatory assessment

The experience sampling method (ESM) is applied in ambulatory assessment to prompt subject self-reporting. Existing mobile apps provide time-triggered prompts but lack event-triggers. Hence, the sampling might not occur in moments that are of interest for a psychologist. To identify relevant sensor sources and contexts we conducted an online survey with ambulatory assessment experts. Most relevant for these experts are time, date and user activity, followed by location, notifications and accelerometer. A feasibility test proved that all relevant sources are accessible on Android phones. We also assessed the desired granularity of the data gathered from each sensor source. Our results are a first step towards an ESM platform to create context-aware Android apps for ambulatory assessment.

GPS-triggered electronic diaries and neuroscience to unravel risk and resilience factors of city dwellers mental health in everyday life

Introduction Given the large influx of city dwellers worldwide, the considerably heightened prevalence of psychiatric disorders in cities is a macrosocial challenge. Recent evidence points towards an altered neuronal stress regulation, e.g., in the anterior cingulate cortex as a central underling phenotypic change caused by urbanicity [1]. However, it remains unknown, which specific influences in city dwellers everyday life serve as risk and resilience factors. Aims Thus, aiming to uncover potential risk and resilience factors of urbanicity, we investigate impacts of a broad range of specific everyday life influences such as air pollution, traffic noise, nature environment, social interaction, and positive or stressful events on mental health. In particular, we combine interactive ambulatory assessment, functional magnetic resonance imaging (fMRI) and geoinformatic approaches. As a proof of concept, we investigated neurobiological mechanisms underlying city dwellers positive event related affective changes in everyday life, often referred to as reward reactivity and proven to be a resilience marker for mental health. Methods In the context of the URGENCY study (Impact of Urbanicity on Genetics, Cerebral functioning and -structure in Young people), participants carried GPS-triggered electronic diaries and accelerometers [2,3] in their everyday life’s across 7 days [4]. To maximize the within-subject variance of the processes of interest, our energy efficient smartphone-algorithm traced participant’s locations continuously, applying real-time analyses to trigger e-diary assessments, e.g., on affect and event appraisal every time participant’s environmental surrounding changed [5]. After the study week we collected fMRI data. To analyze participant’s everyday life reward reactivity, we predicted positive affect by positive event appraisal applying a 2-stage multi-level model nesting e-diary assessments within participants, controlling for time of the day and allowing for both random intercepts and slopes. Thereafter, we conducted a second-level regression model with participant’s individual reward reactivity residuals predicting brain activation in the rostral anterior cingulate cortex (rACC) mask during a well established monetary and social incentive delay task with age and sex as nuisance regressors. Results In a sample of 65 healthy participants (52.3% females) aged 23.2 (range: 18-28) years, our multilevel analysis revealed both an overall effect (beta = 0.013; p = neutral contrast (peak voxel at x = 3, y = 35, z = 14; t(61) = 4.01; pFWE = 0.014) when controlling for age and sex. This effect remained stable when controlling for additional covariates such as neuroticism, social network index, current urbanicity, and task performance. Conclusions Our results showed that person’s individual affective reactivity to positive events in everyday life was linked to brain activation in the rostral anterior cingulate cortex suggesting a critical role of those circuits in conveying a resilience mechanism of everyday life reward reactivity. This proof of concept suggests that the combination of interactive ambulatory assessment and functional magnetic resonance imaging is a promising strategy to unravel everyday life risk and resilience factors of city dwellers mental health.

Psychische Erkrankungen bei Leistungssportlern

Besonders im Leistungssport liegt noch ein weiter Weg zum pragmatischen und offenen Umgang mit psychischen Erkrankungen vor uns. Als Wegweiser geben wir im Folgenden einen Überblick über Häufigkeit, Entstehung und Behandlungsmöglichkeiten psychischer Beschwerden bei Leistungssportlern. Dabei skizzieren wir Perspektiven, wie es gelingen kann, neben der körperlichen auch die psychische Gesundheit von Athleten mehr ins Blickfeld sowohl der Sportverantwortlichen als auch der Öffentlichkeit zu rücken.

A comparison of temporal and location-based sampling strategies for global positioning system-triggered electronic diaries.

Self-reporting is a well-established approach within the medical and psychological sciences. In order to avoid recall bias, i.e. past events being remembered inaccurately, the reports can be filled out on a smartphone in real-time and in the natural environment. This is often referred to as ambulatory assessment and the reports are usually triggered at regular time intervals. With this sampling scheme, however, rare events (e.g. a visit to a park or recreation area) are likely to be missed. When addressing the correlation between mood and the environment, it may therefore be beneficial to include participant locations within the ambulatory assessment sampling scheme. Based on the geographical coordinates, the database query system then decides if a self-report should be triggered or not. We simulated four different ambulatory assessment sampling schemes based on movement data (coordinates by minute) from 143 voluntary participants tracked for seven consecutive days. Two location-based sampling schemes incorporating the environmental characteristics (land use and population density) at each participants location were introduced and compared to a time-based sampling scheme triggering a report on the hour as well as to a sampling scheme incorporating physical activity. We show that location-based sampling schemes trigger a report less often, but we obtain more unique trigger positions and a greater spatial spread in comparison to sampling strategies based on time and distance. Additionally, the location-based methods trigger significantly more often at rarely visited types of land use and less often outside the study region where no underlying environmental data are available.