Research Study Abstract

Interactive Ambulatory Behavior Monitoring an Approach to Optimize the Sampling Strategy in Real Time

  • Published on 06/24/2011

Physical activity is an important factor for healthcare intervention delivery and clinical outcome measurement in rehabilitation medicine, rheumatology, aging and cardiovascular medicine. A psychological perspective in ambulatory behavior monitoring does focus on the relation between behavior (physical activity, posture, movement) and phenomena like mood or emotions. Whereas numberless studies propose a positive relation between physical activity and mood, unforgettably most studies assessed this association with subjective ratings of activity and retrospective ratings of mood.

Ambulatory behavior monitoring enables to assess activity of mood in real time using electronic devices. Crucial in such a study is the sampling strategy. whereas activity can be monitored continuously with a high sampling frequency, mood can only be assessed in discrete intervals, like every 30 minutes or every hour. To identify a possible relation between mood and physical activity in every day life, mood must be assessed during episodes of low and high physical activity. When assessing mood only during episodes of medium physical activity, no statistical relation will be found.

To optimize the assessment of possible relation between physical activity and mood, we developed an algorithm which continuously monitored physical activity in every day life. When predefined thresholds were surpassed, the algorithm triggered an alarm that participants had to answer mood questions on their electronic diary. 70 participants wore this system for 24 hours each.

To analyze the effectiveness of the algorithm, we compared the frequency of episodes (10 minutes) with high (>220 mg/min.), medium (>20 mg/min. and <220 mg/min.), and low (<20 mg/min) physical activity as revealed by a randomized sampling strategy to our interactive sampling strategy. Compared to the random sampling strategy (high activity: 9,3%, medium activity: 84%, low activity: 9,4%) the interactive sampling strategy revealed a better distribution ( high activity: 38%, medium activity: 32%, low activity: 30%) of selected episodes.

Interactive ambulatory behavior monitoring can optimize sampling strategies in real time to get a closer look at the relation between behavior (physical activity, posture, movement) and phenomena like mood or emotions.


The Science of Ambulatory Assessment