The 23rd "European College of Sport Science" conference takes place in Dublin on the 4th til the 7th of July. Once again, movisens stands ready (at booth 15) to answer all your questions about our physiological sensors and other research solutions.
An m-health innovation enabled by movisens
The CoCa-Project involves the implementation and piloting of an "m-Health" (mobile-health) platform for participants with ADHD. It aims to observe and promote the chronobiological rhythm and physical activity levels in people with attention-deficit hyperactivity disorder (ADHD), and it's comorbid disease factors.
A European collaboration of researchers have set out to understand the associated diseases prevalent with ADHD. They're studying the features and physical functions in adolescents and young adults with ADHD to better understand it's associated diseases. They'll seek new ways to treat ADHD and help prevent these concomitant diseases.
In conjunction with movisens GmbH, KIT's Institute for Sport and Sports Science developed an m-Health system as part of the CoCA project. The system generates individualized feedback, sending daily reminders to the participants related to their treatment. The system also monitors and increases participant's adherence to their current treatment plan, motivating them to perform the intervention's exercises and the light therapy.
Incorporating Physiological Data
The m-Health system includes classic e-diary components, monitors the therapy delivery, and presents video clips as exercise modules. The study also tracks the physiological data, recorded via the wrist worn Light and activity sensor - LightMove 3. This sensor provides valuable objective data for analysis and assists in the interventions.
An m-health intervention
Incorporating the sensor allows for an innovative m-health intervention. Whilst the sensor records the data for future analysis, it simultaneously calculates and transmits physiological data via Bluetooth smart to the participants smartphone. From there, it's uploaded to a server and processed in real time. Through this innovation, the m-health system generates motivational feedback on the activities performed and transmits them to the participant's smartphone. An innovative ecological momentary assessment study, AND intervention.
The CoCA-Project provided movisens a unique opportunity to demonstrate the complex capabilities of our experience sampling platform. Enlisting movisens' experience with EMA study design, combined with their expertise in physiological sensors enabled the research team to focus on finding the answers that they seek.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 667302.
The 13th conference of the Austrian Society for Psychology (Österreichischen Gesellschaft für Psychologie) (ÖGP) takes place from the 12th til the 14th of April 2018 in the Johannes Kepler Universität in Linz.
The conference focuses on the opportunity of linking newer technologies with classical reactive procedures in order to show new potentials for psychological research. We're looking forward to the interesting presentations, and hope that attendees take a moment to stop by our stand to inform themselves of our innovative methods and research solutions.
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
movisens visits Tübingen this week for the 2018 DGKJP research conference. As always, we're looking forward to the presentations and the chance to engage in interesting discussions around the topic of interventions. The conference runs from the 1st to the 2nd of March 2018, and we invite all the participants to stop by our booth over the two days to discuss how we can help them in their research. We'll display our sensors and resources for capturing ambulatory data, and in particular our tools used for intervention studies.
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
Reliable methods to measure and assess "sedentary behavior"
- Case 1: Known environment – e.g. no possibility to stand and all low physical activity ≙ sitting. In this case attaching a sensor at the hip (Move 3) provides only a rough estimation of sedentary behavior.
- Case 2: Differentiating between sitting/lying & standing. By attaching a sensor (Move 3) to the thigh, the different angles of the axis allow differentiation between sitting/lying and standing. But it is not possible to differentiate between sitting and lying (Byrom, Stratton, McCarthy, & Muelhausen, 2016).
- Case 3: Assessing changes in time distribution of sedentary behavior – requiring the precise distribution of sedentary behavior and physical activity intensity. This case requires the assessment of both body posture and energy expenditure. This is possible by attaching one sensor (Move 3) at the thigh (sitting/lying vs. standing) and one (Move 3) at the upper body/hip (standing/sitting vs. lying). Additionally, the sensor at the upper body/hip provides the data necessary to estimate energy expenditure (Holtermann, et al., 2017)
- Case 4: Assessing changes in time distribution of sedentary behavior with a static load. In this case the use of an ecg-sensor provides additional data to assess energy expenditure. Initially the ecg-sensor requires calibration to estimate energy expenditure with additional load. Attaching one sensor (Move 3) at the thigh (sitting/lying vs. standing) and one physical activity and ecg-sensor (EcgMove 3) at the upper body (standing/sitting vs. lying) provides acceleration data from two positions, and the additional ecg-signal allows improved energy expenditure estimations during static work, due to the linear relationship between cardiorespiratory stress and energy expenditure (Holtermann, et al., 2017).
- Case 5: If an intervention is necessary, or if the research requires additional subjective parameters, we offer the possibility to trigger a questionnaire with our experience sampling platform movisensXS via our SensorTrigger. After the application detects 30 minutes (customizable by the researcher) of sedentary behaviour (<1.5MET) from the sensor, the trigger displays a form on a smartphone app prompting the participant to answer a questionnaire. This offers the possibility to obtain detailed feedback and insights into the daily routine of the study participant.
Added Android 7 and 8 Compatibility (Nougat and Oreo)
We updated our whole code base to be compatible with the new android versions.
Log Apps and Lock other Apps now compatible with Android 5+
We have completely rewritten our internal code to make the "Lock other Apps" functionality and the "logging which app is used (beta)" compatible with newer android versions.
Silent mode warning card
In case a participant accidentally puts their smartphone to silent mode we display a warning card on the home screen to let them know that they might miss alarms.
Notification if location tracking was disabled
In case you use location tracking and a participant accidentally disables location tracking we show a notification until the participant activates the location tracking again.
Added log music condition (Beta)
We added a new beta functionality that logs the music your participant listens to. It logs the artist, the album, and the track currently playing. It has been implemented for several android music apps and has been tested with the largest tree: Google Android Player, Amazon Music and Spotify. Let us know, if you want to try it.
Here's even more movisensXS app improvements
- TimeRangeCondition can now define time ranges that transition days (e.g. 23.00-04.00)
- You can now explicitly allow and disallow apps
- The cards on the home screen are now ordered by priority for the participant
- Fixed the alarm notification to allow the resumption of an incomplete form
- The inactivity timer on forms is deactivated on several item formats (audio recording, timer, display video, presentation, cognition tests, external). Additionally the inactivity time on forms can be disabled by setting the inactivity time on forms to 0
- Alarms and forms will be stopped when study is paused
- Fixed issues that could allow access to the study control screen without a pin code
- Fixed an issue on selected smartphones that could cause movisensXS to crash when opening a form
- Barcode item now detects more formats (UPC-A & E, Code 39 & 93 & 128, EAN-8 & 13, QR Code)
- RTL compatible likert and visual analog scale
- Updating of server status if device is uncoupled/finished
- Various bug fixes
And some improvements of beta features
- Added calendar item and calendar time trigger
- Added option to limit log apps function to a specified set of apps
- Display ON/OFF logging now detects shutdown
- Added presentation item
- Fixed participant time trigger conflicts with time items and some condition blocks in the sampling
- New building blocks for the sampling to trigger based on movisens sensors
- Limiting estimated steps logging to 0-300 steps per minute
- Retry of uploads after unisens upload fails
- We fixed time overflow issues with the stopwatch item
- Notification when location services are disabled but needed