Search Results for sedentary Mood

Sedentary Mood Study – Part 3

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Sedentary Mood Study (Part III)

The following article is part of a series on the "SedentaryMood Study".

Preparations to determine the body position

As part of the "SedentaryMood Study," the Move 3 activity sensor was used for the first time to capture sedentary behavior as a primary target variable. Thus, some preparatory work was necessary in the run-up to the study. These were aimed primarily at the detection of the body position.

In order to determine the most suitable carrying position for the activity sensor and thus to differentiate between sitting and upright body positions, two video studies were carried out. On the one hand in everyday life with different activities and body positions and on the other hand during office work in everyday working life.

The evaluations of both measurements show that a carrying position of the activity sensor Move 3 between the sagittal axis and the longitudinal axis, an upright body position is assumed. If the sensor is in a position that exceeds the angular range of < 20° between the sagittal axis and the longitudinal axis, an upright body position is assumed. If the sensor is in a position that exceeds the angular range of > to the sagittal axis, then a seated / lying body position is specified.

As the measured data of the activity sensor Move 3 analyzed, coupled with the e-Diary movisensXS and triggered with the SensorTrigger you will learn in the next article ...

Sedentary Mood – Study II

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Study Diary "Sedentary Mood-Study"

Sedentary Behavior Study

The following article continues our series on "Sedentary Mood-Studies". Throughout the series we'll take you through a course of sedentary behavior studies, describing the process from planning to results. We'll start with the process of capturing the necessary data to examine the link between sedentary behavior and mood. To do this, we'll detail a particular sedentary behavior study to illustrate the idea.

Part 2: Recording mood and sedentary behavior data

When studying sedentary behavior, it's important to select a time frame that provides enough data to analyse. In this case, the researcher chose a five day period in an ambulatory setting to capture data in everyday life. Over this five day period participants received mood assessments several times per day, and had their activity recorded during waking hours.

To capture the participants mood, it's necessary to use an experience sampling method. For this study, the android based experience sampling app movisensXS displayed the short version of the "Multidimensional Mood Questionnaire (MDMQ)" at random intervals. The MDMQ measures the mood in three dimensions - Valence, Energetic Arousal, and Calmness - and was specifically conceptualized for ambulatory studies (see Wilhelm and Schoebi, 2007, p. 259ff.)

Whilst there's no technical device that captures sedentary behavior (see Kang and Rowe, 2015, p.113), the activity sensor serves as the de facto research instrument of choice. Given the abundance of fitness trackers on the market, it's often tempting to purchase inexpensive devices in order to obtain more data points. However, for research grade data it's important to use research grade devices. Whilst that may mean fewer devices and fewer participants, the quality of the data more than compensates.

The Sedentary Behavior Research Network (SBRN, 2017) defines Sedentary Behavior as: "Sedentary behavior is any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture". So in order to obtain an optimal recording, we need to evaluate both body position and energy expenditure.

In the next part of this series, we'll describe how the guidelines for sedentary behavior were met within this study

“Sedentary Mood Study”

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Study Diary "Sedentary Mood Study"

The following article begins a "Sedentary Behavior Studies" series, in which we'll detail the study process from conception through to results. We'll focus on the link between sedentary behavior and mood. But first, we need to address an important point.

Why research sedentary behavior?

"Whoever sits longer dies younger" and "Sitting is the new smoking". These headlines come from recent behavioral and health related studies. Due to such conclusions sedentary behavior demands the attention of researchers, and the general public looks on with interest. As the understanding of sedentary behavior and it's consequences develops, further research appears illustrating its adverse effects on cardiovascular health, metabolic and muscular parameters, and it's dire risk for overall physical health (Owen et al., 2010, p.3).

Despite these findings, there's little research on the effect of sedentary behavior on psychological health. Preliminary findings indicate a possible connection between the two. As Fuchs et al. suggest (2015. p. 7) "Sitting changes the activity of metabolism and therefore it doesn´t seem absurd, that this can also influence the psychological processes." If the link between sedentary behaviour and adverse psychological health exists, understanding it would prove valuable in the fight against psychosomatic diseases.

"How do we measure sedentary behavior and mood?" ... to be continued in the next articles

Sedentary Behaviour

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Sedentary behavior presents one of the 21st century's most pressing health concerns. Whilst the negative side effects of sedentary behavior feature prominently in recent scientific publications, the sheer extent of these negative effects remain largely unexplored due to the difficulty of accurately determining sedentary time. A range of different scenarios can influence the interpretation of sedentary behavior. If you're interested in contributing to the ongoing research into sedentary behavior we provide a range of solutions tailored to specific scenarios.


Recommendations

Suggestions for the valid and reliable measurement of sedentary behavior

Scenario 1: Low physical activity defined as sitting

When the daily routine of the participants consists primarily of sitting, as in the case with the majority of office employees, then measuring the general activity level of the participant should provide a useful measure of sedentary behavior. For these cohorts, the activity sensor sensor Move 4 worn on the hip provides a convenient way to track the activity levels of the participants. However, relying on this alone provides only a rough estimate of actual "sedentary behavior" time.

Scenario 2: The distinction between sitting/lying & standing

To distinguish between sitting/lying and standing remains a constant challenge for researchers. Through a useful refinement available through our analysis algorithms we've made this distinction possible. To take advantage of this feature requires the activity sensor Move 4 worn on the participant's thigh. Through assessing the different angles of the acceleration axes, our analysis software DataAnalyzer can differentiate between sit / lie & stand. However, it is not possible to distinguish between sitting and lying.

Scenario 3: Capture sedentary behavioral changes and the intensity of physical activity

Whilst extend periods of sitting remain the key focus of most research in sedentary behavior, there's also a strong desire from researchers to detemine the level of activity intensity when participants transition out of sedentary periods. To capture data on both the body position and energy expenditure, researchers can attach a Move 4 activity sensor to the participants thigh (sitting / lying vs. standing) and a Move 4 activity sensor on the upper body or hip (standing / sitting vs. lying) to determine energy expenditure.

Scenario 4: Detection of sedentary behavior changes with additional static load

The limitation of accelerometery in isolation bevomes apparent in cohorts when there's a likelihood that the participants undertake activities that incorporate a static load, such as lifting weights or stationary cycling. To combat this, we can use the ECG and activity sensor EcgMove 4 to capture additional data for determining energy expenditure. The combination of a Move 4 activity sensor on the thigh (sit / lie vs. stand) and the ECG and activity sensor EcgMove 4 on the upper body (standing / sitting vs. lying) provides an excellent overview of the activity level of the participant. The addition of the ECG signal enables better estimation of energy expenditure during static activities due to the linear relationship between cardiorespiratory stress and energy expenditure.

Scenario 5: Capturing sitting and staging interventions

Whilst accurately determining sedentary periods remains a worthy goal, assessing the motivations behind the behavior and implementing interventions seems the logical next stage of such research. When investigating the subjective feelings of participants, it's possible to trigger a questionnaire via the SensorTrigger app on our Experience Sampling Platform movisensXS during sedentary periods. Various triggering algorithms use on different physiological changes to prompt the participant to answer a series of questions. The "Sedentary" trigger sends a questionnaire after a determined amount of time in the sitting position (e.g. 30 minutes) elapses. This makes it possible to get more detailed feedback and thus more accurate insights into the everyday life of volunteers. Eventually, after capturing suitable data, the same process can function as an intervention, sending messages to assist in highlighting awareness of the behavior.

Sample study

Sedentary Mood-Study

What influence does Sedentary Behavior have on mood?

Despite recent research, the extent to which sedentary behavior affects mental health remains relatively unknown. However, the few available findings indicate that a connection could exist. "Sitting changes the activity of metabolism and therefore it doesn´t seem absurd, that this can also influence the psychological processes." If the link between sedentary behaviour and adverse psychological health exists, understanding it would prove valuable in the fight against psychosomatic diseases.


...read more in our Sedentary Mood study articles

SedentaryMood-Study Cover

Useful information

It's clear that moderate physical activity, including exercise, training and everyday activities, provide many health-level benefits. Recent research suggests that sedentary behavior such as prolonged, uninterrupted sitting poses a health risk. Instead of sitting, even light activity, such as standing and walking reduces that risk.

"Sedentary Behavior, Sedentariness or Sedentary Activity" refers to activity with a MET (Metabolic Equivalent of Task) of less than 1.0 to 1.5. International literature discusses sedentariness as a risk factor that works independently of physical activity. Studies suggest that someone who is occasionally active, but sits continuously for longer periods of time during the rest of the day, endangers his health just as much as the one who is inactive but lingers for less.

In order to assess sedentary behavior in a valid and reliable way, both measurement must include BOTH body position and energy expenditure! The daily duration of sedentary behavior remains the most commonly used measure. However, this fails to distinguish the relevance of different temporal patterns such as short phases vs. long periods. Therefore, we recommend measuring the duration of "non-sedentary behavior" as well as categorising physically active phases in addition to the total duration of sedentary behavior.


Overview activity sensors and their validity

Parameter

Wearing place

Move 4

ActiGraph

ActiPal

Body Position
(sitting/lying)

thigh

hip

K=.97

K=.78

-

K=.67

K=.85

-

Sedentary Behaviour

thigh

hip

K=.95

K=.84

-

K=.69

K=.90

-

Giurgiu M. et al. (2019). Journal for the Measurement of Physical Behaviour.

Read more about the validation of activity sensors and about the growing evidence that sedentary behavior is a risk factor for our health: Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

Literature

Ainsworth, B. E., Haskell, W. L., Hermann, S. D., Meckes, N., Basset Jr., D. R., Tudor-Locke, C.,Leon, A. S. (2011). 2011 Compendium of Physical Activities: a second update of codes and MET values. Med. Sci. Sports Exerc., 43, pp. 1575-1581.

Byrom, B., Stratton, G., McCarthy, M., & Muelhausen, W. (2016). Objective measurement of sedentary behaviour using accelerometers. Int. J. Obes, 40(Lond), pp. 1809-1812.

Carson, V., Wong, S. L., Winkler, E., Hearly, G. N., Colley, R. C., & Tremblay, M. S. (2014). Patterns of sedentary time and cardiometabolic risk among Canadian adults. Prev. Med., 65, pp. 23-27.

Fuchs, R. & Schlicht, W. (2015). Seelische Gesundheit und sportliche Aktivität: Zum Stand der Forschung. In R. Fuchs & W. Schlicht (Hrsg.), Seelische Gesundheit und sportliche Aktivität (S. 1-11). Göttingen: Hogrefe Verlag GmbH & Co. KG.

Holtermann, A., Schellewald, V., Mathiassen, S. E., Gupa, N., Pinder, A., Punakallio, A.,Ellegast, R. (2017). A practical guidance for assessments of sedentary behavior at work: A PEROSH initiative. Applied Ergonomics, 63, pp. 41-52.

SBRN. (2012). Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl. Physiol. Nutr. Metab., 37, pp. 540-542.



Move 4

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Activity Sensor

The best tool for measuring physical activity in everyday life!




Move 4 - Activity Sensor


Sensor getragen am Handgelenk

It has never been so easy to capture movement data quickly and precisely in everyday life!


The Move 4 combines excellent signal acquisition with unsurpassed convenience, a benefit for both participants and researchers alike. Begin your study assured in the quality of your data's fitness for publication.

High precision

Obtain precise data from our advanced device platform, capturing a multitude of signals at research level quality. Benefit from our tested and validated algorithms or conduct your own analysis from the raw data.


Whatever your approach, our sensors make data acquisition in every-day life easy and efficient. All the data you need, from when you need it.

Everyday life and real time

Analyse your participants in their every-day life with extensive measurement durations.


In addition, movisens offers the unique option of sensor coupling for all our sensors. Whilst recording the participants physiology, our sensors analyze physiological parameters and transmit the results via Bluetooth to a smartphone equipped with movisensXS. Algorithms within movisensXS process this data and can trigger a questionnaire for the participant when the set conditions occur. If you want to investigate physiological changes such as a high level of activity or sedentary behavior, such events can act as triggers for queries within the study.

Multimodal data analysis

Combine your activity data with experience sampling data.


The Move 4 offers researchers superior flexibility as our analysis algorithms extract accurate activity parameters from a variety of wearing positions. This allows you to find the most convenient solution for your participants and your study. To get the most out of the raw data captured by the sensor, most researchers prefer to analyze it using the movisens DataAnalyzer's, AI-based, validated algorithms.

Documentation

Benefit from our many years of experience in the field of ambulatory assessment and let us put together the right analysis solution for you

Software_DataAnalyzer

DataAnalyzer

Modular software for sensor data analysis and report generation
Smartphone mit movisensXS

movisensXS

The most comprehensive mobile research platform for Experience Sampling
Software DataMerger
DataMerger

Tool for the integration of data assessed with different methods

Our Move 4 is used in the following areas




running person

Activity Monitoring

  • Measurement of steps, activity classes, movement intensity and/or activity level
  • Energy Expenditure estimation in everyday life
  • Joint recording of objective and subjective aspects of physical activity

    • Recommendations for activity monitoring

Sedentary Behaviour




sitting woman



hängematte

Sleep Monitoring

Interactive Ambulatory Assessment

  • Investigating phases of physical activity and inactivity
  • Combining different sensor parameters
  • Trigger algorithms

    • Recommendations for IAA



young scientists



Cover sedentary mood study

Blog - SedentaryMood-Study

    What influence does Sedentary Behavior have on mood?

    The implementation of the study, the course of the study, the measurement methods as well as the measurement recommendations and findings will be presented and described here at regular intervals. The individual articles in the study diary are intended to build on each other to describe the overall course of the study.

    read more in our Sedentary Mood study articles

Look and Feel - Sensor triggered interactive ambulatory assessment

Matching products and services

ECGMove 4
EcgMove 4

Sensor for the acquisition of ECG- and activity data
EdaMove 4 worn
EdaMove 4

Sensor for the acquisition of electrodermal activity and activity data


Accessories and Consumables

Accessories and Consumables for the movisens sensors
Schulungs-Icon
Consulting

Consulting and Training regarding technologies and methods for ambulatory assessment and mobile monitoring
Customizing-Icon
Customizing

Adaption and extension of movisens products to your requirements
Workshops-Icon
Webinars

We offer webinars on themes related to ambulatory assessment, physiological parameters, and experience sampling

Useful Information

Raw data and live data

Raw data

Also known as primary data, this is obtained directly during an observation, measurement or data collection and remains unprocessed. It represents the original information recorded by sensors. Raw data from acceleration, ECG, and EDA are usually displayed as graphs. Calculating output parameters such as HR, steps, and energy expenditure from these raw signals requires algorithms fit for purpose.
Advantage:
Raw data allows long term storage and the implementation of new analysis methods, e.g. for a new algorithm, new AI and it can also be fed into other software.
Disadvantage:
They contain a large amount of data and require more storage space.

Live data

In addition to offline calculation, the Move 4 allows the live analysis of physical activity parameters in conjunction with movisensXS. In live mode, Movement Acceleration, Step Count, and Body Position can be measured, calculated and transmitted via the Bluetooth Smart interface at the rate of 1 value per minute.

Technical Data

Power supply

Lithium-Polymer-Battery

Battery voltage

3,7 V

Number of charging cycles

300 (with 1 C / 1 C > 80%)

Internal memory

4 GB

Maximum recording capacity

4 weeks

Battery run time

~ 7 days

Recharging time

~ 1 hour

Size of sensor (W x H x D)

62,3 mm x 38,6 mm x 11,5 mm

Weight of sensor

25 g

Protection rate

Waterproof (IP64)

Internal sensors

3D acceleration sensor:

Measurement range: +/- 16 g

Output rate: 64 Hz

 

Rotation rate sensor:

Measurement range: +/-2000dps

Resolution: 70 mdps

Output rate: 64 Hz

 

Pressure sensor:

Measurement range: 300 - 1100 hPa

Noise: 0,03 hPa

Output rate: 8 Hz

 

Temperature sensor:

Output rate: 1 Hz

Live analysis

Movement Acceleration

Step count

Indicators

LED, 3-color

Vibration alarm

User Interfaces

Marker (tapping)

Interfaces

Micro-USB, Bluetooth Smart (4.0)

API

Java API for USB (Windows)

Example for Bluetooth Smart (Android)

Wear locations

Hip, Chest, Wrist, Upper Arm, Ankle, Thigh

Wearing systems

Wrist Band, Belt Adapter and Belt

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

Atmospheric pressure:

300 to 1200 hPa absolute

Warranty

2 years

Literature and Validation

  • Influence of motivational interviewing on postoperative mobilization in the enhanced recovery after surgery (ERAS®) pathway in elective colorectal surgery - a randomized patient-blinded pilot study.
    R. Wiesenberger & J. Müller & M. Kaufmann et al. (2024) in: Langenbecks Arch Surg (409). Read more...
  • Impact of a Semi-Rigid Knee Orthotic Intervention on Pain, Physical Activity, and Functional Capacity in Patients with Medial Knee Osteoarthritis.
    B. J. Stetter & J. Fiedler & M. Arndt et al. (2024) in: Journal of Clinical Medicine (13 (6)). Read more...
  • Psychomotor Slowing in Psychosis and Inhibitory Repetitive Transcranial Magnetic Stimulation.
    S. Walther & D. Alexaki & F. Weiss et al. (2024) in: JAMA Psychiatry. Read more...
  • Exploring the Link between Lifestyle, Inflammation, and Insulin Resistance through an Improved Healthy Living Index.
    F. Bruckner & J.R. Gruber & A. Ruf et al. (2024) in: MDPI (16(3)). Read more...
  • Development of a Machine Learning Model to Detect Freezing of Gait in Parkinson Patients.
    K.Y. Hilbrants (2024). Read more...
  • Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years.
    F. Beck & I. Marzi & A. Eisenreich et al. (2023) in: BMC Sports Science, Medicine and Rehabilitation (15, 163). Read more...
  • Momentary within-subject associations of affective states and physical behavior are moderated by weather conditions in real life: an ambulatory assessment study.
    I. Timm & M. Reichert & U. Ebner-Priemer et al. (2023) in: International Journal of Behavioral Nutrition and Physical Activity (20). Read more...
  • Microtemporal Dynamics of Dietary Intake, Physical Activity, and Impulsivity in Adult Attention-Deficit/Hyperactivity Disorder: Ecological Momentary Assessment Study Within Nutritional Psychiatry.
    A. Ruf & A. B. Neubauer & E. D. Koch et al. (2023) in: JMIR Publications (10). Read more...
  • Ecological Momentary Assessment in Nutritional Psychiatry: Microtemporal Dynamics of Dietary Intake, Physical Activity, and Impulsivity in Adult ADHD.
    A. Ruf & A. B. Neubauer & E. D. Koch et al. (2023) in: JMIR Mental Health. Read more...
  • Momentary associations between sedentary bouts, cognitive load and mood in daily life: An ambulatory assessment study.
    M. Giurgio & U. Ebner-Priemer (2023) in: Mental Health and Physical Activity (25). Read more...
  • The Work Lifestyle-integrated Functional Exercise Program for Preventing Functional Decline in Employees over 55 years: Development and Initial Evaluation.
    Y. Ritter & D. Pfister & G.M. Steckhan et al. (2023). Read more...
  • Multicentre, interventional, single-arm study protocol of telemonitored circadian rhythms and patient-reported outcomes for improving mFOLFIRINOX safety in patients with pancreatic cancer.
    M. Bouchahda & A. Ulusakarya & A. Thirot-Bidault et al. (2023) in: BMJ Open (13(6)). Read more...
  • Microstructural white matter biomarkers of symptom severity and therapy outcome in catatonia: Rationale, study design and preliminary clinical data of the whiteCAT study.
    D. Hirjak & G.A. Brandt & R. Peretzke et al. (2023) in: Schizophrenia Research. Read more...
  • Combined physical activity training versus aerobic activity training in unipolar depressive disorder: a quasi-randomised evaluation study.
    A. Berwinkel & M. Driessen & T. Beblo et al. (2023) in: Neuropsychiatr. Read more...
  • At Crossroads in a Virtual City: Effect of Spatial Disorientation on Gait Variability and Psychophysiological Response among Healthy Older Adults.
    C. O. Amaefule & S. Lüdtke & A. Klostermann et al. (2022) in: Gerontology. Read more...
  • The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement: Active and Sitting Time Precursors to Mood in Young Adults.
    B. Clark & E. Winkler & M. Giurgio et al. (2022) in: Journal for the Measurement of Physical Behaviour (5; 4). Read more...
  • Effects of strength exercise interventions on activities of daily living, motor performance, and physical activity in children and adolescents with leukemia or non-Hodgkin lymphoma: Results from the randomized controlled ActiveADL Study.
    D. Gaser & C. Peters & R. Oberhoffer-Fritz et al. (2022) in: Frontiers in Pediatrics. Read more...
  • Effectiveness of an evidence-based care pathway to improve mobility and participation in older patients with vertigo and balance disorders in primary care (MobilE-PHY2): Study protocol for a multicentre cluster-randomised controlled trial.
    C. Horstmannshoff & S. Skudlik & J. Petermann et al. (2022) in: Research Square. Read more...
  • The Behavioral Mapping of Psychomotor Slowing in Psychosis Demonstrates Heterogeneity Among Patients Suggesting Distinct Pathobiology.
    N. Nadesalingam & S. Lefebvre & D. Alexaki et al. (2022). Read more...
  • Measuring catatonia motor behavior with objective instrumentation.
    S. von Känel & N. Nadesalingam & D. Alexaki et al. (2022) in: Frontiers in Psychology. Read more...
  • Mood-enhancing Physical Activity in Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) and Healthy Youths – Daily Life Investigations by Ambulatory Assessment.
    E.D. Koch (2022). Read more...
  • A temporal classification method based on behavior time series data in patients with behavioral variant of frontotemporal dementia and apathy.
    C. Peltier & F. Lejeune & L.G.T. Jorgensen et al. (2022) in: Journal of Neuroscience Methods (376). Read more...
  • The association of stress and physical activity: Mind the ecological fallacy.
    M. Reichert & S. Brüßler & I. Reinhardt et al. (2022) in: German Journal of Exercise and Sport Research (52). Read more...
  • Influence of Sit-Stand Tables in Classrooms on Children’s Sedentary Behavior and Teacher’s Acceptance and Feasibility: A Mixed-Methods Study.
    P. Schwenke & M. Coenen (2022) in: Environmental Research and Public Health (19 (11)). Read more...
  • Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth intervention.
    J. Fiedler & C. Seiferth & T. Eckert et al. (2022) in: German Journal of Exercise and Sport Research. Read more...
  • Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance.
    J.H. Migueles & P. Molina-Garcia & L.V. Torrez-Lopez et al. (2022) in: Scientific Reports (12). Read more...
  • Analysis of self-reported activities of daily living, motor performance and physical activity among children and adolescents with cancer: Baseline data from a randomised controlled trial assessed shortly after diagnosis of leukaemia or non-Hodgkin lymphoma.
    D. Gaser & C. Peters & M. Götte et al. (2022) in: Wiley. Read more...
  • Functional connectivity correlates of reduced goal-directed behaviorsin behavioural variant frontotemporal dementia.
    V. Godefroy & B. Batrancourt & S. Charron et al. (2022) in: Research Square. Read more...
  • Actigraph-Measured Movement Correlates of Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms in Young People with Tuberous Sclerosis Complex (TSC) with and without Intellectual Disability and Autism Spectrum Disorder (ASD).
    T. Earnest & E. Shephard & C. Tye et al. (2020) in: Brain Sciences (8). Read more...
  • Accuracy of Sedentary Behavior–Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study.
    M. Giurgiu & C. Niermann & U. Ebner-Priemer et al. (2020) in: JMIR mHealth and uHealth (8).
  • Mood and dysfunctional cognitions constitute within - subject antecedents and consequences of exercise in eating disorders.
    M. Reichert & S. Schlegel & F. Jagan et al. (2020) in: Psychotherapy and Psychosomatics (89).
  • OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake.
    P.-V. Rouast & H. Heydarian & M.-T.-P. Adam et al. (2020).
  • Improving mobility and participation of older people with vertigo, dizziness and balance disorders in primary care using a care pathway: feasibility study and process evaluation.
    E. Seckler & V. Regauer & M. Krüger et al. (2020) in: Research Square.
  • Fear of Physical Activity, Anxiety, and Depression. Barriers to Physical Activity in Outpatients With Heart Failure?.
    H. Spaderna & J. M. Hoffmann & S. Hellwig et al. (2020) in: European Journal of Health Psychology (27). Read more...
  • The Freiburg sport therapy program for eating disorders: a randomized controlled trial.
    A Zeeck & S. Schlegel & F. Jagan et al. (2020) in: Journal of Eating Disorders (8). Read more...
  • A neural mechanism for affective well-being: Subgenual
    cingulate cortex mediates real-life effects
    of nonexercise activity on energy.
    R. Markus & U. Braun & G. Gan et al. (2020) in: Science advances (6).
  • Development of a classification system for assessing apathy’s degree in patients with behavioral variant of frontotemporal dementia.
    P. Fulcher (2020) in: MASTER’S DEGREE THESIS.
  • Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior.
    M. Giurgiu & J.B.J. Bussmann & H. Hill et al. (2020) in: Journal for the Measurement of Physical Behaviour (Volume 3: Issue 3). Read more...
  • Breaking Up Sedentary Behavior Optimally to Enhance Mood.
    M. Giurgio & E.D. Koch & R.C. Plotnikoff et al. (2020) in: Medicine & Science in Sports & Exercise (52).
  • Real-Time Detection of Spatial Disorientation in Persons with Mild Cognitive Impairment and Dementia.
    J. Schaat & P. Koldrack & K. Yordanova et al. (2019) in: Gerontology (1).
  • Dynamics of Intraindividual Variability in Everyday Life Affect Across
    Adulthood and Old Age.
    M. Katana (2019).
  • Embodied learning in the classroom: Effects on primary school children's attention and foreign language vocabulary learning.
    M. Schmidt & V. Benzig & A. R. Wallman-Jones et al. (2019) in: Psychology of Sport and Exercise (43). Read more...
  • Neural correlates of individual differences in affective benefit of real-life urban green space exposure.
    Heike Tost & Markus Reichert & Urs Braun et al. (2019) in: Nature Neuroscience (7). Read more...
  • Using Acceleration Data for Detecting Temporary Cognitive Overload in Health Care Exemplified Shown in a Pill Sorting Task.
    L. Kohout & M. Butz & W. Stork (2019) in: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS).
  • Sedentary behavior in everyday life relates negatively to mood: An ambulatory Assessment study.
    Marco Giurgiu & Elena D. Koch & Jörg Ottenbacher et al. (2019) in: Scandinavian Journal of Medicine & Science in Sports (29). Read more...
  • Promotion of physical activity-related health competence in physical education: study protocol for the GEKOS cluster randomized controlled trial.
    Stephanie Haible & Carmen Volk & Yolanda Demetriou et al. (2019) in: BMC Public Health (19). Read more...
  • Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors.
    M. Armbruster & P. Anastasopoulou & S. Altmann et al. (2018) in: American Journal of Sports Science (6). Read more...
  • Individual Differences in the Competence for Physical-Activity-Related Affect Regulation Moderate the Activity–Affect Association in Real-Life Situations.
    Gorden Sudeck & Stephanie Jeckel & Tanja Schubert (2018) in: Journal of Sport and Exercise Psychology (40). Read more...
  • Intermittent Fasting (Alternate Day Fasting) in Healthy, Non-obese Adults: Protocol for a Cohort Trial with an Embedded Randomized Controlled Pilot Trial.
    Norbert J. Tripolt & Slaven Stekovic & Felix Aberer et al. (2018) in: Advances in Therapy (35). Read more...
  • Bright light therapy versus physical exercise to prevent co-morbid depression and obesity in adolescents and young adults with attention-deficit / hyperactivity disorder: study protocol for a randomized controlled trial.
    Jutta S. Mayer & Katharina Hees & Juliane Medda et al. (2018) in: Trials (19). Read more...
  • A novel algorithm for detecting human circadian rhythms using a thoracic temperature sensor Article history :.
    Aly Chkeir & Farah Mourad-chehade & Jacques Beau et al. (2017) in: Advances in Science, Technology and Engineering Systems Journal (2). Read more...
  • Physical Activity and Depressive Mood in the Daily Life of Older Adults.
    Andrea E. Gruenenfelder-Steiger & Marko Katana & Annika A. Martin et al. (2017) in: GeroPsych (30). Read more...
  • Measuring Fear of Physical Activity in Patients with Heart Failure.
    Jeremia M. Hoffmann & Susan Hellwig & Vincent M. Brandenburg et al. (2017) in: International Journal of Behavioral Medicine (25). Read more...
  • Lightweight Visual Data Analysis on Mobile Devices - Providing Self-Monitoring Feedback.
    Simon Butscher & Yunlong Wang (2016) in: VVH 2016 - 1st International Workshop on "Valuable visualization of healthcare information": from the quantified self data to conversations (in conjunction with AVI '16). Read more...
  • Contributions à l’élaboration d’un système d’aide médico-sociale à l’aide d’un robot humanoïde.
    Louise Devigne (2015). Read more...
  • Situationsadaptive Navigationsassistenz für Menschen mit Demenz.
    P. Koldrack & R. Henkel & K. Zarm et al. (2015) in: AAL-Kongress 2015. Read more...
  • Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.
    Dimitrios Triantafyllopoulos & Panagiotis Korvesis & Iosif Mporas et al. (2015) in: Journal of Medical Systems (40). Read more...
  • Bewegungsangst bei chronischer Herzinsuffizienz – Erste Ergebnisse zur Validierung eines Messinstruments.
    H. Spaderna & S. Hellwig & D. Hennig et al. (2015) in: 12. Kongress der Fachgrupppe Gesundheitspsychologie - Abstracts. Read more...
  • Fitness, kognitive Leistungsfähigkeit und Wohlbefinden bei jungen Erwachsenen - Interventionsstudien zum Einfluss von Ausdauertraining.
    Katrin Walter (2015). Read more...
  • Validation and comparison of two methods to assess human energy expenditure during free-living activities.
    P. Anastasopoulou & M. Tubic & S. Schmidt et al. (2014) in: PLOS (PLoS ONE 9(2): e90606). Read more...
  • Erfassung körperlicher Aktivität mittels Akzelerometrie - Möglichkeiten und Grenzen aus technischer Sicht.
    Stefan Hey & Panagiota Anastasopoulou & Birte von Haaren (2014) in: Bewegungstherapie und Gesundheitssport (30(02)). Read more...
  • Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study).
    L. Shammas & T. Zentek & B. von Haaren et al. (2014) in: Biomedical engineering online (13). Read more...
  • Detection of Parameters to Quantify Neurobehavioral Alteration in Multiple Sclerosis Based on Daily Life Physical Activity and Gait Using Ambulatory Assessment.
    Layal Shammas & Birte von Haaren & Angela Kunzler et al. (2014) in: Zeitschrift für Neuropsychologie (25). Read more...
  • Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer.
    Panagiota Anastasopoulou & Sascha Härtel & Mirnes Tubic et al. (2013) in: Wireless Mobile Communication and Healthcare.
  • A Comparison of Two Commercial Activity Monitors for Measuring Step Counts During Different Everyday Life Walking Activities.
    P. Anastasopoulou & S. Härtel & S. Hey (2013) in: International Journal of Sports Science and Engineering (Vol. 07 (2013) No. 01). Read more...
  • The Association between Short Periods of Everyday Life Activities and Affective States: A Replication Study Using Ambulatory Assessment.
    Thomas Bossmann & Martina Kanning & Susanne Koudela-Hamila et al. (2013) in: Frontiers in Psychology (4). Read more...
  • Characteristics of the activity-affect association in inactive people: an ambulatory assessment study in daily life.
    B. von Haaren & S.N. Loeffler & S. Haertel et al. (2013) in: Frontiers in Movement Science and Sport Psychology (4).
  • Acute and medium term effects of a 10-week running intervention on mood state in apprentices.
    Katrin Walter & Birte von Haaren & Simone Löffler et al. (2013) in: Frontiers in Movement Science and Sport Psychology (4). Read more...
  • Measurement of daily mobility under fampridine-therapy with Movisens-system in patients with multiple sclerosis.
    R. Kempcke & T. Schultheiß & S. Sobek et al. (2012) in: 28th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS).
  • Kindergarten in Bewegung. Zur Qualität von Bewegungskindergärten.
    R. Schwarz (2012) in: Kita aktuell.
  • Assessment der Mobilität im Alltag zur Unterstützung von MS-Patienten.
    Shammas, L. & Bachis, S. & Anastasopoulou, P. et al. (2012) in: 15. Jahrestagung der dvs-Kommission Gesundheit, Leipzig.
  • Assessment of Human Gait Speed and Energy Expenditure Using a Single Triaxial Accelerometer.
    Panagiota Anstasopoulou & Shammas Layal & Stefan Hey (2012) in: Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on. Read more...
  • Aktuelle Messverfahren zur objektiven Erfassung körperlicher Aktivitäten unter besonderer Berücksichtigung der Schrittzahlmessung.
    D. Rosenbaum (2012) in: Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (55). Read more...
  • A new method to estimate energy expenditure using accelerometry and barometry-based energy models.
    P Anastasopoulou & L. Shammas & J. Stumpp et al. (2011) in: 45. DGBMT Jahrestagung. Freiburg.
  • Validity of the kmsMove-sensor in calculating energy expenditure during different walking intensities.
    B. von Haaren & J.-P. Gnam & S. Helmholdt et al. (2011).
  • Estimation of energy expenditure using accelerometers and activity-based energy models - validation of a new device.
    S. Härtel & J. P Gnam & S. Löffler et al. (2011) in: European Review of Aging and Physical Activity (Volume 8). Read more...
  • Trends und Möglichkeiten zur Erfassung körperlicher Aktivität im Alltag.
    S. Hey & U. Großmann & J. Ottenbacher et al. (2011) in: Kinder bewegen - wissenschaftliche Energien bündeln. Jahrestagung der dvs-Kommission Gesundheit, Karlsruhe.
  • Bewegungskindergärten: empirische Befunde und praktisches Wissen.
    R. Schwarz (2011) in: S. Baadte, K. Bös, S. Scharenberg, R. Stark, A. Woll (Hrsg.), Kinder bewegen - Energien nutzen (S. 65-75). Landau: VEP..
  • Energieumsatzmessung mit Aktivitätssensoren – Validität des kmsMove-Akzelerometers.
    B. von Haaren & J.-P. Gnam & S. Härtel et al. (2011) in: Kinder bewegen - wissenschaftliche Energien bündeln.
  • Einsatz sensorgestützter Verfahren im Gesundheitswesen: Herausforderungen und Lösungsansätze.
    D.I.D.S. Saboor & M.F.H.M. Schallhart (2011). Read more...

All publications can be find here.

Interactive Ambulatory Assessment

Posted on

We are proud to offer the only research grade sensor triggering functionality for Experience Sampling studies.
This feature allows you to capture the contextual information from a participant, enabling you to fully understand the objective physiological data. Instead of looking at the data and wondering what that spike or dip was in a particular metric, you can use this feature to ask the participant themselves at the moment it occurs.

Our research grade sensors analyze physiological parameters as the sensor gathers the data (in realtime), and then transmits the results via BluetoothSmart to a smartphone installed with movisensXS.
movisensXS evaluates the data within your customisable algorithm, and as a result of the evaluation, can trigger a questionnaire or an intervention on the smartphone.

Using our simple drag and drop interface, you select what physiological parameter (or parameters!) you’d like to utilize and build your own triggering algorithm. When investigating physiological changes such as high activity levels, sedentary behavior, changes in heart rate variability (RMSSD), heart rate thresholds, or electrodermal activity, these parameters can act as triggers within your study.

sensortrigger function

Recommendations for IAA

Investigating phases of physical activity and inactivity

Whilst accurately determining sedentary periods remains a worthy goal, assessing the motivations behind the behavior and implementing interventions seems the logical next stage of such research. When investigating the subjective feelings of participants, it's possible to trigger a questionnaire via the SensorTrigger app on our Experience Sampling Platform movisensXS during sedentary periods. Various triggering algorithms use on different physiological changes to prompt the participant to answer a series of questions. The "Sedentary" trigger sends a questionnaire after a determined amount of time in the sitting position (e.g. 30 minutes) elapses. This makes it possible to get more detailed feedback and thus more accurate insights into the everyday life of volunteers. Eventually, after capturing suitable data, the same process can function as an intervention, sending messages to assist in highlighting awareness of the behavior.

people sitting

Examination of stress

Thanks to movisens solutions, it is possible to collect physiological and subjective data together in everyday life. All movisens mobile sensors are able to transmit the results of the measured and analysed data in real time to a smartphone via a Bluetooth interface. From there, questionnaires can be triggered in movisensXS to start a query. Interactive Ambulatory Assessment makes it possible to record subjective data precisely at the times when something of physiological interest (heart rate increase or HRV change) is measured. By combining HRV measurement and IAA in everyday life, small interventions can be performed quickly and easily.

stressed on conference

Detecion of emotional situations

Measurement of electrodermal activity (EDA) provides an excellent insight into the activity of a person's autonomic nervous system. As a result, EDA is an accurate method for detecting emotional arousal. In combination with the movisensXS experience sampling platform, it provides an ideal way to collect objective and subjective data together in an interactive ambulatory assessment. The sensor transmits the calculated EDA values to a smartphone, on which the user can use various algorithms to launch a questionnaire in certain physiological situations, for example, in order to collect further data.

emotions in mountain

Combining different sensor parameters

The combination of different sensors in the Interactive Ambulatory Assessment offers a wide range of additional options for detecting specific situations in which a trigger can be activated in the Experience Sampling Platform. For example, the combination of heart rate and physical activity can be used to define a trigger algorithm that triggers when a change in heart rate or HRV is detected, but the physical activity does not exceed a certain threshold.

jogging-person

Integration of sensor control functions

To ensure the smooth running of the study, a number of control mechanisms have been incorporated to monitor the functioning of the sensors.

  • SensorTrigger detects that the sensor has disconnected
  • SensorTrigger detects the sensor cannot start measuring
  • Sensor battery empty
  • mobile phone battery empty
sensorcontrol

Trigger algorithms

You have the freedom to select different algorithms for each individual participant, and also further customize the existing algorithms within groups. There's a range of existing algorithms developed for specific research projects that provide a small sampling of what is possible with this innovative technology. If you have an existing algorithm that you would like to utilize, or an idea for a physiological marker that you would like to have as a trigger, please contact us to discuss its implementation.

Trigger Algorithm


IAA with movisens

Would you like to receive specific information on the topic of sensor triggered experience sampling?
Then take the opportunity and watch our webinars on this topic.



Sample studies

SedentaryMood-Study

What influence does Sedentary Behavior have on mood?
Despite recent research, the extent to which sedentary behavior affects mental health remains relatively unknown. However, the few available findings indicate that a connection could exist. "Sitting changes the activity of metabolism and therefore it doesn´t seem absurd, that this can also influence the psychological processes." If the link between sedentary behaviour and adverse psychological health exists, understanding it would prove valuable in the fight against psychosomatic diseases.


...read more in our Sedentary Mood study articles

SedentaryMood-Study Cover

Useful information

Whether Interactive Ambulatory Assessment (IAA), Ecological Momentary Assessment (EMAs) or Ecological Momentary Interventions (EMIs), with movisens you’ll realize innovative research ideas and capture insightful data at the right time, in real time and in everyday life .

IAA uses technology to monitor, collect and analyse data from individuals in real time as they go about their daily activities, usually outside of a clinical setting. This method enables continuous monitoring of various parameters such as physiological, behavioural and environmental factors.
The sensor and software solutions developed and produced by movisens are ideal for this data collection.

  • Move 4 - Activity Sensor → Activity Monitoring
  • EcgMove 4 - ECG and Activity Sensor → HRV Monitoring
  • Eda Move 4 - EDA and Activity Sensor → EDA Monitoring
  • LightMove 4 - Light and Activity Sensor → Sleep Monitoring
  • movisensXS - Experience Sampling Platform → Experience Sampling

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    Any questions? - moviDocs have the right answer!

    Answers and instructions for use can be found on our Docs.

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    Publications and validations

    All publications and validations here.

    DataAnalyzer

    Posted on

    DataAnalyzer Software, Box

    The movisens DataAnalyzer processes raw sensor data to calculate physiological parameters with a selectable output interval in just a few clicks.

    The DataAnalyzer extracts the selected parameters into .csv format, allowing researchers to delve deeper and process the data further in Excel or SPSS. For a big picture overview, our pdf reports provide a great summary of the key information extracted from the sensor in easy to read charts and tables.

    The software package works with raw sensor data saved in the unisens format, and complements our range of physiological sensors (Move 4, EcgMove 4, LightMove 4, EdaMove 4). The DataAnalyzer also allows the batch processing of an entire study cohort, automatically displaying the available parameters based on the sensor type, and wear position.

    As with our sensors, the DataAnalyzer is a one-off purchase with no renewal or subscription fees. Once you pay for it, you own it. It comes standard with algorithms that allow the calculation of physical parameters derived from the data gathered by the class leading accelerometers featured in all of our sensors.

    The following additional modules are available for individual purchase:

    • Energy Expenditure
    • Cardio/HRV
    • Electrodermal Activity
    • Sleep
    • Ambient Light
    • Physical Activity Metrics



    DataAnalyzer Software, Box
    Excerpt from activity report: activity classes and body position
    Excerpt from activity report: energy expenditure and MET level
    Excerpt from heart rate and HRV report: MET level, activity classes, heart rate, HRV spectrum

    Top features

    • Batch analyze complete studies with one click
    • Selectable output parameters
    • Configurable output intervals
    • Optimized output that's suitable for further processing (Excel, SPSS)
    • Integrated generator of informative PDF reports

    Applications

    • Analysis of activity, ECG and EDA measurement data
    • Activity recognition and energy expenditure calculation
    • Heart rate and heart rate variability
    • Electrodermal activity
    • Research of the autonomic nervous system (ANS)
    • Behavioral monitoring
    • Psycho-physiological stress monitoring

    Matching products and services

    Move 4
    Activity Sensor

    Assessment of physical activity

    EcgMove 4
    ECG and Activity Sensor

    Assessment of ECG and physical activity

    LightMove 4
    Light and Activity Sensor

    Assessment of ambient light and physical activity

    EdaMove 4
    EDA and Activity Sensor

    Assessment of EDA and physical activity

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    Workshops

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    Product Customization

    Description of Modules

    Included as standard with the DataAnalyzer software licence, the algorithms allow the analysis of the physical activity data collected from the Move 4, LightMove 4, EcgMove 4, and EdaMove 4 sensors. You can select from the following output parameters:

    • Body Position
    • Acceleration along the body axis
    • Steps
    • Activity class
    • Inclination of body
    • Altitude
    • Vertical speed
    • Physical activity report (PDF) - A detailed report including hourly summaries of body position, activity intensity, steps, etc… presented in an easy to read chart format.

    With the Energy Expenditure Module you can analyze physical activity data of the sensors Move 4 and EcgMove 4 with regards to energy expenditure. The following output parameters are available:

    • Activity Energy Expenditure
    • Total Energy Expenditure
    • Metabolic Equivalent of Task / MET
    • Energy expenditure summary
    • Physical activity and energy expenditure report (PDF) – A detailed report displaying the physical activity (activity intensity, body position, steps, etc…) in addition to the energy expenditure of the participant/s. Charts depict an hour by hour analysis of Energy expenditure and MET levels.

    With the Cardo/HRV-Module you can analyze the ECG signal data obtained by the EcgMove 4, either by generating a detailed Heart Rate Variability report or extracting information on the following parameters:

    • ECG R peaks
    • Normal beats and intervals
    • Beat by beat heart rate
    • Heart rate
    • HRV parameter Low Frequency (LF)
    • HRV parameter High Frequency (HF)
    • HRV parameter Low to High Frequency Ratio (LF/HF)
    • HRV parameter SDNN
    • HRV parameter RMSSD
    • HRV parameter SD1
    • HRV parameter SD2
    • HRV parameter SD2/SD1
    • HRV report (PDF) - A detailed report displaying a HRV Spectogram in addition to charts displaying Heart Rate, the Baevskii Stress Index, LF to HF ratios, and activity classes of a participant/s. The report concludes with a table displaying the overall HRV parameters and Activity classes of the participant/s.

    With the EDA Module you can analyze the EDA data captured by the sensor EdaMove 3. The following output parameters are available:

    • Electrodermal Activity report as text file
    • Skin conductance level
    • SCR amplitudes
    • SCR rise times
    • SCR Energies
    • SCR half recovery times
    • Number of SCR
    • Mean of SCR Amplitudes
    • Mean of SCR rise times
    • Mean of SCR energy
    • Mean of SCR recovery times

    Whith the Sleep-Module you can analyze the data captured by the sensor Move 4, LightMove 4, ECGMove 4 und EdaMove 4, Move 3, LightMove 3, ECGMove 3 und EdaMove 3. The following output parameters are available:

    • Sleep/wake detection
    • Lights out detection

    Whith the Ambient Light-Module you can analyze the data captured by the sensor LightMove 3. The following output parameters are available:

    • Illumination
    • Color Temperature
    • Light Situation detection

    With the Physical Activity Metrics you can analyze acceleration data from all of the movisens sensors and calculate commonly used physical activity metrics. Physical activity metrics are algorithms to aggregate raw accelerations signals acquired by acceleration sensors to values that correlate with the intensity of physical activity of a person wearing the acceleration sensor. The following output parameters are available:

    • Euclidian Norm (EN)
    • Eucldian Norm Minus One (ENMO)
    • HFEN, BFEN, HFEN+
    • Zero Crossing
    • Proportional Integrating Measure (PIM)
    • Mean Amplitude DeviationMean (MAD)
    • ActiWatch 4 counts

    Downloads

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    • We constantly strive to develop and improve all of our products. To benefit from these improvements, please subscribe to our newsletter so that we can keep you fully informed. Register now.
    Software
    Documentation and Support
    Data examples
    Example reports
    External Tools

    System Requirements

    The DataAnalyzer needs the following:

    • A PC with Microsoft Windows 7 or higher, 64bit
    • Microsoft Excel for reports in Excel format
    • Administrator rights during installation
    • A minimum of 2.1 GB free space on hard disc

    Change history

    Download
    • Bugfix: Make sure that the parameter threeHipPositions is taken into consideration when generating report.
    Download
    • Add new output parameter called paMetricActigraphCountsVectorMagnitude, that computes the vector magnitude of ActiGraph counts, averaged over the output interval.
    • Expand the possibilities of physical activity metrics to choose from for the calculation of Activity Level (ActivityLevelFromPaMetric).
    • Bugfix: output parameters paMetricActigraphCountsActiLifeDown, paMetricActigraphCountsActiLifeForward, paMetricActigraphCountsActiLifeRight are now shown in Results.xlsx file.
    Download
    • Correct any missing explanations for output parameters.
    Download
    • Implement the ActiLife Algorithm from https://github.com/actigraph/agcounts.git to calculate the ActiGraph counts.
    • Add additional output parameters for DataAnalyzer: PaMetricActigraphCountsActiLifeDown, PaMetricActigraphCountsActiLifeForward, PaMetricActigraphCountsActiLifeRight.
    Download
    • Bugfix for altitude calculation: Ensure that indices are always positive.
    Download
    • Changes affect the output parameter NonWearSleepWake. They improve the detection of extended periods when sensor is not worn.
    Download
    • Bugfix in activity pdf reports (ReportActivity.pdf and ReportActivityEe.pdf). The 'total' parameter in activity class table shows now the full day as 24 hours.
    Download
    • Make alignment at midnight optional for output parameters ReportActivityEeSummary.xlsx and ReportActivitySummary.xlsx.
    Download
    • Add new column to Results.xlsx table that documents the markers set in signal marker.csv.
    Download
    • Bugfix in activity excel reports (ReportActivityEeSummary.xlsx and ReportActivitySummary.xlsx). The parameter "Average of full days" now shows the last day as a full day when the recoding of the last day is exactly 24 hours.
    Download
    • Bugfix in plot energy expenditure summary.
    Download
    • New Parameter: ActivityLevelFromPaMetric
    Download
    • Bugfix: Check minimal amount of data needed for Step Count Summary.
    • Bugfix in function listing available activity classes for reports.
    Download
    • Make additional outputs visible for DataAnalyzer: BeatDropReason, EcgValidSeg, HrvIsValid, HrIsValid.
    • Output ReportSleepPdf even when body position is not calculable.
    • Separate light activity level time into not active level time and light level time in ReportActivityEESummaryExcel.
    • Calculate reports when the parameter threeHipPositions is set to true.
    • Set ColorTemperature to -1 if cannot be calculated.
    Download
    • Add additional outputs HrvIsValid and HrIsValid
    Download
    • Activated PaMetrics in Test License
    • Optimized license code parser
    • Fix error in CalcReportSleep
    • Make body positions optional for ReportSleepPdf
    • Change reportSleep name to reportSleepPdf
    • Add description for reportSleep
    • Set colorTemperature to -1 if it is not calculable
    Download
    • Fixed bug in ReportActivityEeSummaryExcel by separating light activity level time into not active level time and light level time
    • Fix Bug in HRV Segmentation, Welch algorithm for small windows
    • For non wear detection output enum nonWear always when sensor is charging
    • Add new report for sleep
    • Add movement acceleration information for reports ActivitySummaryExcel and ActivityEeSummaryExcel
    • Add reports ActivitySummaryPdf and ActivitySummaryShortPdf for sensor location ankle, upper arm and thigh without EE and Met plots
    • Improve Altitude Filtering
    • PaMetricPim: Allow calculation of PaMetricPim with all acceleration signal sample rates (other than 64Hz)
    • PaMetricAiAbs: remove noise floor according to sensor type
    Download
    • Add MovementAcceleration to ReportActivitySummaryExcel and ReportActivityEeSummaryExcel
    • Bug fix in wear detection
    Download
    • Enable ReportActivitySummaryPdf and ReportActivitySummaryShortPdf for sensor location ankle
    Download
    • Bugfix: DataAnalyzerCmd now considers option targetPath
    Download
    • Bugfix: ReportActivityPdf now works for sensor location ankle
    • Bugfix: Added missing translations for measurement parameters in GUI
    Download
    • Bugfix: Correct calculation of absolute date/time after more than ~24days in Results.xlsx
    • Bugfix: Correct unit for EdaScrHalfRecoveryTimes
    • Show metEpoch length in ReportEeSummaryExcel
    Download
    • Added new module for physical activity metrics
    • Add cycling detection
    • Added new output parameter for sedentariness
    • Add functions to generate 3 body positions for sensor location hip
    • Add body position calculation for ankle
    • Improve bodyposition calculation
    • Bugfix: Allow summary reports longer than 9 days
    • Bugfix: Custom algorithm configurations now work for DataAnalyzerCmd
    Download
    • Bugfix: Correct output of Results.xlsx Excel-Files for empty SignalEntries at the end of measurements
    Download
    • Added functionality to allow custom algorithm configurations
    • Added parameters for hrv frequency bands
    • Fixed missing translations of footer in reports
    • Improved sanity check for MET values
    • Fixed translations in StepCount summary plot
    • Added missing sensor position left_side_hip for Move II
    • Added New Excel Summary Report without Ee data in Base module
    • Added handling of manual marked artifacts in ECG
    Download
    • Added support for 4th sensor generation
    Download
    • Bugfix in CalcLightsOut Parameter
    • Bugfix in ReportTableHrvAndActivity Parameter
    • Bugfix in Baevsky Stress Index Algorithm for very small variabilities
    Download
    • Bugfix: output ReportHrvPdf for all days of measurement
    • Bugfix: Calculate Baevsky Stress Index correctly for small variabilities
    • Added 4k display support
    • Bugfix: Stop calculation if message box is closed
    • Bugfix: Prevent deletion of destination folder (path not editable)
    • Bugfix: Load attributes (age, gender, etc.) from measurement in batch mode
    • Added new module Sleep with sleep/wake detection
    • Added support for ambient light sensor LightMove3
    • Added algorithms for ambient light: illuminance, color temperature, lights out detection
    • Improved ECG RR filter
    • Improved wear detection algorithm
    • New output EDA SCL signal
    • Added NonWear, MVPA and MVPA bouts to Summary report
    • Added support for unisens-CSV format
    • Improved colors in reports for better readability
    • Improved report for MET level
    • Improved tables in reports
    • Better support of different sensor locations in reports
    • Improved body position detection for sensor location thigh
    • Fixed bug for missing values at the end of Results.xslx
    Download
    • New PDF reports
    • Support for new sensors and sensor positions
    • New report generator for PDF reports
    • Added wear time detection algorithms
    • Improved filtering of RR intervals
    • Improved HRV parameter RMSSD
    • New algorithms for ambient light sensors (illumination and color temperature)
    • No need of LaTeX and Excel as system requirements
    Download
    • Improved filtering of RR list
    • Improved HRV spectral parameter calculation
    • Improved AEE, TEE and MET Calculation by using additional Model for slopes
    • Bugfixes in PDF reports
    • Support for new sensors
    • Improved EDA SCR detection
    • Added missing parameter EdaScl (Skin Conductance Level)
    • New parameter ECG derived respiration (EDR)
    • Considering output interval for EdaArousal
    • Added sitting/standing detection (sensor position thigh)
    • Added temperature parameter
    • Added HRV Parameter pNN50
    Download
    • Output of all selected parameters as Excel including clearly laid out column descriptions (Results.xslx)
    • New module for Cardio/HRV with all common parameters, Baevskii Stress Index and a new HRV report as PDF
    • New module for EDA with all relevant EDA parameters, including new arousal parameter
    • All PDF reports revised and now available in German and English
    • New PDF overview report for physical activity
    • Improved detection of body positions
    • Improved energy expenditure calculation while resting/sitting
    • Improved plots and layout in all reports

    Literature and Validation

    • Feasibility and usefulness of postoperative mobilization goals in the enhanced recovery after surgery (ERAS®) clinical pathway for elective colorectal surgery.
      R. Wiesenberger & J. Müller & M. Kaufmann et al. (2024) in: Langenbeck's Archives of Surgery (409). Read more...
    • Does it need an app? – Differences between app-guided breathing and natural relaxation in adolescents after acute stress.
      D. Schleicher & I. Jarvers & M. Kocur et al. (2024) in: Psychoneuroendocrinology (169). Read more...
    • The use of music for Solace, its connection to Openness and its moderating effects on music listening and stress.
      S. Gorgi (2024). Read more...
    • Modeling occupants’ metabolic rate in office buildings by implementation of smart wearable sensors considering personal thermal comfort.
      N. Pivac (2024). Read more...
    • Predicting the onset of psychotic experiences in daily life with the use of ambulatory sensor data – A proof-of-concept study.
      F. Strakeljahn & T. Lincoln & K. Krkovic et al. (2024) in: Schizophrenia Research (267). Read more...
    • Impact of a Semi-Rigid Knee Orthotic Intervention on Pain, Physical Activity, and Functional Capacity in Patients with Medial Knee Osteoarthritis.
      B. J. Stetter & J. Fiedler & M. Arndt et al. (2024) in: Journal of Clinical Medicine (13 (6)). Read more...
    • Exploring the Link between Lifestyle, Inflammation, and Insulin Resistance through an Improved Healthy Living Index.
      F. Bruckner & J.R. Gruber & A. Ruf et al. (2024) in: MDPI (16(3)). Read more...
    • Acute Fasting Modulates Autonomic Nervous System Function and Ambulatory Cardiac Interoception.
      A. Schwerdtfeger & Rominger C. (2024) in: Biological Psychology. Read more...
    • Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years.
      F. Beck & I. Marzi & A. Eisenreich et al. (2023) in: BMC Sports Science, Medicine and Rehabilitation (15, 163). Read more...
    • The cardiac correlates of feeling safe in everyday life: A Bayesian replication study.
      A. Schwerdtfeger & C. Rominger (2023) in: International Journal of Psychophysiology (196). Read more...
    • Spontaneous infant crying modulates vagal activity in real time.
      A. Madden-Rusnak & M. Micheletti & A. Dominguez et al. (2023) in: Developmental Psychobiology (65, Issue 7). Read more...
    • Human uncertainty in interaction with a machine: establishing a reference dataset.
      A. Rother & G. Notni & A. Hasse et al. (2023). Read more...
    • Momentary within-subject associations of affective states and physical behavior are moderated by weather conditions in real life: an ambulatory assessment study.
      I. Timm & M. Reichert & U. Ebner-Priemer et al. (2023) in: International Journal of Behavioral Nutrition and Physical Activity (20). Read more...
    • Rethinking Learning Experience: How Generally Perceived Life Stress Influences Students’ Course Perceptions in Different Learning Environments.
      M. Gellisch & T. Schäfer & I. Yahya et al. (2023) in: European Journal of Investigation in Health, Psychology and Education (13(8)). Read more...
    • Microtemporal Dynamics of Dietary Intake, Physical Activity, and Impulsivity in Adult Attention-Deficit/Hyperactivity Disorder: Ecological Momentary Assessment Study Within Nutritional Psychiatry.
      A. Ruf & A. B. Neubauer & E. D. Koch et al. (2023) in: JMIR Publications (10). Read more...
    • Ecological Momentary Assessment in Nutritional Psychiatry: Microtemporal Dynamics of Dietary Intake, Physical Activity, and Impulsivity in Adult ADHD.
      A. Ruf & A. B. Neubauer & E. D. Koch et al. (2023) in: JMIR Mental Health. Read more...
    • The Work Lifestyle-integrated Functional Exercise Program for Preventing Functional Decline in Employees over 55 years: Development and Initial Evaluation.
      Y. Ritter & D. Pfister & G.M. Steckhan et al. (2023). Read more...
    • Off-the-shelf wearable sensing devices for personalized thermal comfort models: A systematic review on their use in scientific research.
      A. Costantino & M. Ferrara & M. Arnesano et al. (2023) in: Journal of Building Engineering (70). Read more...
    • Does Being Ignored on WhatsApp Hurt? A Pilot Study on the Effect of a Newly Developed Ostracism Task for Adolescents.
      D. Latina & A. Goreis & P. Sajko et al. (2023) in: Journal of Clinical Medicine (12 (5)). Read more...
    • Interactive teaching enhances students' physiological arousal during online learning.
      M. Gellisch & G. Morosan-Puopolo & O.T. Wolf et al. (2023) in: Annals of Anatomy (247). Read more...
    • Tranquillity, transcendence, and retreat: the transformative practice of listening at Evensong.
      K. King (2023) in: Magdalen College, University of Oxford. Read more...
    • Momentary feelings of safety are associated with attenuated cardiac activity in daily life: Preliminary evidence from an ecological momentary assessment study.
      A. Schwerdtfeger & L. Paul & Rominger C. (2022) in: International Journal of Psychophysiology (182). Read more...
    • Flow bei der Arbeit greifbar machen.
      J. Schlicksupp (2022) in: Wirtschaftspsychologie heute. Read more...
    • Measuring catatonia motor behavior with objective instrumentation.
      S. von Känel & N. Nadesalingam & D. Alexaki et al. (2022) in: Frontiers in Psychology. Read more...
    • Mood-enhancing Physical Activity in Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) and Healthy Youths – Daily Life Investigations by Ambulatory Assessment.
      E.D. Koch (2022). Read more...
    • Decreased sympathetic cardiovascular influences and hormone-physiological changes in response to Covid-19-related adaptations under different learning environments.
      M. Gellisch & O.T. Wolf & N. Minkley et al. (2022) in: American Association for Anatomy. Read more...
    • Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.
      D. Kumar & A. Peimankar & K. Sharma et al. (2022) in: Elsevier (221). Read more...
    • The association of stress and physical activity: Mind the ecological fallacy.
      M. Reichert & S. Brüßler & I. Reinhardt et al. (2022) in: German Journal of Exercise and Sport Research (52). Read more...
    • Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia.
      L. Rösler & G. Van der Lande & J. Leerssen et al. (2022) in: Sleep Research Society 2. Read more...
    • The dynamical association between physical activity and affect in the daily life of individuals with ADHD.
      E.D. Koch & C. M. Freitag & J. S. Mayer et al. (2022) in: European Neuropsychopharmacology (57). Read more...
    • How does it feel to walk in Berlin? Designing an Urban Sensing Lab to explore walking emotions through EDA sensing.
      W. Blum & P. Fried (2021). Read more...
    • Assessing New Methods to Optimally Detect Episodes of Non-metabolic Heart Rate Variability Reduction as an Indicator of Psychological Stress in Everyday Life: A Thorough Evaluation of Six Methods.
      S. B. Brown & J. F. Brosschot & A. Versluis et al. (2020) in: Frontiers in Neuroscience (14).
    • Effects of exercise training on heart rate variability in children and adolescents with pulmonary arterial hypertension: a pilot study.
      J. Siaplaouras & M. Frerix & A. Apitz et al. (2020) in: PMC (11 (4)). Read more...
    • Effects of Biophilic Interventions in Office on Stress Reaction and Cognitive Function: A Randomized Crossover Study in Virtual Reality.
      Jie Yin & Nastaran Arfaei & Piers MacNaughton et al. (2019) in: Indoor Air (0). Read more...
    • Dynamics of Intraindividual Variability in Everyday Life Affect Across
      Adulthood and Old Age.
      M. Katana (2019).
    • Neural correlates of individual differences in affective benefit of real-life urban green space exposure.
      Heike Tost & Markus Reichert & Urs Braun et al. (2019) in: Nature Neuroscience (7). Read more...
    • Sedentary behavior in everyday life relates negatively to mood: An ambulatory Assessment study.
      Marco Giurgiu & Elena D. Koch & Jörg Ottenbacher et al. (2019) in: Scandinavian Journal of Medicine & Science in Sports (29). Read more...
    • Brute Force ECG Feature Extraction Applied on Discomfort Detection.
      Guillermo Hidalgo Gadea & Annika Kreuder & Carsten Stahlschmidt et al. (2018) in: Information Technology in Biomedicine: Proceedings 6th International Conference, ITIB'2018, Kamień Śląski, Poland, June 18--20, 2018. Read more...
    • An experience sampling study on the nature of the interaction between traumatic experiences, negative affect in everyday life, and threat beliefs.
      Katarina Krkovic & Björn Schlier & Tania Lincoln (2018) in: Schizophrenia Research (201). Read more...
    • Immediate and sustained effects of intermittent exercise on inhibitory control and task-related heart rate variability in adolescents.
      Sebastian Ludyga & Uwe Pühse & Stefano Lucchi et al. (2018) in: Journal of Science and Medicine in Sport (22). Read more...
    • Transcutaneous vagus nerve stimulation and emotional inhibition of return.
      Bart Verkuil & Andreas Michael Burger (2018).
    • A mixed-methods study of physiological reactivity to domain-specific problem solving: methodological perspectives for process-accompanying research in VET.
      Tobias Kärner (2017) in: Empirical Research in Vocational Education and Training (9). Read more...
    • Does a 20-week aerobic exercise training programme increase our capabilities to buffer real-life stressors? A randomized, controlled trial using ambulatory assessment.
      Birte von Haaren & Joerg Ottenbacher & Julia Muenz et al. (2015) in: European Journal of Applied Physiology (116). Read more...
    • Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study).
      L. Shammas & T. Zentek & B. von Haaren et al. (2014) in: Biomedical engineering online (13). Read more...
    • Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer.
      Panagiota Anastasopoulou & Sascha Härtel & Mirnes Tubic et al. (2013) in: Wireless Mobile Communication and Healthcare.
    • Classification of Human Physical Activity and Energy Expenditure Estimation by Accelerometry and Barometry.
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    • A new method to estimate energy expenditure using accelerometry and barometry-based energy models.
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    • Validity of the kmsMove-sensor in calculating energy expenditure during different walking intensities.
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    • Estimation of energy expenditure using accelerometers and activity-based energy models - validation of a new device.
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    • Energieumsatzmessung mit Aktivitätssensoren – Validität des kmsMove-Akzelerometers.
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    • Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology..
      (1996) in: Circulation (93).

    You can find more publications here.

    Study Diary (VII)

    Posted on

    SedentaryMood-Study (Part VII)

    The following article is part of a series about the "SedentaryMood-Study".

    In the last article the practical implementation of the study was described. In the following and last blog post the result of the study is presented.

    Result

    The results indicate that there is a significant negative influence on the mood dimension alertness-fatigue and on the good-bad mood. In general terms, this means that sedentary behaviour contributes to higher fatigue. Sedenary units (≥ 30 minutes) in which no interruption took place have a particularly negative effect compared to interrupted units.
    With regard to the good-bad mood, the results also point to a significant negative influence of sedentary time. This means that the sedentary time can contribute to a worse mood. Especially negative is the effect of the sedentary units (≥ 30 minutes), in contrast to interrupted sedentary units, on the good-bad mood.

    Study Diary VI

    Posted on

    SedentaryMood-Study (Part VI)

    The following article is part of a series about the "SedentaryMood-Study".


    The practical implementation of the SedentaryMood-Study

    In the last article the applied investigation plan was explained. The following article describes the practical implementation of the study.


    Step by step

  • Preparation of the ethics proposal
  • Preparation of the respondent information and questionnaires
  • Creation of the study concept via the Ambulatory Assessment Platform movisensXS








  • Preparation of the necessary research equipment and the associated materials
  • Configuring and Starting Sensors











  • Install TriggerApp
  • Bluetooth low energy Establish connection between sensor and smartphone and select algorithm (Sedentary)








  • Pair the smartphone with movisensXS and load the created design on the smartphone
  • Instruction and instruction of test persons at the workplace
  • Start study!


    • you can find out more about the study in the next article...



    Study Diary V

    Posted on

    SedentaryMood-Study (Part V)

    The following article is part of a series about the "SedentaryMood-Study".

    Measurement times of the real-time study

    In the last article, the sedentary sedimentary triggered e diaries and the randomly selected queries were described. In the next step, the study plan used for the SedentaryMood-Study is explained. Here, the frequency of the mood polls during the course of a day plays an important role.

    The exact number of queries depends on the individual participant's level of activity and thus, as in this study, up to 12 queries per day can be expected. More mood queries per day over a longer period of time are not to be recommended in order not to overstrain the associated willingness of the test persons to participate and thus not to endanger the data quality.

    On the basis of study results, about three to five days - of which at least one weekend day - are necessary for a representative recording of sedentary behaviour. In view of this, the survey period, the SedentaryMood Study, lasted five days (three working days and two weekend days).

    The sample was recruited from the University of Newcastle (UoN, Australia) and the Karlsruhe Institute of Technology (KIT, Germany). Participation in the study was linked to the following inclusion criteria: official co-worker of the institution, no illness or injury, and the work was performed predominantly in a sedentary body position.

    more about the practical implementation of the study can be found in the next article...